1 DECISION MAKING FOR SUSTAINABLE LOCATION OF SEMI DESIRABLE FACILITIES WITH APPLICATION TO CEMENT PLANTS By MARYAM MIRHADI FARD A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFIL LMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Maryam Mirhadi Fard
3 To my husband, Amin, whose kind supports can never be appreciated and to my parents for their endless support and prayers
4 ACKNOWLEDGMENTS I am profoundly grateful to my chair, Dr. Kibert for his extraordinary support and sharing his vast knowledge with me during my studies and research. I would also like to thank my co chair, Dr. Chini, for his continuous and generous su pport and feedback during my research and providing me with resources. I have been lucky to have both of them as my chair and co chair. I would like to extend my thanks to my other committee members, Dr. Obonyo and Dr. Smith for their thoughtful review an d suggestions. I am also grateful for the time and input of all the participants in the survey conducted in the research. I would like to take this opportunity to thank colleagues and friends who assisted me during this research.
5 TABLE OF CONTENTS pag e ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 Background ................................ ................................ ................................ ............. 14 Motivation and Contribution ................................ ................................ .................... 16 Scope of Work ................................ ................................ ................................ ........ 17 2 LITERATURE REVIEW ................................ ................................ .......................... 20 Sustainable Development ................................ ................................ ....................... 20 Facility Location (Site Selection) ................................ ................................ ............. 24 Geographic Information System (GIS) and Facility Location ............................ 29 Multi Criteria Decision Making (MCDM) and Facility Location .......................... 36 Sustainable Facility Location ................................ ................................ ............ 44 Practices and Guidelines of Sustainable Siting ................................ ................ 49 Summary ................................ ................................ ................................ ................ 53 3 RESEARCH METHODOLOGY ................................ ................................ ............... 58 Principal Research Steps ................................ ................................ ........................ 58 Data Requ irements ................................ ................................ ................................ 60 Assumptions ................................ ................................ ................................ ........... 61 4 FRAMING SUSTAINABLE SEMI DESIRABLE FACILITY LOCATION ................... 66 Siting Algorithm ................................ ................................ ................................ ....... 67 Evaluation Criteria ................................ ................................ ................................ .. 77 5 CASE STUDY ................................ ................................ ................................ ......... 84 General Characteristics of the Cement Industry ................................ ..................... 85 Sustainability Challenges of the Cement Industry ................................ ................... 86 Framing the Pr oblem of Siting a Cement Plant in the State of Florida .................... 90 Implementation of the Site Selection Model ................................ ............................ 91
6 Primary ESCIA of a Cemen t Plant ................................ ................................ .... 91 Customizing the Proposed Evaluation Criteria. ................................ ................ 94 Macro Level Analysis ................................ ................................ ....................... 94 Macro Level Screening Analysis ................................ ................................ 95 Macro Level Suitability Analysis ................................ ................................ 96 Weighting the Decision Criteria ................................ ................................ 105 Comparison of the Feasible Counties ................................ ...................... 108 Micro Level Analysis ................................ ................................ ...................... 109 M icro Level Screening Analysis ................................ ............................... 109 Micro Level Suitability Analysis ................................ ................................ 111 Comparison of the Candidate Parcels ................................ ..................... 120 Discussion of the Results ................................ ................................ ............... 122 Sensitivity Analysis ................................ ................................ ......................... 124 Reviewing the Proposed Sit ing Model ................................ ................................ .. 125 Summary ................................ ................................ ................................ .............. 126 6 CONCLUSIONS, LIMITATIONS AND RECOMMENDATIONS ............................ 167 Conclusion ................................ ................................ ................................ ............ 167 Limitation ................................ ................................ ................................ .............. 172 Recommendations for Future Studies ................................ ................................ ... 172 APPENDIX A VALUES OF FEASIBLE COUNTIES AGAINST THE SUITABILITY CRITERIA ... 175 B RESEARCH QUESTIONNAIRE ................................ ................................ ........... 180 C SUMMARY OF THE ANSWERS TO THE QUESTIONNAIRE .............................. 187 LIST OF REFERENCES ................................ ................................ ............................. 208 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 219
7 LIST OF T ABLES Table P age 2 1 Average R andom I ndex ................................ ................................ ..................... 54 2 2 Proposed actions for site assessment of a cement plant ................................ ... 54 2 3 Punjab pollution control guideline for b rick k ilns, c ement p lants/ g rinding u nits .. 55 3 1 The types and sources of the data used in the cas e study ................................ 63 5 1 Feasible counties ................................ ................................ ............................. 128 5 2 Macro level suitability indices ................................ ................................ ........... 128 5 3 The priority vector and consistency ratio of the dimensions comparison .......... 129 5 4 The priority vector and consistency ratio of the technical sub themes comparison ................................ ................................ ................................ ....... 129 5 5 The priority vector and consistency ratio of the social sub themes comparison 129 5 6 The summary of consistency ratios ................................ ................................ .. 130 5 7 Weight values of dimensions and sub themes ................................ ................. 130 5 8 W eight values of technical indices ................................ ................................ .... 131 5 9 W eight values of environmental indices ................................ ........................... 131 5 10 W eight values of s ocial indices ................................ ................................ ......... 132 5 11 W eight values of economic indices ................................ ................................ ... 132 5 12 Normalized values of feasible counties against the suitability indices .............. 133 5 13 The score of the feasible countie s ................................ ................................ .... 137 5 14 Priorities of the feasible counties ................................ ................................ ...... 137 5 15 The list of the screening criteria for micro level analysis ................................ ... 137 5 16 The result of the screening phase at micro level analysis ................................ 138 5 17 Glades c ounty zoning ordinance ................................ ................................ ...... 139 5 18 Hendry c ounty zoning ordinance ................................ ................................ ...... 140
8 5 19 Status of candidate parcels against the suitability indices ................................ 141 5 20 Definitions of FEMA f lood z one d esignations ................................ ................... 144 5 21 Suitability scores of the candidate p arcels for cement plant purpose ............... 145 5 22 The results of sensitivity analysis ................................ ................................ ..... 146 A 1 values of feasible counties against the suitability criteria ................................ .. 175 C 1 Su mmary of themes comparisons ................................ ................................ .... 187 C 2 Summary of technical sub themes comparison ................................ ................ 191 C 3 Summary of social sub themes comparison ................................ ..................... 197 C 4 Summary of economic sub themes comparison ................................ ............... 204
9 LIST OF FIGURES Figure P age 2 1 The main components of sustainable development ................................ ............ 56 2 2 The pillars of sustainable development ................................ .............................. 56 2 3 A classification of multi criteria location models ................................ .................. 56 2 4 High level considerations a new cement plant site assessment ......................... 57 4 1 Decision algorithm for semi desirable facility location ................................ ........ 80 4 2 Siting decision criteria ................................ ................................ ......................... 81 5 1 CO 2 emission in 2008 ................................ ................................ ....................... 148 5 2 Gross CO 2 emission per ton clinke ................................ ................................ ... 148 5 3 Environmental, social and economic impacts of a cement plant ....................... 149 5 4 The list of decision criteria in the case study ................................ .................... 150 5 5 Florida limestone map ................................ ................................ ...................... 153 5 6 Feasible areas ................................ ................................ ................................ .. 153 5 7 Florida counties in 300km buffer of Polk feasible area ................................ ..... 154 5 8 Florida counties in 300km buffer of Hendry feasible area ................................ 154 5 9 Florida Seaports ................................ ................................ ............................... 155 5 1 0 areas ................................ ................................ ................................ ................ 155 5 11 Clip spatial data of Miami Dade candidate areas ................................ ............. 156 5 12 Academic level of participants in the survey ................................ ..................... 156 5 13 Characterization of participants in the survey ................................ ................... 15 7 5 14 Combination of scores and priorities of the feasible counties ........................... 157 5 15 Selected suitable counties for micro level analysis ................................ ........... 158 5 16 Collier feasible areas ................................ ................................ ........................ 159
10 5 17 Glades feasible areas ................................ ................................ ....................... 159 5 18 Hendry feasible areas ................................ ................................ ....................... 160 5 19 Collier potential and candidate parcels ................................ ............................. 160 5 20 Glades candidate parcels ................................ ................................ ................. 161 5 21 Hendry candidate parcels ................................ ................................ ................. 161 5 22 Collier topographic map ................................ ................................ .................... 162 5 23 Glades topographic map ................................ ................................ .................. 162 5 24 Hendry topographic map ................................ ................................ .................. 163 5 25 DVI map of Collier c ou nty ................................ ................................ ................. 163 5 26 DVI map of Glades c ounty ................................ ................................ ................ 164 5 27 DVI map of Hendry c ounty ................................ ................................ ............... 164 5 28 Florida rural areas ................................ ................................ ............................ 165 5 29 The result of sensitivity analysis on 7km buffer distance ................................ .. 165 5 30 The result of sensit ivity analysis on land size and land use .............................. 166
11 LIST OF ABBREVIATION S AHP Analytical Hierarchy Process CLIP Critical Land and W ater Identification Project CSI Cement Sustainability Initiatives DEO Department of Economic Opportunity DVI Drastic Vulnerability Index EIA Environmental Impact Assessment ESCIA Environmental, Social and Economic Impact Assessment FDEP Florida Department of Environmental Protection FDOR Florida Department of Revenue FEMA Federal Emergency Management Agency FF A CQUIRED Florida Forever Acquisitions FFBOT Florida Forever Board of Trustees FGDL Florida Geographic Data Library GIS Geographic Information Systems IRB Institutional Review Board MCDM Multi C riteria Decision Making MUNICODE Municipal Code Corporation NAICS North American Industry Classification System PCA Portland Cement Association RACEC Rural Areas of Critical Economic Concern S AW Simple Additive Weighting
12 Abstract of Dissertation Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DECISION MAKING FOR SUSTAINABLE LOCATION OF SEMI DESIRABLE FACILITIES WITH APPLICATION TO CEMENT PLAN TS By Maryam Mirhadi Fard August 2013 Chair: Charles J. Kibert Cochair: Abdol Reza Chini Major: Design, Construction and Planning As a decision making process, facility location deals with specifying the most appropriate location for one or multiple fac ilities. The main theme of this research is the improvement of facility location theories by adopting the requirements of sustainable development to better respond to today's increasingly complex environmental challenges. Although considering environmental economic, and social criteria in sustainable facility location decisions makes the results more practical and efficient, it may result in a complex decision making process. The novelty of sustainable facility location calls for a clear definition and fra ming of the problem. This research aims to frame the problem by developing a supportive model for sustainable site selection decisions for semi desirable facilities. The model will be illustrated by presenting a case of siting a cement plant in the state o f Florida. One of the key steps in the model is identifying sustainable facility location indices, which are measurable criteria that evaluate sustainability characteristics of a semi desirable plant from the location point of view. As the compliance with the requirements of sustainable
13 development is followed in the facility location problem, a wide range of environmental, economic and social criteria and spatial information will be taken into account. In the case study, Geographic Information System (GIS ) analysis was utilized to analyze the spatial data of site alternatives and screen inappropriate options out accordingly. The Analytical Hierarchy Process (AHP) approach was used in the first level of the case study to weight the evaluation criteria, and consequently, the feasible alternatives were ranked to find the appropriate choices of counties E ight counties were then selected for the second level of analysis based on the outcomes of the first level of analysis The second level of analysis resulted in a few candidate parcels in Glades County. To increase the number of the candidate parcels, sensitivity analysis was performed to measure the impacts of the decision criteria on the results.
14 CHAPTER 1 INTRODUCTION Background To achieve the global object ives of sustainable development, companies should fundamentally re assess their business operations and strategic plans. The long term survival of industries is linked to the compliance of their strategic plans to the principles of sustainable development. Site selection decisions can be regarded as one of the strategic decisions with significant impact that can no longer be made in isolation and needs to be made with regard to the requirements of sustainable development. S ustainable facility location is a complex process that should comply with different types of criteria and, sometimes, conflicting requirements, such as technological constraints, governmental regulations, global objectives of sustainable development, and the needs of key stakeholders. Mos t of the facility location theories examine the optimum site alternative that minimizes the associated cost or maximizes serviceability of one or more facilities. However, in some facility location studies, social and environmental factors, in addition to conventional criteria, were taken into account. Most of these studies are associated with undesirable facilities. The studies of Gros (1975) Muntzing (1976) Barda et al. (1990) Queiruga et al. (2008) Sumathi et al. (2008) and Achillas (2010) are some examples. Nevertheless, the existing literature of facility location theories does not effectively address all the requirements of sustainable development. The purpose of this research is to define and frame the sustainable facility location pro blem for semi desirable facilities. This research will develop a supportive
15 model for site selection decisions. Semi desirable facilities are desirable facilities for people in terms of their services, but these facilities cause inconveniences to the neigh boring areas (Colebrook & Sicilia, 2007) This research focuses on semi desirable facilities for several reasons which are discussed below. First, despite the undesirability of these types of facilities in terms o f their locations, they should be developed and be located somewhere due to the national (CBI and RICS, 1992, p. 11 12) Therefore, on the one hand, semi desirable facilities have crucial roles in economic development of nations; and on the other hand, they present negative social and en vironmental impacts. Second, from a sustainability perspective, semi desirable facilities have the potential to contribute to sustainable development. These facilities provide goods and services for nations, create job opportunities, and provide a source of income directly and indirectly for companies, governments and investors (Worrall, Neil, Brereton, & Mulligan, 2009) McPherson (1995) believed that industrial devel opment is a major concern for people because the desire of most persons is to have access to job markets, to have high living standards, or to live in places where there are econom ic and employment opportunities. Third, in 2011, approximately 31% of the Un consumption was attributed to the industrial sector (U.S. EIA, 2012) and, in 2009, emissions were from industry and electricity generations secto rs (EPA, 2010b) Although these numbers are
16 associated with all types of facilities, the consumption of semi desirable facilities makes up a significant portion of this number. Therefore, employing sustainable development practices in these types of facilities is indispensable. A case study will be conducted as a part of this research to demonstrate the model. The case study is targeted to analyze the sustainability characteristics of multiple alternatives and to identify the most appropriate ones for locating a cement plant in the state of Florida. The Geographic Information System (GIS) technique will be utilized to evaluate the spatial characteristics of site candidates. Examples of these characteristics include technica l requirements of sites, such as access to raw material and market, and environmental requirements in terms of distance from certain areas, or popula tion centers. GIS analysis helps in effectively screening out inappropriate site options Motivation and C ontribution The main motivation of this research is to minimize the environmental footprint of industrial facilities through strategic decisions. Site selection is one of the strategic decisions with long term impacts on companies, environment, and local c ommunities. Having a comprehensive approach combined with sustainability considerations in site selection ensures that the adverse impacts of facilities on their surrounding environment are minimized. It can also help decision makers to make better locatio n decisions to minimize construction, operational, and maintenance costs of various facilities. By framing the concept of sustainable facility location, this study provides a foundation from which long term sustainability goals can be built into location decisions. By proposing a siting model for semi desirable facilities and by
17 implementing the model in a case study, this research contributes to both theories and practices of facility location and sustainable development. In summary, the main contribution of this dissertation to the body of knowledge is to frame the concept of sustainable facility location through a holistic approach towards the requirements of sustainable development. To achieve the purpose of this research, the following key questions n eeds to be answered: What characteristics define a sustainable location for semi desirable facilities? What is the best model for siting a semi desirable plant in compliance with the requirements of sustainable facility location? What criteria measure the sustainability of a location in a comprehensive manner? What criteria can be used to assess the complian ce of a location decision for a cement plant with the requirements of sustainable development? Scope of Work To frame the concept of sustainable facilit y location for semi desirable plants, a model consisting of appropriate steps and corresponding tools will be developed. The model will provide agencies with a decision support mechanism, which will facilitate the process of selecting appropriate sites for their plants from the sustainable development prospective. By conducting the case study of locating a cement plant in the state of Florida, the model will be evaluated and potential improvement areas will be identified. The use of Geographic Information S ystem (GIS) and Multi Criteria decision Making ( MCDM ) approach in the case study helps in evaluating the suitability of site candidates. The stepwise scope of this research is outlined below:
18 Conduct a literature review of the related subjects including sustainable development, facility location, the application of GIS and MCDM techniques in site selection problems, and practices or guidelines of sustainable siting Frame the concept of sustainable semi desirable facility location Develop a preliminary model for sustainable location of semi desirable facilities Propose a list of criteria for assessing sustainability of a location for a semi desirable facility Frame the sustainable siting of a cement plant in the state of Florida Customize the siting deci sion criteria for winnowing and ranking the location candidates and identifying the suitable ones Categorize the criteria into four groups of macro screeni ng, macro suitability, micro screening, and micro suitability criteria Run the macro level (state le vel) analysis to identify candidate counties Run the micro level (county level) analysis based on the results of the previous step to identify suitable parcels Review and revise the preliminary siting model based on the results of the case study As mention ed earlier, the scope of this research is limited to developing a decision support model for semi desirable facilities by adopting the principles of sustainable development. Therefore, this research is not intended to develop a comprehensive and prescripti ve methodology for siting all types of facilities. Conditions of site selection decisions vary based on geography, society, economic, and environment; therefore, the model should be customized based on the circumstances of problems. In the case study, som e of the criteria, such as the quality of limestone, are compromised for the sake of simplicity because considering all criteria makes the problem too complex to be solved.
19 In Chapter 2 a literature review of the rele vant subjects is presented. Chapter 3 describe s Chapter 4 illustrate s t he proposed siting model along with the list of decision criteria In Chapter 5, the methodology and the results of the case study are present ed The last c hapter, Chapter 6, summarize s the contributions, and limitations of the research, and recommendations for future research studies.
20 CHAPTER 2 LITERATURE REVIEW Sustainable Development In 1983, World Commission on Environment and Development (WCED), chaire d by Gro Harlem Brundtland, was formed to address the environmental UN Conference on Enviro nment and Development held in Rio de Janeiro resulted in establishing the Rio sustainability action plan, Agenda 21. The plan provides countries with general sustainability principles for strategic plans and practices. Agenda 21 includes four categories of 1. Social and economic development, 2. Resource management, 3. Strengthening the participation of major groups, and 4. Means of implementation (Kirkby, O'Keefe, & Timberlake, 1995) However, t he book the term sustainability (Goldsmith, Allen, Allaby, Davoll, & Lawrence, 1972) To achieve sustainable development, first, its definition should be presented. There is no single unified interpretation of the concept of sustainable development. As Mitchell (1997) (p.28) Barbier (1987) (p.10 3) A well known definition of sustainable development belongs to the prominent the needs of the present generation without compromising the ability of future generations to m (WCED, 1987, p.43) There are several
21 challenging questions about this definition including: what is transferred between generations? Is it all about natural resources or it also includes other things, such as human culture? (Elliott, 2006) In addition, as Elliot (2006) have the same meaning for different people. According to the inte rpretation of Kirkby et al. (1995) the main components of Brundtland definition of sustainable development are environment, growth, and equity ( Figure 2 1 ). A common crit ique about the definition of sustainable development used by the World Conservation Strategy (1980) is that it is entirely concerned about ecological issues rather than all aspects of sustainability (Hopwood, Mellor, & O'Brien, 2005) R epetto (1986) that manages all assets, natural resources, and h uman resources, as well as financial and physical assets, for increasing long term wealth and well (p.15) According to Repetto, sustainable development has the goal of rejecting activities that would result in poorer future generation. Pezzey (1992) believed that the main focus of survivability is to maintain welfare above the threshold limit; whereas, the focus of sustainable development is that the wel fare be a non decreasing trend. According to Munda (2005) harmonisation or simultaneous realisation of economic growth and environmental elieved that the concept of sustainable development encompasses all social, economic, ecological, technical, and ethical dimensions.
22 Glavic and Lukman (2007) evo lution of human society from the responsible economic point of view, in (p.1884) Rogers et al. (2008) su ggested the following nine ways to achieve sustainability: Develop so as to not overwhelm the carrying capacity of the system. Sustainability will take care of itself as economi c growth proceeds (Kuznets). Polluter and victim can arrive at an efficient solution by themselves (Coase). Let the market take care of it. Internalize the externalities. Let the national economic accounting system reflect defensive expenditures. Rein vest rents for nonrenewable resources (weak and strong sustainability). Leave future generations the option or the capacity to be as well off as we (p. 28) The sustainable development concept is the result of growing concerns about key societal problems such as poverty, inequality, and rising consumption trends of natural resources. To map the concerns of sustainable development, Haughton (1999) defined five categories of equity principles. Haughton believed that without achieving these equities, listed below, it is impossible to move towards sustainable development: Inter generational equity (futurity) The concept of this equity can be drawn from the B the needs of the present generation without compromising the ability of future (WCED, 1987, p.43)
23 Intra generational equity (social justice) The main focus of this equity is to address the main causes of contemporary social injustice. Geographical equity (transfrontier responsibility) The focus of this equity is to simultaneously consider local and globa l environmental problems. Procedural equity This equity is closely linked to geographical equity and it holds that rules and regulations should be developed and applied in such a way that they treat all people openly and fairly. In other words, political boundaries should not be misused to evade local sustainability regulations and transfer problems to other jurisdictions. Inter species equity The focus of this principle is to preserve ecosystem integrity and biodiversity. Although there is no consensus a bout the exact definition of sustainable development, many agree on three pillars of sustainability environmental, economic, and social dimensions ( Figure 2 2 ) (Azapagic & Perdan, 2000) To achieve sustainable development, translation of its concept into decision making processes and its implementation are crucial. One of the translation methods is to define the sustainability criteria that are relevant to each decision pro cess and to the section in which the decision is taking place. As indicated by Chapter 40 of agenda 21, (UNCED, 1992) developing criteria for assessing compliance with sustainability is necessary. By defining the criteri a, the sustainability of each decision can be measured and evaluated. For the purpose of defining the sustainability criteria, several approaches, such as the approaches taken in the works of Clayton (1996) and Auty (1997) have been
24 proposed in the literature. However, there is no specific methodology that is applicable for every decision in different sectors. Pezzey (1989) suggested three considerations in developing sustainability assessment criteria: criteria should be long term, the focus of criteria should be equity for intra generations and/or inter generations, and the criteria to be considered as constraints. However as mentioned earlier, sustainability criteria cannot be the same for all decisions. Sustainability environment. Facility Location (Site Selection) Facility location or site selection is the process of identifying a suitable location for a specific facility. Facility location problem, as an interdisciplinary problem, has been studied by different scientific groups. In the literature, there are two main approaches to facility location theories; the approach of urban planners and the approach of operations researchers. Facility location is a common term mostly among operations researchers; whereas, site or land suitability analysis and site selection are the common terms among u rban planners. For the literature review of this dissertation, all the aforementioned terms were used as the keywords. involves finding an optimal location for a facility by minim izing the associated costs and/or maximizing desirability of facilities with respect to some constraints. Therefore, operations researchers utilize mathematical optimization to solve facility location problems (Te rouhid, Ries, & Fard, 2012) Although the primary location theories, such as the theories presented in the works of Weber (1929) and Isard (1956) were mostly focused on mi nimizing the
25 serving cost or maximizing the profit, currently numerous facility location models with different approaches have been developed. The applications of the models vary based on the conditions of decisions. Some of the important factors in select ing a location model include: the types of facilities, the number of facilities, the type of the location associated with the decision space (e.g. continuous, network, discrete), the types of coverage, decision restrains, such as the capacity constraint, r elation between new and existing facilities, and the goals of location problems (Hamacher & Nickel, 1998) For instance, for locating noxious facilities, the main objective is usually maximizing the facility dist ance to urban areas; whereas, for locating a retail center, minimizing average times to demand centers might be the primary objective. Some of the objectives considered in the developed location decision theories are as follows (Brandeau & Chiu, 1989; R. Z. Farahani, SteadieSeifi, & Asgari, 2010) : Minimize average travel time or distance Minimizing maximum travel time or distance Maximizing minimum time or distance Minimizing the number of new facilities Maximizing responsiveness Minimizing cost (e.g. set up cost, operational cost, fixed cost, and travel cost) Maximizing profit Some examples of the relevant publications on facility location theories include the publications of Dr ezner (1995) Francis et al. (1983) and Farahani and Hekmatfar (2009) Some examples of the review papers on normative models include the studies of Brandeau and Chiu (1989), Current et al. (1990), Schilling et al. (1993), and Farahani et al. (2010). Equation 2 1 is an example of a simple integer programming model for an un capacitated facility location decision adapted fr om the work of Mirchandani and
26 Francis (1990) The objective of the problem is to locate a subset of facilities in order to maximize the total profit and satisfy all demands. There is no constraint regard ing the service capacity of facilities. Objective function : (2 1) Constraints: Where: f j = fixed cost of locating a facility at site j c ij = profit of servicing client i from facility j x j =1 if a facility is locat ed at site j, otherwise xj=0 y ij =1 if the demand of client i is satisfied from facility j, otherwise yj=0 The first Constraint guarantees that the demand of each client is satisfied and the second constraint ensures that every client is supplied only from open facilities. As mentioned earlier, site suitability analysis is the term mostly used by planners for facility location problems. Church and Murray (2009) have defined site of systematically identifying or rating potential (p.108) Planners, usually use descriptive methods and mapping tools, such as GIS, in site suitability decis ions to specify the most suitable location. The descriptive approach focuses on the agglomeration
27 properties of locations (Church & Murray, 2009) In general, applying only mathematical models will not effectively support sustainable facility location decisions; therefore, the tools and methods of site suitability analysis, such as GIS, should also be utilized. According to Church and Murray (2009) researchers interested in facility location problems should know both GIS and optimization techniques. In the literature, McHarg (1969) is recognized as the originator of basic mapping concepts. McHarg used these concepts for site s was to overlay thematic maps to create a configuration, based on which the most suitable location can be selected. Each map represents a theme, such as a wetland or a soil property. The work of Von Thunen (1826) is also recognized as the inception of descriptive approach to facility location theories. In his model, Van Thunen addressed the land use allocation. tudies formally dates back to 1909 with the book of Alfred Weber (1929) which can also be involved finding a fourth point to locate a factory in a way that minimizes the weighted distance between the point and the other three points, one point representing a demand point and two points representing two raw material points. Below, some of the reviewed site selection (facility location) p rocedures are briefly explained. In his book commonly used by planners, LaGro (2011) proposed the following site selection process:
28 Clarify project objectives and requirements Determine the site sele ction criteria Data Collection and Analysis Identify potential sites Rank the alternative sites Select the best site and document the results Test project feasibility ( p.48) In their book, Church and Murray (2009) suggested the principle steps of site suitability analysis as follows: Identify the major attribute layers with influencing roles in site suitability and collect the re levant data for each location alternative Determine the site suitability method Conduct the site suitability evaluation to rank location alternatives By the use of multi criteria approach and GIS technique, Sumathi et al. (2008) proposed an algorithm to identify the most appropriate location in an urban area for a landfill site. The decision criteria in their proposed methodology were classified to two sets of constraint parameterization, which eliminates unsuitab le locations and key evaluating criteria. This elimination process facilitates the identification of the most appropriate location for the facility. The evaluation criteria classified into land use, hydro geologic, and air quality criteria. For siting larg e wind projects, Berkhuizen (1988) proposed that the decision be processed at three levels of national, regional, and local scales to determine the most suitable area. In locating a power plant in Maryland, C alzonetti et al. (1987) proposed to conduct the site selection process in five stages including: 1. Exclusionary screening for eliminating certain areas 2. Discretionary screening for selecting candidate areas
29 comparison for selecting final sites, and 5. Candidate site analysis for characterizing sites. Church and Murry (2009) be lieved that in general, there are some common laws of location theories. They classified these laws into three groups: Due to a reason, some locations are better than other alternatives. According to this law, decision makers of facility location problems should seek the optimal or near optimal site options. Location efficiencies can be affected by spatial characteristics. In a multi facilities location problem, sites should be selected at the same time. Geographic Information System (GIS) and Facility Loca tion A Geographical Information System (GIS) is a technological tool for storing, integrating, organizing, analyzing, and visualizing geographical data as well as attribute data. Moreover, various levels of information can be combined and analyzed in GIS f or supporting decision making processes. Some of the powerful capabilities of GIS in the manipulation of spatial data include (Church & Murray, 2009) : Conversion : Integrating different layers of spatial informati on with different coordination systems Aggregation : C ombin ing data into higher levels Overlay : Integrating different input layers of data into one output layer Interpolation : Estimating the location data where there is no available information Two forms of raster and vector spatial data are available to be used in the GIS environment. Raster data are grid cell images composed of millions of cells. The vector format consists of points, lines, and polygons. According to LaGro (2011) the
30 raster format is mostly used for environmental data analysis and site gradients, and vector format is usually used for cultural data, such as land parcels and roads. Recently, GIS has been recognized as an effective tool for supporti ng location decisions and its application in that area is intensified (Sumathi et al., 2008) According to Church (2002) the success of location problems is tied to the application of GIS. Having the ability to manage and manipulate numerous spatial data from different sources, GIS is considered as a powerful tool for site election processes It is notable that effectively considering principles of sustainable development is deeply tied to spatial data. Providing a wide range of spatial information, GIS is one of the most effective and reliable tools for under taking sustainable development led activities, such as sustainable facility location. It is worth mentioning that GIS is considered as one of the analyzing tools of the environmental data in Rio sustainability action plan, Agenda 21, chapter 40.9 (Campagna, 2006) The first application of GIS in location science dates back to 1970s with the research works of Kiefer (1973) Voelker (1976) and Dobson (1979) For instance, Dobson (1979) worked on the problem of identifying site alternatives for locating a power plant in the state of Maryland. I n solving the problem, he followed the approach of McHarg and created the maps of site alternatives, based on the evaluating parameters, such as soil, presence of water, distance to transmission grid, and population density. The application of GIS in sit e selection decision has significantly increased due to its potential capabilities. Some of the applications of GIS in facility location
31 problems can be seen in the research studies of Charnpratheep, Zhou, & Garner (1997) Muttiah et al. (1996) Sadek et al. (2001) Leao et al. (2001) and Wakefield & Elliott (2000) However, based on the types of facilities, approaches in using GIS for location decision vary. For instance, in retail sector, GIS is mostly used for demand assessment, competitor analysis, and customer penetration; whereas, in housing development decision, the main focus is on housing and land price, and accessibility to transit systems. In the following, a number of case studies are reviewed to compare the applied approaches for different facility location problems. In thei r research, Benoit and Clarke (1997) used a GIS application for a retail siting decision. They used mapping, overlaying, and buffering techniques to analyze the spatial data. Due to the nature of the retail busine ss, the main considered evaluation criteria were population, household numbers, demand, market penetration, and the locations of other competitors. This consideration can also be seen in the research work of Tayman and Louise Pol (2011) These researchers addressed the The research study of Zhang et al. (2009) can be considered as a housing development case study, based on GIS analysis. In their article, Zhang et al. proposed the potential areas for affordable housing development in Wuhan, China. They used GIS tool integrated with spatial decision support system (SDSS) for analyzing the data. For afforda ble housing site selection proce ss, they considered the following main criteria: Distance to rapid transit line Urban planning area (population density)
32 Land grade and land cost Housing market price Geographic characteristics of sites to avoid river, lake, etc. Public participate Residen tial segregation prevention Jiang and Eastman (2000) utilized multi criteria approach to conduct GIS based industrial site suitability analysis in Nakuru, Kenya. In their study, they applied fuzzy approach in Bool ean and weighted linear combination evaluation techniques. The considered evaluation criteria in their research were as follows: Proximity to road, for the transportation of raw materials and finished products Proximity to the town, to consider labor avai lability Slope gradients, because of its impacts on construction cost Distance from Lake Nakuru Park for preservation of wildlife and a tourist attraction For proximity to the town, they preferred short distance due to labor availability. However, in rea lity, trade labor availability, which pulls the manufacturing plant towards urban areas, and environmental considerations pushing the plant away from urban areas. Although the authors emphasized on their multi criteria approach in their research, they used only four evaluation criteria to simplify the problem, and these four criteria were not sufficiently reflecting the circumstances of the problem. In fact, industrial area development is one of the crucial decisions in urban planning, which requires that environmental, economic, and social factors be taken into account. Based on the review of the literature, some examples of the factors governing in industrial location decisions are as follows: Pub lic health Community economic performance Unemployment rates Distance to critical hydro and geological areas
33 Distance to sensitive areas, such as archeological and cultural areas For locating a geothermal power plant in Iran, Yousefi and Ehara (2007) used GIS as the decision support tool. For this purpose, they categorized the spatial data into three classes of physical, socioeconomic, and technical datasets. The physical dataset included slope, rivers, and fau lts. The socioeconomic dataset included population centers, and access roads. Wells, hot springs, and anomaly were considered as part of technical datasets. GIS has also been extensively applied in locating undesirable facilities, such as incineration faci lities for hazardous waste materials and solid waste landfills. Some examples include the research studies of Hodgart (2003) Senner et al. (2006) Sumathi et al. (2008) Wang et al. (2009) and Alada Almeida et al. (2009) For identifying the potential suitable landfill sites in the vic inity of Ankara, Turkey, Sennert et al. (2006) integrated 16 thematic maps, such as topography, settlements roads, railways, airport, wetlands, infrastructures (pipelines and power lines), slope, geology, land use, floodplains, aquifers, and surface water In their research, Sumathi et al. (2008) used GIS for locating a municipal solid waste landfill. As the first step, they identified different criteria and constraints, which should be met in location decisions For each criterion, they created a thematic map in GIS to analyze the site alternatives. By integrating AHP technique with a GIS application, Wang et al. (2009) identified the optimal areas and back up areas for s iting a landfill in Beijing, China. The considered thematic maps included residential areas, surface water bodies, ground water, price of lands, airport areas, land use, slope of the land surface, highway, road, and existing landfill suitability.
