A study on the efficiency of carbon emission policy implementation in China based on a novel network super-efficiency EBM-Malmquist index

As the world’s largest developing country, manufacturing hub, and energy consumer, China has consistently ranked among the top global emitters of carbon dioxide. While China currently maintains a high level of control over carbon emissions, carbon emission policies remain a critical step in environmental protection. Therefore, we employ a novel network-based ultra-efficient EBM method combined with the Malmquist index to analyse panel data from 2012 to 2022, evaluating the implementation efficiency of China’s carbon emission policies over the past decade. This approach enables a more scientific quantification of their implementation efficiency and reveals the underlying mechanisms of efficiency losses, thereby promoting a transition in policy from ‘broad-based emissions reduction’ to ‘precision-based governance.’The results of the study show that: (1) the larger the size of the province, the higher the efficiency of carbon emission policy implementation. (2) In the process of regional implementation, the higher the technical efficiency and the larger the scale efficiency of the province, the higher the efficiency of carbon emission policy implementation.

1 Introduction

With the increasing severity of global climate change, countries are gradually recognising the importance of carbon emission reduction in the process of promoting sustainable development. In order to address the global challenges posed by climate change, the international community has adopted a series of agreements and commitments in an attempt to control global temperature rise and reduce greenhouse gas (GHG) emissions. The Paris Agreement, as an important framework for global climate governance, sets temperature-control targets and imposes hard and fast constraints on carbon emission pathways. However, developing countries face complex conflicts between economic development and emission reduction targets. China, as the world’s largest developing country, has not only demonstrated its international responsibility to actively address climate change by proposing a “dual-carbon” target, but has also brought about profound structural challenges between domestic economic growth and carbon emission reduction.

Against this backdrop, how to optimise the implementation of carbon emission policies while ensuring sustainable economic development has become a key issue in China’s efforts to achieve its ‘dual carbon’ goals. Zhou et al. (2013) found that China is in a critical period of economic transformation, with a high reliance on tradition (Zhou et al., 2013). And the research of Yuan et al. (2022) shows that low-carbon and green industries have not yet fully possessed sufficient competitiveness (Yuan et al., 2022). The research of Mulligan et al. points out that: how to balance the relationship between economic development and carbon emission reduction, to improve the efficiency of the policy implementation, and to reduce the cost of emission reduction has become the core problem that needs to be solved urgently (Mulligan, 2005).

From the perspective of economic output, the relationship between carbon emission policies and GDP growth has become increasingly close. The traditional development model is often closely linked to carbon emissions, as Ren et al. (2022) mentioned in their study: with the proposal of the “double carbon” target, the green development concept promotes the gradual contraction of highcarbon industries, while low-carbon industries begin to accelerate the development of (Ren et al., 2022). In this process, the efficiency of policy implementation will directly determine the pressure of emission reduction on economic growth and the pace of structural adjustment. Spadafora et al. (2012) pointed out that: efficient policy implementation can not only alleviate the short-term contraction impact of high-carbon industries, but also provide institutional support for the long-term development of lowcarbon industries (Spadafora, 2012). However, most of the existing studies focus on macro-level analyses, as Wu et al. (2017) mentioned in their study that the existing literature lacks an in-depth exploration of the efficiency of policy implementation, especially in distinguishing between short-term costs and long-term benefits is still insufficient (Wu et al., 2017). Therefore, this study aims to explore the impact of policy implementation efficiency on the relationship between carbon emissions and economic growth using a novel network EBM-Malmquist model, and to analyse the differentiated effects of policy implementation in different regions.

