How does the fiscal pressure of local governments affect China's PM2.5 emissions?

Other's shoes also fit well: AI technologies contribute to China's blue skies as well as carbon reduction

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM2.5 and SO2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance.

Will economic development come at the cost of environmental pollution under fiscal pressure? evidence from resource-based cities in China

The traditional view is that local governments are inclined to ease environmental regulations in response to fiscal pressure (FP) and alleviate FP by sacrificing the environment for economic development (ED). This paper takes Chinese Mainland resource-based cities (RBC) as the research sample but draws different conclusions. The research results are as follows: firstly, for RBC, although FP reduces environmental pollution (EP), it is not conducive to ED. Compared with non-resource-based cities (NRBC), the economic negative effect of FP in RBC is more obvious. Secondly, FP is detrimental to ED by reducing EP, and RBC have not sacrificed the environment to promote ED in the context of FP. This paper summarizes that the development path of RBC should be "optimizing government behavior → increasing FP → improving environmental quality → reducing ED level." Thirdly, the impact of FP on cities with slower economic growth and smaller economic gap is greater, and the impact of FP on cities with better environment is more obvious. This paper argues that facing FP, RBC in China will not trade EP for ED; at the same time, we suggest RBC in other countries in the face of the FP, not only thinking of promoting ED at the expense of the EP and ease the FP, but also the development of green ecological requirement, overall consideration of the relationship between EP and ED, and then achieve green and sustainable ED without degrading the environment as far as possible.