Regional spatial patterns and influencing factors of environmental auditing for sustainable development: summaries and illuminations from international experiences
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Environmental auditing (EA) is an efficient tool for supervising environmental governance for the realization of economic and social sustainable development. However, the extent to which EA influences the implementation of environmental protection projects and the dominating socioeconomic factors affecting the implementation of EA are unclear. Due to limited data availability, reports investigating this issue are relatively scarce. Based on annual investigation data from the International Organisation of Supreme Audit Institutions regarding the implementation of world EA projects between 2003 and 2013, this paper performed exploratory spatial data analysis to analyze the spatial correlations of EA projects in 204 countries/regions worldwide. In addition, spatial regression models for the year 2013 were established. The findings suggest that (I) high–high regions were mainly concentrated in North America, Europe, and parts of Latin America. The local implementation of EA had strong spatial interactive effects and was heavily influenced by adjacent regions. An experimental area that has already implemented EA can influence its adjacent regions, and the surrounding regions may learn from such implementation. (II) The spatial regression coefficient of the spatial lag model was 0.323 and highly significant (p < 0.001) compared to the traditional regression model. After considering the spatial variables, the importance of CO2 emissions decreased by 1.7%, while environmental input and per capita income decreased by 0.7% and 1%, respectively. The influence of these factors on the implementation of EA was driven by the factor of spatial proximity, which was a critical factor influencing the results. (III) In general, socioeconomic development level, environmental protection investment, and other factors, such as CO2 emission level and degree of social informatization, all increased the efficiency of EA implementation. These conclusions emphasize the important role of EA in helping local governments with environmental management and sustainable development.
KeywordsSpatial effect Exploratory spatial data analysis (ESDA) Spatial regression model Environmental auditing Environmental management Sustainable development
We are grateful to two anonymous reviewers who contribute in ameliorating the original version of this manuscript in the peer review of this work. This research was jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19040502), the National Natural Science Foundation of China (Grant No. 41701505), and the Major Program of National Natural Science Foundation of China (Grant No. 41690143).
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Conflict of interests
The authors declare that they have no conflict of interests.
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