Analyzing the Mechanism of Decentralized Energy Governance Strategy to Reduce Carbon Emissions: Evidence from China

Tianle Yang, Luobin Zhang, Qunyang Du

Economics of Energy & Environmental Policy2026https://doi.org/10.5547/2160-5890.15.1.tyanarticle
AJG 1ABDC B
Weight
0.50

Abstract

The transition to new energy sources is critical for addressing global carbon emissions. However, there is limited study on how decentralized energy governance (DEG) strategies can effectively promote this transition and mitigate carbon emissions. This study investigates the impact and mechanism of China’s New Energy Demonstration City (NEDC) policy, a prominent DEG strategy, on reducing carbon emissions. Using city-level data from 283 Chinese cities spanning 2006 to 2021, we employ the difference-in-differences method to analyze the policy’s effects. The results suggest that the DEG strategy significantly reduces carbon emissions, primarily through renewable energy innovation and energy transitions. Heterogeneity analysis further highlights that the policy’s effectiveness is more pronounced in non-resource- based cities, R&D-intensive cities, and those with strong environmental regulations. These findings underline the importance of expanding DEG strategies and investing in resources to foster innovation and accelerate energy transitions, providing valuable policy recommendations for achieving carbon neutrality.

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https://doi.org/https://doi.org/10.5547/2160-5890.15.1.tyan

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@article{tianle2026,
  title        = {{Analyzing the Mechanism of Decentralized Energy Governance Strategy to Reduce Carbon Emissions: Evidence from China}},
  author       = {Tianle Yang, Luobin Zhang, Qunyang Du},
  journal      = {Economics of Energy & Environmental Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.5547/2160-5890.15.1.tyan},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.