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Establishment and optimization of an evaluation index system for brownfield redevelopment projects: An empirical study
Environmental Modelling & Software 74 (2015) 173e182
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Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft
Establishment and optimization of an evaluation index system for brownfield redevelopment projects: An empirical study Yuming Zhu a, Keith W. Hipel b, Ginger Y. Ke c, *, Ye Chen d a
School of Management, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada c Faculty of Business Administration, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3X5, Canada d College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2015 Received in revised form 21 September 2015 Accepted 21 September 2015 Available online xxx
Brownfield redevelopment has recently become the focus of attention of governments, communities, environmental advocates, scientists, and researchers around the world. The purpose of this study is to provide a framework for establishing and optimizing an evaluation index for brownfield redevelopment projects (BRPs). This framework involves three steps: the initial design, testing and optimization, and verification. With the help of two standard statistical software packages, the reliability and validity of the initialized index system are established, and then the optimization of the initial index system is carried out by means of Factor Analysis. The effectiveness of the optimization of the index system is verified through Structural Equation Modeling. Furthermore, an illustration example is used to show how to apply the established index system in the real world. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Brownfield redevelopment project Evaluation index system Establishment and optimization Factor analysis Structural equation modeling
Software availability
Data availability
Name: SPSS 16.0 and AMOS 8.0. Developer: IBM Software Group. Contact address: you can get the local contact information from the website: http://www-01.ibm.com/software/analytics/spss/. First available: first version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. Operation System: Windows 8, Windows 7, Windows Vista， Windows XP, Mac OS X The latest versions: IBM SPSS Statistics e v22.0 (released on August 2013). Add-on: AMOS 22.0 which allows modeling of structural equations and covariance structures, path analysis, and has the more basic capabilities such as linear regression analysis, ANOVA and ANCOVA. Availability: http://www14.software.ibm.com/webapp/downlo ad/byproduct.jsp?pgel¼ibmhzn1&cm_re¼masthead-_-supdl-_-dltrials Cost: 30-day free trial.
Data: collected by questionnaire survey. Number of questionnaires sent out: 500. Number of questionnaires returned: 340. Number of valid questionnaires returned: 335. Target of questionnaire survey: major stakeholders of brownfield redevelopment, which included relevant government divisions, brownfield owners, brownfield developers, the financial sector, public representatives, and others. Time of survey: from January 2009 to June 2009. Location of survey: Cities in China: Xi'an, Beijing, Shanghai, Shenzhen, Jinan.
* Corresponding author. E-mail address: [email protected] (G.Y. Ke). http://dx.doi.org/10.1016/j.envsoft.2015.09.012 1364-8152/© 2015 Elsevier Ltd. All rights reserved.
1. Introduction After decades of effort, the concept of sustainable development has been accepted and adopted as policy by governments, for-profit organizations, non-profit organizations, and individuals (WBCSD, 2014; UNDSD, 2009). The World Business Council on Sustainable Development (WBCSD) (2014) defined sustainable development as “forms of progress that meet the needs of the present without compromising the ability of future generations to meet their
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needs”. Brownfield redevelopment continues a concrete application of the concept of sustainable development (Wedding and Crawford-Brown, 2007). Sardinha et al. (2013) proposed a sustainability redevelopment framework that illustrates how the integration of different perspectives and approaches can lead to a locally adapted sustainable development overview that can support the redevelopment planning of a brownfield in a rural setting. Many countries have given their own definitions of brownfields according to specific characteristics (Alker et al., 2000). Among these definitions, the most commonly cited is the one from the US Environmental Protection Agency (USEPA), which defines brownfields as “abandoned, idle, or underutilized commercial or industrial properties where an active potential for redevelopment is restrained by known or suspected environmental contamination caused by past actions (USEPA, 2005)”. Brownfields exist in very large numbers and pose serious environmental and health risks in industrialized countries around the globe. For example, the United States is believed to contain between 500,000 and 1,000,000 brownfield sites and Germany about 362,000 (NRTEE, 2003). Canada may have up to 30,000 brownfields, including the sites of almost-forgotten industrial enterprises such as coal gasification plants, locations where toxic substances were used or stored, and former gas stations and mining operations (De Sousa, 2001). Undoubtedly, restoration and redevelopment of brownfields can provide a range of economic, social, and environmental benefits, including restoration of environmental quality and improvement of quality of life for citizens, elimination of health threats, provision of land for housing or commercial purposes, creation of employment opportunities, expansion of the tax base for all levels of government, and reduction in the pressure on urban centers to expand into greenfields (NRTEE, 2003). Thus, brownfields have recently become the focus of attention for governments, communities, environmental advocates, scientists, and researchers around the world. Considerable research has addressed brownfield redevelopment issues including development of remediation technologies, environmental assessment, risk assessment and management, financial arrangements, and community and public involvement (Brebbia, 2006). Many methodologies from the fields of social science and management science have been utilized to handle challenging environmental management problems. Chen et al. (2009) used the rough-set approach to classify cities facing brownfields redevelopment issues. BenDor et al. (2011) presented a system dynamics model of the redevelopment process that illustrates how delays compound before realizing financial benefits from investment in core urban areas. Chrysochoou et al. (2012) presented an indexing scheme to screen brownfield sites in wide areas which help develop initial planning strategies for fund allocation and rede€ berl et al. (2013) developed an assessment method velopment. Do based on effectiveness-cost analysis to support decision making in contaminated site management and to implement the principles of sustainability into the selection of remediation options in Austria. Ruelle et al. (2013) used a community investigation approach to study the relationship of landscape quality and €dler et al. (2011) used an intebrownfields redevelopment. Scha grated assessment model to design sustainable and economically €dler et al. (2012) attractive brownfield revitalization options. Scha used a decision support method to build a framework of assessment methods and models which would support an efficient early judgment about whether and how brownfields could be assigned €dler et al. (2013) a sustainable and marketable land use. Scha proved that a spatially explicit algorithmic evaluation of sustainability indicators might significantly improve the applicability, comprehensiveness and reliability of the indicator-based evaluation of sustainability with a case study. Among all the
methodologies that have been applied to environmental issues, the project evaluation method is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency (Rossi et al., 2004). Based on a contextual analysis of the market segments, Guarini and Battisti (2014) outlined a methodological approach to assess the financial sustainability of redevelopment projects on brownfield sites. The approach has been tested on a restructuring proposal for the “Corviale” (Rome) building-city housing development. All of these studies are concrete examples of how to use certain evaluation index systems to assess the value of the BRP, so as to make a sound decision. However, all of these studies focused on the evaluation method itself, yet paid very little attention on how to build an evaluation index system, which is the foundation of any project evaluation. Our research fills this gap by emphasizing on the process of establishing and optimizing an evaluation index system specifically for BRPs. As the first and main step of project evaluation, the establishment of an evaluation index system (i.e., a set of structured indices or criteria), plays a very important role and also paves the way for further project evaluation tasks. Researchers have proposed several index systems from different perspectives. For example, Syms (1999) identified six groups of decision-making factors which are relevant to the redevelopment of brownfield land; De Sousa (2000) proposed an index system based on three aspects: environmental benefits, social benefits and economic benefits; Wedding and Crawford-Brown (2007) proposed an index system to assess the success of redevelopment in meeting sustainability goals, including multi-stakeholder perspectives, green building elements, and sitelevel details. Despite the previous effort, all existing studies were qualitative and thus failed to provide objective and conclusive judgement for subsequent applications. Herein, we propose a framework of an evaluation index system for BRPs which integrates both qualitative and quantitative analyses. To the best of our knowledge, this is the first time that such an index system is empirically developed and applied to the brownfield redevelopment area. Fig. 1 depicts the structure of this framework, from which we can see the generality in the process of deriving this framework. Therefore the corresponding index system can be applied to most of the BRPs in general, by serving as a basis for designing further index systems, or as a starting point for various project evaluation techniques. The rest of the paper is organized as follows: the initial evaluation index system for BRPs is established in Section 2 (top row in Fig. 1); the testing and optimization of the index system is given in Section 3 (the second row in Fig. 1); the effectiveness of the optimized index system is verified in Section 4 (the third row in Fig. 1); an example is given in Section 5 (bottom row in Fig. 1) to show how to use the established evaluation index system to assess the proposed redevelopment plans. Finally, conclusions and future research are presented in Section 6. 2. The initial establishment of the evaluation index system for brownfield redevelopment projects In this section, we start from the concept of sustainable development (Section 2.1), identify the relevant stakeholder (Section 2.2), and then establish an initial evaluation index system for BRPs (Section 2.3). 2.1. Evaluating the brownfield redevelopment projects from the perspective of sustainability After the brownfield has been properly remediated and
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Fig. 1. The framework of establishment and optimization of an evaluation index system for BRPs.
redeveloped for useful purposes, the reused land can bring economic, social, and environmental benefits. For example, if apartment buildings are constructed on a formal brownfield property, the landlord will collect rent from the residents, the people living in the apartment will spend money in the community, and the city where the apartment is located will have a larger tax base and enhance its reputation with respect to environmental stewardship. All these activities make the redevelopment sustainable. A BRP is very different from development projects on clean land. First, it is critical to remediate the project site and ensure that it complies with environmental standards in consonance with its subsequent development. Second, there are special requirements for the BRP to prevent environment contamination. The environmental index must be considered within an integrated evaluation process to make sure that all stakeholders of the BRP benefit from the redevelopment, and thus, to achieve the goal of sustainable development. Traditional evaluation methods emphasize the economic evaluation, which usually uses a financial index, such as Net Present Value (NPV), Return on Investment (ROI), or Payback Period (PP), to measure the project, but rarely take the environmental and ecological indices into consideration. This omission may significantly increase the possibility of having more environmental pollution, ecological deterioration and resources exhaustion. Therefore, the evaluation of a BRP should not only be viewed from the economic perspective, but also be integrated with environmental, social, national economic, financial, and risk evaluation. Details of the aforementioned evaluation concepts are described as follows. (1) Environmental Evaluation refers to the analysis of the BRP with respect to environmental restoration. The BRP environmental evaluation should focus on the improvement of soil quality and the increasing percentage of green land. (2) Social Evaluation. The sustainable development viewpoint makes people conscious of the importance of harmoniously developing human projects with nature. The evaluation of the BRP should take both the economic benefits and the goal of fairness among stakeholders into consideration. (3) Economic Evaluation refers to evaluating the economic benefits based on the principle of rational resource allocation, which may be indirectly reaped from the BRP. The economic evaluation of a BRP is mainly concerned with the
influence of the BRP on the local tax base, employment rates, and land value of neighborhoods. (4) Financial Evaluation assesses direct gains from BRP, such as the rate of return on investment and the investment recovery period of the BRP. (5) Risk Evaluation. In the course of implementation, an engineering project will deal with all kinds of the uncertainty. Risk evaluation is an effective way to reduce uncertainty and enhance project management. Generally, a BRP has a long life span and therefore usually faces high uncertainty. So the risk evaluation is crucial in this case.
