China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere

Chensheng Lyu

doi:doi:10.11648/j.ss.20251404.24

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China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere

Chensheng Lyu^*

Published in Social Sciences (Volume 14, Issue 4)

Received: 26 June 2025 Accepted: 21 July 2025 Published: 8 August 2025

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Abstract

To address climate challenges, China implemented its National Emissions Trading System (ETS) in July 2021, initially targeting the power sector that accounts for 40% of national carbon emissions. While existing research has predominantly examined regional pilot programs, empirical evidence on the national market's initial effectiveness remains limited. This study fills this gap by analyzing provincial panel data (2019-2024) through a difference-in-differences (DID) approach to assess the ETS's nationwide emission reduction impact. Our methodology selects the six provinces with the lowest clean energy shares (Shanghai, Beijing, Tianjin, Anhui, Shandong, Shaanxi) as the treatment group, using others as controls, while employing a two-way fixed effects model to account for provincial and temporal heterogeneity - with rigorous verification of parallel trends via dynamic event studies and joint significance tests. Key findings reveal: (1) significant power sector emission reductions (average 0.252%) attributable to the national ETS, displaying dynamic "surge-then-adjustment" characteristics with an initial sharp decline followed by partial rebound; (2) heterogeneous impacts concentrated in carbon market pilot areas with negligible effects elsewhere, indicating path dependence in policy efficacy; and (3) economic development level and population size as core emission drivers. This research contributes novel insights by providing the first quantitative assessment of the national ETS's decarbonization impact on the power sector and validating the critical importance of prior pilot experience for policy effectiveness. The results highlight the need for differentiated policy reinforcement in non-pilot regions to achieve nationwide decarbonization goals.

Published in	Social Sciences (Volume 14, Issue 4)
DOI	10.11648/j.ss.20251404.24
Page(s)	433-439
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

China Carbon Market, Power Sector Emissions, Emission Trading System, Difference-in-Differences, Pilot Policy Effectiveness

1. Introduction

The world is facing a rising threat of climate change, and effective global action is needed to reduce greenhouse gases. China has already committed to reducing its emissions by 2030. The country's efforts are being supported by the implementation of the emissions trading system, which is the cornerstone of the global effort to address climate change. China’s national carbon market started to operate in July 2021

[1]

. It is based on the successful model used by the European Union. The country's initial phase included only the power industry, which contributed around 40% of the country's total emissions

[2]

The implementation of China's national carbon market marks a significant policy milestone, requiring thorough assessment of its early-phase effects to evaluate performance and inform future improvements. Current research primarily focuses on regional pilot carbon markets, leaving substantial knowledge gaps regarding the national scheme's initial outcomes. Based on empirical analysis of the EU carbon market, Ellerman & Buchner (2008) revealed that allowance trading in electricity generation can lower emission reduction costs, establishing fundamental support for market-based approaches

[3]

. In the Chinese context, Li & Niu (2024) found the pilot carbon trading policy reduced power sector emissions by approximately 24.53 metric tons compared to non-pilot areas

[4]

, while Yu, Z. et al (2023) examined interactions between carbon trading and coal power phase-out policies, demonstrating that combined policy effectiveness varies by ETS design

[5]

. Recent studies further indicate that shifting to a cap-and-trade model after 2030 may decrease power sector decarbonization costs and improve efficiency

[6]

, though Li, G., et al. (2023) identified rebound effects in power sector decarbonization that negatively correlate with carbon price levels

[7]

. Liu & Zhang (2021) discovered ETS impacts on renewable energy development differ across technology types and regions, with particularly significant promotion of hydropower and solar PV generation

[8]

. Utilizing the real-time emissions data from Liu, Z. et al (2020), this paper develops a provincial difference-in-differences model to examine policy effect variations, thereby advancing theoretical understanding of equitable transitions in carbon market mechanisms.

