碳排放约束下我国工业水资源利用效率及其影响因素研究

doi:10.13776/j.cnki.resourcesindustries.20231102.002

资源与产业 ›› 2023, Vol. 25 ›› Issue (5): 50-60.DOI: 10.13776/j.cnki.resourcesindustries.20231102.002

碳排放约束下我国工业水资源利用效率及其影响因素研究

田泽，王颢霏，任阳军

（河海大学商学院，江苏南京 211100）

收稿日期:2022-05-27 修回日期:2022-12-27 出版日期:2023-10-20 发布日期:2023-10-20
通讯作者: 王颢霏，硕士生，主要从事低碳经济研究。E-mail:1663510310@hhu.edu.cn
作者简介:田泽，博士、教授，主要从事低碳经济政策研究。E-mail:tianze@126.com
基金资助:
2021年国家社会科学基金后期资助项目（21FJYB047）；中央高校基本科研业务专项（B200207035、B210207018）。

CHINA’S INDUSTRIAL WATER RESOURCE USE EFFICIENCY AND FACTORS UNDER CARBON EMISSION CONSTRAINTS

TIAN Ze, WANG Haofei, REN Yangjun

(Business School, Hohai University, Nanjing 211100, China)

Received:2022-05-27 Revised:2022-12-27 Online:2023-10-20 Published:2023-10-20

摘要/Abstract

摘要： 本文运用考虑非期望产出的动态DEA模型、Dagum基尼系数、核密度估计以及Tobit回归模型等方法探究我国2010—2019年的工业水资源利用效率及其影响因素。结果表明：1）我国工业水资源利用效率总体处于中等水平，在时间上呈现波动的上升趋势，且在空间上呈现东部、西部、中部依次递减的变化规律；2）中国工业水资源利用效率总体差异呈先上升后下降的趋势，区域内差异是我国工业水资源利用效率总体差异的主要来源；3）经济发展水平和技术创新对我国工业水资源利用效率均有显著正向促进作用，水资源要素禀赋对工业水资源利用效率产生负向效应，工业化程度和环境规制对不同区域工业水资源利用效率的影响存在明显差异。

关键词: 碳排放约束, 工业水资源利用效率, 动态DEA, Dagum基尼系数, Tobit回归

Abstract: This paper uses non-desired dynamic DEA model, Dagum Gini coefficient, kernel density estimation and Tobit model regression to study China ‘s industrial water resource use efficiency and factors during 2010 to 2019. The results show that China ‘s industrial water resource use efficiency is generally at a middle level, in a waved rising trend over time, decreasing from eastern, western to central spatially. The overall variance of China ‘s industrial water resource shows a rising-then-falling trend, mainly contributed by the regionally internal differences. Economic development level and technical innovation largely promote China ‘s industrial water resource use efficiency, offset by elements occurrence of water resource. Industrialization and environmental regulations impact industrial water resource use efficiency, variable with areas.

Key words:

carbon emission constraints, industrial water resource use efficiency, dynamic DEA, Dagum Gini coefficient, Tobit regression

中图分类号:

F424.6

田泽, 王颢霏, 任阳军. 碳排放约束下我国工业水资源利用效率及其影响因素研究[J]. 资源与产业, 2023, 25(5): 50-60.

TIAN Ze, WANG Haofei, REN Yangjun.

CHINA’S INDUSTRIAL WATER RESOURCE USE EFFICIENCY AND FACTORS UNDER CARBON EMISSION CONSTRAINTS [J]. Resources & Industries, 2023, 25(5): 50-60.

参考文献

陈关聚, 白永秀, 2013. 基于随机前沿的区域工业全要素水资源效率研究[J]. 资源科学, 35(8): 1593-1600.〔CHEN G J, BAI Y X, 2013. Research on regional industrial total factor water resources efficiency based on stochastic frontier [J]. Resources Science, 335(8): 1593-1600.〕

董巧珍, 2018. 我国工业水资源利用效率及影响因素分析[D]. 杭州: 浙江财经大学.〔DONG Q Z, 2018. Analysis of industrial water resources utilization efficiency and its influencing factors in China [D]. Hangzhou: Zhejiang University of Finance and Economics.〕

姬志恒, 于伟, 2022. 中国工业用水效率的空间差异及驱动机制[J]. 工业技术经济, 41(12): 86-93.〔JI Z H, YU W, 2022. Spatial differences and driving mechanisms of industrial water use efficiency in China [J]. Industrial Technical Economics, 41(12): 86-93.〕

雷玉桃, 黄丽萍, 张恒, 2017. 中国工业用水效率的动态演进及驱动因素研究[J]. 长江流域资源与环境, 26(2): 159-170.〔LEI Y T, HUANG L P, ZHANG H, 2017. Study on dynamic evolution and driving factors of industrial water use efficiency in China [J]. Resources and Environment in the Yangtze Basin, 26(2): 159-170.〕

