中国工业生态效率时空分异特征及其影响因素解析

doi:10.13249/j.cnki.sgs.2020.03.001

Abstract

Abstract:

Industrial added value of China has been the largest in the world, and industrial sectors consumed a lot of energy and resources, which led to the destruction of the ecological environment. Thus, improving the industrial eco-efficiency is the important measure to realize the sustainable development. Eco-efficiency was first applied to measuring the environment performance of economic activities. The core connotation of eco-efficiency is to maximize economic benefits while minimizing environmental pollution and resources consumption, and the ultimate goal is to achieve sustainable development. Ecological efficiency has become an important tool for analyzing the impact of economic activities on the environment. This article takes different province as the research object and measures the industrial eco-efficiency with the aid of data envelopment analysis. Different spatial weight matrixes were constructed, and then the spatial evolution was analyzed by spatial autocorrelation analysis. On the basis of the optimal spatial weight matrix, spatial Durbin model was used to analyze the direct effect, space spillover effect, total effect of different influencing factors. Some conclusions were drawn as follows. The average value of the industrial eco-efficiency showed an obvious fluctuation trend during 2000-2015, and the absolute difference showed the similar trend, and the relative difference presented an “N” type change trend. The spatial distribution of the industrial eco-efficiency was characterized by “high in the southeast and low in the northwest”. The mean industrial eco-efficiency of Beijing and Shanghai was the highest, while the mean industrial eco-efficiency of Ningxia was the lowest. The spatial correlation feature of the industrial ecological efficiency was more accurately reflected under the comprehensive weight matrix combining geography and economy. The phenomenon of high and low clustering space club was also obvious. The overall effect of economic development, scientific and technological innovation and fiscal decentralization was positive, and showed that these 3 factors were the important driving force for promoting the improvement of overall regional industrial eco-efficiency, while the opening up had a negative impact on the improvement of industrial eco-efficiency. The direct effect value of fiscal decentralization was the highest, and opening to the outside world and fixed assets were the main factors to restrain the improvement of regional industrial eco-efficiency. Scientific and technological innovation and fiscal decentralization had positive spillover effect. Industrial agglomeration and opening to the outside world have negative spillover effects. On the basis of our study, we can find that industrial ecological efficiency had a spatial spillover effect, which was not only affected by various influencing factors in its region, but also affected by other regional influencing factors. Therefore, when formulating relevant countermeasures and suggestions, not only the regional influencing factors should be reasonably planned, but also the influence of different influencing factors in other regions should be taken into account.

Key words: industrial eco-efficiency, DEA-Window, spatial effect, China

CLC Number:

F427

Zhang Xinlin, Qiu Fangdao, Tan Juntao, Wang Changjian. Spatial Pattern Change and Influencing Factors of China’s Industrial Eco-efficiency[J].SCIENTIA GEOGRAPHICA SINICA, 2020, 40(3): 335-343.

