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Resource curse and green growth in China: Role of energy transitions under COP26 declarations

Author

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  • Yu, Siming
  • Wan, Kang
  • Cai, Cheng
  • Xu, Lingli
  • Zhao, Tuanjie

Abstract

COP 26 pointed out the multilateral consensus, primarily emphasizing practical measures and accelerating green growth (GG) to cope with environmental issues confronting economic progress. The question of whether green transformation expedites the COP26 goals has now captured the attention of economies globally. Therefore, the study scrutinizes the asymmetric linkage between natural resources (NRR), renewable energy consumption (REC), human development, research and development (R&D) expenditures, and GG in China. Utilizing the quarterly data from Q1-1990 to Q4-2021, this study employs the Quantile-Autoregressive Distributed Lag (Q-ARDL) modelling in addressing the non-abnormal and structural variation issues. The findings of Q-ARDL reveal that NRR affirms resource blessing at higher GG quantiles while confirming the resource-curse problem at lower and middle quantiles in the long-run. Among the other promoting factors, REC significantly stimulates GG across all quantiles; however, the impacts are greater at higher quantiles. In contrast, R&D and human capital encourage sustainable economic growth at all innovative GG quantiles, while their influential effects are more pronounced at the lower to middle quantiles. Moreover, consistent outcomes have been observed for short-run parameters with different sizes and significance levels, except for the NRR. The error correction term significantly shifts all model variables into stable, long-run associations across all quantiles. The study's findings provide interesting and pertinent policy recommendations for all stakeholders.

Suggested Citation

  • Yu, Siming & Wan, Kang & Cai, Cheng & Xu, Lingli & Zhao, Tuanjie, 2023. "Resource curse and green growth in China: Role of energy transitions under COP26 declarations," Resources Policy, Elsevier, vol. 85(PA).
    • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pa:s0301420723004798
    DOI: 10.1016/j.resourpol.2023.103768
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