2.2. Stakeholder analysis of a BRP Stakeholder theory constitutes an interpretation of organizational management and business ethics that addresses morals and values in managing an organization (Freeman, 2003). The Stanford Research Institute (SRI) first proposed the concept of stakeholder in 1963 (Freeman, 1984). At that time, researchers from different areas, such as business administration (Freeman, 1984; Bowie, 1988; Clarkson, 1995), natural resources management (Grimble and Wellard, 1997; Gass et al., 1997), health policy (Varvasovszky and Brugha, 2000), and project management (ODA, 1995; PMI, 2000), gave their own definitions of stakeholders according to their particular characteristics. In the area of brownfield redevelopment, stakeholders are the involved parties with conflicting objectives, which may cause a BRP to fail. Therefore, it is of great importance to comprehensively consider each stakeholder's benefit, and hence to achieve a preferable result for all the stakeholders. The stakeholders of BRPs mainly include: (1) Government bodies. The problems of externalities and information asymmetry in the process of brownfield redevelopment cannot be resolved by the market economy system on its own, so the government's supervision and control are needed. The government oversees the overall performance of the BRP. The governmental benefits lie in improving the quality of life, thereby raising the employment rate, increasing financial income, and enhancing the government's image.
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(2) Investors and developers. The investor provides capital for the project, and the developer is the executor of the project. It is the participation of investors and developers that changes wasted land into something of value and promotes the development of the economy and society. (3) Communities. The communities include the owner of the brownfield and the residents of the urban areas, which respectively care about the ownership transfer with corresponding compensation and their individual interests such as health issues. They are both stakeholders in BRPs and the ultimate consumers of the ecological environment. (4) Non-Governmental Organizations. Engaging in public welfare, Non-Governmental Organizations (NGOs) participate in such fields as environment protection, medical health and education. In a BRP, NGOs are concerned with enhancing the social, health and environmental benefits.
2.3. The initial integrated evaluation index system for brownfield redevelopment projects In light of the evaluation concepts and relevant stakeholders from the previous two sections (Sections 2.1 and 2.2), we first determine the four dimensions that the index system, i.e., criteria, can be categorized to. (1) The Social and Economic dimension was used to evaluate the effect on economic growth and the vulnerable groups of the stakeholder, mainly from the perspective of enhancing the government's image, raising the employment rate, increasing financial income, and improving the quality of life. (2) The Financial and Accounting dimension was used to evaluate the cash flow, development potential and profitability of the BRP, mainly from the perspective of net present value, return on investment, payback period, as well as the total cost of brownfield remediation and construction. (3) The Environmental and Health dimension was used to appraise the environmental improvement of the area around the brownfield, mainly from the perspective of enhancing the soil quality and increasing the green cover percentage, and so on. (4) The Prospective value dimension was used to evaluate the geographical position of the brownfield, as well as the difficulties in the remediation of the redevelopment and the financial backing from the government, largely from the perspective of location, scale, and infrastructure support of the brownfield. Next, a structured meeting based on the procedure of Delphi Method (Linstone and Turoff, 1975) was conducted to derive the initial index system. More specifically, a panel of experts, consisting of environmental scientists, social scientists, financial experts, managerial experts, and urban-planning experts, was hired to participate in the group decision making process. First, the experts fill in a pre-defined survey independently according to their own knowledge base and the references provided by the facilitator, including Syms (1999), De Sousa (2000), and Wedding and Crawford-Brown (2007). Then the facilitator collected all the surveys and prepared an anonymous summary, which was sent out to the experts again for revisions or additional opinions. After three iterations, a consensus among all the experts was achieved, which identified the initial set of factors affecting the brownfield redevelopment projects. This initial system and the corresponding
measurement are shown in Table 1. Please note that the last column shows the reliability test results, which are discussed in the next section.
3. Testing and optimization of the evaluation index system for brownfield redevelopment projects 3.1. Reliability and validity analysis of initial proposed evaluation index system for BRPs The reliability of the proposed index system was tested by a reliability coefficient, which was calculated based on the data collected from a well-designed questionnaire consisting of all the criteria listed in Table 1. Stakeholders are requested to evaluate the criteria according to a nine-level approach, which is different from the typical five-level Likert-scale item (Likert, 1932). More specifically, the nine levels are: 1-Strongly Disagree; 2-Somewhere between Strongly Disagree and Disagree; 3-Disagree; 4-Somewhere between Disagree and Neither Agree or Disagree; 5-Neither Agree or Disagree; 6-Somewhere between Neither Agree or Disagree and Agree; 7-Agree; 8-Somewhere between Agree and Strongly Agree; and 9-Strongly Agree. The questionnaire was distributed to 500 key stakeholders in five major cities of China: Xi'an, Beijing, Shanghai, Shenzhen, and Jinan. Out of the 340 questionnaires that were returned, 335 were valid, giving a validity rate of 98.53%. The questionnaire data were then analyzed using Cronbach's alpha (Cronbach, 1951), one of the most commonly used reliability coefficients. The coefficient is calculated as:
0 a¼
k B B B1 k 1@
1 Pk
varðiÞC C C varðiÞ A
i¼1
(1)
where k is the number of components (K-items or testlets) and i is the component for the current sample of people. As a correlation coefficient, Cronbach's alpha ranges in value from 0 to 1. Normally, the reliability of the questionnaire is regarded as questionable when the value falls within the range of 0.60e0.65. A value within 0.65e0.70 implies minimum acceptability. A value within 0.70e0.80 means fairly good. A value within 0.80e0.90 or above indicates very good (Li and Ma, 2007). By using the software SPSS 16.0, we calculated the values of Cronbach's alpha for all four dimensions. The results are listed in the last column of Table 1. We can see that the value of Cronbach's alpha for every dimension is higher than 0.850, which indicates that our proposed index system has a very high reliability. Validity refers to the degree to which evidence and theory support the interpretations of scores verified by using appropriate tests (AERA, 1999). In this study, our major concern is the construct validity, which refers to the extent to which our constructed surveys and questionnaire actually explain and measure the evaluation index system. We herein use Factor Analysis, a statistical method, to examine the validity, and then to optimize the initially proposed evaluation index system for BRPs. The Factor Analysis starts with KMO (Kaiser-Meyer-Olkin) (Kaiser, 1970; Cerny and Kaiser, 1977; Dziuban and Shirkey, 1974) and Bartlett's test (Snedecor and Cochran, 1983), which measures the strength of the relationships among criteria. More particularly, KMO measures the sampling adequacy, and Bartlett's test tests the null hypothesis that the correlation matrix is an identify matrix. Given the results of the test (Table 2), one can see that the KMO value very close to 1 (i.e., the factor analysis should
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Table 1 Initially established evaluation index system for BRP and its reliability coefficients. Initial evaluation index system for BRP
Dimensions
Criteria
Measurement
Reliability coefficients
Social and Economic
Improve the image of local community and government Matchup with city planning Improve living quality of local residents Improve local security status Increase local employment rates Increase land value of neighborhood Increase local tax base Ease the pressure on green land development Protect and recycle the land/soil resource Improve their mediation technologies Net present value (NPV) Return on investment (ROI) Payback period (PP) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to the total cost Lower the health risks of local residents Improve soil quality Enhance the quality of groundwater Improve air quality Increase green cover percentage Size of the brownfield Location of the brownfield Transportation convenience of the brownfield Status of infrastructure facilities of the brownfield Influence from other nearby contamination hazards Technological difficulties and time requirement of brownfield remediation Influence from the policy and legislation
Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Quantitative Quantitative Quantitative Quantitative Quantitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative
No. of Items ¼ 10 Cronbach's Alpha ¼ 0.859
Financial and accounting
Environmental and health
Prospective Value
No. of Items ¼ 5 Cronbach's Alpha ¼ 0.856
No. of Items ¼ 5 Cronbach's Alpha ¼ 0.898
No. of Items ¼ 7 Cronbach's Alpha ¼ 0.853
Qualitative
Table 2 Results of KMO and Bartlett's test. Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett's test of sphericity
yield distinct and reliable factors), and the Bartlett's test of sphericity is significant (i.e., the significance level is small enough to reject the null hypothesis). Thus, we can say that our procedure is valid, and the data collected from the questionnaires are appropriate for proceeding with Factor Analysis.
3.2. Optimization of the initially proposed evaluation index system for BRPs To facilitate better applications of the index system, the number of criteria is reduced from 27 to 24 by using Factor Analysis (Gorsuch, 1983), the most commonly used data reduction technique. Details of this technique are discussed as follows. Suppose that there are n variables, consisting of x1, x2, …,xn. According to the requirements of Factor Analysis, all of these variables have been standardized. Suppose that n variables can be a linear combination of k factors, f1, f2, …,fk. Therefore:
8 x ¼ a11 f1 þ a12 f2 þ ::: þ a1k fk þ ε1 > > < 1 x2 ¼ a21 f1 þ a22 f2 þ ::: þ a2k fk þ ε2 ::: > > : xn ¼ an1 f1 þ an2 f2 þ ::: þ ank fk þ εn
0.929 4.805E3 351 0.000
Approx. chi-square Degree of freedom Significant level
The method that the authors used for factor extraction (optimization) was principal components analysis (PCA), which is used as a tool in exploratory data analysis and involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, after mean centering the data for each attribute. The results of a PCA are discussed in terms of component scores and loadings. Please note that the eigenvalue associated with each factor shows the variance explained by that particular factor. For example in Table 3, the eigenvalue of factor 1 is 10.622, which explains 39.340% of total variance. According to the Kaiser rule, the factors which satisfy “eigenvalue >1” are extracted (Bandalos and BoehmKaufman, 2008). From Table 3, only six factors can meet the criteria, and thus these six factors were extracted, which can explain more than 66 per cent of total variance. All the remaining factors are not significant.