As the ETS is initially implemented solely in the power sector, provinces with lower clean energy generation shares will experience more pronounced effects. Consequently, this study designates six regions—Shanghai, Beijing, Tianjin, Anhui, Shandong, and Shaanxi (exhibiting the lowest clean energy ratios, see Figure 1)—as the treatment group, while the remaining regions serve as the control group, enabling an analysis of ETS-induced heterogeneity in power sector carbon emissions.

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Figure 1. Proportion of clean energy power generation in each province.

2. Methodology

This study employs the difference-in-differences (DID) methodology as the primary empirical approach to examine the causal impact of China's national carbon emissions trading scheme (ETS) on provincial-level power sector emissions. The DID design compares outcome variables between treatment and control groups before and after policy implementation, effectively controlling for both unobserved temporal trends and potential biases arising from inherent inter-group differences. This approach enables more precise isolation of the policy's net effect. The DID framework is particularly suitable for analyzing the specific policy shock created by the national ETS launch in June 2021, especially when examining its effects on the power sector as a key emissions-regulated industry

[9]

. The methodology capitalizes on the natural experiment created by variation in both: (1) the timing of policy implementation (June 2021), and (2) whether provinces were included in the national market's first compliance cycle. This quasi-experimental design provides robust causal identification. The baseline DID specification is specified as follows:

Ln (Carbon {Emissions}_{it}) = α + β {ETS}_{it} + γ X_{it} ​ + {μ ​}_{i} + {λ ​}_{t} + ε_{it} ​

(1)

The subscripts

i

and

t

represent the province and time, respectively. The explanatory variable

{Carbon Emissions}_{it}

represents the carbon emissions of the power sector in the

i

th province in the

t

period. The core explanatory variable is the interaction term

{Treat}_{i} \times {Post}_{t}

, and its coefficient

β

is the average treatment effect of the national ETS policy on carbon emissions in the power sector;

{Treat}_{i}

is the dummy variable of provincial grouping, and

{Post}_{t}

is the dummy variable of policy time. The vector

X_{it}

represents a set of time-varying control variables at the provincial level that may affect carbon emissions. The model further incorporated provincial fixed effect (

μ_{i}

) and temporal fixed effect (

λ_{t}

) to control for provincial heterogeneity and common temporal trend factors that did not change with time, respectively.

ε_{it}

is a random error term.

The validity of the DID approach crucially depends on satisfying the parallel trends assumption - requiring that power sector emissions in treatment and control groups followed similar trajectories prior to policy implementation. To rigorously verify this assumption, we first examine the policy's dynamic effects. Figure 2 visually demonstrates the evolution of emission differences between groups across multiple pre- and post-policy periods. From 2019 to 2024, the explanatory variables showed no significant differences between groups before policy implementation, but became statistically significant afterward.

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Figure 2. Diagram of CO₂ emission trend in the treatment group and control group.

To further validate the parallel trends assumption, we conduct both joint significance tests and Student's t-tests on the explanatory variables

[10]

. Table 1 demonstrates no statistically significant differences in explanatory variables between treatment and control groups in any pre-policy year

[11]

. Table 2 presents an F-test statistic of 0.63 (p=0.6026) for the joint significance of pre-policy dummy variables, failing to reject the null hypothesis of parallel trends. These consistent results showing no significant pre-policy differences between groups - provide robust evidence supporting the parallel trends assumption, thereby strengthening the validity of our DID estimates

[10]

Table 1. Results of independent testing.

Year	Ha: diff < 0	Ha: diff != 0	Ha: diff > 0
2019	P(T < t) =0.270	P(\|T\| > \|t\|)=0.540	P(T > t) =0.730
2020	P(T < t) =0.271	P(\|T\| > \|t\|)=0.542	P(T > t) =0.729
2021	P(T < t) =0.290	P(\|T\| > \|t\|)=0.581	P(T > t) =0.711

Table 2. Results of joint testing.