34 By usin g ArcGIS 9.0, Nas et al. (2010) analyzed eight thematic maps to evaluate potential sites for municipal solid waste in Konya, Turkey. The maps tance from transportation routes and rails, distance from archaeological sites, distance from (Nas et al., 2010, p.491) Some of the capabilities of GIS for modeling location decisions are briefly summarized below (Church, 2002; Church & Murray, 2009; Misra, 2002; Sumathi et al., 2008) : GIS provides digital information for various sites characteristics. Therefore, potential candidates can be analyzed based on different spatial criteria. GIS is one of the most effective tools for assessing environmental criteria, such as habitat, biodiversity, erosion patterns, and stability. Since handling and managing complex geographical data sets are significantly facilitated by GIS, the time and cost of site selection process will be essentially reduced. GIS helps location decision makers systematically screen out unsuitable sites based on the combined spatial data. The results of geographical analysis can be visualized through GIS applications; GIS assists in maintaining and updating the spatial data records of any selected site; therefore, siting, relocating, or planning transportation routes will be significantly facilitated. Collected data in GIS can be utilized for different purposes, even simultaneously. Therefore, the cost of collec ting and maintenance of data can be shared among projects. GIS based site selection is a complex decision making process, which requires diverse spatial data for analyzing site candidates. Some of the challenges faced by decision makers during this process include the following :
35 Data acquisition from diverse data sources can be considered as one of the main barriers of GIS based location decisions. For each evaluation spatial criterion, at least one dataset should be captured and integrated in the GIS based decision system. Downloading appropriate data from digital libraries is a very time and effort consuming process. Unavailability of spatial data when decision models have to use certain pre specified data can be a challenge. Errors in GIS projects are a m ajor source of problems (Griffith, 1989) Therefore, validating the quality of the captured data is one of the crucial steps, which can be done through metadata sources (Radk e et al., 2000) In most GIS based analyses, one of the basic assumptions is that the input data are precise and accurate. However, data validation in cases where the sensitivity of the results to the input data are high or in cases where high levels of p recision is needed can be considered as another source of challenge (Malczewski, 2004) Since the required data should be acquired from disparate datasets and sources, then integrated in a single dataset, checkin g the accuracy of the overlaid thematic maps in GIS can be a challenge. Interoperability is another source of challenge, which needs to be addressed properly. Decision makers need to ensure that different spatial data can be translated to one single file f ormat for integration purposes. The sizes of spatial data files are large; therefore, other difficulties are associated with data processing and analyzing time. Availability of computer facilities with high processing capacities is crucial. Briefly, in dat a acquisition process, the following issues should be considered: The types of spatial data required to be collected for each identified criteria The sources of data that are available The availability of Metadata to check the quality of data and its cont ent The amount of data errors that can be acceptable to policy makers. Some of the spatial data errors include data transformation, interpolation, aggregation, and errors in data attributes The availability of high technology computers to facilitate analyz ing processes
36 Multi Criteria Decision Making (MCDM) and Facility Location Despite the numerous capabilities of GIS, there are limitations in its analysis capabilities for location decisions (Jankowski, 1995) In fact, sustainable location decisions, as strategic decisions, affect the survival of companies, and has long term consequences on local communities and the environment To evaluate site candidates and identify the most suitable ones in terms of sustainabil ity, a set of appropriate and sometimes conflicting criteria should be considered. For instance, parameters for siting an industrial plant; whereas, the crucial factors for indus tries might be economic ones. Therefore, sustainable location decisions require evaluating and comparing several alternatives against multiple criteria, calling for the multi criteria approach in the decision process. In the last decade, multi criteria loc ation models have gained considerable attention in the location science for different types of facilities. In their paper, Farahani et al. (2010) proposed a classification system for multi criteria lo cation problems ( Figure 2 3 ). Some of the advantages of having the multi criteria approach for sustainable facility location are listed below (Figueira, Greco, & Ehrgott, 20 05) : By considering a broad spectrum of criteria, this approach helps decision makers avoid neglecting various aspects of sustainability. The multi criteria approach can consider different value systems as different objectives. The multi criteria approac h facilities debates on weights, rejection levels, aspiration levels, and veto of the predefined set of criteria during the decision making process.
37 A decision process with the MCDM approach involves three main fundamental concepts, which are succinctly de scribed below (Figueira et al., 2005; Zopounidis, 2010) : Alternatives or potential actions The objects, towards which decision process is directed Criterion and family of criteria: Well defined indices, based on which alternatives are evaluated and compared Problem formulation: The way of envisaging and solving the problem Since an MCDM approach can deal with conflicting decision criteria, one of the applications of MCDM techniques is th e case of undesirable and semi desirable facility location problems. Some examples of the location studies that employ an MCDM approach are the works of Barda and Haluk (1990) Current (1990) Erkut (1991) Alada Almeida (2009) Ataei (2005) and Abdullahi et al. (2012) Banias et al. (2010) utilized the ELETRE III methodology to locate a waste water plant in Greece. For achieving sustainable industrial siting, Dudukovic et al. (2005) proposed the use of Spatial Decision Support System (SDSS), which is a combination of GIS tool and the multi criteria approach. Hokkanen and Salminen (1997) used the PROMETHEE de cision tool for locating a waste treatment facility. By applying the Analytical Hierarchical Process (AHP), Erkut and Moran (1991) solved the problem of siting a sanitary landfill. Before comparing the alternative s of a decision, the evaluation criteria should objectives. There are many techniques that can be used for weighting the criteria based on the nature of problems. Since in the c urrent research, AHP was selected as
38 an MCDM method, for weighting the decision criteria of the case study, the AHP technique is briefly described below. AHP, as an MCDM method for group decision making, was developed by Thomas Saaty (1980) This method was selected in this research for weighting the criteria of the first level analysis (the county selection process); because it helps derive the relative importance of criteria based on the opinions of a panel of experts and by pair wise comparisons of criteria in a hierarchical process. In addition, creating a balance among the weights of conventional and sustainability criteria was one of the objectives of this research; therefore, soliciting expert judgments fro m different academic and industrial sections was crucial to make balance among judgments. In this regard, AHP method significantly facilitated the process. AHP, known as an effective and flexible method for ranking qualitative and quantitative data, has b een used in many site selection studies. Wang et al. (2009) utilized AHP and GIS techniques for a landfill site selection. In their research, Moeinaddini et al. (2010) used AHP to select solid waste disposal site in Iran. By using GIS and AHP, Jack (2012) identified the potential sites for a Limited Impacted Design storm water management project. In summary, the main steps of AH P include: Identify the goal of the problem and contributing attributes Identify the participants based on the nature of the problem Establish the hierarchical structure of decision factors from the goal to the criteria and sub criteria Identify the relati ve quantitative importance of the primary criteria, based on their impacts on the identified goal. For prioritization of the criteria, conduct pair wise comparison among criteria in each level and for the purpose of comparison, respondents should be provid ed with a unique scale of importance.
39 Prioritize the sub criteria with respect to their upper level criteria. Compute the importance weight of the criteria (vector of priorities) based on the judgments. According to Saaty (1990) decision makers may decide to eliminate some of the criteria due to their low relative importance values, in this case, priorities should be recalculated with or without revising the judgments. Although there are several ways to calculate the vector of priorities from the comparison results, according to Saaty (1980) the following method gives more precise result compared to the other methods: 1. Multiply the elements of each row in comparison t ables, 2. Take the n th root of the results, and 3. By normalizing the resulting figures, the priorities values will be estimated. One of the key steps of the AHP method, is checking the overall consistency of the results through the consistency ratio. In f act, with checking the consistency ratio, the consistency of judgments is validated and it enables decision makers to modify judgments and improve the overall consistency, if it is needed. Saaty (1990) suggested that in order to keep the risk of inconstancy low, the number of elements in pairwise comparison should be small and not to be more than nine Another important step for ensuring the consistency of AHP results is to conduct pairwise comparison among homoge nous elements rather than disparate ones (Saaty, 1990; Zopounidis, 2010) For this purpose, several clustering methods have been proposed (For more details, see the work of Saaty (1990) ). According to Zopounidis and Pardalos (2010) Saaty and Sodenkamp proposed the following three steps to fix inconsistent judgments: Identify the most inconsistent jud gment Find the value range, within which the inconsistency will be improved Request the expert to revise his/her judgment, based on the values in the range The consistency index and consistence ratio are calculated based on Equation 2 2 and Equation 2 3 (2 2) (2 3) In which,
40 The principal eigenvalue = Random Index, which should be extracted from Table 2 1 proposed by Saaty (1980) If th e consistency ratio exceeds 0.10, the comparison matrix should be re assessed. In summary, the main reasons to use AHP method in facility location and site suitability problems include (Carr & Zwick, 2007; Malczewski, 1999) : The technique is simple and easy to understand and implement. It has potentials to facilitate group decision makings by participating a large group of stakeholders, who can represent the whole community. According to Saa ty (1980) (2004) and Banai (1993) believe that by AHP, large numbers of site candidates cannot be assessed. After weighting the criteria, the alternatives can be ranked based on different multi attribute techniques, such as SAW and COPRAS, which are described briefly below. SAW method SAW (Simple Additive Weighting) is the oldest and the most common used method (Podvezko, 2011) This simple method ( Equation 2 4 ), which does not need complicated c alculations, can be applied for most of the multi attribute problems (Ginevicius & Podvezko, 2007) (2 4) Where
41 = Weights of the suitability criteria = Normalized j th alternative common measurable units to be able to compare the alternatives, based on the criteri a. In the SAW method, Equation 2 5 is used for normalization of values of minimizing criteria, which are preferred with lower values and Equation 2 6 is used for the normalization of values of maximizing criteria, which are preferred with larger values (Podvezko, 2011) (2 5) (2 6) Where: = j th alternative be positive and negative values need to be transformed to positive values; therefore, positive values. However, for normalization of the evaluation values, other normalization methods such as Equation 2 7 for maximizing criteria and Equat ion 2 8 for minimizing criteria might be used (Chatterjee, Athawale, & Chakraborty, 2011) (2 7) (2 8)
42 Where: = j th feasible alternative COPRAS method This method includes the following principal steps Preparing the decision making matrix with normalized values Where: = Normalized j th alternative In this technique, Equation 2 9 is used for normali zation of all values. (2 9) Where: = j th alternative Calculation o f the weighted normalized decision making matrix through Equation 2 10 (2 10) Where: = Weight of the suitability criterion j Normalized j th alternative Order the decision making matrix in a way that the maximizing criteria are placed first, and the minimizing criteria will be placed next Add the weighted normalized values of maximizing criteria. Calculations should be done for each alternative separately and through Equation 2 11
43 ; k is the number of maximizing criteria (2 11) Add the weighted normalized values of minimizing criteria. Calculations should be done for each alternative separately and through Equation 2 12. ; n is the number of minimizing criteria (2 12) Identify the minimal value o f Ri R min Calculate of the relative weight of each alternative i (Qi) through Equation 2 13. (2 13) Determine the maximum Qi K= max Qi; Calculate the utility degree of each alternative through Equation 2 14. (2 14) In COPRAS method, the impacts of maximizing and minimizing criteria are evaluated separately. The utility degree of each alternative in COPRSA method is not a linear function of minimizing criteria. According to Podvezko (2011) when there is data variation, COPRAS might be unstable; therefore, the obtained results might vary from the results of other methods. Each multi criteria method has disadvantages and ad vantages, and no single method can be designated as the best method (Podvezko, 2011) ; therefore, it is the techniques in co mbination allow for comparison of the results and choosing the most stable one.
44 Sustainable Facility Location The focus of this section of the literature review is on the facility location studies, which have addressed the concept of sustainability with or without premeditation. In 2005, Dudukovic et al. (2005) used the term sustainable industrial siting. The main focus of that study was developing a site suitability map for placing industrial facilities in a ca se study. The study did not differentiate between desirable and undesirable industrial facilities. In this study, one of the considered evaluation criterion was proximity to high population density areas, which is in contrast with the targets of undesirabl e facility location decisions. In addition, some of the key technical and sustainability criteria, such as the closeness to raw material, market, and more importantly criteria pertaining to biodiversity, were overlooked. One of the other relevant studies i s the work of Tsoutso et al. (2007), in which they proposed a sustainable siting procedure for Small Hydroelectric Plants (SHPs) in Greek. According to them, some of the influencing sections on sustainable sitting of Hydroelectric Plants (Hps) are plannin g procedure, cases of obstructing the procedure, legal framework, bodies of implementation (public or private), and measures (social, environmental, and technical). In 2010, Farahani et al. (2010) rec to measure these attributes [related to social and environmental objective functions]. Therefore, we can think of a term lik (R. Z. Farahani et al., 2010, p.17) In another article, Arabani and Farahani (2011) suggested that the concept o f sustainable facility location be considered in future research studies. As they
45 off must be set between handling changing (Arabani & Farahani, 2011, p.417) In their study, Cattafi and Gavanelli (2011) found a sustainable site for locating a biomass power plant. By taking into acc ount a set of environmental criteria, such as proximity to water wells, unstable terrain, and archeological sites, they found a feasible area in the GIS environment. By using linear integer programming and considering mostly economic factors, such as deman d and profit, they then tried to find the optimum location for the plant. There are numerous studies in the literature that have addressed various aspects of sustainability, such as social, economic, or environmental factors, without any premeditation to i mplement the concept of sustainability with a holistic approach. Some of the relevant studies are the ones that focus on undesirable and semi desirable facilities. Before reviewing those articles, the concept of undesirable and semi desirable facilities ar e briefly explained below. As noted by Erkut and Newman (1989) the earliest reference to the concept of partially noxious facility location was the study of Goldman and Dearing (1975) Erkut and Newman (1989) also used the term semi desirable facility in their paper. However, in solving facility location problems, they did not differentiate between the undesirable and semi desirab le facilities. According to their definition, semi desirable and undesirable facilities are necessary for society in terms of their services; however, they provide, to some extent, a disservice to neighboring individuals. Nevertheless, undesirable faciliti es have strong negative impacts on the neighboring
46 population and create a threat to human beings. Examples include chemical plants, nuclear reactors, and land fill sites In the operations research literature, most of the studies of purely undesirable facility location problem interact with maximizing the distance of the facility from the closest demand point (Erkut & Neuman, 1989) ; whereas, most of the semi desirable facility location studies deal with balancing between two or more objectives, such as transportation cost and environmental or social cost (Melachrinoudis, 1999; Yapicioglu, Smith, & Dozier, 2007) In the following, some of the studies that have fully or partially addressed the requirements of sustainability; without having the intention to implement the concept of sustainability, a re reviewed. In his research, Muntzing (1976) addressed the environmental and social concerns of nuclear plants locations. One of the relevant statements of Muntzing, which conforms to the concept of sustainabili no impact is solely economic, and no impact is isolated. Nor does the environment (Muntzing, 1976, p.1) Susskind (1985) proposed five factors to be able to partially create balance among environmental and economic benefits and costs in locating power plants. The proposed factors include: Be inventive a bout strategies for reducing or spreading risk Stress the enforceability of promises to mitigate adverse effects Experiment with new forms of compensation (p.4)
47 Berkhuizen at al. (1988) proposed a procedure for locating large wind facilities by considering some environmental and social criteria, such as avoiding physical restrictions and distance to population density. For location decisions of thermal power plants, Brada et al. (1990) suggested the following four socioeconomic criteria to be considered: impact on agriculture l and, priority of the site in terms of tourism, proximity to urban areas to access to the labor force, and existence of residential houses on the site. Erkut and Moran (1991) considered three dimensions of environme ntal, social, and economic for site selection of a landfill. The major considered environmental and social factors include hydrogeology, drainage, ecology, population, community structure (amenities and land use conflicts), social acceptability, and proper ty values. In the economic dimension, only the associated costs of each alternative were evaluated. By including some sustainability criteria, Banias et al. (1993) identified the best suitable location for a constr uction and demolition waste management facility. Briassoulis (1995) identified the influencing environmental criteria in locating industrial facilities and proposed a classification for them. The only consider ed sustainability criterion in the study of Queiruga et al. (2008) for locating electronic waste recycling plants, was the availability of environmental grant. According to Wang et al. (2009) some of the key criteria in the site selection of landfills should include distance from residential areas and main roads, initial cost, land slope, and the availability of solid waste.
48 Achillas (2010) proposed a decision support system for locating electrical waste treatment plant. The major sustainability criteria considered in their study include unemployment population and financial status of local population as the indictors of social a cceptance. For locating municipal landfills, Sumathi et al. (2008) considered some social and environmental factors including water bodies, ground water table and quality, infiltration, air quality index, and se nsitive sites. By studying the influential factors in locating undesirable or semi desirable facilities, Garrone and Groppi (2012) addressed some of the social factors. In their case study, they considered potent ial voice, awareness, and willingness of residents to accept unwanted facilities as social criteria. For measuring the awareness of considered: Percent of high school graduates Pe rcent of families under threshold Average income Moreover, the potential voice of residents was estimated based on homeowners and political activism (votes). As it is emphasized by Kirkby et al. (1995) equity is one of the key themes of sustainability and there are various ways to frame the concept of equity in decisions. It is notable that social equity has been addressed in several facility location papers, such as the study of Norese (2006) for locating waste landfills. Mumphrey et al. (1971) is recognized as the initiator of farming the concept of equity in location decisions. The concept of equity in facility location
49 groups can be based on spatial proximity or some attribute associated with the (Marsh & Schilling, 1994, p.3) The dimensions of Groups can be spatial, demographic, physical, and temporal. The difference in response time for emergency f acilities is an example of spatial equity, and distributions of recreational facilities among low income families is an example of demographic equity (Marsh & Schilling, 1994, p.3) In their paper, Mar sh and Schilling (1994) reviewed the proposed models for measuring equity in facility location problems. According to them, various equity measurement models have been developed and there is no consensus on the best model. To effectively address the concept of equity in a sustainable facility location decision, environmental, social, and economic impacts of the facility should be analyzed as the first step. The equity classification proposed by Haughton (1999) might be a good basis to measure the identified environmental, social, and economic impacts on each class of equity. Practices and Guidelines of Sustainable Siting In this section, some of the reviewed gui delines and practices in association with industrial locations are briefly described. In 1999, the Cement Sustainability Initiatives (CSI) was organized as one of the key management initiatives for the cement industry. CSI was established with the coopera tion of ten major cement companies and under the supervision of the World Business Council for Sustainable Development (WBCSD). The main target of this
50 initiative is to examine the operations and policies of cement plants across the world in pursuit of the sustainability goals. In 2002, CSI published its first sustainability (Cement Sustainability Initiative, 2002) this report was to establish guidelines for cement companies across the world for conducting their business in the sustainability realm (Humphreys & Mahasenan, 2002; H. Klee, 2004) The report includes 13 sub studies, one of which is more relevant to the current research study: Sub Study 11: Management of Land Use, Landscape, and Biodiversity. By illustrating several case studies, sub study 11 recommended the land management practices, which con tribute to sustainable development in each phase of the plant lifecycle. Two of the basic recommended land management actions for improving the sustainable development during locating a cement plant are: Using techniques, such as GIS, to manage ecosystem processes for siting a monitoring land use patterns including habitation, housing, agriculture patterns (Misra, 2002, p.vi) Minimizing the area, which is affected by plant and quarrying operations. In summary, the report proposed several actions for better management of land use and biodiversity while siting a new cement plant ( Table 2 2 ). One of the other valuable resources developed by CSI is the Environmental and Social Impact Assessment (ESIA) guidelines (WBCSD/ CSI, 2005) The purpose of the guideline is to address positive and negative impacts of a cement plant on the environment and the local community during the different phases of cement facility development, operation, and closure. Figure 2 4 illustrates the ESI A high level considerations in the site assessment phase of locating a cement plant.
51 I t is notable that the main focus of recent publications is cement production technologies and progress reports associated with CO 2 reduction in the manufacturing pr ocess. CSI Product Category Rules (PCR) for concrete (CSI, 2013) CO 2 Accounting and Reporting Standard for the Cement Industry (CSI, 2011) and Cement Technology Roadmap (CSI, 2009) are example publications Therefor e, a limited number of updated publications were available for conducting the current research. Punjab pollution Control board (2009) has also developed a guideline for location of industries t o prevent and control pollution. The guideline has three main parts of 1. General guidelines 2. Industry specific guidelines, and 3. City/ Area specific guidelines. One of these guidelines is associated with brick Kilns, cement plants/grinding units ( Table 2 3 ). Rajasthan State pollution control board (2008) has framed a guideline for location and pollution control of the stone crusher industry. Some of the prescribed suitability criteria for location decisions are outlined below: The nearest Arial distance from the proposed stone crusher site to the state/ national highways must be at least 500 meters and 100 meters from the other roads. The nea rest Arial distance from the proposed site to the national parks and sanctuaries must be at least 2.0 Km and 200 meters form the reserve forests/ protected forests/ unclassified forests. The nearest Arial distance from the proposed site to any prominent pu blic sensitive water body/ prominent places of worship/ school /hospital/ notified archeological monuments must be at least 500 meters. In the report prepared for addressing siting issues of gravel mines and asphalt plants in Thurston County, Washington, the following suggestions are prescribed for
52 the location of gravel mines and asphalt plants (Cole, T., Earle, Ch.,Kohlenberg, L., Nelson, S., 2009) : The site should be away from unstable lands, such as steep or hazardous slopes, seismic zones, subsidies areas, and areas with instable soil. Mines and plants should not be located in critical areas, such as flood plains and coastal flooding areas. The site should be away from wetla nds, aquifer recharge areas, and groundwater wells providing water for domestic purposes. Consider buffer around streams and rivers. Consider distance to residential areas and buffers to isolate mining from other land uses and recreational use areas. Consi der distance to vulnerable residents and residential areas. The site should be away from critical or endangered species habitats, state/federal natural area preserves, and state/federal wildlife refuges. In 2005, Washington State Departments of Ecology dev eloped a set of siting criteria for incinerating waste management facilities. Some of the prescribed key location requirements are as follows (Washington State Department of Ecology, 2005) : 500 feet from natural areas 500 feet from prime agriculture soil and agriculture districts Not to be located in hazardous or steep slope, marine bluff, erosion, and subsidence 500 feet from a fault and shorelines Not in 100 year flood zone Not in 500 yea rs flood plain Not over sole source of aquifer 500 feet from groundwater protection areas 500 feet from perennial water bodies Not in channel migration zone Not in high ground water hazard Not in coastal flooding areas 500 feet from wetlands 500 feet fro m critical wildlife habitat, natural areas and wildlife refuges
53 mile from public gathering places Not in historic or archeological places mile from actual residences 200 feet from own property line Summary The current development trend of industries has long term negative environmental and social impacts imposing a variety of costs on current and future generations (Wallner, 1999) Therefore, industries are one of the core areas for sustainable development (WCED, 1987) However, awareness of sustainability is significantly deepened among industries, and sustainable development practices are becoming an integral part of their strategic decisions. They have approached sustainabi lity due to several reasons including existing binding regulations and noncompliance costs, operational costs, satisfying stakeholders, competitive markets, and social responsibilities (Morhardt, Baird, & Freeman, 2002) sustainability concept is facility location decisions. The sustainable facility location concept is a holistic approach encompassing all sustainability dimensions including environmental, economic, and social in addition to the technical dimension. Based on the literature review, the need for framing the concept of sustainability in location decisions is evident. Although various siting guidelines and instructions have been proposed in theories and practices, most of them focused on environmental issues, and only a few of them addressed other aspects of sustainability. Therefore, developing a comprehensive decision making model including a list of evaluation criteria addressi ng all aspects of sustainability is crucial. The model ensures that
54 Table 2 1 Average R andom I ndex 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0.00 0.00 0.5 8 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59 (Adapted from Saaty (1980) ) Table 2 2 Proposed actions for site assessment of a cement plant Actions Proposed Responsib le Agency 1 Techniques like remote sensing coupled with GIS, which offer opportunities to assess and monitor ecosystem processes, could be effectively used at the time of siting and during operation to assess and monitor habitat, biodiversity, erosion pat terns, siltation, land stability etc. GIS techniques are also very valuable in monitoring land use patterns including habitation, housing, agriculture patterns etc. around existing and prospective sites. This will help in more effective development of mana gement plans, especially for projects in sensitive regions. Cement Plant Management 2 Among limestone bearing areas, siting of new projects may be taken up in areas that require development. Cement Plant Management 3 Although project proponents should t ake adequate land for establishment of the plant and quarry in view of minimizing the conflicts with neighbors, efforts should be made to minimize the area actually affected by plant and quarry operation leaving the balance area undisturbed. Cement Plant M anagement 4 If quarrying in a karstic site is unavoidable, the objective must be to find a site with minimum levels of conflict: Sites on isolated limestone hills (cones or towers) should be avoided as such areas commonly have a high degree of endemic or rare species. One large quarry is preferable over a number of small quarries Sites which do not intersect ground water flows should be preferred Detailed assessment of the site must be carried out by qualified scientists who are experienced in karst ter rains. Cement Plant Management (Adapted from Misra (2002) )
55 Table 2 3 Punjab pollution control guideline for b rick k ilns, cement plants/grinding units Distance from Distance Munici pal Corporation Limits 5 Km Class A Town & Cities Limits 2 Km Other Town & Cities Limits 1 Km Village Lal Dora / Phirni 500 Mts. Wild life Sanctuary / Zoo 500 Mts. National Highway 500 Mts. State Highway / Scheduled Road 300 Mts. Residential Are a (15 Pucca Houses) 300 Mts. Educational Institute / Historical Religious Places/ Protected Monuments 300 Mts.