Figures/Tables 5

Fig.1

Fig.2

Table 1

Fig.3

Table 2

References 45

[1]	卢燕群, 袁鹏 . 中国省域工业生态效率及影响因素的空间计量分析[J]. 资源科学, 2017,39(7):1326-1337. doi: 10.18402/resci.2017.07.10
	[ Lu Yanqun, Yuan Peng . Measurement and spatial econometrics analysis of provincial industrial ecological efficiency in China. Resources Science, 2017,39(7):1326-1337.] doi: 10.18402/resci.2017.07.10
[2]	Schaltegger S, Sturm A . Ökologische Rationalität: Ansatzpunkte zur Ausgestaltung von ökologieorientierten managementinstrumenten[J]. Die Unternehmung, 1990,44(4):273-290.
[3]	World Business Council for Sustainable Development. Eco-efficient leadership for improved economic and environmental performance[R]. Geneva: World Business Council for Sustainable Development, 2000.
[4]	Organization for Economic Cooperation and Development. Eco-efficiency[R]. Pairs: Organization for Economic Cooperation and Development, 1998.
[5]	任宇飞, 方创琳, 蔺雪芹 . 中国东部沿海地区四大城市群生态效率评价[J]. 地理学报, 2017,72(11):2047-2063. doi: 10.11821/dlxb201711010
	[ Ren Yufei, Fang Chuanglin, Lin Xueqin . Evaluation of eco-efficiency of four major urban agglomerations in eastern coastal area of China. Acta Geographical Sinica, 2017,72(11):2047-2063.] doi: 10.11821/dlxb201711010
[6]	Alves J L S, Medeiros D D D. Eco-efficiency in micro-enterprises and small firms: A case study in the automotive services sector[J]. Journal of Cleaner Production, 2015(108):595-602.
[7]	Sinkin C, Wright C J, Burnett R D . Eco-efficiency and firm value[J]. Journal of Accounting and Public Policy, 2008,27(2):167-176. doi: 10.1016/j.jaccpubpol.2008.01.003
[8]	Fernández-Viñé M B, Gómez-Navarro T, Capuz-Rizo S F . Eco-efficiency in the SMEs of Venezuela. Current status and future perspectives[J]. Journal of Cleaner Production, 2010,18(8):736-746. doi: 10.1016/j.jclepro.2009.12.005
[9]	成金华, 孙琼, 郭明晶 , 等. 中国生态效率的区域差异及动态演化研究[J]. 中国人口·资源与环境, 2014,24(1):47-54.
	[ Cheng Jinhua, Sun Qiong, Guo Mingjing et al. Research on regional disparity and dynamic evolution of eco-efficiency in China. China Population, Resources and Environment, 2014,24(1):47-54.]
[10]	关伟, 许淑婷 . 中国能源生态效率的空间格局与空间效应[J]. 地理学报, 2015,70(6):980-992. doi: 10.11821/dlxb201506011
	[ Guan Wei, Xu Shuting . Study on spatial pattern and spatial effect of energy eco-efficiency in China. Acta Geographical Sinica, 2015,70(6):980-992.] doi: 10.11821/dlxb201506011
[11]	Moutinho V, Madaleno M, Robaina M et al. Advanced scoring method of eco-efficiency in European cities[J]. Environmental Science & Pollution Research, 2018,25(2):1-18. doi: 10.1007/s11356-017-0863-8 pmid: 29260468
[12]	Zhang J, Liu Y, Chang Y et al. Industrial eco-efficiency in China: A provincial quantification using three-stage data envelopment analysis[J]. Journal of Cleaner Production, 2017(143):238-249.
[13]	Ounsaneha W, Rattanapan C . Defining the eco-efficiency of rubber glove products manufactured from concentrated latex in Thailand[J]. Environmental Progress & Sustainable Energy, 2016,35(3):802-808.
[14]	付丽娜, 陈晓红, 冷智花 . 基于超效率DEA模型的城市群生态效率研究——以长株潭“3+5”城市群为例[J]. 中国人口·资源与环境, 2013,23(4):169-175.
	[ Fu Lina, Chen Xiaohong, Leng Zhihua . Urban agglomerations eco-efficiency analysis based on super-efficiency DEA model: Case study of Chang-Zhu-Tan “3+5” urban agglomeration. China Population, Resources and Environment, 2013,23(4):169-175.]
[15]	吴小庆, 王远, 刘宁 , 等. 基于生态效率理论和TOPSIS法的工业园区循环经济发展评价[J]. 生态学杂志, 2008,27(12):2203-2208.
	[ Wu Xiaoqing, Wang Yuan, Liu Ning et al. Evaluation of circular economy development in industrial park based on eco-efficiency theory and TOPSIS approach. Chinese Journal of Ecology, 2008,27(12):2203-2208.]
[16]	王艳红, 叶文明 . 计及碳排放的电力工业与火电行业生态效率实证分析(2001-2011)[J]. 科技管理研究, 2015(3):215-219.
	[ Wang Yanhong, Ye Wenming. An empirical study on eco-efficiency of electric utility industry based on carbon emissions(2001-2011). Science and Technology Management Research, 2015(3):215-219.]
[17]	Alyamac K E, Ghafari E, Ince R . Development of eco-efficient self-compacting concrete with waste marble powder using the response surface method[J]. Journal of Cleaner Production, 2016(144):192-202.
[18]	Gómez-Calvet R, Conesa D, Gómez-Calvet A R et al. On the dynamics of eco-efficiency performance in the European Union[J]. Computers & Operations Research, 2016(66):336-350.
[19]	Meylan G, Stauffacher M, Krütli P et al. Identifying stakeholders’ views on the eco-efficiency assessment of a municipal solid waste management system[J]. Journal of Industrial Ecology, 2015,19(3):490-503. doi: 10.1111/jiec.2015.19.issue-3
[20]	杨亦民, 王梓龙 . 湖南工业生态效率评价及影响因素实证分析——基于DEA方法[J]. 经济地理, 2017, 37(10): 151-156+196.
	[ Yang Yimin, Wang Zilong. The empirical analysis of human industrial ecological efficiency evaluation and influencing factors—Based on DEA method. Economic Geography, 2017, 37(10): 151-156+196.]
[21]	Hengevoss D, Baumgartner C, Nisato G et al. Life cycle assessment and eco-efficiency of prospective, flexible, tandem organic photovoltaic module[J]. Solar Energy, 2016(137):317-327.
[22]	史丹, 王俊杰 . 基于生态足迹的中国生态压力与生态效率测度与评价[J]. 中国工业经济, 2016(5):5-21.
	[ Shi Dan, Wang Junjie . Measurement and evaluation of China's ecological pressure and ecological efficiency based on ecological footprint. China Industrial Economics, 2016(5):5-21.]
[23]	孙玉峰, 郭全营 . 基于能值分析法的矿区循环经济系统生态效率分析[J]. 生态学报, 2014,34(3):710-717. doi: 10.5846/stxb201306101566