The relationship between urbanisation and carbon emissions is also an important issue in policy design. Yao et al. (2020) found that: in recent years, China has accelerated its urbanisation process, which has promoted the agglomeration of population, resources and industries, but has also led to a rise in the t emissions (Yao et al., 2020). How to reasonably control carbon emission intensity while improving the quality of urbanisation has become an urgent issue. Although traditional policy evaluation methods, such as SFA and DEA, can evaluate the efficiency of resource allocation to a certain extent, their applicability is limited when facing complex regional economic networks, and they are unable to fully reflect the multi-stage process of “energy conversion-economic production-pollution control” mentioned in the study of Mulligan et al. production-pollution control” process mentioned (Mulligan, 2005). Therefore, this study plans to improve the existing assessment methodology by introducing the EBM model, so as to capture the role of policy implementation efficiency in the urbanisation process more accurately and provide a more practical basis for policy formulation. otal amount of energy consumed, which has increased the pressure on carbon.

Regarding the changes in energy structure, especially the changes in the total consumption of crude oil and raw coal, the study by Lei and Ba. (2020) pointed out that the changes in the total consumption of crude oil and raw coal are the determinants of the level of carbon emissions (Lei and Ba, 2020). Coal and oil still dominate China’s energy consumption, and the adjustment of energy structure is crucial to the achievement of emission reduction targets. Wen et al. (2023) concluded that current carbon policies are not only limited to reducing the use of high-carbon energy, but also promote the substitution of clean energy through incentives (Wen et al., 2023). However, Li et al. (2018) study points out that the actual effectiveness of policies is affected by regional differences and the speed of technology diffusion, in which the efficiency of policy implementation plays a key role (Li et al., 2018). Altenburg and Rodrik, (2017) study suggests that effective policies can mitigate capital volatility in the process of divestment of high-carbon energy sources, while promoting green technology innovations with improved investment and return expectations (Altenburg and Rodrik, 2017). Kao and Hwang, (2023) showed that: although studies have explored the relationship between carbon emissions and energy consumption, there is still a lack of in-depth empirical analyses on how the efficiency of policy implementation can drive the restructuring of energy consumption (Kao and Hwang, 2023). Therefore, this study will focus on analysing the impact of policy implementation efficiency on the adjustment of crude oil and crude coal consumption structure, and provide decision support for China’s energy transition.

In summary, this study will focus on the implementation efficiency of carbon emission policy, focusing on analysing its regulating effect on three major areas, namely, economic output, urbanisation development and energy structure adjustment. By introducing an improved and novel network EBM-Malmquist model, a comprehensive assessment framework is constructed, aiming to fill the gaps in existing research and provide empirical evidence for China’s emission reduction policies in different regions, stages and fields. The core objective of the study is to promote the coordinated development of carbon emission reduction and economic development by improving the efficiency of policies, and to provide theoretical support for the green transformation of China’s economy.

2 Research methodology and model construction

2.1 Ultra-efficient EBM index model

The super-efficiency EBM (Epsilon-Based Measure) index model is a cutting-edge method in the field of Data Envelopment Analysis (DEA), which achieves a refined measurement of the efficiency of decision-making units by integrating radial and non-radial distance functions. When the decision-making unit is located in the production frontier plane, the traditional EBM model, similar to the DEA method, gives an upper limit of efficiency value 1, resulting in advanced units that cannot be further distinguished. This limitation is particularly prominent in provincial carbon emission assessment: efficiency values are relatively high in Shanghai and Shanxi, but there are still gradient differences in actual policy implementation effectiveness. Super-efficient EBM allows frontier units to break through the limit of efficiency value 1 by reconstructing the set of production possibilities. Its core lies in removing the evaluated unit from the reference set and constructing a new production frontier whose expression is shown as follows Formulas 1, 2:

$\gamma ·=\mathit{min}\theta -{\epsilon }_x{\displaystyle \sum _{i=1}^m}\frac{{\omega }_i^{-}{s}_i^{-}}{x_{ik}}(1)$