56 Figure 2 1 The main components of sustainable development (Adapted from Kirkby et al. (1995) ) Figure 2 2 The pillars of sustainable development Figure 2 3 A classification of multi criteria location models ( Adapted from (R. Z. Farahani et al., 2010) ) Environment Society Economy Multi Criteria Location Problems Multi objectives location problems Bi objective problems k objective problems (K 3) Multi attiribute location problems ENVIRONMENT GROWTH EQUITY
57 Figure 2 4 High level considerations a new cement plant site assessment (WBCSD/ CSI, 2005) Enviromental and social considerations of Site assessment Stakeholder Mapping Land USe Social Structure and polulation Public Health Biodiversity and ecosystems Cultural heritage and landscapes
58 CHAPTER 3 RESEARCH METHODOLOGY A supportive dec ision making model along with a list of evaluation criteria is proposed for sustainable semi desirable facility location problems. Using GIS combined with the MCDM approach, the proposed model has been illustrated in a case study. The implemented methodolo gy to achieve these objectives is described below. After outlining the principal steps and associated tools, the data requirements for doing the case study are listed. Next, the considered assumptions are described. Principal Research Steps Reviewing liter ature. A comprehensive literature review was conducted on the following related areas: The concept of sustainable development Facility location and site suitability analysis theories Facility location and site suitability studies addressing the requirement s of sustainability The application of GIS and MCDM techniques in site selection problems Existing sustainability practices and guidelines for siting industrial plants Reviewing siting guidelines and practices. The most appropriate and relevant sting guid elines and studies addressing the requirements of sustainability were reviewed to develop the conceptual model and identify the list of criteria. Developing a decision model. After reviewing the literature and siting guidelines, a decision support model al ong with a list of decision criteria was developed. The model includes appropriate steps and tools for the sustainable location decisions of a semi desirable facility. Conducting the case study. For conducting the case study, the following steps were taken :
59 I. Reviewing the general characteristics of the cement industry II. Reviewing the sustainability challenges of the cement industry III. Framing the problem of siting a cement plant in the state of Florida IV. Customizing the proposed criteria for siting a cement plant i n the state of Florida based on the reviewed guidelines and the existing governmental regulations V. Soliciting expert judgments on the list of criteria VI. Classifying the criteria VII. Performing the macro and micro level analyses Macro level analysis As will be d escribed later, the model suggests that location evaluations be performed at both macro and micro levels. In the case study, the purpose of the macro level analysis was to select the candidate counties of Florida with the highest priority for locating a ce ment plant. By conducting the micro level analysis, appropriate land parcels within the selected counties are identified. The evaluation in each level of analysis was also conducted at two phases of screening and suitability. The screening phase was for wi nnowing the location candidates, which were further evaluated by the suitability criteria. In summary, the following steps were taken in the macro level analysis: Collecting and classifying the spatial and non spatial data in association with the identifi ed screening and suitability criteria. Conducting the screening phase to identify the feasible counties. For the spatial analysis, ArcGIS was utilized. Identifying the importance weight of the suitability criteria by using the AHP technique. Conducting su itability analysis to identify the candidate counties. To perform the AHP technique, the following steps were taken: Developing a questionnaire
60 Determining the target population Identifying experts who could create a right balance among themselves for we ighting the criteria due to their backgrounds, educational level, positions, and industries, in which they were involved. In this way, the results would be more trustable. Obtaining the approval of the Institutional Review Board at the University of Florid a (IRB02 office) on the research questionnaire. The IRB02 office is responsible to review research studies conducted at the University of Florida which involve humans or have impacts on them. Distributing the questionnaire among the experts. Checking the v alidity of the responses. Calculating the priority vector (importance weight) of the criteria based on the responses. Checking the consistency ratio of the comparison tables to be less than 0.1. Micro level analysis. This phase was performed by taking the following steps: Collecting and classifying the spatial and non spatial data in association with the identified screening and suitability criteria. Conducting screening phase to identify the potential parcels of the candidate counties. For the spatial anal ysis, ArcGIS was utilized. Conducting suitability phase to identify the appropriate parcels. Performing sensitivity analysis to identify the impacts of precautionary sustainability criteria on the results Discussing the results Improving the decision model The model was refined based on the acquired knowledge and experience through the case study. Data Requirements The required data for conducting the case study includes, but is not limited to: Existing national and state regulations for locating a cement plant in the state of Florida.
61 The spatial and non spatial data of Florida in association with macro level screening and suitability analysis, such as the locations of limestone, environmental status, health status, and unemployment rates of the counties. Existing local regulations for locating a cement plant in the identified candidate counties of Florida. The spatial and non spatial data of Florida in association with micro level screening and suitability analysis, such as proximity to infrastructures, a nd land use. All the required regional and spatial data was extracted from the official data sources, such as: The Florida Geographic Data Library (FGDL) The U.S. Census Bureau Florida Department of Environmental Prot ection (FDEP) The U.S. Geological Survey Local cement companies Table 3 1 summarizes the types and the sources of the data used in the case study Assumptions Assumptions, in all scientific research, play a crucia l role in framing problems and making the results rational. The considered assumptions in this research (including the case study) are outlined below: The facility location decision is made in a standalone situation, not in a supply chain condition. Decisi on making for the location of a facility with regards to its whole supply chain might include different parameters and evaluation criteria. The location decision is a state wide process; therefore, for international or national projects, the proposed model including the criteria, should be customized. The strategic spatial planning is one of the crucial prerequisite for any site selection decision. Therefore, in this research, it is taken for granted that an accurate spatial planning is undertaken. In the case study, only limestone is considered as the primary raw material of a cement plant.
62 located in another county. The existing limestone reserves of Florida is potentially cem ent quality limestone. In other words, the reserves meet the required quality for production of cement. The considered limestone in Florida provides enough raw material for a cement plant operation according to its business plan. The limestone reserves of the candidate counties in the case study are similar in terms of quality.
63 Table 3 1 The types and sources of the data used in the case study Data Source of Data Type of data Data Date Type of access Florida counties US Census Bureau Shape file 2010 http://www.census.gov/geo/ma ps data/data/tiger.html Florida geology map Florida Department of Environmental Protection (FDEP) Shape file 2001 http://www.dep.state.fl.us/gis/d atadir.htm Population US Census Bureau Web Based Table 2010 http://factfinder2.census.gov /fa ces/nav/jsf/pages/searchresult s.xhtml?refresh=t Florida cement plants Florida Department of Environmental Protection (FDEP) Text 2012 Email Electricity rate Florida Public Service Commission Pdf file 2011 h ttp://www.psc.state.fl.us/ Gas rate Enterprise Florida Web Based Table 2011 http://www.floridaprospector.co m/ Wage of production workers Florida Department of Economic Opportunity Excel File 2012 http://www.floridajobs.org/labor market information/data center/statistical programs/current employment statistics Florida seaport s (except for Citrus port) Florida Geographic Data Center (FGDL) Shape file 2004 http://www.fgdl.org/metadataex plorer/explorer.jsp Citrus port Citrus Port Web page 2012 http://portcitrus.com/ Florida major roads & national highways Florida Geographic Data Center (FGDL) Shape file 2011 http://www.fgdl.org/metadataex plorer/explorer.jsp Fl orida railroad Florida Geographic Data Center (FGDL) Shape file 2011 http://www.fgdl.org/metadataex plorer/explorer.jsp Florida navigable waterway network Florida Geographic Data Center (FGD L) Shape file 2011 http://www.fgdl.org/metadataex plorer/explorer.jsp CLIP data Florida Natural Area Inventory (FNAI) Shape file 2011 http://ww w.fnai.org/gisdata.cfm
64 Table 3 1. Continued. Data Source of Data Type of data Data Date Type of access Gini Coefficient County Health Rankings and Road Maps Table 2010 h ttp://www.countyhealthrankin gs.org/node/465/44/archived data/2010 Percent of families under poverty threshold Florida charts Table 2011 http://www.floridacharts.com/c harts/SpecReport.aspx?RepID =1341&tn=24 Rankings of health factors and outcomes County H ealth Rankings and Road Maps Table 2012 http://www.countyhealthrankin gs.org Percent of high school graduates County Health Rankings and Road Maps Table 2012 http://www.countyhealthrankin gs.org Unemployment rates Florida Department of Economic Opportunity Excel File 2011 http://www.floridajobs.org/labor market information/data center/statistical programs/local area unemployment statistics Money income US Census Bureau Web page 2006 2010 http://quickfa cts.census.gov/qf d/states/12/12011.html Florida conservation lands Florida Natural Area Inventory (FNAI) Shape file 2012 http://www.fnai.org/gisdata.cfm Florida Forever Board of Trustees Projects (FFBOT) Florida Natural Area Inventory (FNAI) Shape file 2012 http://www.fnai.org/gisdata.cfm Florida Forever Acquisitions (FF_ Acquired) Florida Natural Area Inventory (FNAI) Shape file 2012 http://www.fnai.org/gisdata.cfm Parcels data Florida Geographic Data Center (FGDL) Shape file 2010 http://www.fgdl.org/metadatae xplorer/explorer.jsp Collier zonin g & future land use Collier County Shape file 2011 http://www.colliergov.net/Index .aspx?page=2713 Glades zoning future land use Zoning & Planning department of Glades County Text 2012 http://www.colliergov.net/Index .aspx?page=2713
65 Table 3 1. Continued. Data Source of Data Type of data Data Date Type of access Hendry zoning & future land use Zoning & Planning department of Hendry County Text 2012 http://www.colliergov.net/Index .aspx?page=2713 Major gas pipe lines National pipeline mapping system Web page 2012 https://www.npms.phmsa.dot.g ov/PublicViewer/composite.jsf Major power transmission lines Florida Geographic Data Center (FGDL) Shape file 2005 http://www.fgdl.org/me tadatae xplorer/explorer.jsp Drastic Vulnerability Index (DVI) Florida Geographic Data Center (FGDL) Shape file 1998 http://www.fgdl.org/metadatae xplorer/explorer.jsp Topographic maps Flor ida Geographic Data Center (FGDL) Shape file 2011 http://www.fgdl.org/metadatae xplorer/explorer.jsp Traffic data Florida department of Transportation (FDOT) Shape file 2012 http://www.dot.state.fl.us/plann ing/statistics/gis/ Flood hazard zones Florida Geographic Data Center (FGDL) Shape file 1996, 2012 http:// www.fgdl.org/metadatae xplorer/explorer.jsp Definitions of flood hazard zones Federal Emergency Management Agency (FEMA) Web page 2012 http://www.fema.gov/ Fault line U.S. Geological Survey (USGS) Map 2012 http://earthquake.usgs.gov/haz ards/qfaults/usma p.php Potable water wells Florida Geographic Data Center (FGDL) Shape file 2012 http://www.fgdl.org/metadatae xplorer/explorer.jsp Emergency medical services Florida Geographic Data Center (FGDL) Shape file 2008 http://www.fgdl.org/metadatae xplorer/explorer.jsp Housing US Census Bureau Web Based Table 2010 http://factfinder2.census.gov/fa ces/nav/jsf/pages/searchresult s.xhtml?refresh=t Races US Census Bureau Web Based Table 2010 http://f actfinder2.census.gov/fa ces/nav/jsf/pages/searchresult s.xhtml?refresh=t
66 CHAPTER 4 FRAMING SUSTAINABLE SEMI DESIRABLE FACILITY L OCATION As mentioned before, the facility location literature in the research areas of operations research and urban planning is very rich with various proposed techniques and algorithms. Linear and non linear optimization, GIS based analysis, MCDM approach, risk management, simulation, and hybrid approaches can be considered as some of common techniques. Although most of the rec ent studies have tried to cover environmental, social, and economic aspects in their models, only a few of them have addressed the sustainability concept with a holistic approach. In this research, sustainable facility location is defined as siting a fac ility in a way to achieve the principles of intergenerational, intra generational, geographical, procedural, and inter species equities. Building upon the methodologies developed by Sumathi et al. (2008) Berkhui zen (1988) Calzonetti et al. (1987) and Cement Sustainability initiatives (CSI) report about land use management ( Misra, 2002) a preliminary algorithm for site selection of a semi desirable facility was developed. In developing the model, the Florida Power Plant Siting Act (The Florida Legislature, 2011) was also (PPSA), ss. 403.501 (FDEP, 2011) FDE the Act, the application process should be investigated at least by the following parties; however, other agencies may also be required to study the application if it is requ ested by FDEP.
67 The Department of Economic Opportunity. The department is responsible may also report on the compatibility of the proposed plant with regional polices, local plans and land development regulations. The Water Management District It is responsible for preparing a report including, but not limited to, investigating the impact of the proposed plant on water Local Government Local government, in whose jurisdiction t he plant will be located, should investigate the compatibility of the proposed plant with the applicable local ordinances, regulations, standers, or criteria. The Fish and Wildlife Conservation Commission This section is also responsible for investigating Regional Planning Council This section is responsible for addressing and The Department of Transportation This section is also responsible for in One of the notable requirements of the Act is that public hearings should be conducted with the certification process. A land use hearing should also be conducted in the selecte d county for siting th e plant. However, according to the Act, by conducting these sessions, requirements for land use plans and zoning ordinances cannot be waived. After conducting the case study, the preliminary model was revised in terms of steps and criteria that are explai ned in Chapter 5. The final model is shown in Figure 4 1 and the associated list of criteria can be seen in Figure 4 2 Siting Algorithm In the following, the steps of the siting algorithm are explained in more detail. Reviewing the primary business plan Developing a primary business plan is one of the key prerequisite s of plant location decisions. A business plan includes key information in association with location decis ions, such as the designed technology,
68 types of facilities, facilities layout, projected profit and cost, level of staffing, material r equirements, and plant sizing. This information helps decision makers analyze the market, determine land requirements, an d more importantly assess the local and global impacts of the plant with regard to the designed technology. Therefore, in the context of sustainable development, a good business plan is an essential prerequisite for a location decision making process. It i s suggested in the methodology section of this dissertation that the primary business plan be reviewed before proceeding to the next step, the Environmental, Social and Economic Impact Assessment (ESCIA). Without a properly developed business plan, ESCIA c annot be conducted effectively. Primary Environmental, Social, and Economic Impact Assessment (ESCIA) It is obvious that sustainability cannot be achieved in a siting decision without considering the impacts of the facility during its whole lifecycle. The refore, as the second step of the siting algorithm, it is suggested that a primary ESCIA be conducted at different phases of the plant, including siting, construction, operation, and closure phases. The purpose of the ESCIA is to identify the likely impact s from locating the facility and plan to minimize the impacts. The corrective action plan might result in considering some precautionary decision criteria in site selection process; thereby, companies will have a prevention approach rather than mitigation or compensation approaches in site selection decisions. In summary, the following actions are proposed to be taken in this step (Misra, 2002) : Identifying persons or things that are affected by the plant during its lifecycle
69 Identifying the tasks and events of the plant that have negative or positive impacts on people/things Identifying those impacts that are directly dependent to the site location Developing criteria to address the identified location dependent impacts ESCIA can be implemented in the context of the equity classification of Haughton (1999) As it is addressed in the sustainability literature, according to Hau ghton, moving towards sustainability requires achieving equity in the following areas: 1. Intergenerational equity (futurity) 2. Intra generational equity (social justice) 3. Geographical equity 4. Procedural equity, and 5. Inter species equity. Consideri ng these five equity categories can prevent ignoring long term impacts of a facility. Risk assessment methods facilitate this process by identifying the positive and negative impacts and their occurrence probabilities during the lifecycle of the plant. Col lect national and state laws governing siting of the plant In every nation, laws play important roles in regulating business practices and personal behaviors. Moreover, with growing environmental concerns, governments are trying to update laws and establi sh more stringent ones to incorporate sustainability requirements. Therefore, considering national and local governing laws and ordinances that apply to siting decisions is not only compulsory, but it is also crucial for the purpose of sustainability. In t he siting methodology of this dissertation, it is suggested that national and state laws governing facility location be collected and reviewed before finalizing the decision criteria.
70 Consult with relevant business development and environmental organizatio ns Before any final decision about the evaluation criteria and in parallel with collecting national and state laws, it is proposed that a consultation from the private and public business development and environmental organizations at the federal and stat e levels be conducted. The main mission of business development organizations is facilitating the economic growth of states. For the purpose of economic growth and development, these organizations usually prioritize counties of a state and accordingly pro pose tax, cash, and bond incentives to companies to place their facilities in top priority development organizations might significantly help companies in their decision making proc esses. In addition to economic development organizations, consulting with the department of environmental protection of the targeted state can be a contributory environment al concerns and permitting processes. It might also influence the evaluation criteria. Customize and classify the siting decision criteria Site evaluation criteria are crucial inputs to any site selection decision. These criteria help decision makers ensu re the compliance of the results of their decisions with their objectives. Therefore, carefully choosing the decision criteria is a prerequisite for a successful rese arch attempted to develop a set of sustainability and technical criteria, which can
71 be utilized by companies and researchers interested in this field. The criteria are shown in Figure 4 2 For the purpose of locati on analysis for a specific project, it is suggested that the criteria be tailored based on the nature of the project, consisting of the identified objectives and requirements, the applied technology, the local circumstances, and legal requirements. Sustain ability requirements include various factors, such as socio economic ones, which need to be addressed within a large area, e.g. a state, rather than a small one, e.g. a few parcels of a region. Therefore, considering a small area at a local scale for susta inable siting of a facility would make the decision process inefficient. In the siting model presented in this dissertation, companies are suggested to conduct the siting analysis at the macro level to identify candidate counties of state and at the micro level to select an appropriate parcel within the candidate counties. In this regard, it is suggested that the criteria be categorized and customized into the following four major classes: Mandatory and discretionary screening criteria for identifying feasi ble counties Suitability criteria for prioritizing feasible counties and identifying the candidate ones Mandatory and discretionary screening criteria for identifying potential parcels in the candidate counties Suitability criteria for selecting an appropr iate parcel among potential ones Identify the feasible counties and prioritize them (macro level analysis) After finalizing the list of the criteria, the spatial and non spatial data of counties in association with the first group of criteria, macro level screening criteria, should be collected to identify the feasible counties. For analyzing the special dataset, a GIS
72 application is recommended to be used due to its various strong analytical power as explained in Chapter 2. For identifying the importance weights of the second class of the criteria, macro level suitability criteria, one of the MCDM techniques, such as AHP, can be utilized. After identifying the importance weights of the suitability criteria and collecting the criteria values for each feasib le county, the priorities of the counties can be determined through one of the multi attribute decision making methods based on the condition of the problem and type of criteria. For this purpose, the data could be centralized in a decision making matrix. The matrix would be a table that lists the elicited from the analytical results of the developed GIS datasets or other data sources. However, another method is centraliz ing all datasets in a GIS application and conducting the final evaluation in that environment. In the next step, the score of each feasible county should be calculated to rank them and identify the suitable ones. Decision makers will determine the number of counties that will be considered as suitable counties. For calculating the final scores of alternative s, various methods, such as SAW and COPRAS, can be used. Identify the candidate parcels and prioritize them (micro level analysis) At this phase, it i s necessary to customize the third and fourth groups of criteria, the micro level screening and suitability criteria based on the siting governing rules and local conditions of the candidate counties.
73 After customizing the list of criteria, the spatial an d non spatial data of the suitable counties relevant to the third group of the criteria, micro level screening criteria should be collected By winnowing the parcels and identifying the potential ones, the suitability evaluation step can be performed to id entify the most suitable parcel. For the evaluation of the potential parcels, the same approach for the evaluation of the feasible counties can be applied. Site evaluation and acquisition Once the most suitable site is selected based on the macro and mi cro level evaluations, the site should be analyzed in more detail. The availability of the land for the acquisition process might be a challenge for companies. Although the main target of the current research is to minimize potential challenges by addressi ng them during the decision making process, companies might still face them during the land acquisition phase. Responding to the concerns of all parties along with satisfying all decision criteria is hardly possible in reality; therefore, a sufficiently ap propriate solution that balances different requirements needs to be pursued. Some of the land acquisition challenges might be as follows (Misra, 2002) : Growing concerns of local governments for negative impacts of plants, and consequently, regulating more stringent laws Unwillingness of communities to have a large plant in their neighboring area due to negative environmental and social impacts of the plant, such as degradation of landscape and displacement of the po pulation Unwillingness of owners to sell their properties due to personal reasons Increasing acquisition cost of lands
74 In some cases, companies may decide to acquire some of the adjacent lands of the targeted parcel for more flexibility and for future dev elopments; therefore, those lands should also be evaluated with regard to the planned use. After checking the availability of the land for acquisition, its physical characteristics should be visually inspected. This phase includes, but is not limited to, t he soil evaluation, hydrological characteristics, topographic maps, and existing vegetation of the site. If the plant is dependent on the on site raw material, the accessibility and quality of the raw material should be examined in detail. Site evaluation will also help in determining any potential obstacles and constraints. For instance, any evidence of dumping and disposal or contamination can alter the decision. Aerial photographs (such as the photographs available on Google Earth), topographic maps, gr ound level photographs, and websites of property appraisers can be good sources for physical assessment of the land. Historical information and photographs of the site can also be considered as helpful evaluation resources. They can assist in determining p revious land uses, the archeological and historical value of the site, historical environmental contamination in the site, and any other ambiguous physical constraints. Although this information might be unofficial or unwritten, it may include crucial elem ents that need to be addressed before placing the facility (LaGro, 2011) Secondary Environmental, Social, and Economic Impact Assessment (ESCIA) At this phase, companies should conduct the secondary ESCIA in assoc iation with the selected site. Knowing the physical characteristics of the site and
75 the local condition, companies can better evaluate the impacts of the plant on the also h elp companies to manage and minimize their adverse impacts in a more effective way during the whole lifecycle of the plant. another essential step in a site selection process. Stakeholde rs are agencies or people who can potentially be affected by or might affect an industry. Therefore, building trusting, open, and inclusive relationships with stakeholders and addressing their concerns are unavoidable parts of siting decision processes (WBCSD/ CSI, 2005) As an example, the following stakeholders can be potentially affected by the siting of a new cement plant (Misra, 2002, p.7) g in the vicinity Statutory and governmental organizations Other industries and business in the neighborhood (P.7) concerns in siting or expansion of semi desirable facilities. Public hearings can be considered great opportunities for companies to interact with stakeholders, understand their concerns, and convince them with their solutions. In addition, these hearings can create a positive image for companies (Misra, 2002) However, companies should be aware that stakeholders mapping is a dynamic process over the life of projects; some stakeholders may disengage and others may jo in at the later stages of a project.
76 At this phase, it is also suggested that local governmental and private agencies involved in local economic development and urban planning be consulted. Examples of these agencies include economic opportunity agencies, chambers of commerce, Department of Environmental Protection, and property appraisers. Investigate other techniques to increase candidate parcels In the evaluation process, decision makers might come up with limited candidate sites to locate their facilit ies, and subsequently, they might face difficulties in land acquisition of the identified candidate sites. In this situation, based on the nature of the problem, companies need to remedy the problem by investigating other solutions. Some of the solutions c an be as follows. One solution for creating more candidate parcels can be considering the other zoning designations that have the flexibility to be converted for the targeted use, such as some of the agricultural zones. By considering precautionary sustain ability criteria, there might be chances to change the zoning designation of the targeted land. Requests for zoning designation change can be sent to local urban planning departments. However, this process can be a very time consuming process. Another solu tion is considering smaller land sizes in the screening phase, and subsequently, acquiring several adjacent small lands to achieve the targeted land size. If companies face challenges in the process of land acquisition they can evaluate different solution s such as trading the targeted land for other lands with equal or greater value; in this case, a win win situation will happen for both companies and land owners.
77 Another approach to find more results is to conduct a sensitivity analysis to investigate the impacts of the decision criteria on the results, and if it is possible, relax some of the criteria. This method has been considered as a separate step in the model presented in this dissertation and is explained in the next section. Conduct sensitivity an alysis In either of the following situations, companies might need to conduct sensitivity analysis and relax some of the criteria with significant impacts to acquire other candidate sites: If the physical characteristics of the candidate sites, which were ranked based on micro level suitability analysis, are not appropriate. If companies cannot find any solution to acquire one of the candidate sites. addressed. In this situation, it is proposed that a sensitivity analysis at micro level be conducted, and if it is possible, relax some of the precautionary criteria to achieve more results. If the need becomes evident, the parcels within the new set of feasible counties with lower prioriti es should be evaluated. Evaluation Criteria T o characterize sustainable facility location and to incorporate this characterization in the siting model for semi desirable facilities, a list of criteria was developed. Figure 4 2 provides the list of criteria that were taken into account Decision criteria are the foundation of any assessment, and measuring the sustainability of locations is not possible without identifying the evaluation criteria. In the process of developing the list of the criteria, first, a primary list including sustainability and conventional decision criteria was developed based on the literature and exiting guidelines. In extracting the criteria from the literature, those that
78 were relevant to the current research and were in line with the assumptions of this research were considered. At the next step, the evaluation criteria were reviewed by the researcher to eliminate the superfluous ones and to classify them. Expert judgments were also solic ited in this phase. Four categories of dimension, theme, sub theme, and indices in a hierarchical order were identified for the classification of the criteria. Dimensions are general objectives of the problem of sustainable semi desirable facility locatio n including social, economic, environmental, and technical principles. Themes, sub themes and indices are grouped under dimensions. Because the sustainability dimensions, including environmental, social, and economic, have limitations that cannot cover th e operation al and technical issues of a facility, such as the market status, the fourth technical dimension is also considered in the framework of the criteria. Themes and sub themes are defined to translate the goals of dimensions into measurable indices Themes and subthemes have more specific meanings and are more concrete compared to dimensions. The main purpose of indices is to provide meaningful measures and information for evaluation and final decision making (Worrall et al., 2009) A good indicator should be specific, measurable, sensitive to change, achievable in terms of available data, and should have analytical and scientific soundness and policy relevance (Niemei jer & de Groot, 2008) Since some of the sustainability indices can be considered in more than one theme or dimension, classifying them into a specific group can cause controversy among decision makers. For instance, unemployment rate can be classified in both
79 social and economic dimension s as an equity indicator as well as an economic structure indicator. Therefore, decision makers should reach a consensus about the final classification of the criteria. The number of the developed evaluation indices from literature was 162. However, since some of the indices could be covered by others or were not useful due to the objectives of the problem, the first screening step was performed to shorten the list of criteria. In the first screening step, one group of re moved criteria was related to environmental management and regulations. Examples include places with a low amount of fines for noncompliance with environmental regulations. The researcher believes that these types of criteria, such as those related to loos e environmental rules and regulations, do not comply with sustainability objectives. It should be noticed that it is not the purpose of this research to prescribe the identified criteria for all types of industries; therefore, for different types of facil ities,
80 Figure 4 1 Decision algorithm for semi desirable facility location
81 Figure 4 2 Siting decision criteria
82 Figure 4 2 Continued
83 Figure 4 2 Continued
84 CHAPTER 5 CASE STUDY The main objective of the case study, which is about sustainable siting of a new cement plant in the state of Florida, is to demonstrate and examine the develop ed site selection model. The cement industry is selected for the case study due to the following reasons: Cement is the key component of concrete, the second most consumed substance after water. Therefore, the cement industry is one of the key industries t hat affect the built environment. The cement manufacturing process has a high Ecological Rucksack index, meaning it requires large quantities of raw materials to be extracted, transported, used, and processed. This leads to the displacement of current popu lation, flora, and fauna. Cement plants require significant initial investments; therefore, the plant size and location factors are crucial in economic feasibility of a cement plant (Holcim (US) Inc., 2002) Ce ment manufacturing generates nearly 0.85 pounds of CO 2 for every pound of cement (WBSCD/CSI, 2009) In 2008, the cement industry accounted for approximately 7% of global CO 2 emissions related to the primary energy supply. I n 2008, the total CO 2 emissions related to the primary energy supply was about 29Gt, a quarter of which were attributed to the industry and fuel transformation. Within industry and fuel transformation, 27% of the emissions were due to cement production ( Figure 5 1 ) (IEA & UNIDO, 2009) Therefore, the destructive environmental impacts of the cement industry is undeniable and this industry has been under scrutiny in recent decades. Figure 5 2 shows the gross CO 2 emission per ton clinker for GNR Participants 1 Cement production yields high levels of dust, which can lead to respiratory problems for local residents. Noise pollution is another envi ronmental impact of the cement industry In the following, the major steps taken in the case study are described in detail: 1 2 and energy performance data.
