Model development of sustainability assessment from a life cycle perspective: A case study on waste management systems in China

JOURNAL OF CLEANER PRODUCTION

卷:210

Article

Zhou, Zhaozhi¹  Chi, Yong¹  Dong, Jun²  Tang, Yuanjun¹  Ni, Mingjiang¹ 

[1] Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou, Zhejiang, Peoples R China

[2] Ecole Mines Albi, Ctr RAPSODEE, Campus Jarlard, F-81013 Albi, France

关键词: Sustainability;    Environment-energy-economy-society model;    Municipal solid waste;    Treatment technology;    Life cycle assessment;    Multi-criteria decision making;   

DOI  :  10.1016/j.jclepro.2018.11.074

来源: Elsevier

【 摘 要 】

Due to the problems of municipal solid waste (MSW) during the expanding urbanization, strategy to find an environmentally friendly, energy efficient, cost-effective, and socially acceptable MSW management system is essential for sustainable development. This study establishes a novel environment-energy economy-society (3E + S) model from a life cycle perspective for sustainability assessment: life cycle assessment for evaluating environmental performance and energy consumption, life cycle costing for recording economic burden, and social life cycle assessment for reflecting social impacts; based on the individual 3E + S results, the final ranking of alternatives is obtained by multi-criteria decision making, which is integrated with analytic hierarchy process and entropy weight method. This model is implemented to identify a sustainable MSW management system among four typical treatment alternatives. Results show that incineration with fluidized bed furnace is the best choice in this study; incineration with moving grate furnace follows after with a slight gap; landfill with and without energy recovery rank the third and the last. The framework of SLCA on MSW field is built in this study and sensitivity analysis is provided for further discussion on social impacts. The calculation method of weight factors reduces man-made disturbances and the sensitivity analysis demonstrates strong robustness of the results and effectiveness of the modification for the model. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free