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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Dong, Liang Ren, Jingzheng Liang, Hanwei Gao, Zhiqiu Sun, Lu |
| Description | Author Affiliation: Ren J ( Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China); Liang H ( Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China.); Dong L ( Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China); Sun L ( Center for Social and Environmental Systems Research, National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba-City, Ibaraki 305-8506, Japan); Gao Z ( State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. Electronic address: zgao@mail.iap.ac.cn.) |
| Abstract | Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. |
| ISSN | 00489697 |
| Journal | Science of The Total Environment |
| Volume Number | 562 |
| e-ISSN | 18791026 |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2016-08-15 |
| Publisher Place | Netherlands |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Environmental Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
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