中国省域空间单元绿色治理效率测度及其空间格局特征

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Abstract The paper constructed a generalized panel three-stage DEA model to empirically measure the green governance efficiency of 31provinces in China from 2008 to 2019, and explored the improvement direction of green governance based on external environment and projection analysis; a spatial autocorrelation model was used to determine the potential association of provincial green governance efficiency; a spatial gravity model and social network analysis were used to clarify the scale of spatial association of green governance efficiency in each province. The green governance efficiency of Chinese provinces had a U-shaped characteristic, and non-managerial factors restrict the improvement of the real efficiency; the technical environment had a positive effect on the elimination of green governance input redundancy, but the adaptation and regulation of the economic must be dialectical; there was a large amount of input redundancy in each province, and the internal variation of redundancy was significant The spatial correlation among provinces in green governance activities was positive, and the increment of spatial correlation was significantly increased, but the internal differentiation and contribution heterogeneity potentially affected the shaping of the overall pattern of green governance; China had basically formed a "day" spatial correlation framework of green governance, and the spatial effect and radiation of central regions such as Beijing and Shanghai help the overall The spatial effect and radiating effect of central regions such as Beijing and Shanghai contributed to the improvement of the overall green governance efficiency.

Key words： green governance efficiency province area spatial pattern broad panel three-stage DEA spatial effect

Received: 02 August 2021

PACS:	X82
	X22

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	LIU Hong-da
	WANG Xiao-xia
	ZHANG Ji-jian
	HUANG Jia-liang

Cite this article:

LIU Hong-da,WANG Xiao-xia,ZHANG Ji-jian等. Measurement of green governance efficiency of China’s provincial spatial units and analysis of spatial pattern characteristics[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(3): 1477-1488.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I3/1477