Table 3 Component scores and loadings.
(2)
where aij are the factor loadings for the ith subject, for j ¼ 1,2 …,k; i ¼ 1,2 …,n; and εi are independently distributed error terms with a mean of zero and finite variance, which may not be the same for all i.
Extraction sums of squared loadings
Rotation sums of squared loadings
Total
% Of Variance
Cumulative %
Total
% Of Variance
Cumulative %
10.622 2.491 1.529 1.154 1.022 1.018
39.340 9.227 5.663 4.273 3.785 3.770
39.340 48.568 54.231 58.504 62.290 66.060
4.548 3.250 3.142 2.592 2.328 1.977
16.844 12.036 11.637 9.599 8.623 7.321
16.844 28.879 40.517 50.115 58.739 66.060
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Table 4 Component matrix. Component
Increase of green cover percentage Return on investment (ROI) Total cost of Brownfield remediation and construction Improvement of remediation technologies Improvement of air quality Transportation convenience of brownfield area Ratio of Brownfield remediation cost to total cost Improvement of soil quality Easing the pressure on green land development Payback period (PP) Lowering the health risk of local residents Technological difficulties and time requirement of brownfield remediation Net present value (NPV) Protecting and recycling the land/soil resource Status of Infrastructure facilities of brownfield area Matchup with city planning Improvement of living quality of local residents Increase local employment rate Increase local tax base Location of brownfield Increase land value of neighborhood Influence from other contamination hazards nearby Size of brownfield Improvement of image of local community and government Improvement of local security status Influence from policy and legislation Improvement of the quality of groundwater
1
2
3
0.716 0.678 0.675 0.674 0.674 0.669 0.666 0.659 0.650 0.649 0.644 0.642 0.639 0.634 0.628 0.618 0.615 0.610 0.604 0.604 0.604 0.585 0.584 0.554 0.528 0.495 0.588
0.373 0.342 0.219 0.385 0.448 0.353 0.247 0.532 0.309 0.427 0.156 0.164 0.275 0.360 0.280 0.135 0.394 0.160 0.337 0.102 0.263 0.591
0.145 0.235
0.390 0.233 0.378 0.104 0.175 0.478 0.453 0.235 0.125 0.299 0.254 0.494 0.371 0.116
4
5
6
0.101 0.113 0.199
0.140 0.295 0.302
0.146 0.345 0.114 0.113 0.312 0.187 0.244
0.159 0.117 0.296
0.137
0.168 0.243 0.360 0.190
0.128 0.338 0.210
0.382 0.357 0.131 0.179 0.101
0.136 0.252
0.202 0.157 0.176 0.101
0.420 0.237 0.145 0.245
0.397 0.378 0.197 0.195 0.240 0.128
0.301 0.149 0.281 0.180
0.384 0.158 0.139 0.378 0.116 0.223 0.177
Extraction Method: Principal Component Analysis. Result: 6 components extracted.
The component matrix (Table 4) helps to explain the degree of correlation between the component and the factor. Pure variables have loadings of 0.3 or greater on only one factor. Complex variables may have high loadings on more than one factor, which makes interpretation of the results more difficult. In this case, rotation would be necessary. Varimax rotation, where the factor axes are kept at right angles to each other, is most frequently chosen in principal component analysis and Factor Analysis. It maximizes the sum of the variances of the squared loadings (Kaiser, 1958). Ordinarily, rotation reduces the number of complex variables and improves interpretation. However, in this analysis, the rotated solution still includes several complex variables (see Table 5). As can be seen in the table, Factor 1 comprises 7 items with factor loadings ranging from 0.477 to 0.856. Factor 2 consists of 5 items with factor loadings going from 0.594 to 0.756. Factor 3 comprises 4 items with factor loadings ranging from 0.667 to 0.743. Factor 4 consists of 3 items with factor loadings going from 0.641 to 0.727. Factor 5 comprises 4 items with factor loadings ranging from 0.451 to 0.707. Factor 6 comprises 4 items with factor loadings ranging from 0.483 to 0.726. The final step in the Factor Analysis is to remove unnecessary items (optimization) and to label factors. From the previous step, 6 factors have been generated. By a rule of thumb in exploratory Factor Analysis, loadings should be 0.5 or higher to confirm that independent variables identified a priori are represented by a particular factor (Gorsuch, 1983). Hence, to optimize the evaluation index system for BRP, we deleted the items (criteria) whose factor loading were less than 0.5: Improvement of living quality of local residents (0.477), Technological difficulties and time requirement of brownfield remediation (0.451), and Influence from policy and legislation (0.483) (see Table 5). Then, according to the characteristics of the items included in each extracted factor, we labeled factors 1 to 6 as Environmental and
Health Benefit Indicators, Financial Indicators, Brownfield Site Indicators, Societal Stability Indicators, Policy and Technical Indicators, and Performance Indicators, respectively. So far, we have obtained the optimized evaluation index system for BRPs, which is depicted in the first three columns of the numerical entries in Table 5. 4. Effectiveness verification of the optimized evaluation index system for brownfield redevelopment projects Having been optimized, the index system still needs to be tested regarding the feasibility of its employment in the evaluation of BRPs. The authors adopted the Structural Equation Modeling (SEM) method (Pearl, 2000), which is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions, to confirm the effectiveness of the optimization of the evaluation index system for BRPs. With the assistance of AMOS software, a parametric test and the comprehensive evaluation of the model were executed. 4.1. Parametric test The parametric test mainly conducts an inspection of the representativeness and rationality of parameters, as well as the meaning and rationality of evaluation parameters. Test of the representativeness of parameters in the Structural Equation is similar to the parametric representative assessment in the linear regression equation, namely, a test of the parameter t. Test of the rationality of parameters is used to assess the realistic meaning of the selected parameters. AMOS provides an easy and convenient method by using CR (Critical Ration) to test the representativeness of parameters. The results of the parametric estimation can be found in Table 6. From the Un-Standardized Regression Coefficients in Table 6, all
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Table 5 Optimized evaluation index system for BRP and rotated component matrix. Optimized evaluation Factors/indicators Index system for BRP
Criteria
Component 1
Environmental and health benefits
Improvement of the quality of groundwater Improvement of soil quality Improvement of air quality Lowering the health risk of local residents Increase of green cover percentage Improvement of remediation technologies Improvement of living quality of local residents Financial Payback period (PP) Return on investment (ROI) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to total cost Net present value (NPV) Brownfield site Location of brownfield Status of infrastructure facilities of brownfield area Transportation convenience of brownfield area Size of brownfield Societal Stability Increase local employment rate Increase local tax base Improvement of local security status Policy and Technical Protecting and recycling the land/soil resource Influence from other contamination hazards nearby Easing the pressure on green land development Technological difficulties and time requirement of brownfield remediation Performance Matchup with city planning Improvement of image of local community and government Increase land value of neighborhood Influence from policy and legislation
2
3
4
0.856 0.807 0.776 0.729 0.719 0.600 0.477
5
6
0.514 0.348 0.326 0.756 0.720 0.320 0.704 0.663 0.594 0.356 0.743 0.722 0.717 0.667 0.727 0.725 0.641
0.430
0.707 0.580 0.302 0.486 0.568 0.309 0.436 0.451 0.477
0.726 0.674
0.334
0.581 0.443 0.483
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Result: Rotation converged in 9 iterations.
P values are less than 5%, so all Path Coefficients pass the test of representativeness. The rationality of parameters should be determined before evaluating the Goodness-of-Fit of a model. So it is necessary to test whether the parametric estimation exceeds the acceptable range. The results of estimated coefficient of error variance are shown in Table 7. Referring to the definition by Hair et al. (1998), the
parameters in Table 7 should satisfy the following 2 conditions: The negative error variance doesn't exist; The Standardized Coefficient doesn't exceed 1 or isn't too close to 1 (over 0.95). According to Table 7, no negative error variance exists in
Table 6 Un-standardized and standardized regression coefficient. Un-standardized regression coefficient
F2)F0 F1)F0 F3)F0 F4)F0 F13)F1 F23)F2
Standardized regression coefficient
Un-standardized coefficient
Standardized error
Probability
Mark
1.044 1.000 0.949 1.125 1.049 0.963
0.105
Critical portion value 9.971
***
par_3
0.099 0.122 0.062 0.070
9.591 9.190 17.001 13.715
*** *** *** ***
par_4 par_5 par_1 par_2
Standardized coefficient F2)F0 F1)F0 F3)F0 F4)F0 F13)F1 F23)F2
0.832 0.707 0.775 0.777 0.804 0.745
*Represents P < 0.05,** represents P < 0.01,*** represents P < 0.001.
Table 7 Estimated coefficient of error variance (partial).
F0 e27 e29 e2 e4 e26 e28 e30 e3 e5
Un-standardized coefficient
Standardized error
Critical Portion value
Probability
Mark
0.883 0.530 0.384 0.868 1.032 0.427 0.734 0.343 1.061 1.051
0.144 0.087 0.108 0.083 0.088 0.078 0.133 0.083 0.098 0.096
6.113 6.109 3.545 10.494 11.741 5.454 5.523 4.131 10.799 10.933
*** *** *** *** *** *** *** *** *** ***
par_24 par_26 par_28 par_32 par_34 par_25 par_27 par_29 par_33 par_35
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Table 8 The Fit indices. Absolute fit index
Comparative fit Index
Parsimonious fit Index
Model
c2
c2 /d f
RMSEA
IFI
CFI
TLI
PNFI
PCFI
AIC
1 2 Evaluation Standard
0.849 0.875 >0.90
0.848 0.888 >0.90
0.832 0.889 >0.90
0.723 0.751 >0.5
0.768 0.792 >0.5
1201.788 848.843 The smaller, the better.