85 Reviewing the general characteristics of the cement industry Reviewing the sustainability challenges of the cement industry Reviewin g existing sustainability practices and guidelines associated with location decisions of cement plants Framing the problem of siting a cement plant in the state of Florida Customizing and classifying the proposed criteria (in Chapter 4) for siting a cement plant in the state of Florida Implementing the proposed siting model (in Chapter 4) General Characteristics of the Cement Industry According to the North American Industry Classification System (NAICS), cement manufacturing has a NAICS code of 327310. Th e main products of this industry are Portland, natural, masonry, Pozzolanic, and other hydraulic cements. The primary raw material for the cement production is calcium carbonate (CaCO3) (U.S. Census Bureau, 20 12) The production of cement involves six major processes of 1. Quarrying 2. Proportioning, blending and grinding the raw material 3. Preheating the raw material 4. Heating the raw material in rotary kilns, and 5. Cooling clinker and final grinding 6. Pa ckaging and shipping the cement (PCA, 2009) Cement is the main component of concrete, which is the most consumed material after water. Therefore, the cement industry is one of the key industries that affect the built envir onment. In 201 2 the United States produced and imported 79.1 million metric tons of Portland and blended cement (Van Oss & Kraft, 2013) Although, due to the economic crisis, cement consumption has been significa ntly reduced (PCA, 2009; Van Oss & Kraft, 2012; Van Oss & Kraft, 2013; Van Oss & Kraft, 2013) the economic status of the United States is recoverin g (HeidelbergCement, 2012) It is noteworthy that, in 2012, 6.3 million tons of cement were imported into the United States (approximately
86 8% of total domestic production and imported cement). According to Holc im Inc. (2002) 5.8 million tons of imported cement can result in $1.5 billion lost income for cement manufacturers of the United States. Therefore, filling this gap is crucial for job creation and the econo mic recovery of the United States. Sustainability Challenges of the Cement Industry The cement industry is one of the most controversial industries due to its destructive environmental impacts. As Klee and Coles (2004) faced with increasingly strong legislative and stakeholder pressure, specifically (, p.114 ) The major environmental issues of the cement industry are energy consumption and air emissions (Marlowe, I., Mansfield, D., 2002) Therefore, the location decision of a cement plant is very challe nging and can be totally affected by local communities and other involved stakeholders. Based on the public hearing report of Carolinas Cement Company (2008) some of the concerns of local communiti es regarding the location of cement plants included: future air quality, potential long term impacts on the local public health, endangered species, wildlife, aquifers, archeological and historical resources, natural conservation tourism activity, infrastr ucture, growth of the community, economic impacts, and employment opportunities of the plant. It is notable that, in 2010, the EPA issued the first national regulation for reducing mercury and other toxic emissions from cement plants. According to the EPA website, by fully implementing this regulation in 2013, the annual emissions will be reduced as follows (EPA, 2010a) : Mercury 16,600 pounds or 92 percent Total hydrocarbons 10,600 tons or 83 percent
87 Particulate Matter 11,500 tons or 92 percent Acid gases (measured as hydrochloric acid): 5,800 tons or 97 percent Sulfur dioxide (SO2) 110,000 tons or 78 percent Nitrogen oxides (NOx) 6,600 tons or 5 percent However, when a regulator focuses on decreasing one particula r risk, risks in other areas may become unintentionally greater as a result of this policy. This situation risk trade risk transfer rather than solving it. Cement companies mig ht move from the United States to offshore areas to evade the national/state environmental regulations, which do not guarantee that the act will have an overall positive environmental, social, and economic impact on a global scale. In other words, it might lead to the procedural inequity, as noted by Haughton (1999) Considering the three pillars of sustainability, some of the advantages and disadvantages of the EPA act perspective s are discussed below: Positive Economic impacts The EPA estimates that the rule will yield $6.7 billion to $18 billion in health and environmental benefits, with the estimated cost of $926 million to $950 million annually in 2013. Another EPA analysis es timates emission reductions and costs will be lower, with costs projected to be $350 million annually (EPA, 2010a) Sundseth et al. (2009) have conducted a more focused study on t he economic benefits of reducing mercury, which are mostly related to economic benefits resulted from improved health records. R&D and investment in technologies that are used for removing mercury and other emissions from cement will be fostered. Job creat ions in related areas are expected. Negative Econom ic impacts. Cement production varies from nation to nation depending on many factors, such as the quality of feedstock material, the source of electricity used for cement manufacturing, or the type of plan t in question (such as shaft or rotary kiln, dry or wet technology). Importing cements from other countries with probable lower
88 quality will have adverse economic effects on construction projects, thus the economy. The compliance with the new requirements set forth by the EPA will need significant capital investments by cement manufacturers, impacting their economic positions. Some cement manufacturers are expected not to be able to afford implementing required changes on their manufacturing process to com ply. Therefore, plant closures should not be unexpected. This will have significant economic implication on many communities as well as the industry. Price increase is also a possibility. Constraining effects on the economy by negating the US government's efforts to stimulate the economy, create jobs, and renovate the nation's infrastructure. Taking any inappropriate actions to address technological complications of compliance with the new regulation will have economic implications. Positive Environmental i mpacts. The new regulation is expected to significantly reduce the amount of mercury in cement, thus in the built environment. This reduction will have tangible health benefits. Mercury ends up being in the water on the planet where it changes into methylm ercury, which is a highly toxic substance. The regulation is expected to drastically reduce the amount of methylmercury in the United States. This dangerous substance builds up in fish and is transferred to the human body after eating contaminated fish. De creased levels of mercury and other emissions will result in lower toxic substances in nature, thus healthier and cleaner environment. Encourages other countries to adopt similar regulations and move toward cleaner production of cement, thus creating a hea lthier environment for all. Encourages other industries to move toward cleaner technologies prior to any similar regulations become enforced to their industry. Negative Environmental impacts. It can be envisioned that the new EPA regulation will push cemen t production to other countries with less regulations and more relaxed standards, which on a global scale will increase emissions and lifecycle greenhouse gases, negating the goals of the EPA in reducing harmful substances. For example, according to Cho an d Spohn (Cho & Giannini Spohn, 2007, p.1) cement kilns used for the production of clinker: vertical shaft and rotary kilns. Most of the cement produced in China is made in relatively inefficient and polluting
89 Cement production in many other countries consumes more energy per unit production on average than production in the United States. Importing cements produced in o ther countries will require additional efforts in transporting the product from sites of manufacturing to coastal ports of the United States and also transporting to sites of use. This means more fuel consumption, more carbon production, and a more stresse d environment in a global perspective. A study cited by Lofstedt (2010) producing cem ent in China and shipping it to the United States adds 465 pounds of carbon per metric ton of cement produced. Offshore cement production for a state like California means approximately 25 percent or more CO 2 emissions than had the cement been produced in California. Importing cements produced in other countries with less stringent pollution example, in 2002, particulate emission limits were set at 100 milligrams per cubic meter exhaust in China, while European cement plants have a limit of 50 milligrams (Shah & Ries, 2009) This statistic confirms that shifting cement production to other countries might create a more stressed planet in many respects. Positive Social impacts. The new regulation is expected to significantly reduce the amount of mercury; therefore, in the long term, the build up of mercury in the human body will be reduced. This is especially important for newborns and preg nant women, since mercury is detrimental and dangerous to children's developing brains. Therefore, the new regulation will cause improved health records for areas exposed to lower levels of mercury. Particle pollution causes serious health effects, such as asthma, irregular regulation is expected to make improvements in terms of reducing particle pollutions, thus reducing many diseases. This will improve the quality of life for many people. Negative Social impacts. Probable plant closures as a result of the new regulation will bring about many job losses, adversely affecting the job market and thus impacting social development. t national limits on mercury and other toxic emissions from cement plants will arguably make the global community of poorer quality, due to the reasons listed above.
90 The rule could actually cause more health problems worldwide, creating more stressed commu nities. International equity will be violated, since the citizens of other countries with less stringent environmental regulations may end up paying the price. In summary, for the purpose of achieving sustainability, setting environmental regulations, such comprehensive risk risk analysis be conducted to devise a more manageable strategy Framing the Problem of Siting a Cement Plant in the State of Florida The state of Florida has six exis ting cement plants with eight active kilns located in the counties of Alachua, Hernando, Miami Dade, Sumter, and Suwannee. According to the report of Van OSS and Kraft (2012) in February 2013, Florida was the third cement producing state with the total blended and Portland production of 359,428 metric tons, and the third cement consuming state in the United States with the total Portland cement consumption of 350,884 metric tons. In 201 2 total Portland and bl ended cement production in Florida was about 3,846,862 metric tons, which accounted for 5.3% of the total cement production of the United States. In 201 2 the total Portland and Blended cement consumption in Florida was about 3 883 252 metric tons, which a ccounted for 5 % of total Portland and Blended cement c onsumption of the United States (Van Oss & Kraft, 2013) The purpose of the current study is to hypothetically locate a new cement plant in the state of Florid a with consideration of sustainability criteria. It is worth remembering the assumptions considered for the case study: In the problem, only limestone is considered as the primary raw material for cement production. e, can be transported to a cement plant located in another county.
91 In the demand analysis, the population of Florida is the only factor that determines the demand. It is also assumed that exporting cement to other states or countries will have the same con dition for all site alternatives. All the existing limestone reserves of Florida are potentially cement quality limestone. In other words, the reserves meet the required quality for production of cement. The considered limestone reserves in different areas of Florida provide enough raw material for a cement plant to operate according to its business plan. The limestone reserves of the site alternatives are similar in terms of quality. Implementation of the Site Selection Model For simplification purposes an d also due to resource limitations, only the main steps of the proposed model have been implemented in this study. In the following, the taken steps are described: Primary ESCIA of a Cement Plant Environmental, social, and economic impacts assessment has a crucial role in site selection decisions. Therefore, in the proposed siting model, it was suggested that a primarily impact analysis of the targeted facility be conducted during its whole lifecycle. This assessment helps companies identify the major posit ive and negative impacts of the facility and customize the site evaluation criteria accordingly. Thereby, companies would have a prevention approach rather that mitigation or compensation approaches in reducing the impacts of their facilities. Production o f cement has positive and negative; direct and indirect, social, environmental, and economic impacts at the national and local levels. For the purpose of the case study, the major expected environmental, social, and economic impacts of a new cement plant d uring its operation phase are outlined below.
92 The main environmental issues of cement plants are greenhouse gas emissions (e.g. CO2), air emissions (e.g. Nox, SO2, and dust/ particulate), and local nuisance (Noise/vibration, dust, and visual impact) (Marlowe, I., Mansfield, D., 2002; WBCSD/ CSI, 2005) Toxic emissions of cement plants are very harmful to humans and wildlife (Cho & Giannini Sp ohn, 2007) The primary greenhouse gas emission is CO2 generated in significant amounts. The main sources of CO2 emission in cement plants are the manufacturing process, com bustion for manufacturing operations (40%), transportation (5%), and fossil fuel combustion for electricity generation (5%) (Humphreys & Mahasenan, 2002) The main sources of dust emissions are kilns, raw mills, c linker coolers, and cement mills; and the principal sources of noise are blasting or drilling operation of quarrying and the cement grinding equipment. Cement plants have negative traffic impacts due to hauling of raw materials and final products. These im pacts can create soil contamination, noise, vibration, and dust causing potential health and safety risks. According to Misra (2002) a cement plant does not significantly contribute to water pollution; and waste water emissions are limited to surface runoffs. The main environmental concerns of quarrying activities of a cement plant are associated with dust pollution, noise, vibration, and impacts on land use and biodiversity. Cement plants impact land use and biod iversity in the following manners: 1. Displacement of current activities, flora, and fauna on the land where quarry and cement plant are to be located 2. Land degradation and disturbance of biodiversity on
93 the land, and 3. Creation of a land use that is in compatible with adjacent land uses, thereby affecting neighboring landscapes (Misra, 2002) Cement plants may be located in karst areas; therefore, they may create threats to ical formations related to erosion and (WBCSD/ CSI, 2005, p.44) One of the key attributes of karst areas is that they have ric h and productive soil for agriculture uses, and they are respected as valuable lands. Some of the karst landscapes are valuable sources of information from past environmental and historical conditions (WBCSD/ CSI, 200 5) Moreover, according to the study of Misra (2002) one quarter of all people worldwide get their water supplies from karst areas. In terms of social and economic impacts, the cement industry is very labor i ntensive (Marlowe, I., Mansfield, D., 2002) ; therefore, a new cement plant can provide significant employment opportunities for the local community and it can enhance local lant can also be a source of income and welfare for companies, local communities, and governments. In addition, locating a cement plant in a region makes the most consumed construction material readily available for local industries. Nevertheless, a cement plant may have negative social impacts; for instance, it might cause the migration of strangers to the local community, and subsequently, create adverse impacts on the cohesion and social networks of the community and its values. It may also increase the possibilities of deviant behavior and crimes. Cement plants
94 may also increase the local traffic load, which has adverse impacts on the amenities of people. Figure 5 3 It should be noted that the outlined impacts are basic and they are not prescriptive for all cement projects. The detailed impacts are directly dependent to several issues, such as the business plan and national or local environmental conditions. Customi zing the Proposed Evaluation Criteria The location decision for a new cement plant involves several influencing production factors, such as the proximity of limestone or its alternatives, the differences in fuel and power cost, the availability of water, and transportation costs (Das, 1987) However, due to the heavy weights of the raw materials used for cement production and the high transportation cost, proximity to the raw material is a crucial decision criterion. P roximity to raw materials has a more important role in the location decision of a cement plant than the role of other production and technical factors. In some cases, this issue has resulted in clustering of cement plants in a region with limestone (Das, 1987) According to the study of Das (1987) in 1987, 60% of cement plants in India were located within 20 km transporting distance of the raw materials. According to the primary ESCIA and the aforementioned considerations, the proposed criteria in Chapter 4 were customized and classified for conducting the case study. Expert judgments were solicited for evaluation of these criteria. Figure 5 4 shows the customized and categorized criteria. Macro Level Analysis In this phase, both the screening and suitability evaluations were performed. First, according to the screening criteria, the list of counties in which cement production was
95 feasibl e was identified, the suitability of these feasible counties were evaluated, and they were then ranked based on the suitability criteria. In the following sections, the evaluations are explained in more detail. Macro Level Screening Analysis As discussed earlier, due to the heavy weight of raw materials and high transportation costs, the proximity of raw materials to a cement plant is a crucial factor in its economic feasibility. Proximity to raw materials reduces fossil fuel consumption. Moreover, proximi ty to raw materials provides a potential for a regular and consistent material supply as well as savings of storage spaces and costs. Therefore, in this case study, the availability of limestone was considered as the screening criterion for identifying the feasible areas and counties in the state of Florida. The areas within 5 km buffer distance of limestone were identified as the feasible areas, and the counties, in which the feasible areas are located, were considered as feasible counties. The 5 km buffer distance, for the proximity of plants to limestone, was selected based on the reviewed cases outlined below: In one of the case studies of the CSI report sub study 11 (Misra, 2002) regarding the decision process of siting a cement plant, the distance of 5 km from the raw material was considered. According to the Bakhshi (2008) the average distance of Iranian cement plants from the raw material is about 3 km. Based on o ne of the transportation reports of Government of India (1966) in 1962 63 seven Indian cement plants from eight were located in less than 10 km of raw materials and only one of them was 12 km far. In his diss ertation, Mathur (2010) stated that the raw material of cement plants is plants and only a few cases are located up to 50 km.
96 Figure 5 5 depicts the location of limestone in the state of Florida. The identified feasible counties resulted from the screening phase are shown in Table 5 1 and Figure 5 6 As shown in the figure and the table, out of 67 counties of Florida, 40 counties were identified as feasible counties. Putnam and Union counties are the only counties that do not have limestone, but portions of thei r areas intersect 5 km buffer distance of limestone located in other counties. Macro Level Suitability Analysis In this phase, the suitability of feasible counties were evaluated to select the ones with high priorities. The evaluation was done based on the macro level suitability indices listed in Table 5 2 The values of feasible counties against each suitability indices can be seen in Appendix A. Technical Evaluation The main considered sub themes in the technical dimension include demand, competitors, accessibility of infrastructures, and operational cost. The results of the technical evaluation is described in the following paragraphs. D emand Future Demand analysis ensures business profitability; therefore, it i s one of the key components of industrial business plans, and subsequently, a crucial input of facility location decisions. Since the local market can be defined in terms of population, payrolls, and normal needs (McPherson, 1995) the cement demand in this study was considered as the population of Florida counties falling within 300km buffer distance from the centers of the feasible areas. The buffer distance was assumed to be 300 km for the following reason :
97 I n addition to the high transportation cost of raw materials, land transportation of the final product, cement is also very expensive. Therefore, the demand for cement, which is usually met by land transportation, is considered within 300 km buffer of cemen t plants (Marlowe, I., Mansfield, D., 2002) The European Cement Association (CEMBUREAU, 2012) has also placed emphasis on this issue; according to them, cement cannot economically be transported via land beyond 200 km or at most 300 km. The population data was extracted from 2010 data of the Unites States Census Bureau and ArcGIS was utilized to analyze the demand. Figure 5 7 and Figure 5 8 respectively show Florida counties in 300km buffer of Polk and Hendry counties. Competitors. industrial economics (Bergen & Peteraf, 2002) Moreover, identifying existing decisions. In this study, the maximum kiln capacities of the existing cement plants i n the feasible counties are assumed as the evaluation index. For the purpose of locating a new cement plant, the lower the index is, the better it is. The maximum kiln capacity data of the existing plants was extracted from their air permit documents avail able on the website of Florida Department of Environmental Protection (FDEP). Infrastructures According to PCA (2009) approximately 98% of US cement is shipped to the market by truck and 2% is shipped by barge and rail systems. However, Holcim Inc. (2002) one of the largest cement plants in the Unites States, believed that the most cost efficient transportation method for cement is barge shipment and then rail
98 shipment. In addition, they believed that barge transportation is safer and has less negative environmental impacts compared to the other transportation methods. In this study, the availability of main roads, navigable waterways, and rail roads were considered as th e transportation decision criteria. Since exporting cement through seaports is another available transportation option for Florida cement companies, the availability of seaports have also been considered in this study. There are fifteen public seaports in 2011, Florida was ranked fourth in the nation in terms of exporting (FDOT & FPC, 2012) For the purpose of measuring the availability of infrastructure, the distances betwe en the centers of the feasible areas in each feasible county and the nearest infrastructure were measured in the ArcGIS environment. In the following, the spatial data used for the infrastructure evaluation is briefly described: ted from FGDL website, was derived from 2011 version of the FDOT Road characteristics Inventory (RCI). RCI is a large computerized database containing the data of Florida road networks (FGDL, 2011) Navigable waterways and Rail Network datasets, were downloaded from FGDL website, were in Florida subsets of the National Waterway Network and the National Rail Network of the United States. Seaport data was extracted from a dataset of FGDL, entitled Intermodal Terminal Faciliti es. Since the dataset was not complete, the spatial data of some seaports, such as Citrus and Key West ports, was added by the researcher. Figure 5 9 depicts Florida
99 seaports and Figure 5 10 is a sample of in the candidate areas. Operational cost. The main operational cost of a cement plant is electricity, kiln fuel (such as coal, natural gas, or waste material), and labor cost (Coito & Powell, 2005) Das (1987) believed that the portion of labor costs to the total cost is approximately 9 10% on average. In this study, electricity rate, natural gas rate, a nd labor costs were considered as the operational cost indices. Since Florida does not have coal, and coal should be imported from out of the state, the coal cost is assumed the same for all locations within the state of Florida. For the electricity rates of the feasible counties, the industrial rates of utilities for 400,000 kwh were considered. If there were more than one utility in a county, the average rate of utilities was calculated as the electricity rate. The industrial electricity rates were extra cted from the annual report of Florida Public Service Commission. Not all Florida counties have gas pipelines and the same gas distribution system; therefore, determining the industrial gas rates for all feasible counties was very challenging. Since indust rial gas rates are commonly proportional to the household gas rates, the household gas rates were considered for the evaluation. The required data was acquired from the Enterprise Florida organization website. For the labor cost, the median hourly wage of production workers was considered. The data was achieved from 2012 wage estimates of the Florida Jobs organization. Other operational costs were assumed homogenous for all location alternatives due to the same technology and business plan for all location alternatives.
100 For locating a new cement plant, the feasible counties with lower operations costs were more preferable. Environmental Evaluation Regarding the natural resources of Florida, a project called CLIP has been developed by the cooperation of: Flor ida Natural Areas Inventory (FNAI) University of Florida Center for Landscape Conservation Planning Florida Fish and Wildlife Conservation Commission The CLIP project includes a spatial database of natural resources of Florida. CLIP, which stands for Cri tical Land and Waters Identification Project, can significantly facilitate spatial planning decisions at the state level. In the project, an aggregate priority was identified based upon three resources of biodiversity, landscapes, and surface water. In bi odiversity priority identification, the following four data types were evaluated: Strategic habitat conservation areas. The areas that are required for rare and vulnerable vertebrate species to keep their viable population in Florida. Potential habitat ric hness. The number of rare and vulnerable vertebrate species overlapping at any location. Rare species habitat conservation priorities It indicates rare species with conservation needs. Priority natural communities. It includes upland glades, pine rockland s, seepage slopes, scrub, sandhill, sandhillupland lakes, rocklandhammock, coastal uplands, dry prairie, pine flatwoods, upland hardwood forest, or coastal wetlands. Their prioritization were based on their heritage global status rank.
101 Landscape resource p riorities are based on two major data layers listed below: Florida ecological greenways network Parts of a large landscape (called a hub) or ecological corridor that connects to one or more hubs. Landscape integrity index Higher number indicates larger intact and remote areas, such as the everglades. The surface water resource is the combination of the following three data layers: Significant surface waters It includes aquatic preserves, shellfish harvesting areas, seagrassbeds, springs, public water su pply sources, watersheds important for rare fish species, outstanding Florida waters, national wild and scenic rivers, and national estuaries. Higher priorities are associated with areas adjacent to the significant water resource, and lower priorities are associated with watershed related to significant surface water. Natural flood plain. The areas in a flood plain of a lake or rivers. Higher priorities are associated with higher quality natural areas. Wetlands. Higher priorities indicate wetlands with larg er intact natural landscapes. Since the Aggregate Clip project is very comprehensive and covers key natural resources of Florida, it was selected for evaluating the environmental status of the feasible counties. For this purpose, the following CLIP index ( Equation 5 1 ) was defined: (5 1) In th e CLIP project, priority 1 is the highest priority. As a result of consulting with a CLIP project member, the areas with priorities 1 and 2 were considered in the index as
102 critical environmental areas. In the context of sustainability, the feasible countie s with lower CLIP index were preferable. The spatial data of the clip project, extracted from the Florida Natural Areas Inventory website, was a raster file. Therefore, to be able to analyze the data and calculate the areas of lands with high environmental priorities, the raster file was converted to a vector file using ArcGIS. Figure 5 11 depicts the clip spatial data of Miami Dade County as an example. As this figure shows, some areas have CLIP priority 0, which i s for non designated areas. For environmental evaluation, this type of inaccuracy was unavoidable due to the lack of information. Social Evaluation Poverty, income inequality, public health status, health factors, and social acceptability sub themes were c onsidered for the social evaluation. In the following, they are briefly described. Poverty and income inequality. Many notable sustainability studies, such as the study by the Commission on Sustainable Development of United Nations (2001) have recommended poverty and income inequality be considered as two important sustainability factors. In this case study, poverty was measured by the percent of residents below the 100% poverty line, and income inequality was measured by the Gini Coefficient, which is the most common index for measuring income inequality. The Gini the extent to which the actual distribution of income, consumption expendi ture, or a related variable, differs from a hypothetical distribution in which each person receives (UN DSD, 2001, p.62)
103 Considering the economic objectives of sustainable development, the f easible counties with higher Gini Coefficients and higher percent of families under poverty line were preferred for siting a new plant due to the expected economic growth. A new industrial plant is more desirable for undeveloped communities because it crea tes new jobs and economic opportunities. The data in association with poverty was extracted from Florida Department of Health Division of Public Health Statistics & Performance Management. The Gini data was acquired from the website of County Health Rank ings and Roadmaps organization. Health status and health factor. The County Health Rankings and Roadmaps organization has developed a health ranking system for nearly all U.S. counties. The program can significantly facilitate health improvement decision m aking processes. The location decision of a cement plant, with potential negative environmental impacts on humans, cannot be made in a sustainable manner without considering the health status of the targeted community. By using the County Health Ranking s ystem, the health status of the feasible counties was considered in the decision making process. The counties with better health outcome and health factors were more preferable for locating a cement plant. The health status (health outcome) rankings have b een defined based on the following parameters (County Health Rankings and Roadmaps organization, 2012) : Mortality: Premature death Morbidity: Poor or fair health poor physical health days poor mental health days and low birth weight The list of the criteria considered in the healthcare factor rankings includes (County Health Rankings and Roadmaps organization, 2012) :
104 Health behaviors : Adult smoking adult obesity physical inactivity excessive drinking motor vehicle crash death rate sexually transmitted infections and teen birth rate Clinical care : Uninsured primary care phy sicians preventable hospital stays diabetic screening and mammography screening Social & economic factors : High school graduation some college unemployment children in poverty inadequate social support children in single parent households and violent crime rate Physical environment : Air pollution particulate matter days air pollution ozone days access to recreational facilities limited access to healthy foods and fast food restaurants ) Social (community) acceptability. Social acceptability is about the willingness of the targeted community to accept a facility in their region, and it has a significant impact on the location decision of a new industrial plant (Garrone & Groppi, 2012) In this acceptability of the feasible counties. Higher education enables c ommunities to collect information about potential negative impacts of the plant, and consequently, they will probably have higher propensity to reject the plant. Although the goal of the current study is to locate the cement plant in a sustainable manner, some of the potential negative impacts of the plant on the targeted community are unavoidable. Therefore, social acceptability is a key criterion. After selecting a site, companies should devise strategies to relocate the community or compensate them for a ny potential negative impacts of the plant. The required data for the social acceptability evaluation was acquired from the website of County Health Rankings and Roadmaps organization.
105 Economic Evaluation Regional economic performance and employment stat us are the considered sub themes in the economic dimension. For evaluating the regional economic performance, for locating a plant due to the expected positive economic impac ts of the new plant, such as job creation and improving the financial turnover within the community. For the employment status of the feasible counties, unemployment rates were evaluated. New plants create employment opportunities for communities; therefo re, the communities with higher unemployment rates were preferred in the context of sustainable development. Moreover, in counties with higher unemployment rates, companies will have fewer concerns for labor availability. The income data was taken from the United States Census Bureau and the unemployment rates were acquired from the organization of Florida Jobs. Weighting the Decision Criteria To evaluate the feasible counties, the relative importance of the decision criteria had to be identified. For this purpose, the AHP technique was utilized to solicit To perform the AHP analysis, a questionnaire was developed and distributed among experts (Appendix B). The questionnaire was approved by the Institutional Review Board at the University of Florida (IRB02 office). IRB02 office is responsible to review the research studies conducted at the University of Florida that involve humans or have impacts on them. The main mission of IRB is to evaluate the impacts of the research studies on humans and decide whether the research can be carried out or not (Behavioral/NonMedical Institutional Review Board, 2012)
106 Since there were different and in some cases, conflicting evaluation criteria in the questionnaires were distributed to the experts of different organizations through email, mail, and meetings. 26 accepta ble responds were received. The average total years of experience of the participants was 28.3 years, and 38.5% of the participants were involved in site selection processes. Figure 5 12 shows the educat ional degre e of the participant and Figure 5 13 shows the sector in which participants were involved. The participants were from the following organizations: The University of Florida including the departments of Building Con struction Urban Planning Environmental Engineering Civil Engineering and Warrington College of Business Administration / Dept. of Economics Florida Department of Environmental Protection/ Permitting office Koogler & Associates, Inc. Water & air researc h, Inc. CEMEX Miami Cement Plant Titan America Cement Plant The Institute For Local Self Reliance Liberty concrete & Forming, Inc. Bayside Structures, Inc. Enernoc, Inc. The International Council for Research and Innovation in Building and Construction (W 115: Construction Materials Stewardship) One of the key steps in the AHP method is checking the overall consistency of the results by calculating the consistency ratio. As discussed in Chapter 2, the overall
107 consistency ratio (CR) should be less than 0.1, which means that the pair wise comparisons are relatively consistent and no reevaluation is required. To calculate the weights of the criteria and the consistency ratio of each comparison table based on the responded questionnaires, the following steps wer e taken: 1. A single matrix for each comparison table was developed by calculating the average of the responds for each comparison table. The comparison tables can be seen in the questionnaire, Appendix B. 2. The priority vector (weights) of the criteria in each comparison matrix was calculated. The followed procedure for calculating the weights is outlined below: The values in each row of a matrix were multiplied The n th root of the aforementioned products were calculated, where n is the number of criteria in ea ch comparison table The calculated n th root of the products were normalized to determine the weights The results were checked by adding up the weights, which should be equal to one 3. Measuring consistency index using Equation 2 2 and Equation 2 3. Table 5 3 through Table 5 5 show the calculated parameters for each comparison matrix. As Table 2 1 shows, the Random Index is zero for on e and two dimensional matrices, which means that calculating the consistency ratio is meaningless for these matrices. Since the economic sub theme comparison matrix was 2x2, the consistency ratio was not calculated for this matrix. Table 5 6 depicts the summary of the calculated consistency ratios in the conducted survey. In this study, the AHP method was used only for weighting the dimensions and the sub themes. Because of the limited times of the respondents, us ing AHP to determine
108 the weights of all dimensions, sub themes, and indices was ineffective and very time was applied. Table 5 7 through Table 5 11 show the weights of all dimensions, sub themes, and indices. Comparison of the Feasible Counties After identifying the weights of the criteria and collecting all the data in association with each of them, the feasible counties were evaluated and ranked. To be able to analyze and compare different types of spatial and non spatial data, the data were integrated into a single Excel spreadsheet. The spatial data was analyzed in the ArcGIS environment. The header row in the spreadsheet represented the criteria and the far left column showed the feasible counties. Each cell represented the value of each feasible county against a criterion (Appendix A). Since the data had different measuring units, they w ere normalized first to be able to compare them and add them up for calculating the final scores. Table 5 12 Show the normalized values of feasible counties against the suitability indices. Using the SAW method (Eq uation 2 4 ) combined Equation 2 7 and Equation 2 8 as the normalization techniques, the final scores of the feasible counties were calculated. The feasible counties were also evaluated with the COPRAS method, but consistent with the findings of Podvezko (2011) the rating of the counties changed significantly due to a small variation in data and the results were not stable. Moreover, the results were very different from the SAW method. Therefore, it was d ecided to use the results of the SAW method for calculating the suitability scores.
