Evaluation of the Policy Effects of Low-Carbon City Pilot Projects Based on the PSM-DID Model

In the context of the dual-carbon policy, evaluating the implementation effects of low-carbon city pilot policies is of great significance. Based on panel data from 284 prefecture-level cities in China between 2004 and 2020, this paper integrates the Propensity Score Matching (PSM) method with the Difference-in-Differences (DID) method, referred to as the PSM-DID model. By employing parallel trend tests, placebo tests, robustness checks, and heterogeneity analyses, the implementation effects of the low-carbon city pilot policies are evaluated and compared with those from the traditional DID model. The results indicate that: (1) The low-carbon city pilot policies significantly curb carbon emissions in eastern China, while the effects in central and western regions are relatively weak; (2) Factors influencing carbon emissions differ significantly across regions. In particular, clean energy structure and per capita affluence have a greater impact on the eastern region, while the effect is weaker in the central and western regions; (3) The timing, scope, and regional factors of implementation significantly affect the policy outcomes. Policies implemented early yield more significant results than those implemented later, with regional development levels and scope of implementation playing key roles in shaping policy outcomes; (4) The PSM-DID model yields more reliable results than the DID model.

Authors

Institutions

Publication Details

Journal
Sustainable Development Research
Published
2025-03-24
DOI
https://doi.org/10.30560/sdr.v7n1p136
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of the Policy Effects of Low-Carbon City Pilot Projects Based on the PSM-DID Model

Yu‐Shan Lin, Chaoqiang Jiang, Qisong Xu
Sustainable Development Research
Smart Cities and Technologies
article

Evaluation of the Policy Effects of Low-Carbon City Pilot Projects Based on the PSM-DID Model

Yu‐Shan Lin, Chaoqiang Jiang, Qisong Xu
article en

Abstract

In the context of the dual-carbon policy, evaluating the implementation effects of low-carbon city pilot policies is of great significance. Based on panel data from 284 prefecture-level cities in China between 2004 and 2020, this paper integrates the Propensity Score Matching (PSM) method with the Difference-in-Differences (DID) method, referred to as the PSM-DID model. By employing parallel trend tests, placebo tests, robustness checks, and heterogeneity analyses, the implementation effects of the low-carbon city pilot policies are evaluated and compared with those from the traditional DID model. The results indicate that: (1) The low-carbon city pilot policies significantly curb carbon emissions in eastern China, while the effects in central and western regions are relatively weak; (2) Factors influencing carbon emissions differ significantly across regions. In particular, clean energy structure and per capita affluence have a greater impact on the eastern region, while the effect is weaker in the central and western regions; (3) The timing, scope, and regional factors of implementation significantly affect the policy outcomes. Policies implemented early yield more significant results than those implemented later, with regional development levels and scope of implementation playing key roles in shaping policy outcomes; (4) The PSM-DID model yields more reliable results than the DID model.

Sustainable Development ResearchVol. 7(1)
Nanning Normal University (CN)
Openalex Percentile: Top 93%
Smart Cities and Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.