$s.t.\left\{\begin{array}{l}{\displaystyle \sum _{j=1}^n}{X}_{ij}{\lambda }_j-\theta x_{ik}+s_i^{-}=0i=1,2,···,m\\ {\displaystyle \sum _{j=1}^n}{y}_{rj}{\lambda }_j\ge y_{rk}r=1,2,···,s\\ {\lambda }_j\ge 0,s_i^{-}\ge 0\end{array}\right.(2)$

2.2 Malmquist exponential model

The traditional Data Envelopment Analysis (DEA) model is mainly used to assess the static efficiency of a decision unit at a specific point in time and is suitable for cross-sectional comparisons. In order to analyse the dynamic changes in efficiency over time, new methods need to be introduced. Fare et al. (1994) critically extended the index by combining it with DEA theory for the first time and constructed the DEA-Malmquist productivity index model (Fare et al., 1994). This model utilises panel data to effectively measure and break down the dynamic changes in total factor productivity (TFP) across different periods, thereby enabling a dynamic analysis of efficiency evolution. Its expression is shown as follows (Formula 3)

$Tfpch=M\left(x^{t+1},y^{t+1},x^t,y^t\right)={\left[\frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}\times \frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^t,y^t\right)}\right]}^{\frac{1}{2}}(3)$

When $M\left(x^{t+1},y^{t+1},x^t,y^t\right)>1$, total factor productivity TFP rises; when $M\left(x^{t+1},y^{t+1},x^t,y^t\right)=1$, total factor productivity TFP remains unchanged; when $M\left(x^{t+1},y^{t+1},x^t,y^t\right)<1$, total factor productivity TFP falls.

Under variable economies of scale, the index can be broken down into two components, namely, the technical efficiency change index (Effch) and the technological progress index (Tech), as shown in Formula 4. The technical efficiency change index (Effch) can be further broken down into the scale efficiency index (Sech) and the pure technical efficiency index (Pech).

$\text{Tfpch}=\text{Effch}\times \text{Tech}=\mathrm{Sech}\times \text{Pech}\times \text{Tech}(4)$

2.3 New dynamic network super-efficiency EBM model

The novel ultra-efficient EBM-Malmquist method based on a network structure serves as a core optimization of the traditional DEA framework. By integrating “dynamic dimensional expansion + network structure decomposition + super-efficiency measurement,” it realizes the precise evaluation of efficiency in complex production systems. Its key differences from and innovations compared with the traditional network DEA using cross-sectional data are as follows: Based on panel data, this method constructs a dynamic production frontier, breaking through the limitation that cross-sectional data can only reflect single-period efficiency. It is capable of capturing dynamic characteristics such as intertemporal technological progress and changes in technical efficiency, making the evaluation more consistent with the temporal continuity of actual production. Its core advantage lies in decomposing the production system into two sub-stages and one overall stage. The sources of inefficiency are clarified through three independent calculations: separately measuring the efficiency of the first stage (initial inputs - intermediate outputs), the second stage (intermediate outputs - final outputs), and the overall system. This enables accurate localization of whether the overall inefficiency stems from upstream bottlenecks, insufficient downstream conversion, or the synergistic effect of both, resolving the flaw of traditional models that can only evaluate overall efficiency but fail to trace internal problems. Meanwhile, it introduces super-efficiency EBM measurement, which not only overcomes the bottleneck that all DEA-efficient units have an efficiency value of 1 and thus cannot be distinguished by superiority or inferiority but also combines radial and non-radial functions to take both input surpluses and output shortages into account, improving measurement accuracy. Combined with the Malmquist index, it can decompose efficiency changes into dimensions such as technical efficiency and technological progress, providing targeted basis for formulating optimization strategies. (Liu and Li, 2023).