	[ Sun Yufeng, Guo Quanying . Ecological efficiency analysis of the circular economy system in mining area based on energy analytic approach. Acta Ecologica Sinica, 2014,34(3):710-717.] doi: 10.5846/stxb201306101566
[24]	谷平华, 刘志成 . 基于物质流分析的区域工业生态效率评价——以湖南省为例[J]. 经济地理, 2017,37(4):141-148.
	[ Gu Pinghua, Liu Zhicheng . Evaluation of industrial eco-efficiency based on material flow analysis—As an example in Hunan province. Economic Geography, 2017,37(4):141-148.]
[25]	汪克亮, 孟祥瑞, 杨宝臣 , 等. 基于环境压力的长江经济带工业生态效率研究[J]. 资源科学, 2015,37(7):1491-1501.
	[ Wang Keliang, Meng Xiangrui, Yang Baochen et al. The industrial eco-efficiency of the Yangtze River Economic Zone based on environmental pressure. Resources Science, 2015,37(7):1491-1501.]
[26]	Camarero , Mariam , Castillo et al. Eco-efficiency and convergence in OECD countries[J]. Environmental & Resource Economics, 2013,55(1):87-106.
[27]	Zhou C, Shi C, Wang S et al. Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models[J]. Ecological Indicators, 2018(86):67-80.
[28]	田美玉, 黄海, 张如波 . 长江中游城市群工业转移对工业生态效率影响的实证分析[J]. 统计与决策, 2018,34(4):101-105.
	[ Tian Meiyu, Huang Hai, Zhang Rubo . Empirical analysis of the impact of industrial relocation on industrial ecological efficiency in the middle reaches of Yangtze River. Statistics & Decision, 2018,34(4):101-105.]
[29]	胡嵩 . 基于MinDS模型的中国工业生态效率测算及影响因素研究[D]. 南京: 南京大学, 2016.
	[ Hu Song . Study on China industrial eco-efficiency and influence factors based on MinDS Model. Nanjing: Nanjing University, 2016.]
[30]	王锋, 冯根福 . 基于DEA窗口模型的中国省际能源与环境效率评估[J]. 中国工业经济, 2013(7):56-68.
	[ Wang Feng, Feng Genfu . Evaluation of China's regional energy and environmental efficiency based on DEA Window model. China Industrial Economics, 2013(7):56-68.]
[31]	国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2001-2017.
	[ National Bureau of Statistics of China. China statistical yearbook. Beijing: China Statistics Press, 2001- 2017.]
[32]	国家统计局, 生态环境部. 中国环境统计年鉴[M]. 北京: 中国统计出版社, 2001- 2017.
	[ National Bureau of Statistics of China, Ministry of Ecology and Environment. China statistical yearbook on environment. Beijing: China Statistics Press, 2001- 2017.]
[33]	国家统计局能源统计司. 中国能源统计年鉴[M]. 北京: 中国统计出版社, 2001- 2017.
	[ Department of Energy Statistics, National Bureau of Statistics . China energy statistical yearbook. Beijing: China Statistics Press, 2001- 2017.]
[34]	国家统计局社会科技和文化产业统计司, 科学技术部战略规划司. 中国科技统计年鉴[M]. 北京: 中国统计出版社,2001- 2017.
	[ Department of Social Science and Technology, Cultural Statistics National Bureau of Statistics, Department of Strategy and Planning Ministry of Science and Technology. China statistical yearbook on science and technology. Beijing: China Statistics Press, 2001- 2017.]
[35]	Zhang X, Zhao Y, Wang Cet al. Decoupling effect and sectoral attribution analysis of industrial energy-related carbon emissions in Xinjiang, China[J]. Ecological Indicators, 2019,97:1-9. doi: 10.1016/j.ecolind.2018.09.056
[36]	孙克, 聂坚, 游细斌 , 等. 长江中游城市群生产性服务业的分工特征及空间效应分析[J]. 经济地理, 2018,38(2):123-132.
	[ Sun Ke, Nie Jian, You Xibin et al. Spatial division and spatial effect of producer service in the urban agglomeration in the middle reaches of Yangtze River. Economic Geography, 2018,38(2):123-132.]
[37]	杨仁发 . 产业集聚能否改善中国环境污染[J]. 中国人口·资源与环境, 2015,25(2):23-29.
	[ Yang Renfa . Whether industrial agglomeration can reduce environmental pollution or not. China Population, Resources and Environment, 2015,25(2):23-29.]
[38]	Zhang X, Zhao Y . Identification of the driving factors' influences on regional energy-related carbon emissions in China based on geographical detector method[J]. Environmental Science and Pollution Research, 2018,25(10):9626-9635. doi: 10.1007/s11356-018-1237-6 pmid: 29363033
[39]	贾军 . 基于东道国环境技术创新的FDI绿色溢出效应研究——制度环境的调节效应[J]. 软科学, 2015,29(3):28-32.
	[ Jia Jun . Research on FDI green technology spillovers based on the environmental technological innovation of host country—From the prospect of moderating effects of institutional environment. Soft Science, 2015,29(3):28-32.]
[40]	Wang C, Wang F, Zhang X et al. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang[J]. Renewable and Sustainable Energy Reviews, 2017(67):51-61.
[41]	王少剑, 苏泳娴, 赵亚博 . 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素[J]. 地理学报, 2018,73(3):414-428. doi: 10.11821/dlxb201803003
	[ Wang Shaojian, Su Yongxian, Zhao Yabo . Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions. Acta Geographical Sinica, 2018,73(3):414-428. doi: 10.11821/dlxb201803003
[42]	Wang C, Wang F . China can lead on climate change[J]. Science, 2017,357(6353):764.
[43]	刘炯 . 生态转移支付对地方政府环境治理的激励效应——基于东部六省46个地级市的经验证据[J]. 财经研究, 2015,41(2):54-65.
	[ Liu Jiong . On the inventive effect of ecological transfer payments on environmental governance of local governments: Evidence from 46 eastern prefecture-level cities. Journal of Finance and Economics, 2015,41(2):54-65.]
[44]	张红凤, 周峰, 杨慧 , 等. 环境保护与经济发展双赢的规制绩效实证分析[J]. 经济研究, 2009(3):14-26.
	[ Zhang Hongfeng, Zhou Feng, Yang Huiet al. Regulation performance of the win-win of environmental protection and economic development. Economic Research Journal, 2009(3):14-26.]
[45]	李胜兰, 初善冰, 申晨 . 地方政府竞争、环境规制与区域生态效率[J]. 世界经济, 2014(4):88-110.
	[ Li Shenglan, Chu Shanbing, Shen Chen . Local government competition, environmental regulation and regional ecological efficiency. The Journal of World Economy, 2014(4):88-110.]

