Optimizing Resource Allocation for Regional Employment Governance:A Dynamic Fuzzy-Set QCA Analysis of Low-Carbon Pilot Cities in China

Abstract

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:115%"><span style="font-family:等线"><a name="_Hlk171796579"><span lang="EN-US" style="font-family:"Times New Roman",serif">This study employs a dynamic fuzzy-set qualitative comparative analysis (fsQCA) approach, utilizing panel data from 121 low-carbon pilot cities in China from 2007 to 2019. Grounded in complex systems theory and the triple bottom line framework (Economy-Society-Environment), the research aims to optimize resource allocation to enhance regional employment governance performance. The key findings include that the initial implementation of low-carbon policies resulted in a short-term decline in employment levels, with minimal long-term impact on overall employment figures but a significant effect on high-level urban wages. Significant disparities in employment levels were observed among pilot cities, driven by regional population sizes and economic development levels. Four development models for low-carbon cities were identified: human resources-driven, energy transition-driven, industrial cluster-driven, and comprehensive factor-driven models. These models provide strategic pathways for promoting low-carbon urban development and enhancing employment. The findings offer valuable insights into governance strategies for China’s low-carbon pilot cities, facilitating the context-specific promotion of sustainable urban development and improved employment opportunities.</span></a></span></span></span></p>