3 Indicator selection and data source

This paper uses the new network EBM-Malmquist index method to study the efficiency of carbon emission policy implementation in eight provinces (Tianjin, Hebei, Shanxi, Shanghai, Zhejiang, Fujian, Jiangxi, Hubei) with better development economy scale in China from 2012 to 2022. In this study, the region of China with the better size of China’s development economy is selected as the research object. These regions usually have higher economic density and scale of development, and their carbon emission policy formulation and implementation systems are relatively better. This characteristic makes these provinces more representative and influential in demonstrating the efficiency of China’s carbon policy implementation. Focusing on these regions helps to understand and analyse the core mechanism and efficiency of China’s public policy implementation in greater depth. In the selection of input and output indicators, this study strives to balance the operability and comparability of the indicators with the feasibility of data acquisition. At the same time, special attention is paid to avoiding significant linear correlations among the selected indicators to reduce the potential interference of such relationships on the final analysis results. The data for the study comes from the National Bureau of Statistics, China Statistical Yearbook, Provincial Statistical Yearbook, Provincial Statistical Bulletin, EDGAR (Emission Database for Global Atmospheric Research) and China Energy Statistical Yearbook. In the first stage, the amount of foreign direct investment (USD billion), urbanisation rate (%), raw coal consumption (million tonnes), and crude oil consumption (million tonnes) are selected as input indicators, and carbon dioxide emissions (100,000 tonnes), carbon dioxide emissions intensity (tonnes/million yuan), and total carbon dioxide emissions costs are selected as output indicators to reflect the impact of the efficiency of China’s carbon emissions policy implementation on GDP growth in the short and medium to long term The second stage is based on the output indicators of the first stage. The second stage is to use the output indicators of the first stage carbon dioxide emissions (100,000 tonnes), carbon dioxide emissions intensity (tonnes/million yuan), and total carbon dioxide emissions costs as the input indicators of the second stage, and to select GDP (million tonnes), financial GDP (billion yuan), and the proportion of financial GDP (%) as the output indicators to reflect the impacts of the efficiency of the implementation of the carbon emission policy on the growth of GDP. The third stage is the comprehensive stage. Here, the input indicators from the first stage and the output indicators from the second stage are integrated to conduct a thorough analysis of the impact of the implementation efficiency of carbon emission policies on the environment of 8 provinces. As shown in Table 1 and Figure 1 The flowcharts for each specific stage are shown in Figures 2-4.

Table 1

Table 1. Input and output indicators for evaluating the efficiency of China’s carbon emission policy implementation.

Figure 1

Figure 1. Indicator system for the dynamic study of carbon emission policy implementation efficiency in 8 provinces in China.

Figure 2

Figure 2. Novel network EBM-Malmquist one-stage indicator system of carbon emission policy implementation efficiency in 8 Chinese provinces.

Figure 3

Figure 3. New network EBM-Malmquist two-stage indicator system for the efficiency of carbon emission policy implementation in 8 provinces of China.

Figure 4

Figure 4. EBM-Malmquist total stage index system for the efficiency of carbon emission policy implementation in eight provinces in China.

4 Empirical analysis

4.1 Static analysis of super-efficient CCR model

This paper takes 2012–2022 as the research time, and selects 8 provinces in China as the research object, and the data come from National Bureau of Statistics, China Statistical Yearbook, Provincial Statistical Yearbook, Provincial Statistical Bulletin, and EDGAR (Global Emission Database for Atmospheric Research), and the data of China Energy Statistical Yearbook (2012–2022). In particular, by comparing and analysing the efficiency of carbon emission policy implementation in each province, the internal reasons affecting it are analysed, and corresponding recommendations are given. The comprehensive efficiency value is measured by the software, and the implementation efficiency of each province in 2012–2022 is obtained as shown in Table 2.

Table 2

Table 2. Comprehensive efficiency of economic growth in the context of carbon emission in 8 provinces of China, 2012–2022 (CCR).