Test variables	F-value	Freedom	P-value
First three phases of the policy	0.63	(3,29)	0.6026

3. Data Description

The carbon emissions data of China's power sector were compiled from daily records on the Carbon Monitor platform

[12]

, with the proportion of clean energy generation obtained from the China Energy Statistical Yearbook and control variables collected from national and provincial statistical yearbooks. Table 3 presents the overall characteristics of the dataset.

Regarding control variable selection: (1) per capita GDP was selected to represent regional economic development level, which affects carbon emissions to some extent, higher per capita GDP generally corresponds to higher emissions; (2) year-end total population was included as a control variable due to its significant relationship with carbon emissions; (3) the ratio of science and technology fiscal expenditure was chosen to reflect innovation capacity, where higher innovation levels typically lead to lower emissions

[13]

; (4) the proportion of total import-export volume was selected to measure regional openness and control for trade impacts on emissions; and (5) fiscal expenditure ratio was included to account for local government influence on carbon emissions

[14]

Table 3. Descriptive statistics of variables.

Variable	Obs	Mean	Std.Dev.	Min	Max
CO₂ Emission in Power Industry (Mt)	180	139.317	118.764	1.987	536.828
GDP per capita (10K)	180	8.141	3.645	3.299	20.782
Total Import and Export Value (100M)	180	12664.15	18365.53	22.95521	83773.58
Year End Total Population (10K)	180	4683.323	2962.119	590.44	12755.2
General Public Budget (100M)	180	7328.667	3677.133	1427.89	18533.08
Science and Technology Expenditure (100M)	180	225.883	243.1314	9.4513	1179.142

4. Empirical Results

4.1. Baseline DID Regression

We used a two-way fixed-effect model and misplaced the clustering criteria at the individual level. At the same time, individual and temporal effects have also been introduced into the model to control for geographical disparities and macroeconomic developments, especially the impact of the epidemic in 2020. In the first three columns of Table 3, control variables are gradually added for regression, and it is found that the impact of ETS on carbon emissions is significantly negative. The results show that ETS produces a 0.252% reduction in carbon emissions from the power sector. The signs of the other control variables were consistent with expectations, and lnPGDP and lnPOP were significantly positive, indicating that economic level and population were the main factors affecting carbon emissions in the power sector. The economy and population affect each other, and the more active the economy, the more population tends to bring. And with the entry of talents, it will further stimulate economic development. Both are positively correlated with the electricity demand. The fourth column breaks down the policy effects into years, establishing the following model

[15]

Ln (Carbon {Emissions}_{it}) = α + β_{1} {ETS_post 1}_{i} + β_{2} {ETS_post 2}_{i} + β_{3} {ETS_post 3}_{i} + γ X_{it} ​ + {μ​}_{i} + {λ​}_{t} + ε_{it}

(2)

It can be seen that the policy effect is significant every year, and it can be seen from the size of the coefficient that the policy intensity shows a trend of rapid increase first and then correction.

Table 4. Impact of ETS on CO₂ emission in the power sector.

Variables	(1) Y	(2) Y	(3) Y	(4)
ETS	-0.234** (0.12)	-0.246**(0.102)	-0.252** (0.11)
lnPGDP		0.716** (0.306)	0.965** (0.449)	0.969** (0.452)
lnOPE		-0.283 (0.29)	-0.215 (0.246)	-0.218 (0.25)
lnPOP		3.525** (1.532)	4.072** (1.742)	4.107** (1.769)
lnGIN			-0.406 (0.82)	-0.402 (0.825)
lnTCH			-0.118 (0.154)	-0.117 (0.154)
ETS_post1				-0.220** (0.106)
ETS_post2				-0.274** (0.114)
ETS_post3				-0.261** (0.119)
Constant	4.568*** (0.01)	-23.459** (11.89)	-24.909*(12.39)	-25.217** (12.6)
Individual effect	Y	Y	Y	Y
Time effect	Y	Y	Y	Y
Observations	180	180	180	180
$R^{2}$	0.937	0.938	0.939	0.939

