International empirical studies have demonstrated that CETP directly curtails high-carbon energy consumption and optimizes the energy structure through market mechanisms. For instance, Mansanet-Bataller and Pardo (2008) found that the European Union Emissions Trading System (EU ETS) significantly reduced the share of coal in the power sector while promoting the substitufengtion of natural gas and renewable energy sources. Similarly, Murray and Maniloff (2015) showed that the U.S.Regional Greenhouse Gas Initiative (RGGI) accelerated the transition in energy structures by investing auction proceeds into energy efficiency programs. Kenneth et al. (2020) confirmed the long-term positive effects of the EU carbon market, demonstrating CETP’s capability to reduce reliance on high-carbon energy sources. Focusing on China, Jia et al. (2024) argued that CETP compels enterprises to shift from fossil fuels to non-fossil energy sources through dynamic pricing and demand-response mechanisms (Xu, 2021; Zhao et al., 2018),thus significantly optimizing the energy structure in pilot regions.

Technological innovation constitutes a critical transmission pathway through which CETP fosters the energy transition. While early debates questioned the innovation effect of environmental regulation (Jaffe and Palmer, 1997; Lai and Lorne, 2015; Medema, 2020), subsequent scholarship, grounded in Porter’s Hypothesis and agency theory, generally supports the view that well-designed CETP schemes incentivize breakthroughs in green technology (Schmutzler, 2001; Bhatia and Kumar Jakhar, 2021). Aghion et al. (2016) demonstrated that the EU ETS significantly boosted patent applications for solar and wind technologies,highlighting the catalytic role of carbon markets in clean energy innovation. Similarly, Fowlie and Mar, (2022) found that California’s carbon market facilitated the adoption of Carbon Capture and Storage (CCS) technologies, indirectly reducing fossil fuel dependency in the power sector. Cross-national research has further established a significant positive correlation between carbon pricing mechanisms and the diffusion of green technologies, underscoring the role of policy stability (Popp, 2019). Guo and Hu (2024), Wang et al. (2025a), and others have explored the impact of financial markets and artificial intelligence on carbon emission policies, such as blockchain, have also enhanced carbon market transparency and expedited the globalization of low-carbon innovation networks (Sipthorpe et al., 2022),creating a notable” innovation compensation effect” (Klaus et al., 2024).

CETP can also promote the low-carbon transformation of the energy structure by driving industrial restructuring. Branger et al. (2016) highlighted that the EU ETS significantly reduced emissions from heavy industries through technological upgrades (e.g., electric arc furnace steelmaking) while encouraging the expansion of renewable energy sectors. Ohlendorf et al. (2022) showed that Germany’s carbon price floor mechanisms accelerated industrial restructuring by raising operational costs for carbon-intensive industries and incentivizing low-carbon investments. Quantitative analyses, such as Cantone et al. (2023), found that a $1 increase in carbon price leads to a 1.4%rise in low-carbon technology patent applications. Focusing on China, Herman and Jun (2022) demonstrated that CETP reallocates production factors toward low-carbon industries through quota allocation, while Zhang and Bi, (2023) emphasized the moderating role of carbon price signals in shaping corporate behavior. Moreover, policy synergies—such as integrating artificial intelligence and carbon-neutral technologies—further enhance the transition effects (Hussain et al., 2025).