[1]	刘宏笪,张济建,张茜.全球供应链视角下的中国碳排放责任与形象[J].资源科学, 2021,43(4):652-668. Liu H D, Zhang J J, Zhang X. China's carbon emission responsibility and image from the perspective of global supply chain[J]. Resources Science, 2021,43(4):652-668.
[2]	翟坤周,侯守杰."十四五"时期我国城乡融合高质量发展的绿色框架、意蕴及推进方案[J].改革, 2020,10(11):53-68. Zhai K, Hou J. Green framework, implication and promotion scheme of high-quality development of urban-rural integration in China during the 14th five-year plan period[J]. Reform, 2020,10(11):53-68.
[3]	彭甲超,许荣荣,付丽娜,等.长江经济带工业企业绿色创新效率的演变规律[J].中国环境科学, 2019,39(11):4886-4900. Peng J C, Xu R R, Fu L N, et al. The evolution pattern of green innovation efficiency of industrial enterprises in the Yangtze river economic belt[J]. China Environmental Science, 2019,39(11):4886-4900.
[4]	周力.中国绿色创新的空间计量经济分析[J].资源科学, 2010, 32(5):932-939. Zhou L. Space econometric analysis of green innovation based on a perspective of resource endowment[J]. Resources Science, 2010, 32(5):932-939.
[5]	郭爱君,张娜,邓金钱.财政纵向失衡、环境治理与绿色发展效率[J].财经科学, 2020,(12):72-82. Guo A J, Zhang N, Deng J Q. Vertical fiscal imbalance, environmental governance and green development efficiency[J]. Finance& Economics, 2020,(12):72-82.
[6]	吴传清,周西敏.长江经济带绿色经济效率的时空格局演变及其影响因素研究[J].宏观质量研究, 2020,8(3):120-128. Wu C Q, Zhou X M. Research on the spatiotemporal evolution and the influencing factors of green economic efficiency in the Yangtze river economic belt[J]. Journal of Macro-quality Research, 2020,8(3):120-128.
[7]	马晓君,魏晓雪,刘超,等.东北三省全要素能源效率测算及影响因素分析[J].中国环境科学, 2017,37(2):777-785. Ma X J, Wei X X, Liu C, et al. The analysis of total factor energy efficiency calculation and influence factors of the three northeast provinces[J]. China Environmental Science, 2017,37(2):777-785.
[8]	李敬,陈澍,万广华,等.中国区域经济增长的空间关联及其解释——基于网络分析方法[J].经济研究, 2014,49(11):4-16. Li J, Chen S, Wang G H, et al. Study on the spatial correlation and explanation of regional economic growth in China——Based on analytic network process[J]. Economic Research Journal, 2014,49(11):4-16.
[9]	施本植,汤海滨.中国式分权视角下我国工业污染治理效率及其影响因素研究[J].工业技术经济, 2019,38(5):152-160. Shi B Z, Tang H B. Study on the efficiency of industrial pollution control and its influencing under the chinese style decentralization[J]. Journal of Industrial Technological Economics, 2019,38(5):152-160.
[10]	何好俊,祝树金.制造业集聚是否有利于提升环境治理绩效[J].中国科技论坛, 2016,31(10):59-64. He H J, Zhu S J. Is manufacturing industry agglomeration conducive to improving the performance of environmental governance[J]. Forum on Science and Technology in China, 2016,31(10):59-64.
[11]	梁中,昂昊.中国绿色技术创新效率演化及其空间治理[J].财贸研究, 2019,30(8):16-25,63. Liang Z, Ang H. Green technology innovation efficiency evolution and space governance in china[J]. Finance and Trade Research, 2019,30(8):16-25,63.
[12]	Fried H O, Lovell C A, Schmidt S S, et al. Accounting for environment effects and statistical noise in data envelopment analysis[J]. Journal of Productivity Analysis, 2002,17(1):157-174.
[13]	马生昀,马占新.基于样本前沿面移动的广义DEA有效性排序[J].系统工程学报, 2014,29(4):443-457. Ma S Y, Ma Z X. Ranking of generalized DEA efficiency based on the moving of sample frontier[J]. Journal of Systems Engineering, 2014, 29(4):443-457.
[14]	刘春艳.A股上市银行创新能力技术缺口比率分析——基于广义DEA效率视角[J].会计与经济研究, 2018,32(3):117-128. Liu C Y. Analysis on technological gap ratio of a share listed banks'innovation ability:A perspective of generalized dea efficiency[J]. Accounting and Economics Research, 2018,32(3):117-128.
[15]	高旭阔,张迪.考虑环境因素的城镇污水处理投资效率——基于三阶段DEA的方法[J].中国环境科学, 2018,38(9):3594-3600. Gao X K, Zhang D. Efficiency evaluation of the investment in urban sewage treatment via the three stage DEA method considering environmental factors[J]. China Environmental Science, 2018,38(9):3594-3600.
[16]	王维,郑巧慧,乔朋华.金融环境、政府补贴与中小企业技术创新——基于中小板上市公司的实证研究[J].科技进步与对策, 2014, 31(23):87-92. Wang W, Zhen Q H, Qiao P H. Financial environment, government subsidies and technological innovation of smes——an empirical study on the sme board based on listing corporation[J]. Science& Technology Progress and Policy, 2014,31(23):87-92.
[17]	于丽英,刘宏笪,陈子璇.长江经济带绿色治理效率测度与分析——基于广义面板三阶段DEA模型[J].华东经济管理, 2021,35(6):88-99. Yu L Y, Liu H D, Chen Z X. Measurement and analysis of green governance efficiency in the Yangtze river economic belt:Based on the generalized panel three-stage dea model[J]. East China Economic Management, 2021,35(6):88-99.
[18]	Tobler W. A computer movie simulating urban growth in the detroit region[J]. Economic Geography, 1970,46(2):234-240.
[19]	刘佳,安珂珂.环渤海城市旅游产业绿色创新效率的空间格局[J].华东经济管理, 2020,34(6):27-37. Liu J, An K K. Spatial pattern of green innovation efficiency of tourism industry of cities in Bohai rim region[J]. East China Economic Management, 2020,34(6):27-37.
[20]	史庆斌,谢永顺,韩增林,等.东北城市间旅游经济联系的空间结构及发展模式[J].经济地理, 2018,38(11):211-219. Shi Q B, Xie Y S, Han Z L, et al. Spatial structure and spatial development patterns of urban tourism economic connections in northeast China[J]. Economic Geography, 2018,38(11):211-219.
[21]	周广强,谢英,吴剑斌,等.基于WRF-Chem模式的华东区域PM2.5预报及偏差原因[J].中国环境科学, 2016,36(8):2251-2259. Zhou G Q, Xie Y, Wu J B, et al. Based on WRF-Chem model, the PM2.5 forecast and deviation reason in East China region[J]. China Environmental Science, 2016,36(8):2251-2259.