Note: ***p<0.01,**p<0.05,*p<0.1

4.2. Heterogeneity Analysis

In order to further investigate how the policy works in different regions

[16]

, we divided the sample into two groups according to whether it was the ETS pilot area in June 2013, and divided the original six treatment groups into two sub-samples to continue the regression as treatment groups. We examine the dynamic relationship between ETS and carbon emissions, using a series of dummy variable coefficients in standard regression to reveal the year-over-year impact of ETS on the logarithm of carbon emissions in the power sector:

Ln (Carbon {Emissions}_{it}) = α + β {RT}_{it} + γ X_{it} ​ + {μ​}_{i} + {λ​}_{t} + ε_{it}

(3)

{RT}_{it}

denotes the time of relative policies, the coefficient β represents the magnitude of difference in carbon emissions between treatment and control groups across time periods. To visualize these temporal patterns more clearly, we plot the coefficients with their corresponding confidence intervals in Figure 3. The results reveal distinct patterns: (1) For non-pilot areas, all annual confidence intervals include zero, indicating no statistically significant emission differences between groups; (2) For pilot areas, pre-policy intervals contain zero, while post-policy intervals exclude zero and demonstrate a clear downward trend. These findings demonstrate that the treatment group experienced significant emission reductions relative to the control group following policy implementation. This evidence suggests that the national carbon emission policy has produced increasingly strong emission reduction effects in the power generation industry within pilot areas, while showing no significant impact in non-pilot regions

[17]

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Figure 3. Policy dynamic effect between treatment groups and control groups.

5. Conclusion and Future Study

This study utilizes provincial panel data from 2019 to 2024 within a difference-in-differences (DID) framework to examine the impact of China's national ETS on power sector carbon emissions. The results demonstrate that the ETS significantly reduces emissions in regions relying on traditional energy sources for power generation, with dynamic DID analysis revealing an initial strong policy effect that gradually stabilizes, indicating robust emission reduction performance

[18]

. When comparing pilot versus non-pilot regions, the analysis shows statistically significant emission reductions exclusively in pilot areas, where clear divergence emerges between treatment and control groups, while no meaningful differences are observed in non-pilot regions under the current classification. These findings collectively suggest that while the national ETS has effectively enhanced mitigation potential in pilot areas, its impact remains limited in non-pilot regions given existing policy parameters, highlighting the need for further policy refinement to achieve broader emission reduction effects across all regions.

ETS is the main market-based measure adopted by the Chinese government to control carbon emissions, and its carbon reduction effect in the pilot policy has been recognized by various scholars

[19]

, and it is worth further promoting the policy. However, there is still a lack of research on the effectiveness of national policies, and this paper fills this gap. Our results show that while the ETS further expands its industry scope, it needs to solve the problem of slow response in non-pilot areas to fully unleash China’s carbon reduction potential.

Future studies could focus on analyzing the reasons for the slow response in non-pilot areas, while other environmental policies, such as coal phaseout

[20]

, could be studied alongside ETS to analyze their co-effects.

Abbreviations

ETS	Emission Trading System
DID	Differences-In-Differences

Author Contributions

Chensheng Lyu is the sole author. The author read and approved the final manuscript.

Funding

This work is not supported by any external funding.

Data Availability Statement

The data that support the findings of this study can be found at: https://cn.carbonmonitor.org/power, https://www.stats.gov.cn/sj/ndsj/

Conflicts of Interest

The authors declare no conflicts of interest.

