长江三角洲地区用水量演变与脱钩的驱动效应研究——基于LMDI-Tapio两阶段方法
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摘要
运用LMDI-Tapio两阶段方法分析长江三角洲地区2000—2015年用水量变化驱动效应与脱钩状态,分解脱钩指数,探究脱钩状态变化的驱动因素。研究发现:长江三角洲地区具有较稳定且状况较好的脱钩—弱脱钩—强脱钩变化过程,3省市总体脱钩状态存在差异性:浙江>上海>江苏;上海脱钩因素贡献率为:强度>收入>人口>结构,江苏和浙江的脱钩因素贡献率均为:收入>强度>结构>人口。结果表明,产业用水强度和产业结构是长江三角洲地区用水量与经济增长脱钩的促进因素,经济收入增加和人口规模增长是长江三角洲地区用水量与经济增长脱钩的抑制因素。因此,长江三角洲地区应改善发展结构,优化三大产业的用水比例,积极改变经济发展模式,以保持强脱钩的理想状态。
In this paper,the LMDI-Tapio two-stage method was used to analyze the driving effect and decoupling state of water consumption in the Changjiang River Delta from 2000 to 2015,and the decoupling index was decomposed to explore the driving factors of the decoupling state. The study results showed the Changjiang River Delta had a relatively stable and good decoupling-weak decoupling-strong decoupling process. There were differences in the overall decoupling state of the three provinces: Zhejiang > Shanghai >Jiangsu,Shanghai decoupling factor contribution rate was: Intensity > revenue > population > structure,Jiangsu and Zhejiang were: Income > strength > structure > population. The results showed that the industrial water intensity and industrial structure were the decoupling factors for the decoupling of water consumption from economic growth in the Changjiang River Delta. The increase in economic income and population growth were the depressing factors for the decoupling of water consumption from economic growth in the Changjiang River Delta. Therefore,the Changjiang River Delta region should improve its development structure,optimize the proportion of water used by the three major industries,and actively change the economic development model in order to maintain the ideal state of strong decoupling.
In this paper,the LMDI-Tapio two-stage method was used to analyze the driving effect and decoupling state of water consumption in the Changjiang River Delta from 2000 to 2015,and the decoupling index was decomposed to explore the driving factors of the decoupling state. The study results showed the Changjiang River Delta had a relatively stable and good decoupling-weak decoupling-strong decoupling process. There were differences in the overall decoupling state of the three provinces: Zhejiang > Shanghai >Jiangsu,Shanghai decoupling factor contribution rate was: Intensity > revenue > population > structure,Jiangsu and Zhejiang were: Income > strength > structure > population. The results showed that the industrial water intensity and industrial structure were the decoupling factors for the decoupling of water consumption from economic growth in the Changjiang River Delta. The increase in economic income and population growth were the depressing factors for the decoupling of water consumption from economic growth in the Changjiang River Delta. Therefore,the Changjiang River Delta region should improve its development structure,optimize the proportion of water used by the three major industries,and actively change the economic development model in order to maintain the ideal state of strong decoupling.
引文
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[26]Ang B W,Su B,Wang H. A Spatial-temporal Decomposition Approach to Performance Assessment in Energy and Emissions[J]. Energy Economics,2016,60(8)∶112-121.
[2]佟金萍,马剑锋,刘高峰.基于完全分解模型的中国万元GDP用水量变动及因素分析[J].资源科学,2011,33(10)∶1870-1876.
[3]张陈俊,章恒全,龚雅云.中国结构升级、技术进步与用水量基于改进的LMDI方法[J].资源科学,2014,36(10)∶1993-2002.
[4]陈东景.我国工农业水资源使用强度变动的区域因素分解与差异分析[J].自然资源学报,2012,27(2)∶332-343.
[5]Zhang C J,Zhang H Q. Can Regional Economy Influence China's Water Use Intensity? Based on Refined LMDI Method[J]. Chinese Journal of Population Resources and Environment,2014,12(3)∶247-254.
[6]张陈俊,赵存学,林琳,等.长江三角洲地区用水量时空差异的驱动效应研究[J].资源科学,2018,40(1)∶89-103.
[7]贾绍凤.工业用水零增长的条件分析———发达国家的经验[J].地理科学进展,2001,(1)∶51-59.
[8]陈东景.中国工业用水量强度变化的结构份额和效率份额研究[J].中国人口·资源与环境,2008,18(3)∶211-214.
[9]刘羽中,柏明国.安徽省工业行业用水消耗变化分析基于LMDI分解法[J].资源科学,2012,34(12)∶2299-2305.
[10]张礼兵,徐勇俊,金菊良,等.安徽省工业用水量变化影响因素分析[J].水利学报,2014,45(7)∶837-843.
[11]秦昌波,葛察忠,贾仰文,等.陕西省生产用水变动的驱动机制分析[J].中国人口·资源与环境,2015,25(5)∶131-136.
[12]秦腾,章恒全,佟金萍,等.长江经济带城镇化进程中的水资源约束效应分析[J].中国人口·资源与环境,2018,28(3)∶39-45.
[13]童国平,陈岩.流域内各区域用水量与经济发展脱钩分析及用水量驱动力分解———以淮河流域为例[J].资源开发与市场,2017,33(10)∶1176-1182.
[14]姬生才,陆桂华,何海,等.江苏省太湖流域用水量变化驱动因素研究[J].南水北调与水利科技,2015,13(2)∶231-235,240.
[15]刘怡君,王丽,牛文元.中国城市经济发展与能源消耗的脱钩分析[J].中国人口·资源与环境,2011,21(1)∶74-81.
[16]仲云云,仲伟周.我国碳排放的区域差异及驱动因素分析———基于脱钩和三层完全分解模型的实证研究[J].财经研究,2012,38(2)∶123-133.
[17]盖美,胡杭爱,柯丽娜.长江三角洲地区资源环境与经济增长脱钩分析[J].自然资源学报,2013,28(2)∶185-198.
[18]涂红星,肖序,许松涛.基于LMDI的中国工业行业碳排放脱钩分析[J].中南大学学报(社会科学版),2014,20(4)∶31-36.
[19]汪奎,邵东国,顾文权,等.中国用水量与经济增长的脱钩分析[J].灌溉排水学报,2011,30(3)∶34-38.
[20]马海良,姜明栋,侯雅如.长江经济带城镇化对工业用水的脱钩研究———基于“十一五”和“十二五”时期的对比分析[J].长江流域资源与环境,2018,27(8)∶1683-1692.
[21]孟祥仪,钱会,孙占超.关中地区近十年经济发展与用水效率分析[J].水资源与水工程学报,2016,27(1)∶152-157.
[22]秦丽云.流域水资源与环境经济系统优化研究[J].节水灌溉,2007,32(3)∶44-46.
[23]Wang Q,Zhao M M,Su M. Decomposition and Decoupling Analysis of Carbon Emissions From Economic Growth:A Comparative Study of China and the United States[J]. Journal of Cleaner Production,2018,197(5)∶178-184.
[24]Ang B W,Xu X Y,Su B. Multi-country Comparisons of Energy Performance:The Index Decomposition Analysis Approach[J]. Energy Economics,2015,47(1)∶68-76.
[25]Li A,Hu M,Wang M,et al. Energy Consumption and CO2Emissions in Eastern and Central China:A Temporal and A Cross-regional Decomposition Analysis[J]. Technological Forecasting and Social Change,2016,103(2)∶284-297.
[26]Ang B W,Su B,Wang H. A Spatial-temporal Decomposition Approach to Performance Assessment in Energy and Emissions[J]. Energy Economics,2016,60(8)∶112-121.