李静, 马潇璨, 2014. 资源与环境双重约束下的工业用水效率: 基于SBM-Undesirable和Meta-frontier模型的实证研究[J]. 自然资源学报, 29(6): 920-933.〔LI J, MA X C, 2014. Industrial water use efficiency under resource and environmental constraints: an empirical study based on SBM-Undesirable and Meta-frontier models [J]. Journal of Natural Resources, 29(6): 920-933.〕

李珊, 张玲玲, 丁雪丽, 等, 2019. 中国各省区工业用水效率影响因素的空间分异[J]. 长江流域资源与环境, 28(11): 2539-2552.〔LI S, ZHANG L L, DING X L, et al, 2019. Spatial differentiation of influencing factors of industrial water use efficiency among provinces in China [J]. Resources and Environment in the Yangtze Basin, 28(11): 2539-2552.〕

王保乾, 李昕燃, 2022. 基于水足迹与SBM-Malmquist模型的长江经济带工业水资源绿色效率的研究[J]. 资源与产业, 24(3): 21-31.〔WANG B Q, LI X R, 2022. Study on green efficiency of industrial water resources in the Yangtze River Economic Belt based on water footprint and SBM-Malmquist model [J]. Resources & Industries, 24(3): 21-31.〕

肖磊, 李云, 陈佳蕊, 2020. 中国工业绿色水资源效率的区域差异与收敛性研究[J]. 数量经济研究, 2020, 11(2): 133-149.〔XIAO L, LI Y, CHEN J R, 2020. Study on regional differences and convergence of industrial green water resources efficiency in China [J]. The Journal of Quantitative Economics, 11(2): 133-149.〕

徐敏, 张涛, 王东, 等, 2019. 中国水污染防治40年回顾与展望[J]. 中国环境管理, 11(3): 65-71.〔XU M, ZHANG T, WANG D, et al, 2019. Review and prospect of 40 years of water pollution control in China [J]. Chinese Journal of Environmental Management, 11(3): 65-71.〕

尹庆民, 朱康宁, 2020. 基于EBM模型的长江经济带工业用水效率时空差异及影响因素分析[J]. 中国环境管理, 12(6): 103-109.〔YIN Q M, ZHU K N, 2020. Temporal and spatial differences of industrial water use efficiency and its influencing factors in the Yangtze River Economic Belt based on EBM model [J]. Chinese Journal of Environmental Management, 12(6): 103-109.〕

岳立, 赵海涛, 2011. 环境约束下的中国工业用水效率研究: 基于中国13个典型工业省区2003—2009年数据[J]. 资源科学, 33(11): 2071-2079.〔YUE L, ZHAO H T, 2011. Research on industrial water use efficiency in China under environmental constraints: based on the data of 13 typical industrial provinces in China from 2003 to 2009 [J]. Resources Science, 33(11): 2071-2079.〕

张峰, 薛惠锋, 2021. 基于绿色发展的工业全要素水资源效率时空特征[J]. 资源科学, 43(5): 964-973.〔ZHANG F, XUE H F, 2021. Spatial and temporal characteristics of industrial total factor water resources efficiency based on green development [J]. Resources Science, 43(5): 964-973.〕

郑乐, 杨法暄, 钱会, 等, 2020. 基于超效率DEA模型的宁夏工业水资源利用效率研究[J]. 水资源与水工程学报, 31(2): 81-86.〔ZHENG L, YANG F X, QIAN H, et al, 2020. Research on industrial water resource use efficiency in Ningxia based on superefficiency DEA model [J]. Journal of Water Resources and Water Engineering, 31(2): 81-86.〕

BANKER R D, CHARNES A, COOPER W W, 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis[J]. Management Science, 30(9): 1078-1092.

CHARNES A, COOPER W W, RHODES E, 1978. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 2(6): 429-444.

CHEN Y B, YING W, LIU K, 2021. Regional differences in the industrial water use efficiency of China: the spatial spillover effect and relevant factors[J]. Resources, Conservation and Recycling, 167: 105239.

FÄRE R, GROSSKOPF S, 1996. Intertemporal production frontiers: with dynamic DEA[M]. Dordrecht: Kluwer Academic Publishers.

FARRELL M J, 1957. The measurement of productive efficiency[J]. Journal of the Royal Statistical Society. Series A (General), 120(3): 253-281.

TONE K, 2001. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 130(3): 498-509.

TONE K, TSUTSUI M, 2010. An epsilon-based measure of efficiency in DEA: a third pole of technical efficiency[J]. European Journal of Operational Research, 207(3):1554-1563.

WANG H R, LIU H L, WANG C, et al, 2019. A study of industrial relative water use efficiency of Beijing: an application of data envelopment analysis[J]. Water Policy, 21(2): 326-343.

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碳排放约束下我国工业水资源利用效率及其影响因素研究

CHINA’S INDUSTRIAL WATER RESOURCE USE EFFICIENCY AND FACTORS UNDER CARBON EMISSION CONSTRAINTS

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