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The determinants of urban sustainability in Chinese resource-based cities: A panel quantile regression approach

pmid: 31412517
Improving energy efficiency and reducing environmental pollution emissions are two important ways to alleviate energy problems. Despite the progress in energy efficiency, the growth in energy demand still exceeds the efficiency improvements. This study adopts nonparametric methods to estimate the total factor energy efficiency (TFEE) of 105 resource-based cities covering the period 2010-2016 in China and analyzes the spatiotemporal characteristics of changes in energy efficiency. Furthermore, panel quantile regression is applied to analyze the multiple impacts of economic level, industrial structure, resource endowment, energy price, government intervention and degree of openness on energy efficiency. The main findings are as follows. (1) Each determinant has a different influence on TFEE at different levels; among them, the influence of the fuel and energy price index show an inverted U-shaped distribution as the quantile increases, and that of the GDP per capita shows a stronger heterogeneity than those of other factors. (2) Resource-based cities with lower efficiency are more sensitive to government intervention than are cities with higher efficiency. (3) A city's openness has a negative effect on TFEE, which partly supports the pollution haven hypothesis: the more foreign investment a resource-based city receives, the lower its energy and technology efficiency. Finally, some practical suggestions for the sustainable development of resource-based cities are discussed.
- Hubei Normal University China (People's Republic of)
- Tsinghua University China (People's Republic of)
- Tsinghua–Berkeley Shenzhen Institute China (People's Republic of)
- University of Glasgow United Kingdom
- University of Southampton United Kingdom
330, 338
330, 338
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).122 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