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Lyu, C. (2025). China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Social Sciences, 14(4), 433-439. https://doi.org/10.11648/j.ss.20251404.24

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Lyu, C. China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Soc. Sci. 2025, 14(4), 433-439. doi: 10.11648/j.ss.20251404.24

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Lyu C. China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Soc Sci. 2025;14(4):433-439. doi: 10.11648/j.ss.20251404.24

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@article{10.11648/j.ss.20251404.24,
  author = {Chensheng Lyu},
  title = {China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere
},
  journal = {Social Sciences},
  volume = {14},
  number = {4},
  pages = {433-439},
  doi = {10.11648/j.ss.20251404.24},
  url = {https://doi.org/10.11648/j.ss.20251404.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20251404.24},
  abstract = {To address climate challenges, China implemented its National Emissions Trading System (ETS) in July 2021, initially targeting the power sector that accounts for 40% of national carbon emissions. While existing research has predominantly examined regional pilot programs, empirical evidence on the national market's initial effectiveness remains limited. This study fills this gap by analyzing provincial panel data (2019-2024) through a difference-in-differences (DID) approach to assess the ETS's nationwide emission reduction impact. Our methodology selects the six provinces with the lowest clean energy shares (Shanghai, Beijing, Tianjin, Anhui, Shandong, Shaanxi) as the treatment group, using others as controls, while employing a two-way fixed effects model to account for provincial and temporal heterogeneity - with rigorous verification of parallel trends via dynamic event studies and joint significance tests. Key findings reveal: (1) significant power sector emission reductions (average 0.252%) attributable to the national ETS, displaying dynamic "surge-then-adjustment" characteristics with an initial sharp decline followed by partial rebound; (2) heterogeneous impacts concentrated in carbon market pilot areas with negligible effects elsewhere, indicating path dependence in policy efficacy; and (3) economic development level and population size as core emission drivers. This research contributes novel insights by providing the first quantitative assessment of the national ETS's decarbonization impact on the power sector and validating the critical importance of prior pilot experience for policy effectiveness. The results highlight the need for differentiated policy reinforcement in non-pilot regions to achieve nationwide decarbonization goals.},
 year = {2025}
}

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TY  - JOUR
T1  - China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere

AU  - Chensheng Lyu
Y1  - 2025/08/08
PY  - 2025
N1  - https://doi.org/10.11648/j.ss.20251404.24
DO  - 10.11648/j.ss.20251404.24
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 433
EP  - 439
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20251404.24
AB  - To address climate challenges, China implemented its National Emissions Trading System (ETS) in July 2021, initially targeting the power sector that accounts for 40% of national carbon emissions. While existing research has predominantly examined regional pilot programs, empirical evidence on the national market's initial effectiveness remains limited. This study fills this gap by analyzing provincial panel data (2019-2024) through a difference-in-differences (DID) approach to assess the ETS's nationwide emission reduction impact. Our methodology selects the six provinces with the lowest clean energy shares (Shanghai, Beijing, Tianjin, Anhui, Shandong, Shaanxi) as the treatment group, using others as controls, while employing a two-way fixed effects model to account for provincial and temporal heterogeneity - with rigorous verification of parallel trends via dynamic event studies and joint significance tests. Key findings reveal: (1) significant power sector emission reductions (average 0.252%) attributable to the national ETS, displaying dynamic "surge-then-adjustment" characteristics with an initial sharp decline followed by partial rebound; (2) heterogeneous impacts concentrated in carbon market pilot areas with negligible effects elsewhere, indicating path dependence in policy efficacy; and (3) economic development level and population size as core emission drivers. This research contributes novel insights by providing the first quantitative assessment of the national ETS's decarbonization impact on the power sector and validating the critical importance of prior pilot experience for policy effectiveness. The results highlight the need for differentiated policy reinforcement in non-pilot regions to achieve nationwide decarbonization goals.
VL  - 14
IS  - 4
ER  -

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Chensheng Lyu

School of Statistics, University of International Business and Economics, Beijing, China

Contact Email

http://orcid.org/0009-0000-3158-6890

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Lyu, C. (2025). China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Social Sciences, 14(4), 433-439. https://doi.org/10.11648/j.ss.20251404.24