As can be seen from Figure 5, the mean value of efficiency in economic growth of these eight selected provinces shows an upward trend change from 2012 to 2022. Overall, the integrated efficiency values of all provinces are above 1 in the period 2012–2022. The combined efficiency mean is at its best in 2021, at its worst in 2013, and then it grows year by year until it shows a decreasing trend after 2021. The government’s introduction of carbon emission policies has not led to GDP growth in the short term, a phenomenon that further constrains the overall improvement of the efficiency of carbon emission economic growth in the eight provinces. The mean value of the overall efficiency of carbon emission economic growth varies significantly across provinces, with Shanghai taking the top spot with a mean value of 3.076, which is mainly due to its advanced technological level, well-established policy and management system, and strong economic strength, which provides strong support for emission reduction actions. Shanxi follows with an average value of 2.724, which is attributed to initiatives such as industrial restructuring and energy technology upgrading. Provinces such as Hebei and Jiangxi, on the other hand, have relatively low mean values, probably due to factors such as a heavy industrial structure, insufficient technological innovation capacity, and lack of policy implementation, which limit the efficiency of economic growth. In the time dimension, some provinces show fluctuating changes in economic growth efficiency in the context of carbon emissions. For example, Tianjin goes from 1.403 in 2012 to 2.009 in 2022, with ups and downs but an overall upward trend, which reflects Tianjin’s efforts in industrial restructuring and increasing investment in emission reduction technologies, which have gradually improved its economic growth efficiency. Some provinces have experienced significant fluctuations in efficiency values in specific years, such as Hebei, where efficiency values increased in 2013–2014 and then fluctuated, which may be related to factors such as adaptation adjustments in the early stages of policy implementation and the phased construction of industrial projects, indicating that economic growth efficiency is affected by a variety of complex factors and that consolidation of the effects of the policy faces challenges.

Figure 5

Figure 5. Trend of the average value of the comprehensive efficiency of economic growth in the context of carbon emissions in eight provinces of China, 2012–2022.

From the perspective of efficiency rankings, Shanghai has consistently maintained a leading position, reflecting its advantages in economic growth efficiency under carbon emission constraints and the consistency of its policies. Hebei, on the other hand, has mostly ranked towards the bottom, such as in most years between 2012 and 2022 when it ranked eighth. This indicates that its task of improving economic growth is arduous and requires a thorough analysis and resolution of the constraints. These results provide important foundational information for subsequent in-depth exploration of the relationship between carbon emissions reduction and GDP from the perspectives of output, employment, prices, and investment, aiding in a more comprehensive assessment of the impact of carbon emissions reduction policies on economic growth.

4.2 Analysis of novel dynamic network EBM-Malmquist model

4.2.1 Time analysis

Observing Figure 6, it can be seen that both the total stage and the second stage of eight provinces in China show a trend of rising, then falling, then rising from 2012 to 2017, and show an up and down trend from 2017 to 2022, which have similar fluctuation trends, so that it is mainly the second stage that influences the total stage. The second stage is consistent with its trend from 2018 to 2022, but with a smaller enhancement.

Figure 6

Figure 6. Total stage of economic growth efficiency in the context of carbon emissions 2012–2022 Ultra-efficient EBM-Malmquist line graphs.

As shown in Table 3, the total factor productivity (TFP) for the 2020–2021 period was 1.079, exceeding 1, representing an increase of 7.9%. This marks the period with the highest improvement in economic growth efficiency over the past decade. Further analysis of the efficiency of the two stages during this period reveals that the efficiency of the second stage in 2020–2021 was 1.062, an increase of 6.2%. This indicates that carbon dioxide emissions, emission intensity, and total emission costs reached optimal levels during the second stage, effectively reducing carbon emissions and, to some extent, contributing to the improvement in overall stage efficiency. As shown in Table 4, the total factor productivity (TFP) for the first stage (2021–2022) was 1.341, the highest among all years, primarily driven by technological progress and scale efficiency. However, the impact of the first stage’s TFP on the overall TFP was limited. The second phase from 2020 to 2021 serves as the primary phase for enhancing the overall stage’s economic growth efficiency. Analysing the scale efficiency of the second phase, as shown in Table 5, the scale efficiency of the second phase is 1.043, with an improvement of 4.3%.