The spillover effects of CETP exhibit complex spatiotemporal dynamics. Shi and Zhou (2024) observed that CETP indirectly stimulate residential research and development activities by intensifying firms’ compliance pressures, creating a positive feedback loop of “regulation–innovation.” Conversely, Qi et al. (2022) warned that excessively stringent environmental regulations (e.g., central inspections) could suppress innovation in neighboring regions, underscoring the importance of coordinated policy design. On the international front, Cui et al. (2024) quantified the spillover effects between the China and EU carbon markets, finding that EU carbon price volatility positively influences China’s new energy sector (elasticity coefficient = 0.35). However, Jiang et al. (2023) cautioned that increasing interlinkages among global carbon markets could exacerbate price volatility risks. Wang et al. (2025a), Wang et al. (2025b) suggests re-examining the dynamic impact of carbon emissions trading on the crude oil market and argues that geopolitical and economic policy factors should be considered when discussing the volatility of carbon futures. Furthermore, spillovers are moderated by regional innovation capacities: initially hindered by technical barriers, they eventually transition into facilitation as knowledge accumulates (Li and Du, 2021; Hwang et al., 2023). Meanwhile, Guo and Hu (2024) conducted an in-depth exploration of how energy consumption rights trading and the urban innovation status effect promote ecological civilization and high-quality development. Specifically, regarding relevant policies, Tayyab et al. (2020) provided a sustainability framework for clean textile production systems (wet processing category).

While prior research has substantially enriched our understanding of CETP’s impacts, three critical gaps remain. First, most studies concentrate on emission reduction outcomes, with limited systematic empirical testing of CETP’s direct effects on energy structure transitions. Second, existing analyses typically isolate a single transmission pathway (e.g., technological innovation or industrial upgrading),neglecting the synergistic effects among multiple mechanisms. Third, few studies have addressed the moderating role of structural factors such as urbanization levels or regional economic disparities. Finally, although scholars have begun exploring the application of cutting-edge models in such policy evaluations—such as Guo and Hu (2024), Feng et al. (2024) employing time-varying difference-of-two-models to analyze the dynamic impact of environmental information disclosure on corporate investment value, (Moon et al., 2022; Lv and Guo 2025) employing geometric programming to optimize carbon emission production systems—the vast majority of research in this field remains methodologically constrained by traditional paradigms, with limited innovation in modeling frameworks.

This study addresses these gaps through several key innovations:

Utilizing a Difference-in-Differences (DID) model and decision tree analysis to comprehensively evaluate the net effects of CETP on China’s low-carbon energy transition. Uncovering the dual transmission pathways—scientific and technological inputs, and industrial restructuring—through mediation analysis, thereby moving beyond traditional single-path frameworks. Introducing urbanization as a critical moderating variable and integrating regional heterogeneity within the theoretical framework to better elucidate the economic-geographic mechanisms underlying policy effectiveness, thus providing theoretical guidance for the design of differentiated policies.

Under the carbon emission trading pilot framework, the marginal cost of coal consumption rises significantly. Assuming economically rational behavior, market participants aim to optimize their cost-effectiveness by dynamically adjusting their energy consumption portfolios: reducing reliance on high-carbon energy sources (primarily coal) while progressively increasing the share of lower-carbon alternatives, such as oil and natural gas. Furthermore, enterprises, driven by long-term strategic considerations, expand investments in and applications of renewable energy technologies, including wind and solar power.

This behavioral adjustment leads to a gradual decline in coal’s proportion within the overall energy structure. Two internal mechanisms underlie this process:

First, the policy framework often favors enterprises that meet low-carbon standards by allocating quotas preferentially, allowing such firms to generate surplus emissions reductions and realize economic returns through trading, thus incentivizing continuous investment in low-carbon technologies.

Second, the iterative innovation and large-scale deployment of low-carbon technologies progressively squeeze the market space for high-carbon energy sources, forming a positive feedback loop of “technological innovation - cost reduction - substitution enhancement.”

Hypothesis 1

Carbon emission trading policy promote the low-carbon transition of the energy consumption structure.

First, the institutional incentives embedded within carbon emission trading policy substantially enhance local governments’ financial support for technological research and development (R&D), thereby promoting energy system decarbonization. Numerous empirical studies suggest that once carbon markets surpass critical thresholds in trading volume, local governments’ investments in clean technology R&D experience exponential growth. Through the “emission reduction performance — financial gain” transmission mechanism, local governments optimize fiscal resource allocation structures, transforming environmental compliance pressures into intrinsic motivations for technological innovation. Specifically, governments derive sustainable financial resources from carbon market liquidity cultivation and central fiscal rewards for emission target achievements, thereby establishing a self-reinforcing investment cycle supporting low-carbon technology innovation, which ultimately underpins the low-carbon restructuring of regional energy systems. Figure 2 illustrates the specific channels through which government fiscal expenditures influence the CETP.

