Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning

To scientifically assess the energy-saving effects of China’s zero-waste city pilot (ZWCP) policies and provide empirical evidence and policy insights for advancing pilot policies and accelerating energy conservation and emission reduction goals, this study selected 274 cities in China from 2010 to 2022 as the research sample, employing a double machine learning model to empirically analyze the impact of pilot policies on urban energy consumption intensity. The research results demonstrate that the ZWCP policies significantly reduced energy consumption intensity in pilot areas. Channel analysis reveals that this policy exerted a restraining effect on energy consumption intensity through industrial structure upgrading, green technology innovation, and enhanced environmental awareness. Heterogeneity analysis shows that policy effects were more pronounced in non-urban agglomeration regions, inland areas, and small-to-medium-sized cities. This study provides crucial decision-making references for the promotion and implementation of ZWCP policies during the “14th Five-Year Plan” period.

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Publication Details

Journal
Sustainability
Published
2025-05-30
DOI
https://doi.org/10.3390/su17115039
Citations
8
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
25.05

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article

Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning

Bingnan Guo, Xinyan Guo, Hao Zhang, Yan Qian
8 citations
Sustainability
Energy, Environment, Economic Growth
25.05
article

Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning

Bingnan Guo, Xinyan Guo, Hao Zhang, Yan Qian
article en
8 citations

Abstract

To scientifically assess the energy-saving effects of China’s zero-waste city pilot (ZWCP) policies and provide empirical evidence and policy insights for advancing pilot policies and accelerating energy conservation and emission reduction goals, this study selected 274 cities in China from 2010 to 2022 as the research sample, employing a double machine learning model to empirically analyze the impact of pilot policies on urban energy consumption intensity. The research results demonstrate that the ZWCP policies significantly reduced energy consumption intensity in pilot areas. Channel analysis reveals that this policy exerted a restraining effect on energy consumption intensity through industrial structure upgrading, green technology innovation, and enhanced environmental awareness. Heterogeneity analysis shows that policy effects were more pronounced in non-urban agglomeration regions, inland areas, and small-to-medium-sized cities. This study provides crucial decision-making references for the promotion and implementation of ZWCP policies during the “14th Five-Year Plan” period.

SustainabilityVol. 17(11)
Jiangsu University of Science and Technology (CN)
National Office for Philosophy and Social Sciences
Openalex Percentile: Top 1%
Energy, Environment, Economic Growth
25.05
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Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning — Bingnan Guo, Xinyan Guo, et al. · Sustainability (2025) | TGRS Research Map | TGRS