109 Based on the scores of counties resulted from the SAW method, eight priority groups were identified. Table 5 13 shows the score of the feasible counties and Table 5 14 shows their priorities. The combination of scores and priorities can be seen in Figur e 5 14 For the micro level analysis, the countie s with the highest priority, priority one, were selected as the candidate counties including: 1. Polk 2. Collier 3. Glades 4. Pinellas 5. Putnam 6. Hillsborough 7. Hendry, and 8. Pasco ( Figure 5 15 ) Micro Level An alysis After performing the macro level analysis, the micro level evaluation was conducted on the candidate counties for selecting the appropriate parcels for siting a cement plant with sustainability and technical considerations. In the following sections the evaluation phases of the micro level analysis (screening and suitability) are described in detail. Micro Level Screening Analysis As the first step of the micro level analysis, the screening criteria ( Table 5 15 ) were evaluated to identify the candidate parcels. The screening phase was conducted in 6 steps as follows: Steps 1 and 2. Two of the considered screening environmental criteria include a 7 km buffer distance from Florida urbanized areas which have mo re than 50,000 people population, and a 7 km buffer distance from Florida conservation lands. For evaluating ambient air quality of a cement plant, CSI (Misra, 2002) proposed that 7 to 10 km to be considered as the impacted region. As a result, a 7 km was assumed for the buffer distance.
1 10 Step 3. The other screening environmental criteria were Florida Forever Board of Trustees (FFBOT) projects and Florida Forever Acquisitions (FF Acquired) projects. FFBOT projects include the lands approved by the State's Acquisition and Restoration Council and administered by the Florida Department of Environmental Protection, Division of State Lands, for the State Board of Trustees (BOT). These lands are notable for their distingu ishable natural, historical, and archeological resources. FF Program beginning in 2001. FFBOT and FF Acquired can be considered as mandatory environmental criteria for this c ase study. Step 4. In the screening phase, lands with CLIP priorities 1 and 2 were also excluded from feasible areas. Step 5. After screening the areas that satisfy steps 1, 2, and 3, the parcels falling within those areas were identified as potential parc els. As the next step, the land size for the manufacturing process was considered as a technical criterion for screening the potential parcels. Plant sizing is a major component of the business plan for a cement factory. It is proposed in the siting model that plant sizing be implemented before any site evaluation. Since developing a business plan for a new cement plant was out of the scope of this research, the required land size was based on the minimum land size of the existing cement plants in Florida, 120 acres. Although the acquisition of several adjacent small lands can be another option for locating a cement plant, this was not considered in this study due to its complexity.
111 For land size analysis, the spatial dataset, developed by the Department of Revenue (DOR), was downloaded from the FGDL website. Step 6. Consideration of urban planning designations is a crucial prerequisite for any site selection decision. Accurate spatial planning can preserve natural resources and protect humans from potential hazards and health risks. It may also reduce the risks of unknown spatial factors for cement companies. Therefore, in this study, land use was considered as another screening criterion. For this purpose, industrial (heavy and light manufacturing), minera l, not identified land uses, and land uses for future development were considered. However, this assumption resulted in no potential parcels in all the candidate counties. Therefore, agricultural land parcels were also considered in the screening phase due to the fact that, under special conditions, their land use can be changed. Nevertheless, the code of ordinances of all the candidate parcels were checked in the suitability phase for the purpose of locating a cement plant. Table 5 16 depicts the results of the screening phase in the micro level analysis. As Table 5 16 shows, 11 candidate parcels were identified for the suitability analysis. The candidate parcels were locate d in Collier, Glades, and Hendry counties Figure 5 16 through Figure 5 18 show the feasible areas of Collier, Glades, and Hendry counties. It is worth noting that the feas ible areas were the areas located in 5 km buffer distance of limestone. Figure 5 19 through Figure 5 21 depict the geographical location of the candidate parcels. Micro Lev el Suitability Analysis At this phase, the suitability of 11 candidate parcels, resulting from the screening phase, were evaluated to identify the most appropriate parcel. Below, the evaluation process is explained. The summary results are included in Table 5 19
112 Technical Evaluation Land Zoning. As one of the most important elements of land data, zoning designations show how a development within a community has been planned. They also provide information about the surrounding sites of the targeted land parcel. As discussed in the previous section, the zonings of the identified candidate parcels are not designated for industrial or mining uses; therefore, their zoning codes were checked through codes of ordinances f or the possibility of siting a cement plant. Since the production process of cement is the combination of mining and heavy manufacturing; the zoning ordinances were checked for both uses. ere extracted from the official websites of the local governments. The zoning designations of Hendry candidate parcels were achieved from the urban planning division of the county in llier, Glades, and Hendry counties were extracted from the website of Municipal Code Corporation (Municode). Municode is a common source for municipal laws and ordinances in the United States. Below, the zoning evaluation of the candidate parcels are descr ibed. Zoning of Collier County The zoning designation of all candidate parcels in Collier County is Rural Agricultural District (A). The purpose of zoning A is to provide lands for agricultural, pastoral, and rural land uses. According to zoning planning of earth mining, and related processing and production not incidental to the agricultural ricultural district (A) (The board of county commissioners of Collier County, Florida 2008)
113 However, it seems that the zoning planning of Collier County is not flex ible for siting a cement plant as a heavy manufacturing facility. Zoning of Glades County The zoning designation of all the candidate parcels in Glades County is Open Use Agriculture (OUA). As Table 5 17 shows, si ting a mineral extraction and processing plant is allowable in the OUA zoning with conditional use but siting heavy manufacturing is not permitted (Glades County Government, 2010) Zoning of Hendry County. The zon ing designation of all candidate parcels in Hendry County is General Agriculture (A 2). The same as Glade County, mining with special exception is allowable in A 2 zoning, but heavy manufacturing is not allowed ( Table 5 18 ) (Hendry County Government, 201 3 ) Zoning summary. According to the zoning codes of ordinances, only mining with conditional or special exception is permitted in the zoning districts of all the candid ate (Glades County Government, 2010) Therefore, the flexibility to be converted to industrial parcels. Nevertheless, according to an exp erienced urban planner in Florida, it seems that in some cases, there might be flexibility in land use planning decisions, and the planning designations can be revised. Therefore, by taking sustainability criteria into account in the siting decision proces s, it might be possible to change the planning designations of the candidate parcels under certain conditions.
114 Land Land Slope. The slope of the candidate sites were checked through topographic maps to ensure that they are not very steep. As Figure 5 22 through Figure 5 24 show, all the candidate sites satisfy this criterion Market Proximity to Limestone. As emphasized in the macro level analysis, proximity to limestone i s one of the crucial technical factors for siting a cement plant. In addition, from a sustainability perspective, a shorter distance to raw material means less transportation, and consequently, less fossil fuel consumption and a smaller carbon footprint. I n addition, a shorter distance to raw material restricts dust accumulation and noise disturbance to a small area. It also results in a less land disturbance compared to having two distant places, one for quarrying and another for manufacturing. Therefore, in the case that two land parcels have the same attributes, the one closer to limestone is preferable from both technical and sustainability perspectives. Although all the candidate parcels are located within a 5 km buffer distance of the limestone, their proximity to limestone was again measured in this step to identify the most suitable ones in terms of this criterion. The closest distance between the candidate parcels and limestone were measured in ArcGIS. From the 11 candidate parcels, five of them are located on limestone, which means that siting a cement plant in one of the other six parcels requires acquiring another property for quarrying purpose. As discussed in the Primary ESCIA section, CO 2 and other air emissions from a cement plant are mostly du e to the chemical reactions in kilns. The major environmental concerns of the quarrying process for the neighboring areas are dust and noise. Therefore, for siting the quarrying operation, it is rational to relax some of the criteria to some extent to have other site alternatives fall in the limestone area.
115 Infrastructure Proximity to infrastructure. Proximity to transportation infrastructure not only reduces the operational cost of cement plants, but also significantly contributes to the improvement of su stainable development measures by reducing fuel consumption and carbon footprint. For measuring the proximity of the candidate sites to the infrastructure, the closest distances between each candidate parcel and infrastructure (including the major roads, rail roads, and navigable waterways) were measured. The candidate parcels of Hendry County are nearer to major roads, the most common transportation method. The candidate parcels in Glades County have the shortest distance, approximately 0.1 km, to the Ok eechobee navigable waterway. As addressed before, according to Holcim Inc. (2002) cement transportation via waterways is the most economic transportation method. Since Okeechobee is a manmade waterway, usin g it for transportation causes no major environmental concern. However, the state and local rules and the transportation feasibility through the waterway should be investigated by a group of experts. The parcels in Collier County have the shortest distanc e to the railroad system, approximately 2.5 km. Cement transportation through railroad systems is the second most economic transportation method of cement (2002) Infrastructure Distance to national highway s. Due to negative traffic impacts and pollution emissions, industrial facilities should have a minimum distance from the national highways. Based on the siting guidelines of Punjab and Rajasthan governments, 500 meters is assumed as the suitable distance from the candidate parcels to the national highways in this study. In other words, parcels located beyond
116 500 m of the national highways are considered suitable in terms of this index; otherwise, they are unsuitable. Florida National Highway spatial data w as acquired from the FGDL website. The database is a subset of the National Highway Planning Network, including rural arterials, urban principal arterials, and all national highway system routes. As discussed, proximity to major roads were taken in this di ssertation. For some of the candidate parcels, although the closest major roads were the same as closest national highway, the distances were different. This discrepancy is due to the fact that data was extracted from different data sources. Nevertheless, this discrepancy did not have any impact on the result of the evaluation. Infrastructure Traffic volume. Siting a cement plant will have negative traffic impacts on the local roads due to the need for hauling raw materials and final products. One of the m ost destructive impacts on the environment and local people is traffic congestion and increased carbon emission of vehicles. In addition, heavy traffic can cause soil contamination, and create noise, vibration, and dust, which have potential health and saf ety risks. To evaluate the traffic volume criterion, the annual average traffic volume and truck traffic volume of the closest major road to the candidate parcels were measured. The relevant spatial data were downloaded from Florida Department of Transpor tation (FDOT). It should be noted that since this dataset was different from the major road dataset used for proximity to infrastructures, in some cases, the closest road for annual average traffic volume and truck traffic volume was different from the clo sest road for the purpose of measuring proximity to infrastructures. This discrepancy might be due to
117 the availability of traffic data for FDOT. However, the discrepancy did not have any impact on the result of the evaluation. Based on the analysis, the cl osest roads to all the candidate parcels have relatively low traffic volume compared to the mean traffic volume of the major roads in Florida. The third parcel of Glades County is close to a road with minimum traffic volume compared to the other candidate parcels. Safety Major hazardous transmission lines. To reduce the risks of potential hazards for employees and local people from the proximity of the plant to major hazardous transmission lines, the geographical locations of the lines in candidate parcels and their vicinity areas were investigated. For this purpose, the location status of major gas transmission pipelines, major hazardous liquid pipelines, LNG plants, and power lines were checked. No major line is crossing the candidate lands and their adjac ent areas. The closest distance between candidate parcels and the power lines was 2.4 km, which was in Collier County. Since the spatial data of the other lines was not available, the distances of the candidate parcels from them could not be determined. Th e location status of major gas lines and LNG plants was checked using the website of national pipeline mapping system, and the spatial dataset of major power transmission lines was acquired from the FGDL website. Cost Land cost To compare the land value o f the candidate parcels as the only economic criterion at this phase, the parcel dataset developed by DOR was used. The Table 5 19 shows, Collier c andidate parcels are the most expensive lands based on the DOR data.
118 Environmental Evaluation Hydro & Geology Drastic Vulnerability Index (DVI). DVI is a standardized grading system for the evaluation of ground water pollution potential based on hydrologi c factors, including depth of water, net recharge, aquifer media, soil media, topography, impact of vados zone media, and hydraulic conductivity of the aquifer. The combination of the weighted factors produce DVI. The areas with higher DVI have more potent ial to affect ground water quality. In this study, DVI attributes of the candidate sites were evaluated to check their potential vulnerability to introduce pollution to the ground water. The spatial dataset of DVI was extracted from the FGDL Drastic datase t, developed jointly by the United States Environmental Protection Agency (USEPA) and the National Water Well Association (NWWA). Figure 5 25 through Figure 5 27 show the D VI maps of the candidate parcels. As these figures show, parcels 2 and 3 of Glades County have relatively high DVI compared to the other areas of the county. Therefore, the ground water of parcels 2 and 3 are more vulnerable to pollution. However, as addr essed in the Primary ESCIA and according to Misra (2002) a cement plant does not significantly contribute to water pollution and wastewater and its emissions are limited to surface runoff. Therefore, for the lan ds with high DVI, companies might be able to remedy the problem by developing an efficient water pollution prevention plan. Hydro & Geology Flood hazard zones. Federal Emergency Management Agency (FEMA) has designated flood hazard zones for the purposes of floodplain
119 management, mitigation, and insurance activities. Table 5 20 shows the definitions of the identified flood zones. In this study, the spatial data of the flood zones, extracted from FGDL, were used to investigate the suitability of parcels in term of flood risks. For some of the areas, updated flood data were not available; therefore, the 1996 data were used. Among the candidate parcels, the dominant areas of parcels 3, 4, and 5 of Hendry County have l ow to moderate risk flood areas. The other parcels are located in high risk flood zones. According to Federal Emergency Management Agency (FEMA), mandatory flood insurance is required for high risk flood zones. Hydro & Geology Geologic fault line. There is no geologic fault line in the state perspective. Hydro & Geology Distance to potable water well. The first candidate parcel of Glades C ounty and parcels 4 and 5 of Hendry County are less than 500 m distance from potable water wells. However, in the physical evaluation of the sites, the condition of these wells in terms of water supply and consumption should be investigated, and if one of these parcels is selected as the final parcel, the cement company is responsible for developing a water pollution prevention plan for these water wells. Ecological Impacts CLIP Priority Since the dominant areas of all candidate parcels have CLIP priority 4, they are the same in terms of this factor. Social Evaluation Health Proximity to medical emergency services The proximity of a plant to emergency healthcare facilitates that can provide care for work related or on the job
120 injurie s is evaluated in thi s section. As Table 5 19 illustrates, the candidate parcels in Hendry County have a longer distance to emergency facilities compared to those in Collier and Glades counties. Equity Impacted Population. To investig ate the impacted population from locating a cement plant in each candidate parcel, the total population of the census blocks falling within a 7 km buffer distance of the parcels was counted. As explained earlier, the 7 km buffer distance is an assumption i n this study for the impacted region of a cement plant. The impacted population in Glades parcels is greater than the impacted population in the other parcels. Equity Dominant Race. To compare the racial equity of the impacted population, the dominant rac e of the population impacted by locating a cement plant in each candidate parcel was determined based on the US census data. The dominant race in candidate parcels of Col lier County have a lower percentage of the dominate race (white alone or in combination) compared to the other parcels; therefore, locating a cement plant in Collier candidate parcels would result in less racial inequity. Comparison of the Candidate Parcel s According to the micro level analysis, only the planning ordinance of Glades County has flexibility and allows for the placement of a heavy manufacturing plant in the Gla des County are adjacent, their attributes are approximately the same. Therefore, the other steps of the evaluation method, such as weighting the criteria and ranking the suitability of the parcels, could not be effectively performed for this phase.
121 Neverth eless, decision making about the final parcel is a multi disciplinary process, which should be made by a panel of experts, and in reality, several other key issues need to be considered before making a final decision. The quality and quantity of the availa ble limestone in each parcel is a key technical factor for siting a cement plant. The composition of limestone not only impacts the final products, but also affects the process design, and subsequently, waste, water, and air pollution. The quantity of the limestone greatly impacts the economic feasibility of the plant. Therefore, any final decision regarding the location of a cement plant directly depends on the composition and amount of the available limestone in that area. In this case study, due to the l imited resources, it was not feasible to check this technical factor. Although it is assumed that embracing sustainability aspects might create possibilities to change the planning designations of Collier and Hendry parcels, there is still not one single dominant solution with the best attributes that can take into account all the criteria presented in the micro level analysis. Therefore, the candidate parcels were ranked based on the suitability criteria. Since some of the parcels are located on limestone the parcels were ranked only for the purpose of cement manufacturing and not for the quarrying operation. The performance of the candidate parcels in terms of different suitability criteria was evaluated and in cases where the performance of the candid ate parcels were almost the same in terms of a criterion, that criterion was excluded from the ranking process. Therefore, the following criteria were not considered in the ranking phase: Land slope Traffic volume
122 Major hazardous lines CLIP priority Fault line In addition, DVI and proximity to water wells criteria, for which companies might be able to develop a remedy action plan, were not considered in the ranking phase. For the sake of simplicity, the suitability criteria were weighted equally in this based on Equation 2 7 and Equation 2 8 According to the results, listed in Table 5 21 the parcels of Glades County and p arcels 4 and 5 of Hend r y County have the highest suitability scores, respectively. All these parcels are located in limestone areas Discussion of the Results Based on the macro level analysis, eight counties were selected as the candidate counties. Three counties, Glades, Putnam, and Hendry, were designated as Rural Areas of Critical Economic Concern (RACEC). Under Rural Economic Development Initiative (REDI) program, the Department of Economic Opportunity (DEO) has designated some of the rural areas of Florida as Rural Areas of Critical Economic Concern (RACEC). These areas have been adversely impacted by extraordinary economic events, natural disasters, or they have lack of natural resources (Florida Department of Economic Opportunity, 2013) There are some economic development incentives for these areas, such as the qualified target industry, the tax refund program, the quick response train ing program, the quick response training program for participants in the welfare transition program, transportation projects, the brownfield redevelopment bonus, and the rural job tax credit program (Florida Department of Economic Opportunity, 2013) Moreover, according to
123 Enterprise Florida (2013) funding is provided to these regions for economic research, site selection, and marketin g. Therefore, consulting with business development organizations before finalizing the siting decision criteria might provide important input According to REDI, a rural county or community is: 75,000 or less; or a county with a population of 100,000 or less that is contiguous to a county with a population of 75,000 or less; or an incorporated city or unincorporated federal enterprise community with a population of 25,000 or less and an employme To be designated as RACEC, a rural county must meet at least three of the following economic factors (Florida Department of Economic Opportunit y, 2013) : low per capita taxable values high unemployment high underemployment low weakly earned wages compared to the state average low housing values compared to the state average high percentages of the population receiving publi c assistance high poverty levels compared to the state average a lack of year The following three main areas in Florida are designated as RACEC: Northwest Rural Area of Critical Economic Concern South Central Rural A rea of Critical Economic Concern North Central Rural Area of Critical Economic Concern: Figure 5 28 shows the map of RACECs of Florida. Considering precautionary sustainability criteria in the micro level analysis of the case study resulted in 11 candidate parcels, and only 5 of them were located on limestone. This means that in order to site a cement plant in one of the other candidate
124 parcels, additional property for the purpose of quarrying is required. Since on ly a few candidate parcels are available, companies may face challenges in acquiring one of them. Therefore, as explained in step 14 of the siting model, one of the following methods can be applied to address this issue. Zoning designations. Since the zoni ng designations of the candidate parcels were agriculture, local urban planning departments should be requested for changing the zoning designation of the targeted land. Trading. One of the land acquisition methods is trading the targeted land with other lands with equal or greater values. This can result in a win win situation both for companies and land owners. Land size. In the case that companies cannot find a way to acquire one of the identified candidate parcels, they need to find a way to increase t he number of the candidate counties. For this purpose, companies might consider smaller land sizes in the screening phase and, acquire several adjacent small lands to obtain the targeted land size. Sensitivity analysis. One method of acquiring more candid ate parcels is to conduct a sensitivity analysis to investigate the impacts of the decision criteria on the results. Relaxing some criteria may result in acquiring more candidate parcels. In the next section, the results of a sensitivity analysis conducte d in this study is reported. The main purpose of the sensitivity analysis was to investigate the impacts of some criteria on the number of candidate parcels. Sensitivity Analysis Four of the precautionary sustainability criteria, which resulted in limiting the number of candidate parcels, were the 7 km buffer d istance from conservation lands,
125 the 7 km buffer distance from urbanized areas land size, and land use Therefore, to have more candidate parcels, some compromises are required to be made regarding t hese criteria. For this purpose, it was decided to conduct a sensitivity analysis to investigate the impacts of these criteria on the number of candidate parcels. Table 5 22 and Figure 5 29 demonstrate the relationship between the number of candidate parcels and the buffer distances. In this analysis, all the other screening criteria including FFBOT and FF Acquired projects, CLIP priority 1 and 2, land size, and land use we re kept the same. As shown, reducing the sustainability buffer distances significantly increases the number of candidate parcels. For instance, if the buffer distance is reduced to 3.5 km, the number of candidate parcels comes out to be 59. Figure 5 30 show the relationship among the number of candidate parcels, land size and land use. In this analysis, the criteria of 7 km buffer distance from urbanized areas, FFBOT and FF Acquired projects, and CLIP priority 1 and 2 were the same The number of the candidate parcels without considering land use and land size criteria are 754. By considering land use, the number will decrease to 139, whereas by considering land size the number will decrease to 18. Therefore, the impact of land size is greater than that of land use. Reviewing the Proposed Siting Model Before conducting the case study, a preliminary siting model along with a list of criteria was developed. The model and the criteria were then reviewed and improved based on the acquir ed knowledge and experience gained through the case study. The improved model ( Figure 4 1 ) and the final list of criteria ( Figure 4 2 ) were explained in Chapter 4. The majo r changes made to the preliminary model and the list of criteria are described below.
126 While conducting the case study, the researcher realized that some economic development programs, such as RACEC have been established in the state of Florida for improvin contributory step in sustainable siting decisions, was added to the primary model. However, it should be noti ced that the focuses of business development organizations and environmental departments are mostly on the economic and environmental dimensions, respectively. Therefore, it is the responsibility of decision makers to consider the other dimensions of socia l and technical in customizing the criteria. Based on the outcomes of the case study, it can be concluded that conducting siting decisions at both macro and micro levels combined with screening and suitability evaluations addresses sustainability requirem ents more effectively. Since some of the elements of ESCIA are site related, it was decided that a secondary ESCIA be conducted based on the physical characteristics of the site and the local conditions. As shown in the case study, the process of land acqu isition might be challenging especially if the number of candidate parcels come out to be limited. Therefore, decision makers should increase the number of candidate options and deploy proper land acquisition techniques. This step is added to the primary m odel as a separate step. Summary In this chapter, the siting model, presented in Chapter 4, was examined in a case study of sustainable location of a cement plant in the state of Florida. For this purpose, at first, the general characteristics and sustain ability challenges of the cement industry
127 were reviewed. The primary ESCIA (Environmental, Social, and Economic Impact Analysis) was then conducted. By customizing and categorizing the decision criteria proposed in Chapter 4, the macro and micro level an alyses were conducted. As Figure 4 1 demonstrates, each level of analysis was performed based on two phases of screening and suitability. As a result of the macro level screening phase, 40 counties were identified as feasible counties. By conducting the suitability analysis, eight counties were selected for the micro level analysis. The screening phase of the micro level analysis resulted in 11 candidate parcels. The suitability of the candidate parcels were evalu ated. Although Glades County was the only county whose planning designations permit the placement of a heavy manufacturing plant in candidate sites, the suitability of all 11 parcels were ranked ( Table 5 21 ). To in crease the number of the candidate parcels, sensitivity analysis was performed to measure the impacts of the decision criteria on the results. The chapter concludes by summarizing the changes that appeared necessary to applied on the primary siting model after conducting the case study.