Hypothesis 2

Carbon emission trading policy promote the low-carbon transition of the energy structure by incentivizing local governments to increase financial investments in technological research and development.

Second, carbon emission trading policy play a pivotal role in driving industrial restructuring, serving as a key pathway to achieving carbon emission reduction targets. Through well-designed market mechanisms, CETP simultaneously suppresses the disorderly expansion of high-carbon industries and creates opportunities for the development of low-carbon, high-value-added industries, thereby accelerating the transition and rationalization of industrial structures toward low-carbon trajectories.

Hypothesis 3

Carbon emission trading policy promote the low-carbon transformation of the energy structure by facilitating industrial restructuring.

To assess the impact of the carbon emission trading policy on the low-carbon transition of the energy structure, this study adopts a difference-in-differences (DID) model, treating the CETP policy pilot as a quasi-natural experiment. Given that carbon trading policies commenced pilot programs in 2013, provincial panel data from 2004 to 2021 provide both a sufficient pre-policy baseline trend and a post-policy observation window for the DID model. Furthermore, the relatively comprehensive energy balance sheets and carbon emission accounting information ensure estimation accuracy. This period also features a stable macroeconomic institutional environment and high sample homogeneity, effectively controlling for confounding factors. Furthermore, the carbon market mechanism exhibits path dependency and persistent spillover effects. Consequently, the estimation results from this period can be extrapolated beyond 2025, providing a robust historical reference and policy benchmark for quota allocation, sectoral expansion, and regionally differentiated governance under the current “dual carbon” goals. Considering data gaps and other issues, samples from Tibet and Hong Kong, Macao, and Taiwan were excluded. Consequently, data from 30 provinces (municipalities and autonomous regions) spanning 2004–2021 were ultimately selected as the sample. Among these, Beijing, Shanghai, Tianjin, Chongqing, Hubei, and Guangdong are designated as the treatment group (pilot regions), while the remaining provinces serve as the control group.

The DID model is defined as shown in Equation 1, E s i t = δ 1 + β 1 D I D i t + β x X i t + v t + μ i + ε i t (1)

Among them, D I D i t is the policy variable ( D I D i t = t r e a t i t × p o s t i t ); the explanatory variable e s i t is the energy structure, which is used to portray the low-carbon transition of the energy structure; t r e a t i t is the regional grouping variable, if it is the experimental region, t r e a t i t takes the value of 1, otherwise,   t r e a t i t takes the value of 0; p o s t i t is the policy point variable (The policy pilot program commenced in 2014.) referring to the setting of John Guo, if the policy pilot is in the first 6 months of a certain year, then from that year onwards, p o s t i t takes the value of 1; on the contrary, from the latter year onwards p o s t i t   t akes the value of 1, and the year prior to the implementation of the policy takes the value of 0; is the set of control variables. In the year before the implementation of the policy,   p o s t i t takes the value of 0; X i t is the set of control variables. In model (1), β 1 is the effect variable of policy implementation, β x is the regression coefficient of the control variables, and v t and μ i are the year fixed effect and province fixed effect respectively.

The energy structure of each region is calculated on the basis of the consumption of different energy sources, and standard coal conversion factors for different energy sources. The specific equation for calculating the energy structure is shown in Equation 2. E s i t = e n e r g y i t 1   ×   γ 1 ∑ k   e n e r g y i t k ×   γ k (2)

Where e n e r g y i t 1   is the consumption of coal (10,000 tons) in region i in year, e n e r g y i t k is the consumption of k energy in region i in year t ,   γ 1 is the coal conversion factor,   γ k is the k energy conversion factor.

The explanatory variable in this paper is energy structure ( e s ). In this paper, the calculation of energy structure draws on relevant literature (energy structure is expressed as the share of coal’s CO₂ emissions in total emissions). The policy variable in this paper is the carbon emission trading policy pilot ( D I D ).