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Lyu, C. China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Soc. Sci. 2025, 14(4), 433-439. doi: 10.11648/j.ss.20251404.24

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Lyu C. China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere. Soc Sci. 2025;14(4):433-439. doi: 10.11648/j.ss.20251404.24

Copy | Download

@article{10.11648/j.ss.20251404.24,
  author = {Chensheng Lyu},
  title = {China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere
},
  journal = {Social Sciences},
  volume = {14},
  number = {4},
  pages = {433-439},
  doi = {10.11648/j.ss.20251404.24},
  url = {https://doi.org/10.11648/j.ss.20251404.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20251404.24},
  abstract = {To address climate challenges, China implemented its National Emissions Trading System (ETS) in July 2021, initially targeting the power sector that accounts for 40% of national carbon emissions. While existing research has predominantly examined regional pilot programs, empirical evidence on the national market's initial effectiveness remains limited. This study fills this gap by analyzing provincial panel data (2019-2024) through a difference-in-differences (DID) approach to assess the ETS's nationwide emission reduction impact. Our methodology selects the six provinces with the lowest clean energy shares (Shanghai, Beijing, Tianjin, Anhui, Shandong, Shaanxi) as the treatment group, using others as controls, while employing a two-way fixed effects model to account for provincial and temporal heterogeneity - with rigorous verification of parallel trends via dynamic event studies and joint significance tests. Key findings reveal: (1) significant power sector emission reductions (average 0.252%) attributable to the national ETS, displaying dynamic "surge-then-adjustment" characteristics with an initial sharp decline followed by partial rebound; (2) heterogeneous impacts concentrated in carbon market pilot areas with negligible effects elsewhere, indicating path dependence in policy efficacy; and (3) economic development level and population size as core emission drivers. This research contributes novel insights by providing the first quantitative assessment of the national ETS's decarbonization impact on the power sector and validating the critical importance of prior pilot experience for policy effectiveness. The results highlight the need for differentiated policy reinforcement in non-pilot regions to achieve nationwide decarbonization goals.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - China's Carbon Market Lowers Power Emissions Significantly in Pilot Areas But Not Elsewhere

AU  - Chensheng Lyu
Y1  - 2025/08/08
PY  - 2025
N1  - https://doi.org/10.11648/j.ss.20251404.24
DO  - 10.11648/j.ss.20251404.24
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 433
EP  - 439
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20251404.24
AB  - To address climate challenges, China implemented its National Emissions Trading System (ETS) in July 2021, initially targeting the power sector that accounts for 40% of national carbon emissions. While existing research has predominantly examined regional pilot programs, empirical evidence on the national market's initial effectiveness remains limited. This study fills this gap by analyzing provincial panel data (2019-2024) through a difference-in-differences (DID) approach to assess the ETS's nationwide emission reduction impact. Our methodology selects the six provinces with the lowest clean energy shares (Shanghai, Beijing, Tianjin, Anhui, Shandong, Shaanxi) as the treatment group, using others as controls, while employing a two-way fixed effects model to account for provincial and temporal heterogeneity - with rigorous verification of parallel trends via dynamic event studies and joint significance tests. Key findings reveal: (1) significant power sector emission reductions (average 0.252%) attributable to the national ETS, displaying dynamic "surge-then-adjustment" characteristics with an initial sharp decline followed by partial rebound; (2) heterogeneous impacts concentrated in carbon market pilot areas with negligible effects elsewhere, indicating path dependence in policy efficacy; and (3) economic development level and population size as core emission drivers. This research contributes novel insights by providing the first quantitative assessment of the national ETS's decarbonization impact on the power sector and validating the critical importance of prior pilot experience for policy effectiveness. The results highlight the need for differentiated policy reinforcement in non-pilot regions to achieve nationwide decarbonization goals.
VL  - 14
IS  - 4
ER  -

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