1081.788 740.843 e
3.402 3.012 <3.0
0.172 0.075 <0.08
Fig. 2. Application procedure of the example.
measure error values of error variances. Furthermore, no regression coefficient exceeds 0.95 based on the standardized regression coefficients in Table 6. Therefore, this model can be tested on the Fit Index of the comprehensive evaluation. 4.2. The comprehensive evaluation of the model The comprehensive evaluation is usually implemented by the Fit Index computation. The Fit Index refers to a statistical parameter that is constructed from a certain human perspective, to reflect the Goodness-of-fit of a model. In general, the fit indices include Absolute Fit Index, Comparative Fit Index and Parsimonious Fit Index (Byrne, 2001). The Absolute Fit Index mainly consists of c2 (Chi-square), RMSEA (Root Mean Square Error of Approximation), and SRMR
(Standardized Root Mean Square Residual). The Comparative Fit Index generally includes NFI (Normed Fit Index), CFI (Comparative Fit Index), and NNFI (Non-Normed Fit Index). These two types of indices are used to evaluate the Goodness-of-Fit of a single model. The Parsimonious Fit Index is a more common index, which includes PNFI (Parsimony Normed Fit Index), PCFI (Parsimonious Comparative Fit Index), and AIC (Akaike Information Criterion). The comprehensive evaluation of a model was achieved using diversified Fit indices. Table 8 lists the Absolute Fit Indices, Comparative Fit Indices, and Parsimonious Fit Indices of two models, where Models 1 and 2 respectively refer to the initially established index system and the optimized index system after Factor Analysis. From Table 8, it is noticed that, although the two models do not reach the evaluation standard, they are very close to that.
Table 9 Values of the proposal options. Factors/indicators
Environmental and Health Benefits
Financial
Brownfield Site
Societal Stability
Policy and Technical
Performance
Criteria
Improvement of the quality of groundwater Improvement of soil quality Improvement of air quality Lowering the health risk of local residents Increase of green cover percentage Improvement of remediation technologies Payback period (PP) Return on investment (ROI) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to total cost Net present value (NPV) Location of brownfield Status of infrastructure facilities of brownfield area Transportation convenience of brownfield area Size of brownfield Increase local employment rate Increase local tax base Improvement of local security status Protecting and recycling the land/soil resource Influence from other contamination hazards nearby Easing the pressure on green land development Matchup with city planning Improvement of image of local community and government Increase land value of neighborhood
Proposal options A
B
C
75 70 78 80 77 75 5 21 1500 15 2580 75 85 80 55 90 85 80 75 60 60 70 70 75
85 82 84 89 87 80 7 30 2700 27 3500 85 82 88 80 92 90 75 90 65 90 85 80 70
77 60 65 68 85 90 4 17 2100 18 2600 80 90 90 85 75 72 88 75 60 85 80 85 85
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181
Comparing the two models, Model 2 is a better model, because its Fit Indices are closer to the evaluation standard than Model 1, and some of its Fit Indices have already reached the evaluation standard. For example, RESMA of Model 2 is 0.075, which lies in the range from 0.05 to 0.08, so it is considered to be a good fit.
evaluation index system. Meanwhile, the criteria of the proposed index system can serve as the base of their own primary criteria set, which can be further enhanced by adding and deleting certain criteria if necessary. The practicability of the proposed index system is illustrated by a real world illustration example.
5. An illustration example
Acknowledgments
As previously stated, the proposed evaluation index system can be integrated with designs of other index systems and/or can work as a starting point of most of the project evaluation techniques. Herein, we use a brief example, which embedded our index system into a grey relational evaluation approach, to assist a local government in making a redevelopment decision of an abandoned coking plant located in the downtown area. The application procedure is explained in Fig. 2. The decision process starts from an overview of the situation, from which three alternatives, A) new industrial planning, B) commercial planning, and C) real estate planning, are identified for further evaluation. Next, the local government employs the evaluation index system derived in this research, according to which relevant stakeholders evaluate each alternative based on their own knowledge and experiences. The aggregated (averaged in this case) evaluation record (Table 9) is then used as the input to a grey relational analysis, which computes a grey correlation coefficient for each alternative. First introduced by Deng (1985), Grey relational analysis is an impact evaluation model which analyzes the associativity and similarity among multiple alternatives based on the degree of relation (Ju, 2013). Please note that the detailed computation in grey relational evaluation approach has been omitted to avoid distraction from our index system. The interested readers are referred to Chan and Tong (2007), Liu et al. (2011), and Ju (2013) for details. Finally, in light of the grey correlation coefficients obtained from the previous step, the best alternative is selected. In this example, alternative C, real estate planning, gives the highest coefficient, and thus is selected.
This research was supported in part by the National Social Sciences Foundation of China through the project “Research on Life Span Risk Management of Brownfield Redevelopment Project” (10BJY024), by the Provincial Social Sciences Foundation in Shaanxi, China through the project “Market Mechanism and Policy System for Brownfield Redevelopment in Shaanxi Province” (08E023), and by the Humanities and Social Science Foundation of the Ministry of Education of China through the project “Stakeholders Based Multi-Criteria Decision Analysis of Brownfield Redevelopment” (08JC630066). Backing was also provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada (NSERC-RGPIN-4118-2013 27238), which funded the Research Discovery Grant for the project entitled “Systems Engineering Approaches to Sustainable Environmental Management”. Finally, the Centre for International Governance Innovation (CIGI) located in Waterloo, Ontario, Canada, also furnished support.
6. Conclusions and suggestions regarding the application of the proposed evaluation index system for BRPs The establishment of an evaluation index system plays a very important role in the evaluation of BRPs. Based on the current situation of brownfields in China, the requirements of sustainable development, and the analysis of stakeholders, the authors initially developed the evaluation index system for BRPs, which includes 27 criteria from four dimensions, including Societal and Economic, Financial and Accounting, Environmental and Health, and Prospective Value. With the help of SPSS 16.0, the authors calculated Cronbach's alpha, and conducted KMO and Bartlett's tests. The results of these tests showed that the initialized evaluation index system for BRP has good reliability and validity. By means of principal components analysis, the authors extracted six factors, labeling them as Environmental and Health Benefit Indicators, Financial Indicators, Brownfield Site Indicators, Societal Stability Indicators, Policy and Technical Indicators, and Performance Indicators, and deleted three criteria whose factor loadings are less than 0.5. In this way, the authors built up their optimized evaluation index system for BRPs, whose effectiveness of optimization has been checked and verified by Structure Equation modeling with the assistance of AMOS 8.0. The most common problem in brownfield redevelopment is to select proper planning for a certain brownfield site, which could be mutually beneficial to all stakeholders The established evaluation index system for BRPs provides users with informative reference values by employing the same process to obtain their own
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envsoft.2015.09.012. References Alker, S., Joy, V., Roberts, P., et al., 2000. The definition of brownfield. J. Environ. Plann. Manag. 43 (1), 49e69. American Educational Research Association (AERA), American Psychological Association, & National Council on Measurement in Education, 1999. Standards for Educational and Psychological Testing. American Educational Research Association, Washington, DC. Bandalos, D.L., Boehm-Kaufman, M.R., 2008. Four Common Misconceptions in Exploratory Factor Analysis. In: Lance, Charles E., Vandenberg, Robert J. (Eds.), Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences. Taylor & Francis, pp. 61e87. BenDor, T.K., Metcalf, S.S., Paich, M., 2011. The dynamics of Brownfield redevelopment. Sustainability 3, 914e936. http://dx.doi.org/10.3390/su3060914. Bowie, N., 1988. The moral obligations of multinational corporations. in: Luper-Foy. In: Problems of International Justice. Westview Press, Boulder, pp. 97e113. Brebbia, C.A. (Ed.), 2006. Brownfields III: Prevention, Assessment, Rehabilitation and Development of Brownfield Sites. WIT Press, Southampton, UK. Byrne, B.M., 2001. Structural Equation Modeling with AMOS e Basic Concepts, Applications, and Programming. LEA. ISBN 0-8058-4104-0. Cerny, C.A., Kaiser, H.F., 1977. A study of a measure of sampling adequacy for factoranalytic correlation matrices. Multivar. Behav. Res. 12 (1), 43e47. Chan, W.K., Tong, T.K.L., 2007. Multi-criteria material selections and end-of-life product strategy: grey relational analysis approach. Mater. Des. 28 (5), 1539e1546. Chen, Y., Hipel, K.W., Kilgour, D.M., Zhu, Y., 2009. A strategic classification support system for brownfield redevelopment. Environ. Model. Softw. 24 (5), 647e654. Chrysochoou, M., Brown, K., Dahal, G., 2012. A GIS andindexingschemetoscreenbrownfieldsfor area-wideredevelopmentplanning. Landscapeand Urban Plan. 105, 187e198. Clarkson, M.B.E., 1995. A stakeholder framework for analyzing and evaluating corporate social performance. Acad. Manag. Rev. 20 (1), 92e117. Cronbach, L.J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16 (3), 297e334. De Sousa, C.A., 2000. The Brownfield Problem in Urban Canada: Issues, Approachesand Solutions. University of Wisconsin-Milwaukee (PHD dissertation). De Sousa, C., 2001. Contaminated sites: the Canadian situation in an international context. J. Environ. Manag. 62, 131e154. Deng, J.L., 1985. Grey Control Systems. Huazhong University of Science and Technology Press, Wuhan (in Chinese). € berl, G., Ortmann, M., Frühwirth, W., 2013. Introducing a goal-oriented sustainDo ability assessment method to support decision making in contaminatedsitemanagement. Environ. Sci. Policy 25, 207e217. Dziuban, C.D., Shirkey, E.C., 1974. When is a correlation matrix appropriate for factor
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analysis? Psychol. Bull. 81, 358e361. Freeman, R.E., 1984. Strategic Managementda Stakeholder Approach. Pitman Publishing Inc, Massachusetts, p. 276. Freeman, R.E., 2003. In: Brebbia, C.A. (Ed.), Stakeholder Theory and Organizational Ethics. Berrett-Koehler Publishers. Gass, G., Biggs, S., Kelly, A., 1997. Stakeholders, science and decision making for poverty-focused rural mechanization research and development. World Dev. 25 (1), 115e126. Gorsuch, R.L., 1983. Factor Analysis. Lawrence Erlbaum, Hillsdale, NJ. Grimble, R., Wellard, K., 1997. Stakeholder methodologies in natural resource management: a review of principles, contexts, experiences and opportunities. Agric. Syst. 55 (2), 173e193. Guarini, M., Battisti, F., 2014. Social housing and redevelopment of building complexes on brownfield sites: the financial sustainability of residential projects for vulnerable social groups. Adv. Mater. Res. 869e870, 3e13. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. Multivariate Data Analysis with Readings. Prentice Hall, Englewood Cliffs, NJ. Ju, Y.B., 2013. Methods and Applicationns for Emergency Management under Fuzzy Environment. Beijing Institute of Technology Press, Beijing. Kaiser, H.F., 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23 (3), 187e200. Kaiser, H.F., 1970. A second generation little jiffy. Psychometrika 35, 401e415. Li, J.L., Ma, B.L., 2007. Management Statistics Application and Practice. Tsinghua University Press. Likert, R., 1932. A technique for the measurement of attitudes. Archives Psychol. 140, 1e55. Linstone, H.A., Turoff, M., 1975. The Delphi Method: Techniques and Applications. Adison-Wesley, Reading, Mass. ISBN 9780201042948. Liu, S.F., Xie, N., Forrest, J., 2011. Novel models of grey relational analysis based on visual angle of similarity and nearness. In: Grey Systems: Theory and Application, vol. 1(1), pp. 8e18. National Round Table on the Environment and the Economy (NRTEE), 2003. Cleaning up the Past, Building the Future: a National Brownfield Redevelopment Strategy for Canada. ISBN 1-894737-05-9. Canada, Ottawa. ODA, 1995. Guidance Note on How to Do Stakeholder Analysis of Aid Projects and Programs. Overseas Development Administration, Social Development Department. www.euforic.org/gb/stake1.htm.