In order to accurately analyze the policy effects, avoid omitting variables and ensure the robustness of the empirical results, the regression model introduces the following control variables: urbanization rate ( u r b a n ), expressed as the proportion of urban population to total population. The level of economic development ( l e v e l   o f   e c o n o m i c   d e v e l o p m e n t , measured by the value of real per capita income in each province ( G N I   p e r   c a p i t a ). Degree of openness to the outside world ( o p e n ), measured by the proportion of total import and export to GDP.

In order to further analyze the transmission mechanism of the impact of carbon emission trading policy on the low-carbon transition of energy structure, this paper adopts the following mechanism variable: science and technology input ( R & D   i n v e s t m e n t ), which is measured by the ratio of the local financial expenditure on science and technology to the total local financial expenditure.

The data on carbon emission and energy structure used in this paper come from the China Carbon Accounting Database ( C E A D ), and all other data are sourced from the statistical yearbooks published annually on the official website of the National Bureau of Statistics ( N B S ). The descriptive statistics of each variable are shown in Table 1.

Table 2 presents the baseline regression results. Column (1) reports estimates without control variables, while Column (2) adds all control variables. In all specifications, region and year fixed effects are controlled, and standard errors are clustered at the provincial level.

The key findings are:

In Column (1), the estimated coefficient on the policy interaction term (DID) is −0.079-0.079–0.079 and is statistically significant at the 1% level, indicating that carbon emission trading policy significantly promote the low-carbon transition of the energy structure.

In Column (2), after introducing control variables, the DID coefficient remains significantly negative (−0.042-0.042–0.042) at the 1% level, confirming the robustness of the baseline result.

Thus, the empirical evidence supports Hypothesis 1, verifying that CETP contribute meaningfully to optimizing China’s regional energy structures.

In order to further explore the effect of control variables on the experimental results, the decision tree model in machine learning was used to analyze the available data.

The CART algorithm recursively bisects the data by selecting the Gini coefficient as the feature criterion. As can be seen from this decision tree, the features selected are numerical variables, which are recursively divided using numerical bisection points. The decision tree model constructed in this study has 4 layers and 13 nodes.

The analysis of factors influencing the effects of carbon emission trading policy based on the decision tree model shows that the degree of regional urbanization presents a significant core explanatory power in the system of explanatory variables (characteristic importance 76.9%). By setting an urbanization class division threshold of 1.5 (Gini coefficient of 0.416), the sample is effectively differentiated into two groups (230 high-urbanization samples versus 138 low-urbanization samples), and the high-urbanization group demonstrates a more significant effect of carbon emission trading policy implementation. It is worth noting that although the second level decision node reveals the auxiliary explanatory role of regional GDP per capita and openness to the outside world, its explanatory effectiveness decreases by 61.9%–69.9% compared with the degree of urbanization, which confirms the dominant role of the urbanization level in the mechanism of the policy effect from the structural level of the model.

It is speculated that the strengthening mechanism of urbanization degree on the effect of carbon emission trading policy mainly stems from the scale effect and factor agglomeration, and the optimization of the institutional environment in two dimensions. Highly urbanized areas form economies of scale through industrial concentration and population agglomeration, which reduces the unit cost of promoting low-carbon technologies, and at the same time, the completeness of the infrastructure such as smart grids and carbon capture is greatly improved compared with that of the low-urbanized areas, which provides a physical At the same time, the government regulatory system in highly urbanized areas tends to be more perfect and strict, which will effectively curb policy arbitrage.