Table 3

Table 3. 2012–2022 Economic growth efficiency in the context of carbon emissions at all stages of super-efficiency EBM-Malmquist.

Table 4

Table 4. 2012–2022 economic growth efficiency in the background of carbon emissions, one stage of super-efficiency EBM-Malmquist.

Table 5

Table 5. Economic growth efficiency stage II super efficiency EBM-Malmquist in the context of carbon emissions 2012–2022.

It can be analysed that scale efficiency has a greater impact on total factor productivity and thus plays a major role in promoting the efficiency of economic growth in the second stage.

In contrast, the period 2019–2020 registered a total-stage Total Factor Productivity (TFP) of 0.988, below the threshold of 1, indicating a regression of 1.2%. This represents the most significant decline in economic growth efficiency over the decade. Decomposition into the two sub-stages (TFP1, TFP2) reveals that the efficiency of the second stage in 2019–2020 was 0.935, reflecting a decrease of 6.5%. It can thus be inferred that the decline in economic growth efficiency during this period was primarily attributable to the inability of GDP and financial systems in the second stage to effectively facilitate carbon emission reductions, thereby constraining overall economic growth efficiency.

As further illustrated in Table 5, a decomposition of the second-stage TFP for 2019–2020 shows a technological progress index of 0.921, corresponding to a decline of 7.9%. This suggests that technological progress exerted a suppressive effect on economic growth efficiency in the second stage. The underlying reasons may include high carbon dioxide emissions, elevated emission intensity, and insufficient investment in emission abatement.

Consequently, it is imperative for government authorities to enhance the regulation of carbon emissions. Policy interventions should establish binding limits on carbon emissions and increase public awareness regarding the importance of emission reduction, thereby fostering a transition toward more sustainable economic growth.

As depicted in Figure 7, the contribution of technological progress to total factor productivity was more pronounced prior to 2016, whereas the influence of pure technical efficiency became more significant thereafter. This shift aligns with China’s strategic emphasis on fostering a synergistic transition toward green development and high-quality economic growth during this period.

Figure 7

Figure 7. 2012–2022 economic growth efficiency in the context of carbon emissions stage 1 super-efficiency EBM-Malmquist line Plot.

Observation of Figure 8 found that the impact of scale efficiency on total factor productivity is more relevant before 2017, while the impact of technological progress on total factor productivity is more relevant after 2017, which may be related to China’s promotion of the concepts of Industry 4.0, green manufacturing, and the promotion of high-quality development in 2017.

Figure 8

Figure 8. Economic growth efficiency in the context of carbon emissions 2012–2022 two-stage super-efficiency EBM-Malmquist.

4.2.2 Regional analysis

On the other hand, we also observe the regional level to analyse the super-efficiency EBM-Malmquist of each stage of China’s economic growth efficiency in the context of carbon emissions, and Table 6 shows the super-efficiency EBM-Malmquist of each stage of China’s economic growth efficiency in the context of carbon emissions in the sub-region from 2012 to 2022, where TFP* is the total stage, TFP1 is the first stage, and TFP2 is the second stage. Observing Table 6, it can be seen that the super-efficiency EBM-Malmquist value of Zhejiang Province is 1.046 greater than 1, which is 4.6% progress and ranked first, and through the comparison of its TFP1 and TFP2 indices, it can be seen that the efficiency value of Zhejiang Province’s TFP2 is even higher, 1.051, which is 5.1% progress. From this, it can be concluded that the second stage is the main reason to promote the efficiency of economic growth in the context of carbon emissions in Zhejiang Province, into Table 8 to observe the second stage found that the technical efficiency of Zhejiang Province is 1.023, an improvement of 2.3%; the scale efficiency is 1.051, an improvement of 5.1%.

Table 6

Table 6. Super-efficiency of economic growth efficiency by stages in the context of carbon emissions in 8 Chinese provinces EBM-Malmquist.