Pearl, J., 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 0521773628. PMI, 2000. A Guide to the Project Management Body of Knowledge. Project Management Institute, 130 South State Road, Upper Darby (f creative problem solving). Rossi, P.,H., Lipsey, W.M., Freeman, H.E., 2004. Evaluation: a Systematic Approach, seventh ed. SAGE, Thousand Oaks, CA. Sardinha, I., Craveiro, D., Milheiras, S., 2013. A sustainability framework for redevelopment of rural brownfields: stakeholder participation at SAeO DOMINGOS mine, Portugal. J. Clean. Prod. 57, 200e208. Sch€ adler, S., Morio, M., Bartke, S., et al., 2011. Designing sustainable and economically attractivebrownfield revitalization options using an integrated assessment model. J. Environ. Manag. 92, 827e837. http://dx.doi.org/10.1016/ j.jenvman.2010.10.026. Sch€ adler, S., Morio, M., Bartke, S., Finkel, M., 2012. Integrated planning and spatial evaluation ofmegasite remediation and reuse options. J. Contam. Hydrology 127, 88e100. http://dx.doi.org/10.1016/j.jconhyd.2011.03.003. Sch€ adler, S., Finkel, M., Bleicher, A., et al., 2013. Spatially explicit computation of sustainability indicatorvalues for the automated assessment of land-use options. Landsc. Urban Plan. 111, 34e45. http://dx.doi.org/10.1016/ j.landurbplan.2012.12.002. Snedecor, George W., Cochran, William G., 1983. Statistical Methods, eighth ed. Iowa State University Press. Syms, P., 1999. Redeveloping brownfield land: the decision-making process. J. Prop. Invest. Finance 17, 481e494. United Nations Division for Sustainable Development (UNDSD), 2009. http://www. un.org/esa/sustdev/index.html. (accessed on 27.05.09.). US Environmental Protection Agency (USEPA), 2005. Road Map to Understanding Innovative Technology Options for Brownfields Investigation and Cleanup, fourth ed. EPA-542-B-05e001. Varvasovszky, Z., Brugha, R., 2000. How to do (or not to do) A stakeholder analysis. Health Policy Plan. 15 (3), 338e345. Wedding, G.C., Crawford-Brown, D., 2007. Measuring site-level success in brownfield redevelopments: a focus on sustainability and green building. J. Environ. Manag. 85, 483e495. World Business Council on Sustainable Development (WBCSD), 2014. http://www. wbcsd.org/, (accessed 20.04.14.).
Contents lists available at ScienceDirect
Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft
Establishment and optimization of an evaluation index system for brownfield redevelopment projects: An empirical study Yuming Zhu a, Keith W. Hipel b, Ginger Y. Ke c, *, Ye Chen d a
School of Management, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada c Faculty of Business Administration, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3X5, Canada d College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2015 Received in revised form 21 September 2015 Accepted 21 September 2015 Available online xxx
Brownfield redevelopment has recently become the focus of attention of governments, communities, environmental advocates, scientists, and researchers around the world. The purpose of this study is to provide a framework for establishing and optimizing an evaluation index for brownfield redevelopment projects (BRPs). This framework involves three steps: the initial design, testing and optimization, and verification. With the help of two standard statistical software packages, the reliability and validity of the initialized index system are established, and then the optimization of the initial index system is carried out by means of Factor Analysis. The effectiveness of the optimization of the index system is verified through Structural Equation Modeling. Furthermore, an illustration example is used to show how to apply the established index system in the real world. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Brownfield redevelopment project Evaluation index system Establishment and optimization Factor analysis Structural equation modeling
Software availability
Data availability
Name: SPSS 16.0 and AMOS 8.0. Developer: IBM Software Group. Contact address: you can get the local contact information from the website: http://www-01.ibm.com/software/analytics/spss/. First available: first version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. Operation System: Windows 8, Windows 7, Windows Vista， Windows XP, Mac OS X The latest versions: IBM SPSS Statistics e v22.0 (released on August 2013). Add-on: AMOS 22.0 which allows modeling of structural equations and covariance structures, path analysis, and has the more basic capabilities such as linear regression analysis, ANOVA and ANCOVA. Availability: http://www14.software.ibm.com/webapp/downlo ad/byproduct.jsp?pgel¼ibmhzn1&cm_re¼masthead-_-supdl-_-dltrials Cost: 30-day free trial.
Data: collected by questionnaire survey. Number of questionnaires sent out: 500. Number of questionnaires returned: 340. Number of valid questionnaires returned: 335. Target of questionnaire survey: major stakeholders of brownfield redevelopment, which included relevant government divisions, brownfield owners, brownfield developers, the financial sector, public representatives, and others. Time of survey: from January 2009 to June 2009. Location of survey: Cities in China: Xi'an, Beijing, Shanghai, Shenzhen, Jinan.
* Corresponding author. E-mail address: [email protected] (G.Y. Ke). http://dx.doi.org/10.1016/j.envsoft.2015.09.012 1364-8152/© 2015 Elsevier Ltd. All rights reserved.
1. Introduction After decades of effort, the concept of sustainable development has been accepted and adopted as policy by governments, for-profit organizations, non-profit organizations, and individuals (WBCSD, 2014; UNDSD, 2009). The World Business Council on Sustainable Development (WBCSD) (2014) defined sustainable development as “forms of progress that meet the needs of the present without compromising the ability of future generations to meet their
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needs”. Brownfield redevelopment continues a concrete application of the concept of sustainable development (Wedding and Crawford-Brown, 2007). Sardinha et al. (2013) proposed a sustainability redevelopment framework that illustrates how the integration of different perspectives and approaches can lead to a locally adapted sustainable development overview that can support the redevelopment planning of a brownfield in a rural setting. Many countries have given their own definitions of brownfields according to specific characteristics (Alker et al., 2000). Among these definitions, the most commonly cited is the one from the US Environmental Protection Agency (USEPA), which defines brownfields as “abandoned, idle, or underutilized commercial or industrial properties where an active potential for redevelopment is restrained by known or suspected environmental contamination caused by past actions (USEPA, 2005)”. Brownfields exist in very large numbers and pose serious environmental and health risks in industrialized countries around the globe. For example, the United States is believed to contain between 500,000 and 1,000,000 brownfield sites and Germany about 362,000 (NRTEE, 2003). Canada may have up to 30,000 brownfields, including the sites of almost-forgotten industrial enterprises such as coal gasification plants, locations where toxic substances were used or stored, and former gas stations and mining operations (De Sousa, 2001). Undoubtedly, restoration and redevelopment of brownfields can provide a range of economic, social, and environmental benefits, including restoration of environmental quality and improvement of quality of life for citizens, elimination of health threats, provision of land for housing or commercial purposes, creation of employment opportunities, expansion of the tax base for all levels of government, and reduction in the pressure on urban centers to expand into greenfields (NRTEE, 2003). Thus, brownfields have recently become the focus of attention for governments, communities, environmental advocates, scientists, and researchers around the world. Considerable research has addressed brownfield redevelopment issues including development of remediation technologies, environmental assessment, risk assessment and management, financial arrangements, and community and public involvement (Brebbia, 2006). Many methodologies from the fields of social science and management science have been utilized to handle challenging environmental management problems. Chen et al. (2009) used the rough-set approach to classify cities facing brownfields redevelopment issues. BenDor et al. (2011) presented a system dynamics model of the redevelopment process that illustrates how delays compound before realizing financial benefits from investment in core urban areas. Chrysochoou et al. (2012) presented an indexing scheme to screen brownfield sites in wide areas which help develop initial planning strategies for fund allocation and rede€ berl et al. (2013) developed an assessment method velopment. Do based on effectiveness-cost analysis to support decision making in contaminated site management and to implement the principles of sustainability into the selection of remediation options in Austria. Ruelle et al. (2013) used a community investigation approach to study the relationship of landscape quality and €dler et al. (2011) used an intebrownfields redevelopment. Scha grated assessment model to design sustainable and economically €dler et al. (2012) attractive brownfield revitalization options. Scha used a decision support method to build a framework of assessment methods and models which would support an efficient early judgment about whether and how brownfields could be assigned €dler et al. (2013) a sustainable and marketable land use. Scha proved that a spatially explicit algorithmic evaluation of sustainability indicators might significantly improve the applicability, comprehensiveness and reliability of the indicator-based evaluation of sustainability with a case study. Among all the
methodologies that have been applied to environmental issues, the project evaluation method is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency (Rossi et al., 2004). Based on a contextual analysis of the market segments, Guarini and Battisti (2014) outlined a methodological approach to assess the financial sustainability of redevelopment projects on brownfield sites. The approach has been tested on a restructuring proposal for the “Corviale” (Rome) building-city housing development. All of these studies are concrete examples of how to use certain evaluation index systems to assess the value of the BRP, so as to make a sound decision. However, all of these studies focused on the evaluation method itself, yet paid very little attention on how to build an evaluation index system, which is the foundation of any project evaluation. Our research fills this gap by emphasizing on the process of establishing and optimizing an evaluation index system specifically for BRPs. As the first and main step of project evaluation, the establishment of an evaluation index system (i.e., a set of structured indices or criteria), plays a very important role and also paves the way for further project evaluation tasks. Researchers have proposed several index systems from different perspectives. For example, Syms (1999) identified six groups of decision-making factors which are relevant to the redevelopment of brownfield land; De Sousa (2000) proposed an index system based on three aspects: environmental benefits, social benefits and economic benefits; Wedding and Crawford-Brown (2007) proposed an index system to assess the success of redevelopment in meeting sustainability goals, including multi-stakeholder perspectives, green building elements, and sitelevel details. Despite the previous effort, all existing studies were qualitative and thus failed to provide objective and conclusive judgement for subsequent applications. Herein, we propose a framework of an evaluation index system for BRPs which integrates both qualitative and quantitative analyses. To the best of our knowledge, this is the first time that such an index system is empirically developed and applied to the brownfield redevelopment area. Fig. 1 depicts the structure of this framework, from which we can see the generality in the process of deriving this framework. Therefore the corresponding index system can be applied to most of the BRPs in general, by serving as a basis for designing further index systems, or as a starting point for various project evaluation techniques. The rest of the paper is organized as follows: the initial evaluation index system for BRPs is established in Section 2 (top row in Fig. 1); the testing and optimization of the index system is given in Section 3 (the second row in Fig. 1); the effectiveness of the optimized index system is verified in Section 4 (the third row in Fig. 1); an example is given in Section 5 (bottom row in Fig. 1) to show how to use the established evaluation index system to assess the proposed redevelopment plans. Finally, conclusions and future research are presented in Section 6. 2. The initial establishment of the evaluation index system for brownfield redevelopment projects In this section, we start from the concept of sustainable development (Section 2.1), identify the relevant stakeholder (Section 2.2), and then establish an initial evaluation index system for BRPs (Section 2.3). 2.1. Evaluating the brownfield redevelopment projects from the perspective of sustainability After the brownfield has been properly remediated and
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Fig. 1. The framework of establishment and optimization of an evaluation index system for BRPs.