The results of the mediation effect analysis based on 1,000 b o o t s t r a p samples in Table 4 indicate that science and technology inputs ( R & D   i n v e s t m e n t ), as a mediating variable, play a significant mediating role between carbon emission trading policy and low-carbon transformation of regional energy structure. Specifically, the estimated value of the indirect effect (i.e., the mediating effect) is −0.104, and its 95% confidence interval lies completely below zero and does not cross the zero point, which indicates that the mediating effect is significant. This result reveals that the carbon emission trading policy promotes the optimization of the regional energy structure and low-carbon transition by promoting scientific and technological inputs, which in turn promotes the optimization of the regional energy structure and low-carbon transition. Further analyzing the direct effect, its estimated value is −0.136, and the 95% confidence interval is also completely below zero, without crossing the zero point, which indicates that the direct effect is also significant. In addition, the mediating effect accounts for 43.1% of the total effect, which indicates that the effect is an incomplete mediating effect. This finding verifies hypothesis H2, that is, science and technology inputs have a significant mediating role between carbon emission trading policy and low-carbon transformation of regional energy structure.

Table 5 demonstrates the results of mediating effect analysis based on 1,000 times b o o t s t r a p ampling. In this study, industrial structure ( i s ) is set as the mediating variable to explore the mechanism of the impact of carbon emission trading policy on the low-carbon transition of regional energy structure. Specifically, the estimated value of the indirect effect (in this paper, the mediating effect) is −0.079, and its 95% confidence intervals are all negative (without passing through 0), which indicates that the mediating effect significantly exists. This result reveals that the carbon emission trading policy promotes the optimization of the regional industrial structure by promoting the low-carbon transformation of the regional energy structure. Further analyzing the direct effect, its estimated value is −0.1613, and the 95% confidence intervals are all negative as well, indicating that the direct effect is significant. In addition, the mediating effect accounts for 32.9% of the total effect, which indicates that the effect is an incomplete mediating effect. This finding verifies Hypothesis H3, that is, industrial restructuring has a significant mediating role between carbon emission trading policy and low-carbon transformation of regional energy structure.

The results of the benchmark regression model indicate that the carbon emission trading policy has generally contributed significantly to the low-carbon transition of the regional energy structure. However, given the large differences in the carbon emissions trading markets in the pilot regions, the impact of the policy on the low-carbon transition of the energy structure in the pilot regions may be heterogeneous. Therefore, it is necessary to analyze the heterogeneity of the impact of the carbon emission trading policy in each pilot region. Table 6 demonstrates the regression results of each pilot province and city. The analysis results show that the policy effect of Beijing and Tianjin is significantly negative, which indicates that in these two regions, the carbon emission trading policy can effectively promote the low-carbon transition of the energy structure, and the policy effect of Beijing is the most significant; the policy effect of Shanghai, Hubei, and Chongqing is negative but not significant, which means that in these regions, the carbon emission trading policy does not have a significant effect on the promotion of low-carbon transition of the energy structure; and Guangdong’s policy effect is positive, indicating that in this region, the carbon emission trading policy fails to significantly promote the low-carbon transition of energy structure. To summarize, the policy effect of carbon emission trading in Beijing and Tianjin is significant, while the policy effect in other provinces and cities is not obvious. Table 6 provides specific parameters.

According to administrative divisions, the pilot areas of carbon emission trading policy can be categorized into four municipalities (Beijing Municipality, Tianjin Municipality, Shanghai Municipality and Chongqing Municipality) and two economically powerful provinces (Hubei Province and Guangdong Province). This study further examines whether there are significant differences in the impacts of the carbon emission trading policy on the low-carbon transformation of the energy structure of the above four municipalities and two economic powerhouse provinces. Table 7 demonstrates the results of the regression analysis for the municipality sub-sample and the province sub-sample, where column (1) shows the regression results for the municipality sub-sample and column (2) shows the regression results for the province sub-sample.

The results of the empirical analysis show that the regression coefficients of the carbon emission trading policy are negative for both municipalities directly under the central government and pilot provinces. Specifically, the carbon emission trading policy in municipalities can significantly promote the low-carbon transformation of the regional energy structure; in contrast, the promotion effect of the carbon emission trading policy in provinces is not significant. This suggests that the carbon emission trading policy in municipalities has a more obvious role in promoting the low-carbon transition of energy structure, while the policy effect in provinces is relatively weak. Table 7 provides specific parameters.