128 Table 5 1 Feasible counties No. County No. County No. County No. County 1 Alachua 11 Glades 21 Lee 31 Pinellas 2 Broward 12 Hamilton 22 Leon 32 Polk 3 Calhoun 13 Hendry 23 Levy 33 Putnam 4 Citrus 14 Hernando 24 Liberty 34 Sumter 5 Collier 15 Hillsborough 25 Madison 35 Suwannee 6 Columbia 16 Holmes 26 Marion 36 Taylor 7 Dixie 17 Jackson 27 Miami Dade 37 Union 8 Franklin 18 Jefferson 28 Monroe 38 Wakulla 9 Gadsden 19 Lafayette 29 Palm Bea ch 39 Walton 10 Gilchrist 20 Lake 30 Pasco 40 Washington Table 5 2 Macro level suitability indices Dimension Sub theme Index Technical Demand Population 300 km buffer No. of competitors Max capacity of existing kilns O perational cost Industrial power Rate Gas Rate Median hourly wage of Production Workers Availability of Infrastructure Distance to the nearest major road (km) Distance to the nearest railroad (km) Distance to the nearest sea port (km) Di stance to the nearest navigable waterway (km) Environmental Environmental status % of area with CLIP priority 1,2 Social Poverty Gini Coefficient Percent of families below 100% poverty Public health status The rank of health outcomes Health fact ors The rank of Health factors Social acceptability High school graduation (%) Economic Regional Economic Performance Money income (Per _capita ) Employment status Unemployment Rate%
129 Table 5 3 The priority vector and con sistency ratio of the dimensions comparison Technical Environmental Social Economic 4th root of product Priority Vector Technical 1.00 2.12 2.54 2.81 1.9741 0.4258 Environmental 0.47 1.00 3.02 3.24 1.4652 0.3161 Social 0.39 0.33 1.00 1.93 0.7084 0.152 8 Economic 0.36 0.31 0.52 1.00 0.4880 0.1053 4.14592 C.I. 0.04864 C.R 0.05404 Table 5 4 The priority vector and consistency ratio of the technical sub themes comparison Demand No. of local com petitors Operation cost Infrastructure 4th root of product Priority Vector Demand 1.00 4.18 3.13 2.80 2.4589 0.5194 No. of local competitors 0.24 1.00 1.44 1.64 0.8662 0.1830 Operation cost 0.32 0.70 1.00 2.55 0.8676 0.1833 Infrastructure 0.36 0.61 0.39 1.00 0.5412 0.1143 4.18814 C.I. 0.06271 C.R 0.06968 Table 5 5 The priority vector and consistency ratio of the social sub themes comparison Poverty Public health status Health factors Social acceptability 4th root of product Priority Vector Poverty 1.00 2.69 2.31 2.07 1.8934 0.4277 Public health status 0.37 1.00 2.13 2.17 1.1442 0.2585 Health factors 0.43 0.47 1.00 2.43 0.8389 0.1895 Social acceptability 0.48 0.46 0.41 1.00 0.5502 0.1243 4.23361 C.I. 0.07787 C.R 0.08652
130 Table 5 6 The summary of consistency ratios Main Theme Technical Sub themes Social Sub themes Overall C.R. 0.054 0.069 0.086 Table 5 7 Weight values of dime nsions and sub themes Dimension W.V Sub theme W.V Technical 0.43 Demand 0.52 No. of competitors 0.185 Operational cost 0.185 Availability of Infrastructure 0.11 Environmental 0.32 % of area with CLIP priority 1,2 1.00 Social 0.15 Poverty 0.43 Public health status 0.26 Health factors 0.19 Social acceptability 0.12 Economic 0.10 Regional Economic Performance 0.70 Employment status 0.30
131 Table 5 8 Weight values of t echnical indices Dimension Sub theme W.V Index W.V. Technical 0.43 Demand 0.52 Population 300 km buffer 1.00 No. of competitors 0.185 Max capacity of existing kilns 1.00 Operational cost 0.185 Industrial power Rate 0.33 Gas Rate 0.33 Median hourly wage of Production Workers 0.33 Availability of Infrastructure 0.11 Distance to the nearest major road (km) 0.50 Distance to the nearest railroad (km) 0.17 Distance to the nearest sea port (km) 0.17 Distance to the nearest navigable waterway (km) 0.17 Table 5 9 Weight values of e nvironmental indices Dimension Sub them W.V. Index W.V. Environmental 0.32 Environmental status 1.00 % of area with CLIP priority 1,2 1.00
132 Table 5 10 Weight values of s ocial indices Dim ension Sub theme W.V. Index W.V. Social 0.15 Poverty 0.43 Gini Coefficient 0.50 Percent of families below 100% poverty 0.50 Public health status 0.26 The rank of health outcomes 1.00 Health factors 0.19 The rank of Health factors 1.00 Social acceptability 0.12 High school graduation (%) 1.00 Table 5 11 Weight values of e conomic indices Dimension Sub theme W.V. Index W.V. Economic 0.10 Regional Economic Performance 0.70 Money income (Per_capita ) 1.00 Employment status 0.30 Unemployment Rate% 1.00
133 Table 5 12 Normalized values of feasible counties against the suitability indices County Population 300 km buffer Max capacity of existing kilns Industrial Rate 400,000 kwh Gas Rate Medi an hourly wage of Production Workers Alachua 0.6043 0.4570 0.8427 0.6626 0.0000 Broward 0.8138 1.0000 1.0000 0.1687 1.0000 Calhoun 0.0617 1.0000 0.0000 0.9959 0.5520 Citrus 0.7537 1.0000 0.6854 0.7819 0.0901 Collier 0.8244 1.0000 1.0000 0.0000 0.5381 Columbia 0.5787 1.0000 0.8427 0.8107 0.0901 Dixie 0.5919 1.0000 0.6854 1.0000 0.0901 Franklin 0.3375 1.0000 0.6854 0.6955 0.5520 Gadsden 0.1417 1.0000 0.6854 0.8807 0.8360 Gilchrist 0.5820 1.0000 0.6854 0.7531 0.0000 Glades 0.8815 1.0000 1.0000 0.753 1 0.4111 Hamilton 0.5181 1.0000 0.8427 0.8313 0.0901 Hendry 0.8534 1.0000 1.0000 0.8642 0.4111 Hernando 0.8466 0.4457 0.6854 0.6955 0.5266 Hillsborough 0.9831 1.0000 0.7450 0.4733 0.5266 Holmes 0.0010 1.0000 0.8620 0.9053 0.5520 Jackson 0.0282 1.0000 0.4310 0.8807 0.5520 Jefferson 0.5069 1.0000 0.6854 0.5021 0.8360 Lafayette 0.5690 1.0000 0.6854 0.5885 0.0901 Lake 1.0000 1.0000 0.6854 0.6132 0.5866 Lee 0.8534 1.0000 1.0000 0.4074 0.3603 Leon 0.3737 1.0000 0.6854 0.6379 0.8360 Levy 0.6431 1.0000 0.6854 0.8601 0.0901 Liberty 0.2438 1.0000 0.3427 0.5885 0.5520 Madison 0.5292 1.0000 0.6854 0.8395 0.0901 Marion 0.7205 1.0000 0.6854 0.7407 0.5335 Miami Dade 0.5785 0.0000 1.0000 0.5720 0.5404 Monroe 0.5030 1.0000 1.0000 0.1975 0.4111 Palm Beach 0. 8244 1.0000 1.0000 0.2222 0.6212 Pasco 0.8511 1.0000 0.7152 0.6337 0.5266 Pinellas 0.8483 1.0000 0.7152 0.5473 0.5266 Polk 0.9831 1.0000 0.7450 0.6708 0.1547 Putnam 0.5982 1.0000 1.0000 0.8765 0.0901 Sumter 0.8457 0.6862 0.6854 0.7531 0.0901 Suwannee 0.5690 0.7234 0.8427 0.9342 0.0901 Taylor 0.5801 1.0000 0.6854 0.8971 0.0901 Union 0.5774 1.0000 1.0000 0.8519 0.0901 Wakulla 0.4810 1.0000 0.6854 0.4856 0.8360 Walton 0.0000 1.0000 0.8620 0.4362 0.5520 Washington 0.0265 1.0000 0.8620 0.8148 0.5520
134 Table 5 1 2 Continued. County Distance to the nearest major road (km) Distance to the nearest railroad (km) Distance to the nearest sea port (km) Distance to the nearest navigable waterway (km) % of area with CLIP Priority 1,2 Alachua 0.8517 0.8783 0.41 35 0.3683 0.4093 Broward 0.9979 0.9732 0.9870 0.9681 0.6463 Calhoun 0.9385 0.8997 0.6346 0.8212 0.5159 Citrus 0.7751 0.8421 0.8451 0.9479 0.8328 Collier 0.8427 0.8251 0.2639 0.7262 0.9470 Columbia 0.7284 0.8330 0.3444 0.2652 0.1714 Dixie 0.9747 0.719 3 0.3136 0.8157 0.9388 Franklin 0.9484 0.7417 0.6800 0.9272 1.0000 Gadsden 0.9531 0.9377 0.4088 0.9470 0.8783 Gilchrist 0.8108 0.8666 0.3305 0.6266 0.2754 Glades 0.6905 0.9271 0.4098 0.9312 0.8239 Hamilton 0.8187 0.7351 0.1700 0.0000 0.4199 Hendry 0. 6112 0.2399 0.4672 0.4416 0.6929 Hernando 0.9475 0.9762 0.8506 0.6597 0.7678 Hillsborough 0.9946 0.9820 1.0000 0.9505 0.7373 Holmes 0.9770 0.8046 0.5486 0.6131 0.5248 Jackson 0.8977 1.0000 0.5321 0.6457 0.0000 Jefferson 0.2946 0.4200 0.1668 0.7733 0.9 455 Lafayette 0.7008 0.7074 0.1077 0.5960 0.8073 Lake 0.0000 0.9630 0.6250 0.4497 0.5381 Lee 0.9257 0.9077 0.0000 0.7685 0.6606 Leon 0.4987 0.8593 0.2686 0.8265 0.8417 Levy 0.4706 0.6203 0.5130 0.9035 0.5376 Liberty 0.8028 0.6481 0.5731 0.6672 1.0000 Madison 0.9943 0.9952 0.0184 0.2124 0.2858 Marion 0.9814 0.8165 0.7604 0.9877 0.3230 Miami Dade 1.0000 0.9996 0.7898 0.7532 0.7444 Monroe 0.6982 0.0000 0.5471 1.0000 0.9683 Palm Beach 0.9329 0.8636 0.8212 0.9025 0.6183 Pasco 0.8999 0.7320 0.7348 0.9 588 0.8344 Pinellas 0.9868 0.9566 0.7943 0.9818 0.8532 Polk 0.7878 0.6588 0.6649 0.3966 0.9565 Putnam 0.6157 0.8450 0.5416 0.8694 0.9931 Sumter 0.7381 0.9330 0.8572 0.4344 0.5497 Suwannee 0.8863 0.8806 0.2300 0.3959 0.0431 Taylor 0.3835 0.8405 0.1075 0.8873 0.9891 Union 0.8778 0.8051 0.4392 0.1908 0.1917 Wakulla 0.9017 0.8053 0.3656 0.9358 0.9058 Walton 0.8548 0.6444 0.3769 0.4224 0.4530 Washington 0.9146 0.8306 0.6269 0.5686 0.4104
135 Table 5 12 Continued. County Gini Coefficient Families below 10 0% poverty The rank of health outcomes The rank of Health factors High school graduation (%) Alachua 1.0000 0.7619 0.8254 1.0000 0.3898 Broward 0.6154 0.0893 0.8730 0.9032 0.3414 Calhoun 0.4615 0.6131 0.2540 0.4032 0.0993 Citrus 0.3077 0.2143 0.4444 0. 5484 0.2203 Collier 0.9231 0.0833 1.0000 0.9194 0.3172 Columbia 0.2308 0.2857 0.2222 0.3548 0.1235 Dixie 0.5385 0.2798 0.0159 0.1129 0.6223 Franklin 0.4615 0.8810 0.4286 0.2581 0.3245 Gadsden 0.3846 1.0000 0.0794 0.0323 0.8232 Gilchrist 0.3846 0.5536 0.3175 0.4516 0.0000 Glades 0.3846 0.5238 0.1429 0.1613 0.6901 Hamilton 0.4615 0.6310 0.1270 0.0000 0.7046 Hendry 0.4615 0.9286 0.5079 0.0161 0.2591 Hernando 0.2308 0.2202 0.3810 0.5323 0.3172 Hillsborough 0.6154 0.2024 0.5556 0.6774 0.2373 Holmes 0 .6154 0.5417 0.2063 0.2742 0.2785 Jackson 0.3846 0.5298 0.2381 0.5000 0.2567 Jefferson 0.7692 0.4702 0.3333 0.1935 1.0000 Lafayette 0.4615 0.4286 0.4127 0.4355 0.0920 Lake 0.3077 0.0119 0.8095 0.8710 0.2736 Lee 0.6154 0.1190 0.6825 0.6452 0.2857 Leon 0.6154 0.6667 0.9524 0.9355 0.3511 Levy 0.3846 0.6429 0.1587 0.1452 0.5182 Liberty 0.9231 0.2976 0.4603 0.3226 0.4068 Madison 0.6154 0.6071 0.1111 0.0806 0.6562 Marion 0.3846 0.2679 0.3016 0.3710 0.3462 Miami Dade 0.8462 0.3810 0.9206 0.5968 0.4843 Monroe 1.0000 0.0000 0.7778 0.9677 0.1622 Palm Beach 0.8462 0.0833 0.8413 0.9516 0.2470 Pasco 0.3077 0.0893 0.3968 0.6935 0.2470 Pinellas 0.6154 0.0774 0.5714 0.8387 0.3487 Polk 0.2308 0.2619 0.5397 0.3871 0.4576 Putnam 0.6154 0.7440 0.0317 0.0968 0.4 140 Sumter 0.0769 0.0238 0.6508 0.8871 0.1259 Suwannee 0.3077 0.3869 0.1746 0.2097 0.7119 Taylor 0.3846 0.4940 0.0952 0.1290 0.4455 Union 0.1538 0.6250 0.0000 0.2419 0.3898 Wakulla 0.0000 0.1012 0.7143 0.5806 0.2276 Walton 0.6154 0.2262 0.3492 0.6129 0.2155 Washington 0.3846 0.5060 0.0476 0.2903 0.1864
136 Table 5 1 2 Continued. County Money income (Per_capita ) Unemployment Rate (%) Alachua 0.5261 0.1625 Broward 0.3598 0.3500 Calhoun 0.9387 0.3875 Citrus 0.6197 0.6875 Collier 0.0000 0.4875 Columbia 0.7559 0.4125 Dixie 0.8542 0.8000 Franklin 0.6858 0.1875 Gadsden 0.8638 0.4875 Gilchrist 0.8011 0.4250 Glades 0.8198 0.4750 Hamilton 0.9086 0.6125 Hendry 0.9540 1.0000 Hernando 0.6102 0.8750 Hillsbor ough 0.4269 0.5125 Holmes 0.9304 0.2250 Jackson 0.8495 0.2125 Jefferson 0.7439 0.3125 Lafayette 0.8114 0.2125 Lake 0.5012 0.6000 Lee 0.3250 0.5875 Leon 0.4807 0.2000 Levy 0.7843 0.6125 Liberty 0.8569 0.2375 Madison 0 .8850 0.6375 Marion 0.6269 0.7375 Miami Dade 0.6024 0.6125 Monroe 0.0654 0.0000 Palm Beach 0.1469 0.5500 Pasco 0.5508 0.7000 Pinellas 0.3550 0.5125 Polk 0.6484 0.6500 Putnam 0.7971 0.6875 Sumter 0.5501 0.2625 Suwanne e 0.7809 0.3625 Taylor 0.7866 0.5625 Union 1.0000 0.2250 Wakulla 0.6479 0.2375 Walton 0.3976 0.0875 Washington 0.7942 0.5625
137 Table 5 13 The score of the feasible counties County Score County Score County Score County Score Polk 0.7973 Taylor 0.7070 Leon 0.6658 Madison 0.5060 Collier 0.7553 Jefferson 0.7027 Lake 0.6635 Gilchrist 0.4944 Glades 0.7533 Broward 0.6944 Lafayette 0.6491 Holmes 0.4789 Pinellas 0.7465 Franklin 0.6919 Gadsden 0.6351 Calhoun 0.47 70 Putnam 0.7454 Hernando 0.6814 Miami Dade 0.6298 Union 0.4704 Hillsborough 0.7423 Lee 0.6752 Sumter 0.5959 Columbia 0.4419 Hendry 0.7419 Palm Beach 0.6714 Levy 0.5916 Washington 0.4294 Pasco 0.7364 Wakulla 0.6697 Marion 0.5551 Walton 0.3969 Dixie 0. 7211 Liberty 0.6671 Alachua 0.5424 Suwannee 0.3964 Citrus 0.7107 Monroe 0.6660 Hamilton 0.5388 Jackson 0.3001 Table 5 14 Priorities of the feasible counties County Priority County Priority County Priority County Priority Pol k 1 Taylor 2 Leon 3 Madison 5 Collier 1 Jefferson 2 Lake 3 Gilchrist 6 Glades 1 Broward 2 Lafayette 4 Holmes 6 Pinellas 1 Franklin 2 Gadsden 4 Calhoun 6 Putnam 1 Hernando 2 Miami Dade 4 Union 6 Hillsborough 1 Lee 3 Sumter 4 Columbia 6 Hendry 1 Palm B each 3 Levy 4 Washington 7 Pasco 1 Wakulla 3 Marion 5 Walton 7 Dixie 2 Liberty 3 Alachua 5 Suwannee 7 Citrus 2 Monroe 3 Hamilton 5 Jackson 8 Table 5 15 The list of the screening criteria for mi cro level analysis Dimension T heme Criteria Environmental Ecological Impacts 7 km buffer from conservation lands* Not in Florida Forever scope Not in areas with CLIP Priority 1,2 Social Demographics 7 km buffer from urbanized areas Technical Land Land Size Land Use
138 Table 5 16 The result of the screening phase at micro level analysis County Step 1 Step 2 Step 3 Step 4 No of potential parcels Step 5 No of potential parcels Step 6 No of candidate parcels Conservation lands Urbanized Area 50,0 00 or more people Not in FF &FFBOT scope Don't have CLIP 1,2 Land size Land use Polk 7 km 7 km 0 0 0 Collier 7 km 7 km 405 2 2 Glades 7 km 7 km 108 10 3 Pinellas 7 km 7 km 0 0 0 Putnam 7 km 7 km 0 0 0 Hillsborough 7 km 7 km 0 0 0 Hendry 7 km 7 km 240 6 6 Pasco 7 km 7 km 0 0 0 Total 753 18 11
139 Table 5 17 Glades c ounty zoning ordinance Use OU OUA ARS RF 1 RS RG AR RM RMH C 1 C 2 C RV ID 1 ID 2 PS PD FP Manufacturing, heavy (flammable materials, curing, tanning, etc.) N N N N N N N N N N N N N P N P Manufact uring, light (processing in enclosed building) N N N N N N N N N N C N P P N P Mineral extraction and processing C C N N N N N N N N N N C C N N C=Conditional/ Special exception N=Not permitted P= Permitted
140 Table 5 18 Hendry c ounty zoning ordinance Land Use or Activity Zoning Districts A 1 A 2 A 3 RR RR F RG 1 RG 2 RG 3 RG 4 C 1 C 2 C 3 I 1 I 2 RG 1M RG 2M RG 3M Industrial uses. Auto salvage/junkyard P Light industry S P P Heavy industry S P Warehousing/distribution S P Mining S S Use not permitte d in this district. P Use permitted by right in this district. S Use permitted by special exception in this district.
141 Table 5 19 Status of candidate parcels against the suitability indices Cou nty ID City/Town Size (acres) DOR Land Use Zone Future Land Use Adjacent Land Use Collier Co_1 Immokalee 127.17 Orchard, Groves, Citrus Rural Agriculture district (A) Agricultural / Rural Mixed Use District Agriculture No value R/w Co_2 Immokalee 141 .84 Orchard, Groves, Citrus Rural Agriculture district (A) Agricultural / Rural Mixed Use District Agriculture No value R/w Glades Ga_1 Moore Haven 225.98 Cropland soil class3 Open Use Agriculture (OUA) Transitional & Agriculture Agriculture Agricultur e R/w Other state Ga_2 Moore Haven 230.54 Cropland soil class3 Open Use Agriculture (OUA) Transitional & Agriculture Agriculture R/w Other state Ga_3 Moore Haven 155.61 Cropland soil class3 & Grazing land soil class 1 Open Use Agriculture (OUA) Agri culture & residential Transitional & Agriculture Agriculture Agriculture R/w Other state Hendry He_1 Clewiston 292.37 Orchard, groves, citrus General Agriculture (A 2) Agriculture Agriculture He_2 La Belle 341.59 Orchard, groves, citrus General Agriculture (A 2) Agriculture Agriculture He_3 Clewiston 188.89 Orchard, groves, citrus General Agriculture (A 2) Agricultur e Agriculture Not zoned for Agriculture Residential He_4 La Belle 204.20 Cropland soil class 3 General Agriculture (A 2) Agriculture Agriculture He_5 La Belle 632.81 Cropland soil class 3 General Agriculture (A 2) Agriculture Agriculture He_6 Clewiston 319.02 Cropland soil class 3 General Agriculture (A 2) Agriculture Agriculture Not zoned for Agriculture Residential
142 Table 5 19 Continued. ID Slope Power line Gas line Hazardous Liquid line LNG plants Dominant DVI Distance to limestone (km) Distance to main road Co_1 OK No 2.4 No No No 202 3.93 2.18 Co_ 2 OK No 2.4 No No No 202 4.33 2.45 Ga_1 OK No 10.2 No No No 182 0 1.92 Ga_2 OK No 10.3 No No No 193 0 2.00 Ga_3 OK No 10.5 No No No 193 0 2.00 He_1 OK No 3.8 No No No 195 2.50 0.33 He_2 OK No 3.2 No No No 166 3.03 1.22 He_3 OK No 2.8 No No No 195 3.4 1 0.00 He_4 OK No 6.5 No No No 184 0.00 0.01 He_5 OK No 4.9 No No No 195 0.00 0.01 He_6 OK No 3.3 No No No 195 2.15 0.00 Table 5 19. Continued. ID Annual avg daily traffic (aadt) Truck aadt Truck % Distance to Navigable waterway Distanc e to railroad Distance to national Highway DOR Land value/acre Flood zone Co_1 5300 615.00 11.60% 44.03 2.58 2.51 $1,713 AH Co_2 5300 615.00 11.60% 44.19 2.59 2.51 $1,789 AH Ga_1 6100 952 15.61% 0.12 7.49 1.91 $734 AE Ga_2 6100 952 15.61% 0.13 7.78 1.99 $693 AE Ga_3 1500 360 24.00% 0.14 8.15 1.95 $344 AE He_1 6100 952 15.61% 5.21 12.10 3.17 $1,509 A He_2 6100 952 15.61% 5.50 12.14 3.58 $1,347 A He_3 6100 952 15.61% 6.61 11.93 3.28 $1,180 X He_4 6100 952 15.61% 2.41 8.54 0.03 $654 X He_5 6100 952 15.61 % 3.44 8.55 0.04 $711 X He_6 6100 952 15.61% 5.82 10.27 1.67 $620 A
143 Table 5 19. Continued. ID Fault line Dominant Clip priority H istorical str. In land or adjacent lands Distance to potable water wells Distance to the nearest emergency facility Popula tion Housing units Dominant race & % Co_1 Lowest hazard 4 No 3.08 11.18 1367 435 White alone or in combination 92.173% Co_2 Lowest hazard 4 No 3.26 10.82 1432 474 White alone or in combination 88.198% Ga_1 Lowest hazard 4 No 0.14 11.31 2840 709 White alone or in combination 65.739% Ga_2 Lowest hazard 4 No 0.80 11.64 2726 612 White alone or in combination 64.160% Ga_3 Lowest hazard 4 No 1.73 12.11 1757 612 White alone or in combination 77.632% He_1 Lowest hazard 4 No 3.28 17.74 1210 685 White alo ne or in combination 91.653% He_2 Lowest hazard 4 No 3.49 18.24 1308 731 White alone or in combination 91.284% He_3 Lowest hazard 4 No 2.58 18.83 1103 625 White alone or in combination 90.662% He_4 Lowest hazard 4 No 0.46 14.72 1187 623 White alone o r in combination 90.649% He_5 Lowest hazard 4 No 0.41 15.40 1373 714 White alone or in combination 91.333% He_6 Lowest hazard 4 No 2.23 17.79 1118 615 White alone or in combination 91.771%
144 Table 5 20 Definitions of FEMA f l ood zone d esignations ( Adapted from (FEMA) ) LEVEL OF RISK ZONE DESCRIPTION Moderate to Low B and X (shaded) Area of moderate flood hazard, usually the area between the limits of the 100 year and 500 year floods. Are also used to designate base floodplains of lesser hazards, such as areas protected by levees from 100 year flood, or shallow flooding areas with average depths of less than one foot or drainage areas less than 1 square mile. Moderate to Low C and X (unshaded) Area of minimal flood hazard, usually depicted on FIRMs as above the 500 year flood level. High A Areas with a 1% annual chance of flooding and a 26% chance of flooding over the life of a 30 year mortgage. Because detailed analyses are not performed for such a reas; no depths or base flood elevations are shown within these zones. High AE The base floodplain where base flood elevations are provided. AE Zones are now used on new format FIRMs instead of A1 A30 Zones. High AH Areas with a 1% annual chance of shall ow flooding, usually in the form of a pond, with an average depth ranging from 1 to 3 feet. These areas have a 26% chance of flooding over the life of a 30 year mortgage. Base flood elevations derived from detailed analyses are shown at selected intervals within these zones
145 Table 5 21 Suitability scores of the candidate parcels for cement plant purpose (Excluding quarrying operation) ID Urban planning flexibility Proximity to limestone (km) Proximity to main road Min 500 m distance to national highways Proximity to Navigable waterway Proximity to railroad DOR Land value /acre Glades_3 1.00 1.00 0.18 1.00 1.00 0.42 1.00 Glades_2 1.00 1.00 0.19 1.00 1.00 0.46 0.76 Glades_1 1.00 1.00 0.22 1.00 1.00 0.49 0.73 Hendry_4 0.00 1.00 0.99 0.00 0.95 0.38 0.79 Hendry_5 0.00 1.00 0.99 0.00 0.92 0.37 0.75 Hendry_3 0.00 0.21 1.00 1.00 0.85 0.02 0.42 Hendry_6 0.00 0.50 1.00 1.00 0.87 0.19 0.81 Hendry_1 0.00 0.42 0.87 1.00 0.88 0.00 0.19 Collier _1 0.00 0.09 0.11 1.00 0.00 1.00 0.05 Hendry_2 0.00 0.30 0.50 1.00 0.88 0.00 0.31 Collier _2 0.00 0.00 0.00 1.00 0.00 1.00 0.00 Table 5 21 Continued. ID Low risk flood zone Proximity to medical services Affected population Equity Final Score Glades_3 0.00 0.84 0.62 0.52 7.58 Glades_2 0. 00 0.90 0.07 1.00 7.36 Glades_1 0.00 0.94 0.00 0.94 7.32 Hendry_4 1.00 0.51 0.95 0.05 6.62 Hendry_5 1.00 0.43 0.84 0.03 6.34 Hendry_3 1.00 0.00 1.00 0.05 5.56 Hendry_6 0.00 0.13 0.99 0.01 5.51 Hendry_1 0.00 0.14 0.94 0.02 4.46 Collier _1 0.00 0.95 0 .85 0.00 4.06 Hendry_2 0.00 0.07 0.88 0.03 3.97 Collier _2 0.00 1.00 0.81 0.14 3.95
146 Table 5 22 The results of sensitivity analysis Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 County Buffer from conservation lands (km) Buf fer from urbanized areas (km) Not in FF &FFBO T scope Don't have CLIP 1,2 No of potential parcels Land size No of potential parcels Land use No of candidate parcels Polk 0 0 897 0 0 3.5 3.5 0 0 0 7 7 0 0 0 10 10 0 0 0 Collier 0 0 24,387 101 92 3.5 3.5 5,594 16 15 7 7 405 2 2 10 10 0 0 0 Glades 0 0 2,858 49 36 3.5 3.5 2,147 31 20 7 7 108 10 3 10 10 7 0 0 Pinellas 0 0 96 0 0 3.5 3.5 0 0 0 7 7 0 0 0 10 10 0 0 0 Putnam 0 0 15 2 2 3.5 3.5 3 0 0 7 7 0 0 0 10 10 0 0 0
147 Ta ble 5 22 Continued. Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 County Buffer from conservation la nds (km) Buffer from urbanized areas (km) Not in FF &FFBO T scope Don't have CLIP 1,2 No of potential parcels Land size No of potential parcels Land use No of candidate parcels Hillsborough 0 0 2,529 0 0 3.5 3.5 0 0 0 7 7 0 0 0 10 10 0 0 0 Hendry 0 0 1,153 114 43 3.5 3.5 575 55 35 7 7 24 1 6 6 10 10 182 4 4 Pasco 0 0 3,492 3 0 3.5 3.5 0 0 0 7 7 0 0 0 10 10 0 0 0
148 Figure 5 1 CO 2 emission in 2008 (IEA & UNIDO, 2009) Figure 5 2 Gross CO 2 emission per ton clinker (WBSCD/CSI, 2009)
149 Figure 5 3 Environmental, social and economic impacts of a cement plant
150 Figure 5 4 The list of decision criteria in the case study
151 Figure 5 4 Continued
152 Figure 5 4 Continued
153 Figure 5 5 Florida l imestone map Figure 5 6 Feasible areas (A reas located in 5km buffer of Limestone )
154 Figure 5 7 Florida counties in 300km buffer of Polk feasible area Figure 5 8 Flor ida counties in 300km buffer of Hendry feasible area
155 Figure 5 9 Florida Seaports Figure 5 10 in the candidate areas
156 Figure 5 11 Clip spatial data of Miami Dade candidate areas Figure 5 12 Academic level of participants in the survey 65.38% 19.23% 11.54% 3.85% Degree of Participants Ph.D Ms.C Bs.c College Degree
157 Figure 5 13 Characterization of participants in the survey Figur e 5 14 Combination of scores and priorities of the feasible counties 1 2 3 4 5 6 7 8 0.2500 0.3500 0.4500 0.5500 0.6500 0.7500 0.8500 Polk Glades Putnam Hendry Dixie Taylor Broward Hernando Palm Beach Liberty Leon Lafayette Miami-Dade Levy Alachua Madison Holmes Union washington Suwannee Priority Score Feasible Counties Score Priority
158 Figure 5 15 Selected suitable counties for micro level analysis
159 Figure 5 16 Collier feasible a reas Figure 5 17 Glades feasible areas
160 Figure 5 18 Hendry feasible areas Figure 5 19 Collier potential and candidate parcels
161 Figure 5 20 Glades candidate parcels Figure 5 21 Hendry candidate parcels
162 Figure 5 22 Collier topographic map Figure 5 23 Glades topographic map
163 Figure 5 24 Hendry topographic map Figure 5 25 DVI map of Collier c ounty ( Min: 166 Max: 226 )
164 Figure 5 26 DVI map of Glades c ounty ( Min: 150 Max: 205 ) Figure 5 27 DVI map of Hendry c ounty ( Min: 166 Max: 211 )
165 Figure 5 28 Florida rural areas (Adapted from Enterprise Florida (2013) ) Figure 5 29 The result of sensitivity analysis on 7km buffer distance 173 70 11 4 0 50 100 150 200 0 km 3.5km 7 km 10 km No of candidate parcels Buffer distance from conservation lands& urbanized areas Buffer distance vs. no of candidate parcels
166 Figure 5 30 The result of sensitivity analysis on land size and land use 754 18 11 754 139 11 0 100 200 300 400 500 600 700 800 7 KM BUFFER+ FF + CLIP 1&2 ALL SCREENING CRITERIA AXIS TITLE LAND SIZE & LAND USE VS. NO OF CANDIDATE PARCELS Land sizeland use Land Use-Land size
167 CHAPTER 6 CONCLUSIONS, LIMITAT IONS AND RECOMMENDAT IONS Conclusion To move towards sustainable development, companies should reassess their operation and strategic plans and consider sustainability requirements. Facility location is one of the strategic decisions that is not an exception to this need. Although the existing facility location literat ure is very rich in operations research and urban planning research domains, and many studies have covered various requirements of sustainability, a few of them have holistically addressed the requirements of sustainable facility location The main object ive of this research was to frame the concept of sustainable location for semi desirable facilities. A semi desirable facility location decision involves conflicting decision criteria. On one hand, due to its economic positive impacts, it is desirable to h ave the facility in the neighboring area and on the other hand, due to its destructive environmental impacts, the decision about its location is controversial. However, the magnitude of its negative impacts is not the same as those of undesirable facilitie s. In response to the research objective s and questions sustainable facility location was first defined and a preliminary siting model was developed. Following this step the proposed model was examined and rectified by conducting the case study of susta inable location of a cement plant in the state of Florida. The proposed model consists of a decision making algorithm along with a comprehensive list of evaluation criteria to characterize the sustainable facility location concept and assess the compliance of a location with technical and sustainability requirements.
168 Limiting the siting decision only to a small region may result in the overlooking some of the sustainability requirements. For instance, if companies, without any evaluation, choose the county or community in which they want to place the facility, some of the socioeconomic sustainability requirements, such as health factors and economic status, may not be met. Therefore, to address sustainability requirements more effectively, companies are sug gested to conduct a comprehensive siting evaluation process at both macro and micro levels. The purpose of the macro level analysis is to identify the candidate counties and micro level analysis is conducted to select a suitable parcel in the candidate co unties. With this classification, companies ensure that all technical and sustainability factors are taken into account in their decisions. In this research, it was assumed that a location decision is a state wide decision; however, in reality it might be a nationwide or an international decision; therefore, the macro analysis should be performed at a proper level first, and the siting algorithm and decision criteria should be customized accordingly. Since considering sustainability requirements makes lo cation decisions more complicated, for the sake of simplicity, it is proposed to categorize the evaluation criteria of both macro and micro level analysis into two screening and suitability ones. The purpose of the screening criteria is to identify feasibl e options based on compulsory and cautionary factors. The suitability criteria measure the appropriateness of the feasible options.
169 The framework of the proposed criteria includes four categories of dimension, theme, sub theme, and index in a hierarchical order. Dimensions are general objectives of the problems, which include sustainability and technical principles. The purpose of themes and sub themes is to translate the objectives into measurable indices, which provide meaningful measures of the objective s. By conducting a case study of siting a cement plant in the state of Florida, the implication of the proposed siting model and the evaluation criteria was demonstrated. As the first step in the case study, the major environmental, social, and economic im pacts of a cement plant were outlined. Accordingly, the criteria were customized and classified. For the evaluation phase, the spatial and non spatial data associated with the decision criteria and location options were collected from official data sources The AHP technique was utilized in the macro level analysis to identify the importance weights of the criteria. AHP helped in creating balance among various attributes and expert judgments on the weight of the criteria. By conducting the first level of an alysis, the micro level evaluation, eight counties out of 67 Florida counties were identified as candidate counties. By conducting the micro level analysis, 11 parcels were identified as candidate parcels. Following the suitability analysis, it was found t hat only the planning designations of three of the chosen parcels, all in Glades County, permit the placement of a cement plant that includes quarrying and manufacturing processes. However, even by assuming that changing the planning designations of Collie r and Hendry potential parcels is possible, there is still no one dominant solution that satisfies all the criteria. This judgment is based on the micro level analysis that was conducted.