redeveloped for useful purposes, the reused land can bring economic, social, and environmental benefits. For example, if apartment buildings are constructed on a formal brownfield property, the landlord will collect rent from the residents, the people living in the apartment will spend money in the community, and the city where the apartment is located will have a larger tax base and enhance its reputation with respect to environmental stewardship. All these activities make the redevelopment sustainable. A BRP is very different from development projects on clean land. First, it is critical to remediate the project site and ensure that it complies with environmental standards in consonance with its subsequent development. Second, there are special requirements for the BRP to prevent environment contamination. The environmental index must be considered within an integrated evaluation process to make sure that all stakeholders of the BRP benefit from the redevelopment, and thus, to achieve the goal of sustainable development. Traditional evaluation methods emphasize the economic evaluation, which usually uses a financial index, such as Net Present Value (NPV), Return on Investment (ROI), or Payback Period (PP), to measure the project, but rarely take the environmental and ecological indices into consideration. This omission may significantly increase the possibility of having more environmental pollution, ecological deterioration and resources exhaustion. Therefore, the evaluation of a BRP should not only be viewed from the economic perspective, but also be integrated with environmental, social, national economic, financial, and risk evaluation. Details of the aforementioned evaluation concepts are described as follows. (1) Environmental Evaluation refers to the analysis of the BRP with respect to environmental restoration. The BRP environmental evaluation should focus on the improvement of soil quality and the increasing percentage of green land. (2) Social Evaluation. The sustainable development viewpoint makes people conscious of the importance of harmoniously developing human projects with nature. The evaluation of the BRP should take both the economic benefits and the goal of fairness among stakeholders into consideration. (3) Economic Evaluation refers to evaluating the economic benefits based on the principle of rational resource allocation, which may be indirectly reaped from the BRP. The economic evaluation of a BRP is mainly concerned with the
influence of the BRP on the local tax base, employment rates, and land value of neighborhoods. (4) Financial Evaluation assesses direct gains from BRP, such as the rate of return on investment and the investment recovery period of the BRP. (5) Risk Evaluation. In the course of implementation, an engineering project will deal with all kinds of the uncertainty. Risk evaluation is an effective way to reduce uncertainty and enhance project management. Generally, a BRP has a long life span and therefore usually faces high uncertainty. So the risk evaluation is crucial in this case.
2.2. Stakeholder analysis of a BRP Stakeholder theory constitutes an interpretation of organizational management and business ethics that addresses morals and values in managing an organization (Freeman, 2003). The Stanford Research Institute (SRI) first proposed the concept of stakeholder in 1963 (Freeman, 1984). At that time, researchers from different areas, such as business administration (Freeman, 1984; Bowie, 1988; Clarkson, 1995), natural resources management (Grimble and Wellard, 1997; Gass et al., 1997), health policy (Varvasovszky and Brugha, 2000), and project management (ODA, 1995; PMI, 2000), gave their own definitions of stakeholders according to their particular characteristics. In the area of brownfield redevelopment, stakeholders are the involved parties with conflicting objectives, which may cause a BRP to fail. Therefore, it is of great importance to comprehensively consider each stakeholder's benefit, and hence to achieve a preferable result for all the stakeholders. The stakeholders of BRPs mainly include: (1) Government bodies. The problems of externalities and information asymmetry in the process of brownfield redevelopment cannot be resolved by the market economy system on its own, so the government's supervision and control are needed. The government oversees the overall performance of the BRP. The governmental benefits lie in improving the quality of life, thereby raising the employment rate, increasing financial income, and enhancing the government's image.
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(2) Investors and developers. The investor provides capital for the project, and the developer is the executor of the project. It is the participation of investors and developers that changes wasted land into something of value and promotes the development of the economy and society. (3) Communities. The communities include the owner of the brownfield and the residents of the urban areas, which respectively care about the ownership transfer with corresponding compensation and their individual interests such as health issues. They are both stakeholders in BRPs and the ultimate consumers of the ecological environment. (4) Non-Governmental Organizations. Engaging in public welfare, Non-Governmental Organizations (NGOs) participate in such fields as environment protection, medical health and education. In a BRP, NGOs are concerned with enhancing the social, health and environmental benefits.
2.3. The initial integrated evaluation index system for brownfield redevelopment projects In light of the evaluation concepts and relevant stakeholders from the previous two sections (Sections 2.1 and 2.2), we first determine the four dimensions that the index system, i.e., criteria, can be categorized to. (1) The Social and Economic dimension was used to evaluate the effect on economic growth and the vulnerable groups of the stakeholder, mainly from the perspective of enhancing the government's image, raising the employment rate, increasing financial income, and improving the quality of life. (2) The Financial and Accounting dimension was used to evaluate the cash flow, development potential and profitability of the BRP, mainly from the perspective of net present value, return on investment, payback period, as well as the total cost of brownfield remediation and construction. (3) The Environmental and Health dimension was used to appraise the environmental improvement of the area around the brownfield, mainly from the perspective of enhancing the soil quality and increasing the green cover percentage, and so on. (4) The Prospective value dimension was used to evaluate the geographical position of the brownfield, as well as the difficulties in the remediation of the redevelopment and the financial backing from the government, largely from the perspective of location, scale, and infrastructure support of the brownfield. Next, a structured meeting based on the procedure of Delphi Method (Linstone and Turoff, 1975) was conducted to derive the initial index system. More specifically, a panel of experts, consisting of environmental scientists, social scientists, financial experts, managerial experts, and urban-planning experts, was hired to participate in the group decision making process. First, the experts fill in a pre-defined survey independently according to their own knowledge base and the references provided by the facilitator, including Syms (1999), De Sousa (2000), and Wedding and Crawford-Brown (2007). Then the facilitator collected all the surveys and prepared an anonymous summary, which was sent out to the experts again for revisions or additional opinions. After three iterations, a consensus among all the experts was achieved, which identified the initial set of factors affecting the brownfield redevelopment projects. This initial system and the corresponding
measurement are shown in Table 1. Please note that the last column shows the reliability test results, which are discussed in the next section.