年份	W₁	Z_W₁	W₂	Z_W₂	W₃	Z_W₃	年份	W₁	Z_W₁	W₂	Z_W₂	W₃	Z_W₃
2000	0.230	2.779	0.398	4.435	0.314	5.063	2009	0.254	3.026	0.177	2.168	0.215	3.628
2001	0.172	2.170	0.387	4.322	0.279	4.559	2010	0.290	3.409	0.199	2.391	0.244	4.052
2002	0.214	2.611	0.411	4.568	0.312	5.037	2011	0.346	3.998	0.192	2.323	0.269	4.413
2003	0.224	2.718	0.280	3.226	0.252	4.159	2012	0.338	3.912	0.180	2.205	0.259	4.271
2004	0.171	2.156	0.295	3.383	0.233	3.882	2013	0.360	4.142	0.161	2.006	0.260	4.287
2005	0.094	1.354	0.174	2.136	0.134	2.446	2014	0.142	1.850	0.064	1.013	0.102	1.992
2006	0.177	2.227	0.181	2.209	0.179	3.101	2015	0.350	4.036	0.164	2.035	0.257	4.232
2007	0.197	2.433	0.192	2.324	0.194	3.327	2016	0.373	4.281	0.156	1.958	0.264	4.347
2008	0.273	3.236	0.187	2.276	0.230	3.848

