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

  • Authors
    Cai Q.Corresponding
    Chen W.
    Wang M.Corresponding
    Di K.Corresponding
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  • gnest_06336_final.pdf
  • Paper ID
    gnest_06336
  • Paper status
    Published
  • Date paper submitted
  • Date paper accepted
  • Date paper online
DOI
https://doi.org/10.30955/gnj.06336
Graphical abstract
Abstract

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.

Keywords
motion response Global warming
Cite this article
Cai, Q. et al. (2024) “Optimizing Resource Allocation for Regional Employment Governance:A Dynamic Fuzzy-Set QCA Analysis of Low-Carbon Pilot Cities in China”, Global NEST Journal, 26(8). Available at: https://doi.org/10.30955/gnj.06336.
PDF file
  • gnest_06336_final.pdf