3. Testing and optimization of the evaluation index system for brownfield redevelopment projects 3.1. Reliability and validity analysis of initial proposed evaluation index system for BRPs The reliability of the proposed index system was tested by a reliability coefficient, which was calculated based on the data collected from a well-designed questionnaire consisting of all the criteria listed in Table 1. Stakeholders are requested to evaluate the criteria according to a nine-level approach, which is different from the typical five-level Likert-scale item (Likert, 1932). More specifically, the nine levels are: 1-Strongly Disagree; 2-Somewhere between Strongly Disagree and Disagree; 3-Disagree; 4-Somewhere between Disagree and Neither Agree or Disagree; 5-Neither Agree or Disagree; 6-Somewhere between Neither Agree or Disagree and Agree; 7-Agree; 8-Somewhere between Agree and Strongly Agree; and 9-Strongly Agree. The questionnaire was distributed to 500 key stakeholders in five major cities of China: Xi'an, Beijing, Shanghai, Shenzhen, and Jinan. Out of the 340 questionnaires that were returned, 335 were valid, giving a validity rate of 98.53%. The questionnaire data were then analyzed using Cronbach's alpha (Cronbach, 1951), one of the most commonly used reliability coefficients. The coefficient is calculated as:
0 a¼
k B B B1 k 1@
1 Pk
varðiÞC C C varðiÞ A
i¼1
(1)
where k is the number of components (K-items or testlets) and i is the component for the current sample of people. As a correlation coefficient, Cronbach's alpha ranges in value from 0 to 1. Normally, the reliability of the questionnaire is regarded as questionable when the value falls within the range of 0.60e0.65. A value within 0.65e0.70 implies minimum acceptability. A value within 0.70e0.80 means fairly good. A value within 0.80e0.90 or above indicates very good (Li and Ma, 2007). By using the software SPSS 16.0, we calculated the values of Cronbach's alpha for all four dimensions. The results are listed in the last column of Table 1. We can see that the value of Cronbach's alpha for every dimension is higher than 0.850, which indicates that our proposed index system has a very high reliability. Validity refers to the degree to which evidence and theory support the interpretations of scores verified by using appropriate tests (AERA, 1999). In this study, our major concern is the construct validity, which refers to the extent to which our constructed surveys and questionnaire actually explain and measure the evaluation index system. We herein use Factor Analysis, a statistical method, to examine the validity, and then to optimize the initially proposed evaluation index system for BRPs. The Factor Analysis starts with KMO (Kaiser-Meyer-Olkin) (Kaiser, 1970; Cerny and Kaiser, 1977; Dziuban and Shirkey, 1974) and Bartlett's test (Snedecor and Cochran, 1983), which measures the strength of the relationships among criteria. More particularly, KMO measures the sampling adequacy, and Bartlett's test tests the null hypothesis that the correlation matrix is an identify matrix. Given the results of the test (Table 2), one can see that the KMO value very close to 1 (i.e., the factor analysis should
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Table 1 Initially established evaluation index system for BRP and its reliability coefficients. Initial evaluation index system for BRP
Dimensions
Criteria
Measurement
Reliability coefficients
Social and Economic
Improve the image of local community and government Matchup with city planning Improve living quality of local residents Improve local security status Increase local employment rates Increase land value of neighborhood Increase local tax base Ease the pressure on green land development Protect and recycle the land/soil resource Improve their mediation technologies Net present value (NPV) Return on investment (ROI) Payback period (PP) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to the total cost Lower the health risks of local residents Improve soil quality Enhance the quality of groundwater Improve air quality Increase green cover percentage Size of the brownfield Location of the brownfield Transportation convenience of the brownfield Status of infrastructure facilities of the brownfield Influence from other nearby contamination hazards Technological difficulties and time requirement of brownfield remediation Influence from the policy and legislation
Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Quantitative Quantitative Quantitative Quantitative Quantitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative Qualitative
No. of Items ¼ 10 Cronbach's Alpha ¼ 0.859
Financial and accounting
Environmental and health
Prospective Value
No. of Items ¼ 5 Cronbach's Alpha ¼ 0.856
No. of Items ¼ 5 Cronbach's Alpha ¼ 0.898
No. of Items ¼ 7 Cronbach's Alpha ¼ 0.853
Qualitative
Table 2 Results of KMO and Bartlett's test. Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett's test of sphericity
yield distinct and reliable factors), and the Bartlett's test of sphericity is significant (i.e., the significance level is small enough to reject the null hypothesis). Thus, we can say that our procedure is valid, and the data collected from the questionnaires are appropriate for proceeding with Factor Analysis.
3.2. Optimization of the initially proposed evaluation index system for BRPs To facilitate better applications of the index system, the number of criteria is reduced from 27 to 24 by using Factor Analysis (Gorsuch, 1983), the most commonly used data reduction technique. Details of this technique are discussed as follows. Suppose that there are n variables, consisting of x1, x2, …,xn. According to the requirements of Factor Analysis, all of these variables have been standardized. Suppose that n variables can be a linear combination of k factors, f1, f2, …,fk. Therefore:
8 x ¼ a11 f1 þ a12 f2 þ ::: þ a1k fk þ ε1 > > < 1 x2 ¼ a21 f1 þ a22 f2 þ ::: þ a2k fk þ ε2 ::: > > : xn ¼ an1 f1 þ an2 f2 þ ::: þ ank fk þ εn
0.929 4.805E3 351 0.000
Approx. chi-square Degree of freedom Significant level
The method that the authors used for factor extraction (optimization) was principal components analysis (PCA), which is used as a tool in exploratory data analysis and involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, after mean centering the data for each attribute. The results of a PCA are discussed in terms of component scores and loadings. Please note that the eigenvalue associated with each factor shows the variance explained by that particular factor. For example in Table 3, the eigenvalue of factor 1 is 10.622, which explains 39.340% of total variance. According to the Kaiser rule, the factors which satisfy “eigenvalue >1” are extracted (Bandalos and BoehmKaufman, 2008). From Table 3, only six factors can meet the criteria, and thus these six factors were extracted, which can explain more than 66 per cent of total variance. All the remaining factors are not significant.
Table 3 Component scores and loadings.
(2)
where aij are the factor loadings for the ith subject, for j ¼ 1,2 …,k; i ¼ 1,2 …,n; and εi are independently distributed error terms with a mean of zero and finite variance, which may not be the same for all i.
Extraction sums of squared loadings
Rotation sums of squared loadings
Total
% Of Variance
Cumulative %
Total
% Of Variance
Cumulative %
10.622 2.491 1.529 1.154 1.022 1.018
39.340 9.227 5.663 4.273 3.785 3.770
39.340 48.568 54.231 58.504 62.290 66.060
4.548 3.250 3.142 2.592 2.328 1.977
16.844 12.036 11.637 9.599 8.623 7.321
16.844 28.879 40.517 50.115 58.739 66.060
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Table 4 Component matrix. Component
Increase of green cover percentage Return on investment (ROI) Total cost of Brownfield remediation and construction Improvement of remediation technologies Improvement of air quality Transportation convenience of brownfield area Ratio of Brownfield remediation cost to total cost Improvement of soil quality Easing the pressure on green land development Payback period (PP) Lowering the health risk of local residents Technological difficulties and time requirement of brownfield remediation Net present value (NPV) Protecting and recycling the land/soil resource Status of Infrastructure facilities of brownfield area Matchup with city planning Improvement of living quality of local residents Increase local employment rate Increase local tax base Location of brownfield Increase land value of neighborhood Influence from other contamination hazards nearby Size of brownfield Improvement of image of local community and government Improvement of local security status Influence from policy and legislation Improvement of the quality of groundwater
1
2
3
0.716 0.678 0.675 0.674 0.674 0.669 0.666 0.659 0.650 0.649 0.644 0.642 0.639 0.634 0.628 0.618 0.615 0.610 0.604 0.604 0.604 0.585 0.584 0.554 0.528 0.495 0.588
0.373 0.342 0.219 0.385 0.448 0.353 0.247 0.532 0.309 0.427 0.156 0.164 0.275 0.360 0.280 0.135 0.394 0.160 0.337 0.102 0.263 0.591
0.145 0.235
0.390 0.233 0.378 0.104 0.175 0.478 0.453 0.235 0.125 0.299 0.254 0.494 0.371 0.116
4
5
6
0.101 0.113 0.199
0.140 0.295 0.302
0.146 0.345 0.114 0.113 0.312 0.187 0.244
0.159 0.117 0.296
0.137
0.168 0.243 0.360 0.190
0.128 0.338 0.210
0.382 0.357 0.131 0.179 0.101
0.136 0.252
0.202 0.157 0.176 0.101
0.420 0.237 0.145 0.245
0.397 0.378 0.197 0.195 0.240 0.128
0.301 0.149 0.281 0.180
0.384 0.158 0.139 0.378 0.116 0.223 0.177
Extraction Method: Principal Component Analysis. Result: 6 components extracted.
The component matrix (Table 4) helps to explain the degree of correlation between the component and the factor. Pure variables have loadings of 0.3 or greater on only one factor. Complex variables may have high loadings on more than one factor, which makes interpretation of the results more difficult. In this case, rotation would be necessary. Varimax rotation, where the factor axes are kept at right angles to each other, is most frequently chosen in principal component analysis and Factor Analysis. It maximizes the sum of the variances of the squared loadings (Kaiser, 1958). Ordinarily, rotation reduces the number of complex variables and improves interpretation. However, in this analysis, the rotated solution still includes several complex variables (see Table 5). As can be seen in the table, Factor 1 comprises 7 items with factor loadings ranging from 0.477 to 0.856. Factor 2 consists of 5 items with factor loadings going from 0.594 to 0.756. Factor 3 comprises 4 items with factor loadings ranging from 0.667 to 0.743. Factor 4 consists of 3 items with factor loadings going from 0.641 to 0.727. Factor 5 comprises 4 items with factor loadings ranging from 0.451 to 0.707. Factor 6 comprises 4 items with factor loadings ranging from 0.483 to 0.726. The final step in the Factor Analysis is to remove unnecessary items (optimization) and to label factors. From the previous step, 6 factors have been generated. By a rule of thumb in exploratory Factor Analysis, loadings should be 0.5 or higher to confirm that independent variables identified a priori are represented by a particular factor (Gorsuch, 1983). Hence, to optimize the evaluation index system for BRP, we deleted the items (criteria) whose factor loading were less than 0.5: Improvement of living quality of local residents (0.477), Technological difficulties and time requirement of brownfield remediation (0.451), and Influence from policy and legislation (0.483) (see Table 5). Then, according to the characteristics of the items included in each extracted factor, we labeled factors 1 to 6 as Environmental and
Health Benefit Indicators, Financial Indicators, Brownfield Site Indicators, Societal Stability Indicators, Policy and Technical Indicators, and Performance Indicators, respectively. So far, we have obtained the optimized evaluation index system for BRPs, which is depicted in the first three columns of the numerical entries in Table 5. 4. Effectiveness verification of the optimized evaluation index system for brownfield redevelopment projects Having been optimized, the index system still needs to be tested regarding the feasibility of its employment in the evaluation of BRPs. The authors adopted the Structural Equation Modeling (SEM) method (Pearl, 2000), which is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions, to confirm the effectiveness of the optimization of the evaluation index system for BRPs. With the assistance of AMOS software, a parametric test and the comprehensive evaluation of the model were executed. 4.1. Parametric test The parametric test mainly conducts an inspection of the representativeness and rationality of parameters, as well as the meaning and rationality of evaluation parameters. Test of the representativeness of parameters in the Structural Equation is similar to the parametric representative assessment in the linear regression equation, namely, a test of the parameter t. Test of the rationality of parameters is used to assess the realistic meaning of the selected parameters. AMOS provides an easy and convenient method by using CR (Critical Ration) to test the representativeness of parameters. The results of the parametric estimation can be found in Table 6. From the Un-Standardized Regression Coefficients in Table 6, all
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Table 5 Optimized evaluation index system for BRP and rotated component matrix. Optimized evaluation Factors/indicators Index system for BRP
Criteria
Component 1
Environmental and health benefits
Improvement of the quality of groundwater Improvement of soil quality Improvement of air quality Lowering the health risk of local residents Increase of green cover percentage Improvement of remediation technologies Improvement of living quality of local residents Financial Payback period (PP) Return on investment (ROI) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to total cost Net present value (NPV) Brownfield site Location of brownfield Status of infrastructure facilities of brownfield area Transportation convenience of brownfield area Size of brownfield Societal Stability Increase local employment rate Increase local tax base Improvement of local security status Policy and Technical Protecting and recycling the land/soil resource Influence from other contamination hazards nearby Easing the pressure on green land development Technological difficulties and time requirement of brownfield remediation Performance Matchup with city planning Improvement of image of local community and government Increase land value of neighborhood Influence from policy and legislation
2
3
4
0.856 0.807 0.776 0.729 0.719 0.600 0.477
5
6
0.514 0.348 0.326 0.756 0.720 0.320 0.704 0.663 0.594 0.356 0.743 0.722 0.717 0.667 0.727 0.725 0.641
0.430
0.707 0.580 0.302 0.486 0.568 0.309 0.436 0.451 0.477
0.726 0.674
0.334
0.581 0.443 0.483
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Result: Rotation converged in 9 iterations.