影响因素	直接效应	t_直统计值	间接效应	t_间统计值	总效应	t_总统计值
经济发展	0.0049^***	11.8713	0.0053	1.5629	0.0102^***	2.8788
工业结构	-0.0001	-0.5168	-0.0001	-0.3714	-0.0001	-0.4081
产业集聚	-0.0001	-0.1254	-0.0158^*	-1.7325	-0.0159^*	-1.6571
科技创新	0.0017^*	1.9022	0.0306^***	4.3938	0.0323^***	4.4431
环境规制	-0.0001	-0.0752	-0.0235	-1.6309	-0.0236	-1.5295
对外开放	-0.0010^***	-3.7579	-0.0063^***	-3.2708	-0.0073^***	-3.6217
固定资产	-0.0001^*	-1.8030	-0.0000	-0.5854	-0.0000	-0.7633
财政分权	0.0228^***	3.0950	0.1480^***	3.1836	0.1708^***	3.4282

Spatial Pattern Change and Influencing Factors of China’s Industrial Eco-efficiency

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 45

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Zhang Ziang, Bao Jigang. Effects of Multiple Distances on Inbound and Outbound Tourism Flows in China: A Configuration-based Perspective [J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(1): 13-21.
[2]	Yang Ruozi, Xing Pei, Du Wupeng, Dang Bing, Xuan Chunyi, Xiong Feilin. Climatic Characteristics of Precipitation in North China from 1961 to 2017 [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(9): 1573-1583.
[3]	Liu Yungang, Liu Xuanyu, Zhang Zhengsheng. The Territorial Structure in South China Sea from Fishermen's Perspective [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1062-1071.
[4]	Qiu Fangdao, Zhang Chunli, Guo Mengmeng, Zheng Ziyan, Shan Yongbing. Heterogeneity of the Coupling Relationship Between the Innovation Capacity and Industrial Transformation of Renewable Resource-based Cities in China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1092-1103.
[5]	Zhao Ruyu, Xu Jun. Spatial Agglomeration and Unbalanced Development of Elements in Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1104-1113.
[6]	Wang Ying, Liu Hang, Chen Xiaohong, Song Yunting. Evolution Characteristics and Division of Regional Types of Correlation Coupling of Urban-Rural Systems: A Case Study of the Three Provinces of Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1150-1159.
[7]	Ma Li, Tian Huazheng, Kang Lei, Qi Wei. Assessment of the Social and Economic Support Capacity for the Comprehensive Revitalization of Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 863-873.
[8]	Xia Han, Zhang Wanshun, Peng Hong, Li Lin, Huang Panpan, Xia Jingjing. Evaluation of Ecological Functions of Urbanized Areas Based on the Plan for Major Functional Areas in China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 882-889.
[9]	Liu Jian, Yang Qingshan, Zhang Yu, Liu Jie, Jiang Xiaojun. Spatial Patterns Evolution and Classification of Population Aging in the Three Provinces of Northeast China Based on the County Scale [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 918-927.
[10]	Zhang Hua, Xu Cungang, Wang Hao. Response of Vegetation Change to Meteorological Drought in Northwest China from 2001 to 2018 [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 1029-1038.
[11]	Luo Xiaolong, Cao Shujun, Gu Zongni. The Returning-migrant-induced Urbanization: The New Path of Urbanization in Middle China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 685-690.
[12]	Xue Bing, Xiao Xiao, Li Jingzhong, Xie Xiao. Analysis of Spatial Economic Structure of Northeast China Cities Based on Points of Interest Big Data [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 691-700.
[13]	Wu Yuanyuan, Song Yuxiang. The Evolution of China's Population Aging Spatial Pattern and Its Driving Factors [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 768-775.
[14]	Zuo Xiuling, Su Fenzhen, Wang Qi, Wang Chenliang, Jiang Huiping, Shi Wei. Thermal Stress Temporary Refugia Under Global Change for Coral Reefs in the South China Sea Islands [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 814-822.
[15]	Jiang Xiaojun, Yang Qingshan, Liu Jie, Shen Fang, Liu Jian. Multi-scale and Multi-mechanism Research of Regional Economic Differences in the Three Provinces of Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(3): 383-392.