4.7 Article

Evaluating green development level of nine cities within the Pearl River Delta, China

期刊

JOURNAL OF CLEANER PRODUCTION
卷 174, 期 -, 页码 315-323

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.10.328

关键词

Green development; Index system; Pearl River Delta; Entropy method

资金

  1. Guangdong Province

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Due to the uncertainties resulted from environmental changes, the importance of green development has been paid increasingly attention in the world. However, our knowledge of the green development level of nine cities in the Pearl River Delta (PRD), China, was still limited. Therefore, this study, based on the analysis on the connotation of green development, employed multi-level evaluation method and entropy method to develop the urban green development evaluation index system from five aspects, including enhancement of living environment, treatment and utilization of pollutant, improvement of ecological efficiency, optimization of economic growth and development of innovative potential. This evaluation index system was further applied to evaluate the green development level of nine cities within PRD. Results showed that significant difference in green development level occurred among cities within PRD. Green development level in Shenzhen, Zhuhai, Guangzhou was relatively higher than that in other cities, with the advantages in improvement of ecological efficiency and optimization of economic growth. Green development level in Zhongshan, Dongguan, Huizhou, Foshan averaged in PRD, while Jiangmen and Zhaoqing had a delayed green development level because of their irrational economic structures and extensive growth patterns though these two cities possessed good ecological environment endowments. In the future, it is necessary for PRD to formulate specific and targeted policies and measures to further upgrade energy structure, transform industrial structure, improve public transportation, enhance forestry carbon sink, and eventually construct low-carbon circular industrial system and development pattern. (C) 2017 Elsevier Ltd. All rights reserved.

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