P values are less than 5%, so all Path Coefficients pass the test of representativeness. The rationality of parameters should be determined before evaluating the Goodness-of-Fit of a model. So it is necessary to test whether the parametric estimation exceeds the acceptable range. The results of estimated coefficient of error variance are shown in Table 7. Referring to the definition by Hair et al. (1998), the
parameters in Table 7 should satisfy the following 2 conditions: The negative error variance doesn't exist; The Standardized Coefficient doesn't exceed 1 or isn't too close to 1 (over 0.95). According to Table 7, no negative error variance exists in
Table 6 Un-standardized and standardized regression coefficient. Un-standardized regression coefficient
F2)F0 F1)F0 F3)F0 F4)F0 F13)F1 F23)F2
Standardized regression coefficient
Un-standardized coefficient
Standardized error
Probability
Mark
1.044 1.000 0.949 1.125 1.049 0.963
0.105
Critical portion value 9.971
***
par_3
0.099 0.122 0.062 0.070
9.591 9.190 17.001 13.715
*** *** *** ***
par_4 par_5 par_1 par_2
Standardized coefficient F2)F0 F1)F0 F3)F0 F4)F0 F13)F1 F23)F2
0.832 0.707 0.775 0.777 0.804 0.745
*Represents P < 0.05,** represents P < 0.01,*** represents P < 0.001.
Table 7 Estimated coefficient of error variance (partial).
F0 e27 e29 e2 e4 e26 e28 e30 e3 e5
Un-standardized coefficient
Standardized error
Critical Portion value
Probability
Mark
0.883 0.530 0.384 0.868 1.032 0.427 0.734 0.343 1.061 1.051
0.144 0.087 0.108 0.083 0.088 0.078 0.133 0.083 0.098 0.096
6.113 6.109 3.545 10.494 11.741 5.454 5.523 4.131 10.799 10.933
*** *** *** *** *** *** *** *** *** ***
par_24 par_26 par_28 par_32 par_34 par_25 par_27 par_29 par_33 par_35
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Table 8 The Fit indices. Absolute fit index
Comparative fit Index
Parsimonious fit Index
Model
c2
c2 /d f
RMSEA
IFI
CFI
TLI
PNFI
PCFI
AIC
1 2 Evaluation Standard
0.849 0.875 >0.90
0.848 0.888 >0.90
0.832 0.889 >0.90
0.723 0.751 >0.5
0.768 0.792 >0.5
1201.788 848.843 The smaller, the better.
1081.788 740.843 e
3.402 3.012 <3.0
0.172 0.075 <0.08
Fig. 2. Application procedure of the example.
measure error values of error variances. Furthermore, no regression coefficient exceeds 0.95 based on the standardized regression coefficients in Table 6. Therefore, this model can be tested on the Fit Index of the comprehensive evaluation. 4.2. The comprehensive evaluation of the model The comprehensive evaluation is usually implemented by the Fit Index computation. The Fit Index refers to a statistical parameter that is constructed from a certain human perspective, to reflect the Goodness-of-fit of a model. In general, the fit indices include Absolute Fit Index, Comparative Fit Index and Parsimonious Fit Index (Byrne, 2001). The Absolute Fit Index mainly consists of c2 (Chi-square), RMSEA (Root Mean Square Error of Approximation), and SRMR
(Standardized Root Mean Square Residual). The Comparative Fit Index generally includes NFI (Normed Fit Index), CFI (Comparative Fit Index), and NNFI (Non-Normed Fit Index). These two types of indices are used to evaluate the Goodness-of-Fit of a single model. The Parsimonious Fit Index is a more common index, which includes PNFI (Parsimony Normed Fit Index), PCFI (Parsimonious Comparative Fit Index), and AIC (Akaike Information Criterion). The comprehensive evaluation of a model was achieved using diversified Fit indices. Table 8 lists the Absolute Fit Indices, Comparative Fit Indices, and Parsimonious Fit Indices of two models, where Models 1 and 2 respectively refer to the initially established index system and the optimized index system after Factor Analysis. From Table 8, it is noticed that, although the two models do not reach the evaluation standard, they are very close to that.
Table 9 Values of the proposal options. Factors/indicators
Environmental and Health Benefits
Financial
Brownfield Site
Societal Stability
Policy and Technical
Performance
Criteria
Improvement of the quality of groundwater Improvement of soil quality Improvement of air quality Lowering the health risk of local residents Increase of green cover percentage Improvement of remediation technologies Payback period (PP) Return on investment (ROI) Total cost of brownfield remediation and construction Ratio of brownfield remediation cost to total cost Net present value (NPV) Location of brownfield Status of infrastructure facilities of brownfield area Transportation convenience of brownfield area Size of brownfield Increase local employment rate Increase local tax base Improvement of local security status Protecting and recycling the land/soil resource Influence from other contamination hazards nearby Easing the pressure on green land development Matchup with city planning Improvement of image of local community and government Increase land value of neighborhood
Proposal options A
B
C
75 70 78 80 77 75 5 21 1500 15 2580 75 85 80 55 90 85 80 75 60 60 70 70 75
85 82 84 89 87 80 7 30 2700 27 3500 85 82 88 80 92 90 75 90 65 90 85 80 70
77 60 65 68 85 90 4 17 2100 18 2600 80 90 90 85 75 72 88 75 60 85 80 85 85
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Comparing the two models, Model 2 is a better model, because its Fit Indices are closer to the evaluation standard than Model 1, and some of its Fit Indices have already reached the evaluation standard. For example, RESMA of Model 2 is 0.075, which lies in the range from 0.05 to 0.08, so it is considered to be a good fit.
evaluation index system. Meanwhile, the criteria of the proposed index system can serve as the base of their own primary criteria set, which can be further enhanced by adding and deleting certain criteria if necessary. The practicability of the proposed index system is illustrated by a real world illustration example.
5. An illustration example
Acknowledgments
As previously stated, the proposed evaluation index system can be integrated with designs of other index systems and/or can work as a starting point of most of the project evaluation techniques. Herein, we use a brief example, which embedded our index system into a grey relational evaluation approach, to assist a local government in making a redevelopment decision of an abandoned coking plant located in the downtown area. The application procedure is explained in Fig. 2. The decision process starts from an overview of the situation, from which three alternatives, A) new industrial planning, B) commercial planning, and C) real estate planning, are identified for further evaluation. Next, the local government employs the evaluation index system derived in this research, according to which relevant stakeholders evaluate each alternative based on their own knowledge and experiences. The aggregated (averaged in this case) evaluation record (Table 9) is then used as the input to a grey relational analysis, which computes a grey correlation coefficient for each alternative. First introduced by Deng (1985), Grey relational analysis is an impact evaluation model which analyzes the associativity and similarity among multiple alternatives based on the degree of relation (Ju, 2013). Please note that the detailed computation in grey relational evaluation approach has been omitted to avoid distraction from our index system. The interested readers are referred to Chan and Tong (2007), Liu et al. (2011), and Ju (2013) for details. Finally, in light of the grey correlation coefficients obtained from the previous step, the best alternative is selected. In this example, alternative C, real estate planning, gives the highest coefficient, and thus is selected.
This research was supported in part by the National Social Sciences Foundation of China through the project “Research on Life Span Risk Management of Brownfield Redevelopment Project” (10BJY024), by the Provincial Social Sciences Foundation in Shaanxi, China through the project “Market Mechanism and Policy System for Brownfield Redevelopment in Shaanxi Province” (08E023), and by the Humanities and Social Science Foundation of the Ministry of Education of China through the project “Stakeholders Based Multi-Criteria Decision Analysis of Brownfield Redevelopment” (08JC630066). Backing was also provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada (NSERC-RGPIN-4118-2013 27238), which funded the Research Discovery Grant for the project entitled “Systems Engineering Approaches to Sustainable Environmental Management”. Finally, the Centre for International Governance Innovation (CIGI) located in Waterloo, Ontario, Canada, also furnished support.
6. Conclusions and suggestions regarding the application of the proposed evaluation index system for BRPs The establishment of an evaluation index system plays a very important role in the evaluation of BRPs. Based on the current situation of brownfields in China, the requirements of sustainable development, and the analysis of stakeholders, the authors initially developed the evaluation index system for BRPs, which includes 27 criteria from four dimensions, including Societal and Economic, Financial and Accounting, Environmental and Health, and Prospective Value. With the help of SPSS 16.0, the authors calculated Cronbach's alpha, and conducted KMO and Bartlett's tests. The results of these tests showed that the initialized evaluation index system for BRP has good reliability and validity. By means of principal components analysis, the authors extracted six factors, labeling them as Environmental and Health Benefit Indicators, Financial Indicators, Brownfield Site Indicators, Societal Stability Indicators, Policy and Technical Indicators, and Performance Indicators, and deleted three criteria whose factor loadings are less than 0.5. In this way, the authors built up their optimized evaluation index system for BRPs, whose effectiveness of optimization has been checked and verified by Structure Equation modeling with the assistance of AMOS 8.0. The most common problem in brownfield redevelopment is to select proper planning for a certain brownfield site, which could be mutually beneficial to all stakeholders The established evaluation index system for BRPs provides users with informative reference values by employing the same process to obtain their own
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