SCIENTIA GEOGRAPHICA SINICA ›› 2020, Vol. 40 ›› Issue (3): 335-343.doi: 10.13249/j.cnki.sgs.2020.03.001

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

Zhang Xinlin1, Qiu Fangdao1(), Tan Juntao1, Wang Changjian2   

  1. 1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China
    2. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, Guangdong, China
  • Received:2019-03-13 Revised:2019-08-23 Online:2020-03-10 Published:2020-05-13
  • Contact: Qiu Fangdao
  • Supported by:
    National Natural Science Foundation of China(41671123);National Natural Science Foundation of China(41501144);A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of Jiangsu Normal University(18XWRX004)


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