170 By assuming that changing the planning designations of Collier and Hendry potential parcels is possible, the suitability of all 11 candidate parcels were evaluated and they were ranked for the purpose of locating a cement plant as a heavy manufacturing plant. The analysis showed that the parcels of Glades County and parce ls 4 and 5 of Hend r y County have the highest suitability scores, respectively. All these parcels are located on limestone; therefore, by locating a cement plant in one of these parcels there is no need for another land disturbance for quarrying. However, f or locating quarrying and manufacturing processes, a different approach would be to solve two separate but dependent location problems, one location problem for quarrying and another problem for manufacturing process. This different approach might be more appropriate under certain conditions, since quarrying and manufacturing processes include different operational activities and they have different environmental impacts on their surrounding environment. It should be noticed that the proposed list of criter ia needs to be regarded as a objective, type of facility, the applied technology, plant design, and local circumstances. In addition, in the evaluation phase, the criteria may also need to be customized based on available types of data associated with the targeted region. For instance, in the case study, CLIP data assisted in the integration and consideration of several environmental indices simultaneously. During sustainable s iting process, decision makers may face various difficulties. Considering tight sustainability criteria may significantly limit the number of potential site candidates. For instance, the consideration of the 7 km buffer distance from the
171 urbanized areas an d conservation lands was one of sustainability screening criteria that significantly limited the number of potential site candidates. Therefore, meeting all sustainability and technical criteria in site selection decisions can be a challenging process that requires reaching a trade off among the criteria. In some cases, sustainability or technical constraints need to be relaxed to some extent to increase the number of potential site candidates. For this purpose, in the case study, a sensitivity analysis was performed, and it was showed that by reducing 7 km buffer distance to 3.5 km, the number of potential parcels significantly increases. Creating a balance among the importance weights of sustainability and technical criteria is a crucial step in a siting process. Developing a strategic plan demonstrating how different approaches would be balanced might facilitate this process. An accurate spatial planning by local planning departments is a prerequisite for sustainable facility location decisions. Although sustainability principles were considered in the case study for identifying the candidate parcels, the zoning ordinances of most of the candidate parcels did not allow for the placement of a heavy manufacturing plant. Therefore, addressing these conflicts requires taking a more flexible and holistic approach in spatial planning to facilitate sustainable location decisions. Overall, this research demonstrated that integrating sustainability requirements into location decisions is not o nly possible but is als o necessary. The research provides interested companies and researchers in this field with an insight into how to holistically include the concept of sustainability in location decisions.
172 Limitation The main limitation of the research was the difficulties of implementing all the steps of the proposed model, and moreover, evaluating some of the key criteria, such as the quality and quantity of limestone in the candidate sites. Access to some key data for conducting the case study was challenging. Real world location decisions also face these challenges, perhaps to a higher degree. Therefore, collecting data for all alternatives at macro and micro levels in association with all technical and sustainability criteria can be an energy and a time consuming step i n sustainable facility location decisions. Although the spatial data may be extracted from official data sources, data accuracy might still be a challenge for proper site planning and facility layout. Therefore, in a real case, a more thorough investigatio n of the selected site will be required. This research did not investigate the sustainability requirements for a plant layout. However, a sustainable plant layout may include factors that influence site selection decisions. For instance, in some cases, buf fer spaces or physical barriers around the site may be required to reduce noise transmission, which might impact the required land size. Therefore, companies should consider sustainability requirements in all elements e selecting its location. Recommendations for Future Studies Facility location is intrinsically a complex decision making process; therefore, considering sustainability principles will further complicate the problem. The complexity and novelty of the probl em require additional research. Moreover, sustainability cannot be achieved unless sustainable facility location decisions are implemented in real world problems and the outcomes are investigated through research.
173 As discussed earlier, considering precaut ionary sustainability requirements may result in limiting the number of candidate parcels and making land acquisition a challenging task for companies. Developing a systematic approach for sustainable land acquisition techniques can be considered as a pote ntial area for future research. Framing the concept of sustainable facility location for other types of facilities (desirable and undesirable) can also be considered as future research areas. Although the methodology presented in this research is for sem i desirable facilities, it can be utilized for undesirable facilities with some modification. However, for undesirable facilities, the list of the criteria should thoroughly be reviewed to assess the need for the inclusion of more stringent criteria. In ad dition, in the case of locating undesirable facilities, the political (governmental) aspect might need to be considered as an additional dimension to the problem. Developing an automated decision ma king tool integrated with an MC DA approach and a GIS appli cation for macro and micro level analyses can significantly facilitate the decision making process. It is notable that, Florida has powerful GIS data systems compared to the other states. Therefore, improving and integrating environmental, social, and ec onomic spatial datasets of states and counties can also significantly facilitate real world location decisions. As addressed earlier, one of the assumptions of this research was that the location decision is conducted within a state, but in reality, it mi ght be a national or international decision. Therefore, new research studies can be framed for these types of problems to
174 outline the characteristics of national or international sustainable facility location problems.
175 APPENDIX A VALUES OF FEASIBLE COUN TIES AGAINST THE SUITABILITY CRITERIA Table A 1. Values of feasible counties against the suitability criteria County Population 300 km buffer Max capacity of existing kilns Industrial Rate 400,000 kwh (1,000 KW Consumption ) Gas Rate Median hourly wage of Production Workers Alachua 11,320,186 1,895,000 34,903 489 13.12 Broward 14,734,152 0 31,206 609 8.79 Calhoun 2,479,153 0 54,712 408 10.73 Citrus 13,754,040 0 38,600 460 12.73 Collier 14,906,930 0 31,206 650 10.79 Columbia 10,902,346 0 34,903 453 12 .73 Dixie 11,117,367 0 38,600 407 12.73 Franklin 6,972,399 0 38,600 481 10.73 Gadsden 3,781,423 0 38,600 436 9.50 Gilchrist 10,957,389 0 38,600 467 13.12 Glades 15,837,661 0 31,206 467 11.34 Hamilton 9,915,705 0 34,903 448 12.73 Hendry 15,379,464 0 31,206 440 11.34 Hernando 15,268,883 1,934,500 38,600 481 10.84 Hillsborough 17,492,304 0 37,199 535 10.84 Holmes 1,489,528 0 34,449 430 10.73 Jackson 1,933,305 0 44,581 436 10.73 Jefferson 9,733,457 0 38,600 528 9.50 Lafayette 10,744,503 0 38,600 50 7 12.73 Lake 17,767,791 0 38,600 501 10.58 Lee 15,379,464 0 31,206 551 11.56 Leon 7,562,688 0 38,600 495 9.50 Levy 11,951,824 0 38,600 441 12.73 Liberty 5,446,121 0 46,656 507 10.73 Madison 10,096,527 0 38,600 446 12.73 Marion 13,213,922 0 38,600 47 0 10.81 Miami Dade 10,899,174 3,490,000 31,206 511 10.78 Monroe 9,669,948 0 31,206 602 11.34 Palm Beach 14,906,930 0 31,206 596 10.43 Pasco 15,341,973 0 37,900 496 10.84 Pinellas 15,295,584 0 37,900 517 10.84 Polk 17,492,304 0 37,199 487 12.45 Putna m 11,221,269 0 31,206 437 12.73 Sumter 15,253,020 1,095,000 38,600 467 12.73 Suwannee 10,744,503 965,425 34,903 423 12.73
176 Table A 1. Continued. County Population 300 km buffer Max capacity of existing kilns Industrial Rate 400,000 kwh (1,000 KW Deman d) Gas Rate Median hourly wage of Production Workers Taylor 10,925,325 0 38,600 432 12.73 Union 10,882,419 0 31,206 443 12.73 Wakulla 9,310,739 0 38,600 532 9.50 Walton 1,473,106 0 34,449 544 10.73 Washington 1,904,785 0 34,449 452 10.73 Table A 1. Continued. County Distance to the nearest major road (km) Distance to the nearest railroad (km) Distance to the nearest sea port (km) Distance to the nearest navigable waterway (km) % of area with CLIP Prio 1,2 Alachua 1.13 4.33 98.55 58.97 33.16% Brow ard 0.02 0.98 7.91 3.21 19.86% Calhoun 0.47 3.58 63.60 16.87 27.18% Citrus 1.72 5.61 30.34 5.09 9.39% Collier 1.20 6.21 122.19 25.70 2.97% Columbia 2.08 5.93 109.46 68.55 46.52% Dixie 0.19 9.94 114.34 17.38 3.44% Franklin 0.39 9.15 56.43 7.02 0.00% Gadsden 0.36 2.23 99.29 5.17 6.83% Gilchrist 1.45 4.74 111.67 34.96 40.69% Glades 2.37 2.61 99.13 6.65 9.89% Hamilton 1.39 9.38 137.03 93.20 32.57% Hendry 2.97 26.85 90.07 52.15 17.24% Hernando 0.40 0.88 29.46 31.88 13.04% Hillsborough 0.04 0.67 5.86 4.85 14.75% Holmes 0.18 6.93 77.20 36.21 26.68% Jackson 0.78 0.04 79.80 33.18 56.15% Jefferson 5.39 20.50 137.54 21.33 3.06% Lafayette 2.29 10.36 146.88 37.80 10.82% Lake 7.65 1.34 65.12 51.40 25.93% Lee 0.57 3.30 163.90 21.77 19.05% Leon 3.83 5.00 121.44 16.38 8.89% Levy 4.05 13.43 82.82 9.22 25.96% Liberty 1.51 12.45 73.33 31.18 0.00% Madison 0.04 0.21 160.99 73.45 40.10% Marion 0.14 6.51 43.73 1.40 38.01% Miami Dade 0.00 0.05 39.08 23.19 14.35%
177 Table A 1. Continued. County Distance to the nearest major road (km) Distance to the nearest railroad (km) Distance to the nearest sea port (km) Distance to the nearest navigable waterway (km) % of area with CLIP Prio 1,2 Monroe 2.31 35.32 77.43 0.25 1.78% Palm Beach 0.51 4.85 34.12 9.31 21.43% Pasco 0.77 9.49 47.76 4.08 9.30% Pinellas 0.10 1.57 38.36 1.94 8.24% Polk 1.62 12.07 58.82 56.33 2.44% Putnam 2.94 5.51 78.30 12.39 0.39% Sumter 2.00 2.40 28.42 52.82 25.28% Suwannee 0.87 4.25 127.56 56.39 53.73% Taylor 4.71 5.67 146.92 10.73 0.61% Union 0.93 6.91 94.49 75.47 45.38% Wakulla 0.75 6.91 106.11 6.22 5.29% Walton 1.11 12.58 104.33 53.93 30.71% Washington 0.65 6.02 64.81 40.35 33.10% Table A 1. Continued. County Gini Coefficient Families below 100% poverty Rank of health outcomes Rank of Health factors High school graduation (%) Alachua 52 23.60% 15 5 76.00% Broward 47 12.30% 12 11 78.00% Calhoun 45 21.10% 51 42 88.00% Citrus 43 14.40% 39 33 83.00% Collier 51 12.20% 4 10 79.00% Columbia 42 15.60% 53 45 87.00% Dixie 46 15.50% 66 60 66.40% Franklin 45 25.60% 40 51 78.70% Gadsden 44 27.60% 62 65 58.10% Gilchrist 44 20.10% 47 39 92.10% Glades 44 19.60% 58 57 63.60% Hamilton 45 21.40% 59 67 63.00% Hendry 45 26.40% 35 66 81.40% Hernando 42 14.50% 43 34 79.00% Hillsborough 47 14 .20% 32 25 82.30% Holmes 47 19.90% 54 50 80.60% Jackson 44 19.70% 52 36 81.50% Jefferson 49 18.70% 46 55 50.80% Lafayette 45 18.00% 41 40 88.30% Lake 43 11.00% 16 13 80.80% Lee 47 12.80% 24 27 80.30%
178 Table A 1. Continued. County Gini Coefficient Fam ilies below 100% poverty Rank of health outcomes Rank of Health factors High school graduation (%) Leon 47 22.00% 7 9 77.60% Levy 44 21.60% 57 58 70.70% Liberty 51 15.80% 38 47 75.30% Madison 47 21.00% 60 62 65.00% Marion 44 15.30% 48 44 77.80% Miami Dade 50 17.20% 9 30 72.10% Monroe 52 10.80% 18 7 85.40% Palm Beach 50 12.20% 14 8 81.90% Pasco 43 12.30% 42 24 81.90% Pinellas 47 12.10% 31 15 77.70% Polk 42 15.20% 33 43 73.20% Putnam 47 23.30% 65 61 75.00% Sumter 40 11.20% 26 12 86.90% Suwannee 43 17.30% 56 54 62.70% Taylor 44 19.10% 61 59 73.70% Union 41 21.30% 67 52 76.00% Wakulla 39 12.50% 22 31 82.70% Walton 47 14.60% 45 29 83.20% Washington 44 19.30% 64 49 84.40% Table A 1. Continued. County Money income $ (Per_capita ) Unemployment R ate (%) Alachua 24,741 7.70% Broward 28,631 9.20% Calhoun 15,091 9.50% Citrus 22,551 11.90% Collier 37,046 10.30% Columbia 19,366 9.70% Dixie 17,066 12.80% Franklin 21,005 7.90% Gadsden 16,843 10.30% Gilchrist 18,309 9.80% Glades 17,872 10.20% Hamilton 15,794 11.30% Hendry 14,734 14.40% Hernando 22,775 13.40% Hillsborough 27,062 10.50% Holmes 15,285 8.20% Jackson 17,177 8.10%
179 Table A 1. Continued. County Money income $ (Per_capita ) Une mployment Rate (%) Jefferson 19,647 8.90% Lafayette 18,069 8.10% Lake 25,323 11.20% Lee 29,445 11.10% Leon 25,803 8.00% Levy 18,703 11.30% Liberty 17,003 8.30% Madison 16,346 11.50% Marion 22,384 12.30% Miami Dade 22,95 7 11.30% Monroe 35,516 6.40% Palm Beach 33,610 10.80% Pasco 24,164 12.00% Pinellas 28,742 10.50% Polk 21,881 11.60% Putnam 18,402 11.90% Sumter 24,180 8.50% Suwannee 18,782 9.30% Taylor 18,649 10.90% Union 13,657 8.2 0% Wakulla 21,892 8.30% Walton 27,746 7.10% Washington 18,470 10.90%
180 APPENDIX B RESEARCH QUESTIONNAIRE
187 APPENDIX C SUMMARY OF THE ANSWERS TO THE QUESTIONNAIRE Table C 1 Summary of themes compariso ns 1 Technical Environmental Social Economic Technical 1.00 1.00 4.00 3.00 Environmental 1.00 1.00 4.00 3.00 Social 0.25 0.25 1.00 0.50 Economic 0.33 0.33 2.00 1.00 2 Technical Environmental Social Economic Technical 1 1 1 3 Environmental 1.00 1.00 1.00 5.00 Social 1.00 1.00 1.00 1.00 Economic 0.33 0.20 1.00 1.00 3 Technical Environmental Social Economic Technical 1.00 1.00 1.00 1.00 Environmental 1.00 1.00 1.00 1.00 Social 1.00 1.00 1.00 1.00 Economic 1.00 1.00 1.00 1.00 4 Technical Environm ental Social Economic Technical 1.00 7.00 3.00 5.00 Environmental 0.14 1.00 3.00 5.00 Social 0.33 0.33 1.00 0.33 Economic 0.20 0.20 3.00 1.00 5 Technical Environmental Social Economic Technical 1.00 3.00 3.00 0.14 Environmental 0.33 1.00 3.00 0.20 Social 0.33 0.33 1.00 0.33 Economic 7.00 5.00 3.00 1.00 6 Technical Environmental Social Economic Technical 1.00 1.00 1.00 7.00 Environmental 1.00 1.00 1.00 7.00 Social 1.00 1.00 1.00 7.00 Economic 0.14 0.14 0.14 1.00
188 Table C 1. Continue d. 7 Techni cal Environmental Social Economic Technical 1.00 0.20 0.33 0.20 Environmental 5.00 1.00 7.00 5.00 Social 3.00 0.14 1.00 0.33 Economic 5.00 0.20 3.00 1.00 8 Technical Environmental Social Economic Technical 1.00 1.00 5.00 7.00 Environmental 1.00 1.00 5.00 5.00 Social 0.20 0.20 1.00 1.00 Economic 0.14 0.20 1.00 1.00 9 Technical Environmental Social Economic Technical 1.00 0.33 0.50 1.00 Environmental 3.00 1.00 1.00 7.00 Social 2.00 1.00 1.00 5.00 Economic 1.00 0.14 0.20 1.00 10 Technical Enviro nmental Social Economic Technical 1.00 7.00 5.00 3.00 Environmental 0.14 1.00 3.00 1.00 Social 0.20 0.33 1.00 0.33 Economic 0.33 1.00 3.00 1.00 11 Technical Environmental Social Economic Technical 1.00 3.00 5.00 7.00 Environmental 0.33 1.00 5.00 5.0 0 Social 0.20 0.20 1.00 1.00 Economic 0.14 0.20 1.00 1.00 12 Technical Environmental Social Economic Technical 1.00 0.33 0.33 0.33 Environmental 3.00 1.00 1.00 1.00 Social 3.00 1.00 1.00 1.00 Economic 3.00 1.00 1.00 1.00
189 Table C 1. Continued. 13 Technical Environmental Social Economic Technical 1.00 0.11 0.20 0.20 Environmental 9.00 1.00 0.11 0.11 Social 5.00 9.00 1.00 0.20 Economic 5.00 9.00 5.00 1.00 14 Technical Environmental Social Economic Technical 1.00 1.00 3.00 5.00 Environmental 1.00 1.00 2.00 3.00 Social 0.33 0.50 1.00 1.00 Economic 0.20 0.33 1.00 1.00 15 Technical Environmental Social Economic Technical 1.00 1.00 1.00 7.00 Environmental 1.00 1.00 1.00 7.00 Social 1.00 1.00 1.00 3.00 Economic 0.14 0.14 0.33 1.00 16 Techn ical Environmental Social Economic Technical 1.00 5.00 7.00 3.00 Environmental 0.20 1.00 5.00 0.20 Social 0.14 0.20 1.00 0.14 Economic 0.33 5.00 7.00 1.00 17 Technical Environmental Social Economic Technical 1.00 1.00 1.00 0.33 Environmental 1.00 1. 00 3.00 3.00 Social 1.00 0.33 1.00 1.00 Economic 3.00 0.33 1.00 1.00 18 Technical Environmental Social Economic Technical 1.00 0.20 0.14 0.33 Environmental 5.00 1.00 0.33 3.00 Social 7.00 3.00 1.00 5.00 Economic 3.00 0.33 0.20 1.00 19 Technical Env ironmental Social Economic Technical 1.00 2.00 2.00 1.00 Environmental 0.50 1.00 2.00 3.00 Social 0.50 0.50 1.00 5.00 Economic 1.00 0.33 0.20 1.00
190 Table C 1. Continued. 20 Technical Environmental Social Economic Technical 1.00 5.00 7.00 0.33 Environ mental 0.20 1.00 5.00 0.33 Social 0.14 0.20 1.00 0.33 Economic 3.00 3.00 3.00 1.00 21 Technical Environmental Social Economic Technical 1.00 0.14 0.11 0.20 Environmental 7.00 1.00 4.00 6.00 Social 9.00 0.25 1.00 6.00 Economic 5.00 0.17 0.17 1.00 22 Technical Environmental Social Economic Technical 1.00 0.71 0.33 1.00 Environmental 1.40 1.00 3.00 1.00 Social 3.00 0.33 1.00 1.40 Economic 1.00 1.00 0.71 1.00 23 Technical Environmental Social Economic Technical 1.00 7.00 5.00 9.00 Environmental 0 .14 1.00 3.00 7.00 Social 0.20 0.33 1.00 3.00 Economic 0.11 0.14 0.33 1.00 24 Technical Environmental Social Economic Technical 1.00 3.00 5.00 1.00 Environmental 0.33 1.00 3.00 0.33 Social 0.20 0.33 1.00 0.20 Economic 1.00 3.00 5.00 1.00 25 Technic al Environmental Social Economic Technical 1.00 3.00 5.00 7.00 Environmental 0.33 1.00 5.00 5.00 Social 0.20 0.20 1.00 5.00 Economic 0.14 0.20 0.20 1.00 26 Technical Environmental Social Economic Technical 1.00 0.14 0.14 0.11 Environmental 7.00 1.00 7.00 0.11 Social 7.00 0.14 1.00 0.11 Economic 9.00 9.00 9.00 1.00
191 Table C 2 Summary of technical sub themes comparison 1 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 3.00 3.00 No. of lo cal competitors 0.20 1.00 0.33 0.33 Operation cost 0.33 3.00 1.00 1.00 Infrastructure 0.33 3.00 1.00 1.00 2 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 0.33 1.00 1.00 No. of local competitors 3.00 1.00 3.00 1.00 Opera tion cost 1.00 0.33 1.00 0.33 Infrastructure 1.00 1.00 3.00 1.00 3 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 3.00 8.00 7.00 No. of local competitors 0.33 1.00 8.00 7.00 Operation cost 0.13 0.13 1.00 7.00 Infrastructu re 0.14 0.14 0.14 1.00 4 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 3.00 0.33 No. of local competitors 0.20 1.00 0.20 0.14 Operation cost 0.33 5.00 1.00 0.20 Infrastructure 3.00 7.00 5.00 1.00 5 Demand No. of loc al competitors Operation cost Infrastructure Demand 1.00 5.00 3.00 3.00 No. of local competitors 0.20 1.00 0.33 5.00 Operation cost 0.33 3.00 1.00 5.00 Infrastructure 0.33 0.20 0.20 1.00
192 Table C 2.Contin ue d. 6 Demand No. of local competitors Opera tion cost Infrastructure Demand 1.00 7.00 0.14 3.00 No. of local competitors 0.14 1.00 0.14 3.00 Operation cost 7.00 7.00 1.00 9.00 Infrastructure 0.33 0.33 0.11 1.00 7 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 1.00 3.00 3.00 No. of local competitors 1.00 1.00 0.33 0.33 Operation cost 0.33 3.00 1.00 1.00 Infrastructure 0.33 3.00 1.00 1.00 8 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 8.00 1.00 1.00 No. of local competitors 0.13 1 .00 0.11 0.11 Operation cost 1.00 9.00 1.00 1.00 Infrastructure 1.00 9.00 1.00 1.00 9 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 3.00 1.00 No. of local competitors 0.20 1.00 0.33 0.33 Operation cost 0.33 3.00 1.0 0 0.33 Infrastructure 1.00 3.00 3.00 1.00 10 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 3.00 7.00 1.00 No. of local competitors 0.33 1.00 0.20 1.00 Operation cost 0.14 5.00 1.00 1.00 Infrastructure 1.00 1.00 1.00 1.00
193 Table C 2. Continue d. 11 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 3.00 0.33 0.33 No. of local competitors 0.33 1.00 0.20 0.20 Operation cost 3.00 5.00 1.00 1.00 Infrastructure 3.00 5.00 1.00 1.00 12 Demand No. of l ocal competitors Operation cost Infrastructure Demand 1.00 1.00 1.00 1.00 No. of local competitors 1.00 1.00 1.00 1.00 Operation cost 1.00 1.00 1.00 1.00 Infrastructure 1.00 1.00 1.00 1.00 13 Demand No. of local competitors Operation cost Infrastru cture Demand 1.00 0.11 0.11 0.11 No. of local competitors 9.00 1.00 0.11 0.11 Operation cost 9.00 9.00 1.00 0.11 Infrastructure 9.00 9.00 9.00 1.00 14 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 1.00 1.00 No. of local competitors 0.20 1.00 0.20 0.20 Operation cost 1.00 5.00 1.00 5.00 Infrastructure 1.00 5.00 0.20 1.00 15 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 1.00 5.00 3.00 No. of local competitors 1.00 1.00 1.00 1.00 Op eration cost 0.20 1.00 1.00 1.00 Infrastructure 0.33 1.00 1.00 1.00
194 Table C 2. Continue d. 16 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 9.00 3.00 No. of local competitors 0.20 1.00 3.00 0.11 Operation cost 0.11 0. 33 1.00 0.20 Infrastructure 0.33 9.00 5.00 1.00 17 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 1.00 0.33 5.00 No. of local competitors 1.00 1.00 1.00 0.33 Operation cost 3.00 1.00 1.00 1.00 Infrastructure 0.20 3.00 1.0 0 1.00 18 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 3.00 2.00 1.00 No. of local competitors 0.33 1.00 0.33 0.33 Operation cost 0.50 3.00 1.00 1.00 Infrastructure 1.00 3.00 1.00 1.00 19 Demand No. of local competitor s Operation cost Infrastructure Demand 1.00 5.00 2.00 2.00 No. of local competitors 0.20 1.00 3.00 2.00 Operation cost 0.50 0.33 1.00 3.00 Infrastructure 0.50 0.50 0.33 1.00 20 Demand No. of local competitors Operation cost Infrastructure Demand 1. 00 7.00 1.00 5.00 No. of local competitors 0.14 1.00 0.20 0.33 Operation cost 1.00 5.00 1.00 1.00 Infrastructure 0.20 3.00 1.00 1.00
19 5 Table C 2. Continue d. 21 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 0.20 7.00 5.00 No of local competitors 5.00 1.00 1.00 7.00 Operation cost 0.14 1.00 1.00 1.00 Infrastructure 0.20 0.14 1.00 1.00 22 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 9.00 1.00 1.00 No. of local competitors 0.11 1.00 1.00 1.0 0 Operation cost 1.00 1.00 1.00 1.00 Infrastructure 1.00 1.00 1.00 1.00 23 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 9.00 5.00 9.00 No. of local competitors 0.11 1.00 9.00 5.00 Operation cost 0.20 0.11 1.00 7.00 Inf rastructure 0.11 0.20 0.14 1.00 24 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 5.00 0.33 3.00 No. of local competitors 0.20 1.00 0.14 0.33 Operation cost 3.00 7.00 1.00 5.00 Infrastructure 0.33 3.00 0.20 1.00 25 Demand No. of local competitors Operation cost Infrastructure Demand 1.00 3.00 7.00 5.00 No. of local competitors 0.33 1.00 3.00 5.00 Operation cost 0.14 0.33 1.00 7.00 Infrastructure 0.20 0.20 0.14 1.00
196 Table C 2. Continue d. 26 Demand No. of local compe titors Operation cost Infrastructure Demand 1.00 9.00 7.00 5.00 No. of local competitors 0.11 1.00 0.20 0.33 Operation cost 0.14 5.00 1.00 5.00 Infrastructure 0.20 3.00 0.20 1.00
197 Table C 3 Summary of social sub themes co mparison 1 Poverty Public health status Health factors Social acceptability Poverty 1.00 5.00 5.00 7.00 Public health status 0.20 1.00 1.00 3.00 Health factors 0.20 1.00 1.00 3.00 Social acceptability 0.14 0.33 0.33 1.00 2 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 3.00 Public health status 1.00 1.00 1.00 3.00 Health factors 1.00 1.00 1.00 3.00 Social acceptability 0.33 0.33 0.33 1.00 3 Poverty Public health status Health factors Social acceptability P overty 1.00 3.00 3.00 3.00 Public health status 0.33 1.00 3.00 3.00 Health factors 0.33 0.33 1.00 3.00 Social acceptability 0.33 0.33 0.33 1.00 4 Poverty Public health status Health factors Social acceptability Poverty 1.00 5.00 7.00 0.33 Public heal th status 0.20 1.00 3.00 0.20 Health factors 0.14 0.33 1.00 0.14 Social acceptability 3.00 5.00 7.00 1.00
198 Table C 3. Continued. 5 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 0.33 0.20 Public health status 1.00 1 .00 1.00 0.20 Health factors 3.00 1.00 1.00 0.20 Social acceptability 5.00 5.00 5.00 1.00 6 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 7.00 Public health status 1.00 1.00 1.00 5.00 Health factors 1.00 1.00 1.00 5.00 Social acceptability 0.14 0.20 0.20 1.00 7 Poverty Public health status Health factors Social acceptability Poverty 1.00 0.20 0.33 0.33 Public health status 5.00 1.00 1.00 3.00 Health factors 3.00 1.00 1.00 3.00 Social acceptability 3.00 0 .33 0.33 1.00 8 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 0.17 Public health status 1.00 1.00 1.00 0.25 Health factors 1.00 1.00 1.00 0.25 Social acceptability 6.00 4.00 4.00 1.00
199 Table C 3. Continued. 9 Poverty Public health status Health factors Social acceptability Poverty 1.00 3.00 1.00 5.00 Public health status 0.33 1.00 1.00 5.00 Health factors 1.00 1.00 1.00 5.00 Social acceptability 0.20 0.20 0.20 1.00 10 Poverty Public health status Health fa ctors Social acceptability Poverty 1.00 0.33 0.33 0.14 Public health status 3.00 1.00 3.00 0.20 Health factors 3.00 0.33 1.00 1.00 Social acceptability 7.00 5.00 1.00 1.00 11 Poverty Public health status Health factors Social acceptability Poverty 1. 00 7.00 7.00 0.33 Public health status 0.14 1.00 1.00 0.14 Health factors 0.14 1.00 1.00 0.14 Social acceptability 3.00 7.00 7.00 1.00 12 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 1.00 Public health statu s 1.00 1.00 1.00 1.00 Health factors 1.00 1.00 1.00 1.00 Social acceptability 1.00 1.00 1.00 1.00
200 Table C 3. Continued. 13 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 0.20 0.20 Public health status 1.00 1.00 0.20 0.33 Health factors 5.00 5.00 1.00 0.33 Social acceptability 5.00 3.00 3.00 1.00 14 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 5.00 Public health status 1.00 1.00 1.00 3.00 Health factors 1.00 1.00 1.00 3 .00 Social acceptability 0.20 0.33 0.33 1.00 15 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 1.00 Public health status 1.00 1.00 1.00 1.00 Health factors 1.00 1.00 1.00 1.00 Social acceptability 1.00 1.00 1. 00 1.00 16 Poverty Public health status Health factors Social acceptability Poverty 1.00 0.20 0.11 0.11 Public health status 5.00 1.00 0.33 0.20 Health factors 9.00 3.00 1.00 3.00 Social acceptability 9.00 5.00 0.33 1.00
201 Table C 3. Continued. 17 Pov erty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 1.00 Public health status 1.00 1.00 1.00 0.33 Health factors 1.00 1.00 1.00 3.00 Social acceptability 1.00 3.00 0.33 1.00 18 Poverty Public health status Health facto rs Social acceptability Poverty 1.00 0.33 0.33 0.20 Public health status 3.00 1.00 0.50 1.00 Health factors 3.00 2.00 1.00 0.33 Social acceptability 5.00 1.00 3.00 1.00 19 Poverty Public health status Health factors Social acceptability Poverty 1.00 2.00 2.00 2.00 Public health status 0.50 1.00 5.00 5.00 Health factors 0.50 0.20 1.00 5.00 Social acceptability 0.50 0.20 0.20 1.00 20 Poverty Public health status Health factors Social acceptability Poverty 1.00 3.00 3.00 0.20 Public health status 0 .33 1.00 1.00 0.20 Health factors 0.33 1.00 1.00 0.20 Social acceptability 5.00 5.00 5.00 1.00
