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Does city-county consolidation policy promote energy efficiency? Evidence from a quasi-natural experiment in China

William W. Yu et al.

Environment and Development Economics2026https://doi.org/10.1017/s1355770x26100485article
AJG 2ABDC B
Weight
0.50

Abstract

City-county consolidation is a prevalent practice observed worldwide, serving as a common strategy adopted by policymakers to effectively respond to the development requirements of large metropolises. Existing literature has extensively explored the effects arising from this particular policy. Nevertheless, how consolidation affects energy efficiency in consolidated regions is rarely examined. Based on the large-scale city-county consolidation reform from 2000 to 2017 in China, our study explores the causal implications of this policy on energy efficiency at the county level through the application of a staggered difference-in-difference method. Our outcomes demonstrate that a consolidation reform is tied to a statistically significant reduction in energy efficiency of consolidated counties relative to their non-consolidated counterparts. We provide additional evidence that the rise in energy usage, the relocation of low-tech and energy-intensive industries, and the intensification of economic agglomeration are potential contributors to the decrease in energy efficiency.

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https://doi.org/https://doi.org/10.1017/s1355770x26100485

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@article{william2026,
  title        = {{Does city-county consolidation policy promote energy efficiency? Evidence from a quasi-natural experiment in China}},
  author       = {William W. Yu et al.},
  journal      = {Environment and Development Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s1355770x26100485},
}

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Does city-county consolidation policy promote energy efficiency? Evidence from a quasi-natural experiment in China

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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.