基于两阶段不良固定和产出DEA模型的中国工业水处理效率分析

H. Ma, Baoxia Geng, Yingxiong Fu, Yi-gong Sun, Zhaoqun Sun
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引用次数: 6

摘要

中国是世界上用水量最大的国家,水资源十分匮乏。工业在带动经济发展的同时也带来了严重的水污染,导致了生态环境的破坏。随着环保意识的提高,许多学者将研究方向转向了如何改善生态环境。大多数研究将整个系统视为一个“黑盒子”,而不考虑其内部结构。因此,有必要建立一种识别低效率的方法,并提出了一些优化建议。本文提出了一种两阶段非期望固定和输出数据包络分析(DEA)模型。对2011-2015年的工业化学需氧量(COD)排放量进行了调整,并采用启发式搜索算法计算了效率值。30个省市的效率被划分为东部、中部和西部地区。该模型可以识别工业系统中的低效率阶段,并找到系统低效率的来源。分析表明,东部地区效率最高,整体效率倾向于污染物处理阶段。最后,针对效率较低的地区提出了一些建议,既能节约用水,又能保证经济效益,为减少水污染和改善生态环境提供了新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficiency Analysis of Industrial Water Treatment in China Based on Two-stage Undesirable Fixed-sum Output DEA Model
Abstract China is a country with the most water consumption, so it is lack of water resources. Industry has brought serious water pollution while driving economic development, which leads to the destruction of ecological environment. With the improvement of environmental awareness, many scholars have shifted their research direction to how to improve the ecological environment. Most studies consider the whole system as a “black box”, regardless of its internal structure. Therefore, a method to identify inefficiency is necessary and some suggestions for optimization are given. In this paper, a two-stage undesirable fixed-sum output data envelopment analysis (DEA) model is proposed. The industrial chemical oxygen demand (COD) emission during 2011–2015 are adjusted, and the efficiency values are calculated by heuristic search algorithm. The efficiency of 30 provinces and cities is divided into eastern, central and western regions. The model can identify the inefficient stage in industrial system, and find the source of low efficiency in the system. The analysis shows that the efficiency of eastern region is the highest, while the overall efficiency is inclined to the pollutant treatment stage. Finally, the paper puts forward some suggestions for the low efficiency areas, which can save water while ensuring economic benefits, and provide new direction for water pollution reduction and improve the ecological environment.
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