202 Table C 3. Continued. 21 Poverty Public health status Health factors Social acceptability Poverty 1.00 9.00 9.00 7.00 Public health status 0.11 1.00 9.00 3 .00 Health factors 0.11 0.11 1.00 9.00 Social acceptability 0.14 0.33 0.11 1.00 22 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 1.00 0.20 Public health status 1.00 1.00 1.00 1.00 Health factors 1.00 1.00 1.00 1.6 7 Social acceptability 5.00 1.00 0.60 1.00 23 Poverty Public health status Health factors Social acceptability Poverty 1.00 7.00 7.00 5.00 Public health status 0.14 1.00 9.00 7.00 Health factors 0.14 0.11 1.00 5.00 Social acceptability 0.20 0.14 0.20 1.00 24 Poverty Public health status Health factors Social acceptability Poverty 1.00 1.00 0.33 0.33 Public health status 1.00 1.00 0.33 0.33 Health factors 3.00 3.00 1.00 1.00 Social acceptability 3.00 3.00 1.00 1.00
203 Table C 3. Continued. 25 Pover ty Public health status Health factors Social acceptability Poverty 1.00 7.00 5.00 3.00 Public health status 0.14 1.00 1.00 3.00 Health factors 0.20 1.00 1.00 1.00 Social acceptability 0.33 0.33 1.00 1.00 26 Poverty Public health status Health factors Social acceptability Poverty 1.00 7.00 1.00 1.00 Public health status 0.14 1.00 7.00 7.00 Health factors 1.00 0.14 1.00 5.00 Social acceptability 1.00 0.14 0.20 1.00
204 Table C 4 Summary of economic sub themes comparison 1 Regional economic performance Employment status Regional economic performance 1.00 0.33 Employment status 3.00 1.00 2 Regional economic performance Employment status Regional economic performance 1.00 0.33 Employment status 3.00 1.00 3 Regional econ omic performance Employment status Regional economic performance 1.00 5.00 Employment status 0.20 1.00 4 Regional economic performance Employment status Regional economic performance 1.00 3.00 Employment status 0.33 1.00 5 Regional economic performan ce Employment status Regional economic performance 1.00 3.00 Employment status 0.33 1.00 6 Regional economic performance Employment status Regional economic performance 1.00 1.00 Employment status 1.00 1.00 7 Regional economic performance Employment status Regional economic performance 1.00 0.20 Employment status 5.00 1.00
205 Table C 4. Continued. 8 Regional economic performance Employment status Regional economic performance 1.00 5.00 Employment status 0.20 1.00 9 Regional economic performance Emp loyment status Regional economic performance 1.00 0.33 Employment status 3.00 1.00 10 Regional economic performance Employment status Regional economic performance 1.00 5.00 Employment status 0.20 1.00 11 Regional economic performance Employment stat us Regional economic performance 1.00 5.00 Employment status 0.20 1.00 12 Regional economic performance Employment status Regional economic performance 1.00 0.33 Employment status 3.00 1.00 13 Regional economic performance Employment status Regional economic performance 1.00 0.20 Employment status 5.00 1.00 14 Regional economic performance Employment status Regional economic performance 1.00 0.20 Employment status 5.00 1.00
206 Table C 4. Continued. 15 Regional economic performance Employment status Regional economic performance 1.00 3.00 Employment status 0.33 1.00 16 Regional economic performance Employment status Regional economic performance 1.00 0.20 Employment status 5.00 1.00 17 Regional economic performance Employment status Regional e conomic performance 1.00 0.20 Employment status 5.00 1.00 18 Regional economic performance Employment status Regional economic performance 1.00 1.00 Employment status 1.00 1.00 19 Regional economic performance Employment status Regional economic perf ormance 1.00 5.00 Employment status 0.20 1.00 20 Regional economic performance Employment status Regional economic performance 1.00 1.00 Employment status 1.00 1.00 21 Regional economic performance Employment status Regional economic performance 1.00 7.00 Employment status 0.14 1.00
207 Table C 4. Continued. 22 Regional economic performance Employment status Regional economic performance 1.00 1.00 Employment status 1.00 1.00 23 Regional economic performance Employment status Regional economic perfor mance 1.00 9.00 Employment status 0.11 1.00 24 Regional economic performance Employment status Regional economic performance 1.00 1.00 Employment status 1.00 1.00 25 Regional economic performance Employment status Regional economic performance 1.00 3 .00 Employment status 0.33 1.00 26 Regional economic performance Employment status Regional economic performance 1.00 0.14 Employment status 7.00 1.00
208 LIST OF REFERENCES Abdullahi, S., Mahmud, A. R., & Pradhan, B. (2012). Spatial modeling of site s uitability assessment for hospitals using GIS based multicriteria approach at Qazvin city, iran. Geocarto International, (just accepted) Achillas, C., Vlachokostas, C., Moussiopoulos, N., & Banias, G. (2010). Decision support system for the optimal locati on of electrical and electronic waste treatment plants: A case study in Greece Waste Management, 30(5), 870 879. doi:DOI: 10.1016/j.wasman.2009.11.029 Alada Almeida, L., Coutinho Rodrigues, J., & Current, J. (2009). A multiobjective modeling approach to locating incinerators. Socio Economic Planning Sciences, 43(2), 111 120. Ataei, M. (2005). Multicriteria selection for an alumina cement plant location in east azerbaijan province of iran. Journal of the South African Institute of Mining and Metallurgy, 105(7), 507 514. Auty, R. M., & Brown, K. (1997). Approaches to sustainable development Routledge. Azapagic, A., & Perdan, S. (2000). Indicators of sustainable development for industry: A general framework. Process Safety and Environmental Protection, 78 (4), 243 261. Bakhshi, L. (2008). A preview on Iranian cement industry. 2008 China International Cement Conference, China. 167. Banai, R. (1993). Fuzziness in geographical information systems: Contributions from the onal Journal of Geographical Information Science, 7(4), 315 329. Banias, G., Achillas, C., Vlachokostas, C., Moussiopoulos, N., & Tarsenis, S. (2010). Assessing multiple criteria for the optimal location of a construction and demolition waste management f acility. Building and Environment, 45(10), 2317 2326. Barbier, E. B. (1987). The concept of sustainable economic development. Environmental Conservation, 14(02), 101 110. Barda, O. H., Dupuis, J., & Lencioni, P. (1990). Multicriteria location of thermal power plants. European Journal of Operational Research, 45(2 3), 332 346. doi:DOI: 10.1016/0377 2217(90)90197 J Behavioral/NonMedical Institutional Review Board. (2012). IRB02. Retrieved from http://irb.ufl.edu/irb02/index.html
209 Benoit, D., & Clarke, G. (1997). Assessing GIS for retail location planning. Journal of Retailing and Consumer Services, 4(4), 239 258. Bergen, M., & Peteraf, M. A. (2002). Competitor identification and competitor ana lysis: A broad based managerial approach. Managerial and Decision Economics, 23(4 5), 157 169. Berkhuizen, J. C., de Vries, E. T., & Slob, A. F. L. (1988). Siting procedure for large wind energy projects. Journal of Wind Engineering and Industrial Aerodynamics, 27(1 3) 191 198. doi:DOI: 10.1016/0167 6105(88)90035 9 Boloori Arabani, A., & Farahani, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408 420. Brandeau, M. L., & Chiu, S. S. (1989). An overview of representative problems in location research. Management science, 35(6), 645 674. Briassoulis, H. (1995). Environmental criteria in industrial facility siting decisions: An analysis. Environmental Management, 19(2), 297 311. Calzo netti, F. J., Sayre, G. G., & Spooner, D. (1987). A reassessment of site suitability analysis for power plant siting: A Maryland example. Applied Geography, 7(3), 223 241. doi:DOI: 10.1016/0143 6228(87)90035 X Campagna, M. (2006). GIS for sustainable deve lopment CRC. Carolinas Cement Company. (2008). Carolinas cement company public scoping comments. Carr, M. H., & Zwick, P. D. (2007). Smart land use analysis: The LUCIS model land use conflict identification strategy Esri Pr. Cattafi, M., Gavanelli, M., M ilano, M., & Cagnoli, P. (2011). Sustainable biomass power plant location in the Italian Emilia Romagna region. ACM Transactions on Intelligent Systems and Technology, 2(4), 33. CBI and RICS. (1992). Shaping the nation. Report of the Planning Task Force, L ondon: CBI. CEMBUREAU. (2012). Cement industry main characteristics. Retrieved from http://www.cembureau.eu/about cement/cement industry main characteri stics Cement Sustainability Initiative. (2002). Towards a sustainable cement industry. World Business Council for Sustainable Development (WBCSD), Geneva, Switzerland,
210 Charnpratheep, K., Zhou, Q., & Garner, B. (1997). Preliminary landfill site screening using fuzzy geographical information systems. Waste Management & Research, 15(2), 197 215. Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data methods. Material s & Design, 32(2), 851 860. Cho, J. M., & Giannini Spohn, S. (2007). Environmental and health threats from cement production in C hina. Church, R. L. (2002). Geographical information systems and location science. Computers & Operations Research, 29(6), 541 562. Church, R. L., & Murray, A. T. (2009). Business site selection, location analysis, and GIS Wiley Online Library. Clayton, A. M. H., & Radcliffe, N. J. (1996). Sustainability: A systems approach Kogan Page. Code of ordinances county of Glades, Flor ida, Part II Ordinance No. 2010 5, Â§Â§ 125 IV (2010) Code of ordinances of Hendry C ounty, Florida, Part I Ordinance No. 2013 04, Â§Â§1 53 1 53 3 (2013) Coito, F., & Powell, F. (2005). Case study of the California cement industry. 2000 ACEEE Summer Study on E nergy Efficiency in Industry, Cole, T., Earle, Ch.,Kohlenberg, L., Nelson, S. (2009). Siting issues for gravel mines and asphalt plants, Subcommittee report to the Thurston county planning commission Colebrook, M., & Sicilia, J. (2007). Undesirable faci lity location problems on multicriteria networks. Computers & Operations Research, 34(5), 1491 1514. County Health Rankings and Roadmaps organization. (2012). County health rankings. Retrieved from http://www.countyhealthrankings.org CSI. (2009). Cement technology roadmap. CSI. (2011). CO2 accounting and reporting standard for the cement industry. CSI. (2013). CSI product category rules (PCR) for concrete. Current, J., Min, H., & Schilling D. (1990). Multiobjective analysis of facility location decisions. European Journal of Operational Research, 49(3), 295 307. Das, K. (1987). Cement industry of India New Delhi: Ashish Publishing House.
211 Dobson, J. E. (1979). A regional screening proced ure for land use suitability analysis. Geographical Review, 69(2), 224 234. Drezner, Z. (1995). Objectives in location problems. Facility location: a survey of applications and methods (pp. 151 180) Springer. Dudukovic, J., Stanojevic, M., & Vranes, S. (2005, November). Decision Aid for Sustainable Industrial Siting. EUROCON 2005. The International Conference on Computer as a Tool (Vol. 2, pp. 1085 1088). IEEE. Elliott, J. A. (2006). An introduction to sustainable development Psychology Press. Enterp rise Florida. (2013). Rural areas. Retrieved from http://www.eflorida.com/floridasregionsSubpage.aspx?id=400 EPA. (2010a). EPA sets first national limits to reduce merc ury and other toxic emissions from cement plants source. Retrieved from http://yosemite.epa.gov/opa/admpress.nsf/e77fdd4f5 afd88a3852576b3005a604f/ ef62ba1cb3c8079b8525777a005af9a5 EPA. (2010b). Greenhouse gas emissions. Retrieved from http://www.epa.gov/climatechange/ghgemissions/ Erkut, E., & Moran, S. R. (1991) Locating obnoxious facilities in the public sector: An application of the analytic hierarchy process to municipal landfill siting decisions. Socio Economic Planning Sciences, 25(2), 89 102. Erkut, E., & Neuman, S. (1989). Analytical models for locating undesirable facilities. European Journal of Operational Research, 40(3), 275 291. Farahani, R. Z., & Hekmatfar, M. (2009). Facility location: Concepts, models, algorithms and case studies Springer. Farahani, R. Z., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689 1709. doi:DOI: 10.1016/j.apm.2009.10.005 FDEP. (2011). The power plant siting act (PPSA). Retrieved from http://www.dep.state.fl.us/siting/power_plants.htm FDOT & FPC. (2012). Florida seaports charting our future. FEMA. Definitions of FEMA flood zone designations. Retrieved from https://msc.fema.gov/webapp/wcs/stores/servlet/info?storeId=10001&catalogId=1 0001&langId= 1&content=floodZones&title=FEMA%20Fl ood%20Zone%20Designations
212 FGDL. (2011). Florida Department o f Transportation major roads in F lorida (RCI derived) FEBRUARY 2011 (FGDC ) / etat.majrds_F eb11 (ISO). Retrieved, 2012, from http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId=%7B09581016 BABA 4C42 B7C0 F4517793636E%7D&loggedIn=false Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple c riteria decision analysis: State of the art surveys Springer Verlag. Florida Department of Economic Opportunity. (2013). Programs eligible for special designation score on FFY 2012 small cities CDBG applications. F rancis, R. L., McGinnis, L. F., & White, J. (1983). Locational analysis. EUROP.J.OPER.RES., 12(3), 220 252. G arrone, P., & Groppi, A. (2012). Siting locally unwanted facilities: What can be learnt from the location of Italian power plants. Energy Policy, 45, 176 186. Ginevicius, R., & Podvezko, V. (2007). Some problems of evaluating multicriteria decision methods. International Journal of Management and Decision Making, 8(5), 527 539. Glades County Government. (2010). 2020 comprehensive plan. Glavic, P., & Lukman, R. (2007). Review of sustaina bility terms and their definitions. Journal of Cleaner Production, 15(18), 1875 1885. Goldman, A., & Dearing, P. (1975). Concepts of optimal location for partially noxious facilities. Bulletin of the Operational Research Society of America, 23(1), B85. G oldsmith, E., Allen, R., Allaby, M., Davoll, J., & Lawrence, S. (1972). Blueprint for survival. Boston, MA: Houghton Mifflin Co. Govt. of India. (1966). India. Committee on transport policy and coordination.Govt. of India, Planning Commission. Griffith, D. A. (1989). Distance calculations and errors in geographic databases. Accuracy of Spatial Databases, 81 90. Gros, J. (1975). Power plant siting: A paretian environmental approach. Nuclear Engineering and Design, 34(2), 281 292. doi:DOI: 10.1016/0029 549 3(75)90125 9 Hamacher, H. W., & Nickel, S. (1998). Classification of location models. Location Science, 6(1 4), 229 242. Haughton, G. (1999). Environmental justice and the sustainable city. Journal of Planning Education and Research, 18(3), 233 243.
213 Hei delbergCement. (2012). Annual general meeting 2012. HeidelbergCement. Hodgart, R. L. (2003). Modelling a single type of environmental impact from an obnoxious transport activity: Implementing locational analysis with GIS. Environment and Planning A, 35, 9 31 946. Hokkanen, J., & Salminen, P. (1997). Locating a waste treatment facility by multicriteria analysis. Journal of Multi Criteria Decision Analysis, 6(3), 175 184. Holcim (US) Inc. (2002). Enviromental assessment Hopwood, B., Mellor, M., & O'Brien, G. (2005). Sustainable development: Mapping different approaches. Sustainable Development, 13(1), 38 52. Humphreys, K., & Ma hasenan, M. (2002). Towards a sustainable cement industry substudy 8: Climate change. World Business Council for Sustainable Development (WBCSD), Geneva, Switzerland, IEA, & UNIDO. (2009). Technology roadmap: Carbon capture and storage in industrial appli cations. Isard, W. (1956). Location and space economy. IUCN, U., WWF. (1980). World conservation strategy: Living resource conservation for sustainable development. Gland, Switzerland: IUCN. Jack, A. (2012). Low impact development (LID) siting methodolog y: A guide to siting LID projects using A GIS and AHP. Jankowski, P. (1995). Integrating geographical information systems and multiple criteria decision making methods. International Journal of Geographical Information Systems, 9(3), 251 273. Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173 184. Kiefer, R. W., & Robbins, M. L. (1973). Computer based land use suitable maps. Journal o f the Surveying and Mapping Division, 99(1), 39 62. Kirkby, J., O'Keefe, P., & Timberlake, L. (1995). The earthscan reader in sustainable development Earthscan Publications London. Klee, H. (2004). The cement sustainability initiative. In proceedings I ns titution of C ivil E ngineers Engineering S ustainability (vol. 157, pp. 9 12). Institution of Civil E ngineers.
214 Klee, H., & Coles, E. (2004). The cement sustainability initiative implementing change across a global industry. Corporate Social Responsibility an d Environmental Management, 11(2), 114 120. LaGro, J. A. (2011). Site analysis: A contextual approach to sustainable land planning and site design Wiley. Leao, S., Bishop, I., & Evans, D. (2001). Assessing the demand of solid waste disposal in urban regi on by urban dynamics modelling in a GIS environment. Resources, Conservation and Recycling, 33(4), 289 313. Lofstedt, R. (2010). EPA's proposed NESHAP for Portland cement: Ignoring the Risk Risk trade off. Malczewski, J. (1999). GIS and multicriteria dec ision analysis Wiley, New York. Malczewski, J. (2004). GIS based land use suitability analysis: A critical overview. Progress in Planning, 62(1), 3 65. Marlowe, I., Mansfield, D. (2002). Towards a sustainable cement industry substudy 10: Environment hea lth, and safety performance improvement. Marsh, M. T., & Schilling, D. A. (1994). Equity measurement in facility location analysis: A review and framework. European Journal of Operational Research, 74(1), 1 17. doi:DOI: 10.1016/0377 2217(94)90200 3 Mathu r, S. N. (2010). Working capital management of cement industry in India A comparative analysis of selected units, thesis PhD, Saurashtra University (Doctor of Philosophy in Management, Saurashtra University). McHarg, I. L., & American Museum of Natural History. (1969). Design with nature Published for the American Museum of Natural History [by] the Natural History Press. McPherson, E. M. (1995). Plant location selection techniques William Andrew. Melachrinoudis, E. (1999). Bicriteria location of a semi obnoxious facility. Computers & Industrial Engineering, 37(3), 581 593. Mirchandani, P. B., & Francis, R. L. (1990). Discrete location theory Misra, K. K. (2002). Towards a sustainable cement indus try substudy 11:Management of land use, landscape, and biodiversity. World Business Council for Sustainable Development (WBCSD), Geneva, Switzerland, Mitchell, B. (1997). Resource and environmental management. Addison Wesley Longman Ltd.
215 Moeinaddini, M., Khorasani, N., Danehkar, A., & Darvishsefat, A. A. (2010). Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj). Waste Management, 30(5), 912 920. Morhardt, J. E., Baird, S., & Freeman, K. (2002). Scoring corporate environmental and sustainability reports using GRI 2000, ISO 14031 and other criteria. Corporate Social Responsibility and Environmental Management, 9(4), 215 233. Mumphrey, A. J., Seley, J. E., & Wolper t, J. (1971). A decision model for locating controversial facilities. Journal of the American Planning Association, 37(6), 397 402. Munda, G. (2005). Multiple criteria decision analysis and sustainable development. In Multiple criteria decision analysis: State of the art surveys (pp. 953 986). Springer New York. Muntzing, M. (1976). Siting and environment: Towards an effective nuclear siting policy. Energy Policy, 4(1), 3 11. Muttiah, R., Engel, B., & Jones, D. (1996). Waste disposal site selection using GIS based simulated annealing. Computers & Geosciences, 22(9), 1013 1017. Nas, B., Cay, T., Iscan, F., & Berktay, A. (2010). Selection of MSW landfill site for konya, turkey using GIS and multi criteria evaluation. Environmental Monitoring and Assessment, 160(1), 491 500. Niemeijer, D., & de Groot, R. S. (2008). A conceptual framework for selecting environmental indicator sets. Ecological Indicators, 8(1), 14 25. Norese, M. F. (2006). ELECTRE III as a support for participatory decision making on the loca lisation of waste treatment plants. Land use Policy, 23(1), 76 85. PCA. (2009). Overview of the cement industry. Retrieved from http://www.cement.org/basics/cementindustry.asp Pe zzey, J. (1989). Economic analysis of sustainable growth and sustainable development. Environment Department Working Paper, 15 Pezzey, J. (1992). Sustainable development concepts. World, 1, 45. Podvezko, V. (2011). The comparative analysis of MCDA method s SAW and COPRAS. Engineering Economics, 22(2), 134 146. Public Health Statute, Title XXIX FL. Stat., Â§Â§ 403 501 518 (2011) Punjab pollution Control board. (2009). Guidelines for location of industry. Retrieved from http://www.ppcb.gov.in/industry_specific_guidelines.php
216 Queiruga, D., Walther, G., Gonzalez Benito, J., & Spengler, T. (2008). Evaluation of sites for the location of WEEE recycling plants in Spain Waste Manag ement, 28(1), 181 190. Radke, J., Cova, T. J., Sheridan, M. F., Troy, A., Lan, M., & Johnson, R. (2000). Application challenges for geographic information science: Implications for research, education, and policy for emergency preparedness and response Na tional Emergency Training Center. Rajasthan State pollution control board. (2008). Guidelines for location & prevention & control of pollution in stone crusher industry. Repetto, R. C. (1986). World enough and time: Successful strategies for resource mana gement Yale Univ Pr. Rogers, P. P., Jalal, K. F., & Boyd, J. A. (2008). An introduction to sustainable development Earthscan/James & James. Saaty, T. L. (1980). Analytic hierarchy process Wiley Online Library. Saaty, T. L. (1990). How to make a decision : The analytic hierarchy process. European Journal of Operational Research, 48(1), 9 26. Sadek, S., El Fadel, M., & El Hougeiri, N. (2001, October). Optimizing landfill siting through GIS application. In Seventeenth International Conference on Solid Waste Technology and Management (pp. 21 24). Philadelphia, USA: The Journal of Solid Waste Technology and Management. geographic information systems. Environmental Geology, 49(3), 3 76 388. Shah, V. P., & Ries, R. J. (2009). A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States The International Journal of Life Cycle Assessment, 14(4), 313 327. Siting Criteria for Dangerous Waste Management Facilities, Title 173 Washington Administrative Code (WAC), Â§Â§173 303 173 303 282 (2009). Sumathi, V., Natesan, U., & Sarkar, C. (2008). GIS based approach for optimized siting of municipal solid waste lan dfill. Waste Management, 28(11), 2146 2160. Susskind, L. E. (1985). The siting puzzle: Balancing economic and environmental gains and losses. Environmental Impact Assessment Review, 5(2), 157 163. doi:DOI: 10.1016/0195 9255(85)90040 X Tayman, J., & Pol, L. (2011). Retail site selection and geographic information systems. Journal of Applied Business Research (JABR), 11(2), 46 54.
217 Terouhid, S. A., Ries, R., & Fard, M. M. (2012). Towards sustainable facility Location A literature review. Journal of Sustaina ble Development, 5(7), p18. Thabrew, L., & Ries, R. (2009). Application of life cycle thinking in multidisciplinary multistakeholder contexts for cross sectoral planning and implementation of sustainable development projects. Integrated Environmental Asse ssment and Management, 5(3), 445 460. doi:10.1897/IEAM_2008 064.1 The Collier C ounty land development code, Ordinance No. 08 11, Â§Â§ 2 2 03 01 (2008) U.S. Census Bureau. (2012). NAICS definitions. Retrieved from http://www.census.gov/eos/www/naics/ U.S. EIA. (2012). Annual energy revi ew 2011. UN DSD. (2001). Indicators of sustainable development: Guidelines and methodologies. Commission for Sustainable Development, United Nations, New York, U SA, UNCED. (1992). Agenda 21, United Nations conference on environment and development Rio de Janeiro, June 1992 Rio de Janeiro. Van Oss, H. G., & Kraft, R. H. (2012). Mineral industry surveys. USGC. Van Oss, H. G., & Kraft, R. H. (2013). Mineral indus try surveys cement in February 2013. USGC. Voelker, A. (1976). Indices, a technique for using large spatial data bases. Von Thunen, J. H. (1826). Der isolierte staat in beziehung auf landwirtschaft und nationalokonomie (the isolated state with respect to agriculture and political economy). F.Perthes, Hamburg, Wakefield, S., & Elliott, S. J. (2000). Environmental risk perception and well being: Effects of the landfill siting process in two southern Ontario communities. Social Science & Medicine, 50(7 8), 1139 1154. Wallner, H. P. (1999). Towards sustainable development of industry: Networking, complexity and eco clusters. Journal of Cleaner Production, 7(1), 49 58. Wang, G., Qin, L., Li, G., & Chen, L. (2009). Landfill site selection using spatial inform ation technologies and AHP: A case study in Beijing china. Journal of Environmental Management, 90(8), 2414 2421. WBCSD/ CSI. (2005). Environmental and social impact assessment (ESIA) guidelines World Business Council for S ustainable Development (WBCSD) WBSCD/CSI. (2009). Global cement database on CO2 and energy information. World Business Council for Sustainable Development: Retrieved from
218 http://w bcsdcement.org/index.php?option=com_content&task=view&id=57&Itemi d=118 WCED. (1987). Our common future. Oxford: World Commission on Environment and Development (WCED). Weber, A. (1929). Theory of the location of industries University of Chicago Press. Worrall, R., Neil, D., Brereton, D., & Mulligan, D. (2009). Towards a sustainability criteria and indicators framework for legacy mine land. Journal of Cleaner Production, 17(16), 1426 1434. doi:DOI: 10.1016/j.jclepro.2009.04.013 Yapicioglu, H., Smith, A. E., & Dozier, G. (2007). Solving the semi desirable facility location problem using bi objective particle swarm. European Journal of Operational Research, 177(2), 733 749. Yousefi, H., & Ehara, S. (2007). Geothermal power plant site selection using GIS i n S abalan area, NW IRAN. Paper presented at the Proceedings, Map Asia 2007, 6th Annual International Conference on Geographical Information Technology and Applications, KLCCKuala, 14 16. Zhang, Z., Liu, Y., Li, J., & Chen, B. (2009, July). Application of GIS and spatial decision support system for affordable housing. ICCSE'09. 4th International Conference on Computer Science & Education (pp. 1110 1115). IEEE. Zopounidis, C. (2010). Handbook of multicriteria analysis Springer Verlag.
219 BI OGRAPHICAL SKETCH after her graduation from the most prominent engineering school in her home country, Sharif University of Technology. She had the opportunity to be part of variou s planning and cost engineering teams in different domestic and international construction projects for four years. Afterwards, she had the opportunity to work for two years in Sasol Company, the largest petrochemical corporation in South Africa. She star ted her Ph.D. program at the University of Florida in the fall 2010. During the 2010 and 2011 academic calendar, Maryam worked as a research assistant for the Energy Efficient Housing Research project awarded from Department of Energy to the M.E. Rinker, S r. School of Building Construction after that, she was the research assistant in the Powell Center for Construction and Environment till end of her PhD program. In 2011, she was awarded the membership of the SLX (Sigma Lambda Chi), the national honorary society of Building Construction for outstanding students. Duri ng her PhD academic path, she author ed/ co author ed of several papers. In addition, she was awarded several fellowships from professional institutes, such as PMI and AACE, and the M.E. Rinker, S r. School of Building Construction Maryam is a Project Management Professional (PMP) and a Green Globe Professional (GGP).