Observing Table 6 shows that the super-efficiency EBM-Malmquist value of Shanxi Province is 0.983 less than 1, a regression of 1.7%, and ranked last, the efficiency value of TFP2 of Shanxi Province is even lower, 0.999, a regression of 0.1%, which can be obtained, the second stage is the main reason for the decline in the efficiency of the economic growth of Shanxi Province, Located in the second place in the ranking of the efficiency value of Fujian’s TFP1 is much more lower, observing Table 7 shows that Fujian Province’s TFP is 1.019, a progress of 1.9%, entering Table 8 to observe the second stage finds that Shanxi Province’s TFP is 0.976, a regression of 2.4%. The reason may be that the administration has invested too little in industrial structure, forestry ecology, and technological R&D and innovation regarding carbon emissions.

Table 7

Table 7. Super-efficiency of the first stage of economic growth efficiency in the context of carbon emissions in eight Chinese provinces EBM-Malmquist.

Table 8

Table 8. Super-efficiency of the second stage of economic growth efficiency in the context of carbon emissions in eight Chinese provinces EBM-Malmquist.

5 Conclusion and policy recommendations

5.1 Core conclusions

1. Regarding the stage effects: The economic growth efficiency in the second stage plays a more decisive role in the overall stage impact, whereas the first stage shows a negative correlation with the subsequent two stages. The technological efficiency in the second stage becomes the core driving force for Zhejiang’s efficiency improvement. In contrast, insufficient R&D investment in the first stage leads to a decline in Shanxi’s efficiency.

2. Regional disparities: Significant differences exist in policy implementation efficiency across provinces. Shanghai maintains its leading position by leveraging technological, policy, and economic advantages, whereas Hebei has long lagged behind due to its heavy industrial structure and insufficient technological innovation.

3. From a holistic perspective: Total Factor Productivity (TFP) demonstrates the most significant impact on policy implementation efficiency. The first stage marks a critical inflection point: scale effects were more pronounced earlier, while technological progress became the primary driver thereafter. In the second stage, technological advancement paradoxically constrained economic growth efficiency, a phenomenon closely tied to high emissions and low governance investment.

5.2 Policy recommendations

1. While prioritizing carbon emission efficiency, equal attention should be given to economic growth to prevent insufficient R&D funding that could lead to reduced efficiency. It is recommended to enhance the synergy between carbon emission control and economic output, and to establish a dynamic monitoring mechanism covering the entire industrial chain.

2. Implement differentiated governance by promoting the sharing of efficient low-carbon innovation experiences between Shanghai and Zhejiang; simultaneously expand the application and learning of low-carbon technologies in provinces with lower efficiency; and promote the circular economy model in central provinces. Establish a unified carbon reduction data platform, expand the scale of green finance, and facilitate cross-regional emission reduction cooperation.

3. We examine how total factor productivity (TFP) influences policy implementation efficiency. Scale, technological innovation, and economic growth all require long-term monitoring. Our research demonstrates that scale and technological innovation significantly impact economic growth, which in turn substantially affects the effectiveness of carbon emission policies.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author contributions

WP: Data curation, Methodology, Writing – original draft. TL: Formal Analysis, Funding acquisition, Writing – original draft. YC: Investigation, Software, Writing – review and editing. LY: Validation, Visualization, Writing – review and editing. QL: Investigation, Validation, Writing – review and editing. TW: Data curation, Resources, Writing – original draft.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was support by following funds which we thanks for. And the authors would take all responsibility for this paper. Guangzhou Huashang College Key Project - (Number: 2025HSLJ2); Guangzhou Huashang College Featured Convergence Courses: (HSRHKC2024136); Guangdong Planning office of philosophy and Social Sciences Project (Youth) (Project Number: GD20YGL09); Guangzhou Huashang College Key Disciplinary Team Project - (Number: 2025HSELTD3).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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