Abstract
To promote the green development of the global economy and solve the global energy and climate problems, the green transformation of the regional economy is the only way to solve development challenges. Carbon emission trading policies, as an important market mechanism for promoting carbon emission reduction, can further promote green economic development. This study takes the pilot carbon emission trading policy in China as a natural experiment to explore the impact of the carbon emission trading policy on the green transformation of the regional economy and its mechanism. This study accurately measures the efficiency of green transformation of the regional economy. The empirical results indicate that the carbon emission trading policy can promote the green transformation of the regional economy; carbon emission trading policies affect the green transformation of the regional economy through energy structure, urbanization level, and the interaction between energy structure and urbanization level; the model results are robust. Moreover, due to regional differences in original resource endowments, the effect of carbon emission trading policy on regional economic green transformation presents heterogeneity. Therefore, certain policy recommendations can promote the green development of the regional economy, which has important implications for interdisciplinary research, solving energy and climate problems, and high-quality growth of the world economy.
HIGHLIGHTS
This study adopts Super-SBM to measure green transformation.
Emission trading policies can promote the green transformation of the economy.
The effect of different regions presents heterogeneity.
Energy structure, urbanization level, and their interaction are mediating variables.
INTRODUCTION
Under the realistic background of prominent climate risks, intensifying greenhouse effect, resource and energy depletion, and intensifying environmental constraints, solving the contradiction between economic growth and environmental constraints and achieving green transformation of economic development is the key path to breaking through the development dilemma (Kuriqi et al. 2021; Sheng & Liu 2023). Green economic transformation is not only a problem in developing countries, but also affects some developed countries. The international community is making unprecedented efforts to actively engage in international cooperation to jointly deal with the climate crisis. As a result, countries around the world have come up with carbon-neutral targets. By 2030, the US government has vowed to reduce greenhouse gas emissions by 50–52% from 2005 levels, with the goal of becoming carbon neutral by 2050. The United Kingdom has vowed to reduce greenhouse gas emissions by 78% by 2035 compared to 2005 levels. By 2030, Canada wants to reduce greenhouse gas emissions from 2005 levels by 40–45%. In response to global climate change, China has suggested the aim of ‘attaining the peak of carbon emissions by 2030 and becoming carbon neutral by 2060’ as the world's greatest energy user and carbon emitter (Zhou et al. 2018b). Regional green transformation may be viewed as a worldwide ecological environment and economic challenge, with significant implications for multidisciplinary research, energy and climate problem resolution, and high-quality global economic growth (Lis & Mackiewicz 2023).
Over the past few years, this topic has generated considerable academic and policy interest worldwide. Many studies believe that the influencing factors of economic green transformation are closely related to economic growth rate, energy structure, and technical level (Ambec et al. 2013). Some studies discussed the driving factors for reducing carbon emissions and realizing low-carbon transition from the aspects of economic and social support, natural foundation, policy and technology, and industrial organization. On this basis, the influence of some other external factors on the green transformation of the regional economy is also getting more and more attention. For example, environmental regulation and FDI, technological innovation mode selection, local government competition behavior, environmental regulation and clean technology innovation, etc (Yu et al. 2022b). Given that the heart of economic green transformation is the reduction of carbon emissions and carbon intensity, it can be observed that the influencing elements of economic green transformation are comparable to the driving forces of lowering carbon emissions (Zhou et al. 2019). However, from the existing research, there are few studies on the green transformation development of the regional economy from the perspective of carbon emission trading policy. The implementation of carbon emission trading policies is crucial for promoting the green growth of the regional economy, as it serves as a significant market mechanism for reducing carbon emissions. The global economic and energy climate challenges can be resolved by analyzing the mechanism via which carbon emission trading policies impact green economic growth on a regional scale. In order to do this, the research in this study has determined the areas of cutting research and knowledge gaps in the economic green transformation.
Carbon emission trading policies are seen to be the most economically advantageous measure to combat climate change (Baark 2019). The Chinese government released the ‘Notice on Pilot Carbon Emission Trading’ in 2011, allowing pilot carbon emission trading in seven provinces and cities, in order to encourage the execution of China's greenhouse gas emission reduction objectives. The goal of the policy is to establish a national carbon market based on the results of local pilot projects. The first carbon trading market went live in Shenzhen in June 2013, and the other six followed within a calendar year. China's National Carbon Emission Trading Market (NCET) was formally opened on 16 July 2021 (Wu et al. 2019). The carbon market, sometimes referred to as the market for carbon emission trading, is not only a pricing mechanism for greenhouse gas emissions, but also an important policy instrument, which has the advantages of flexibility, cost-saving, and innovation incentives (Ma & Zhang 2019). As a result, it is important to explore carbon emission trading policies and regional economy green transformation, as well as its action path, both theoretically and practically (Chang et al. 2020; Tang et al. 2020).
The aim of this research is to investigate the impact of carbon emission trading policy on the green transformation of the regional economy and explore its important mechanism. Based on the pilot of China's carbon emission trading policy as a natural experiment, this study constructs a Superefficiency model (Super-SBM), a difference-in-differences (DID), and a mediation model to investigate the effect of carbon emission trading policies on the green transformation of the regional economy, using data from 30 Chinese provinces and cities between 2005 and 2018. Super-SBM with non-expected output accurately measures the efficiency of regional economic green transformation. The study of the DID found that carbon emission trading policies can promote the green transformation of regional economies. The research results of the mediation model indicate that carbon emission trading policies affect regional economic green transformation through energy structure, urbanization level, and the interaction between energy structure and urbanization level. On this basis, the robustness of the model results was verified through more discussions. Research has found that due to differences in regional raw resource endowments, the impact of carbon emission trading policies on the green transformation of regional economies exhibits heterogeneity. This further enriched the research results.
This research focuses on China because its relevant lessons have implications for the rest of the world. The reasons are as follows: first, as the emerging country with the highest carbon emissions, China is representative. Despite China's remarkable economic growth over the past few decades, the quality of economic development is still a cause for concern. If China's carbon emission problem is solved, the global carbon emission problem will be alleviated a lot. Second, China has actively responded to environmental and climate issues by taking a series of countermeasures and strengthening its supervision and law enforcement on environmental issues (Farooq et al. 2023). The green development of China's economy faces stricter environmental regulations and standards. A range of policies and initiatives have been implemented to support the green transition, including financial incentives, carbon taxes, subsidies and green credits (Cao et al. 2019). Third, environmental issues are a key concern for China, which has long made reducing carbon emissions a top priority for national development. According to the report of the 20th National Congress of the Communist Party of China, ‘Chinese-style modernization is the modernization of harmonious coexistence between man and nature,’ and we must accelerate the transformation of the green development mode (Liu et al. 2023; Yu 2023). Therefore, it is necessary to focus on China, the largest developing country in the world. By studying the influence and mechanism of its carbon emission trading policy on the green transformation of the regional economy, this study provides an effective reference for the macro-management and policy formulation of other countries.
This research's marginal contribution is mostly evident in three aspects: to begin, there are several measurement methodologies for regional economic green transformation, however, the calculation results vary. In this study, Super-SBM with non-expected output accurately measures the efficiency of regional economic green transformation, taking into account the accuracy of the calculation findings and carrying out a more suitable evaluation of the amount of regional economic green transformation. Second, the relationship between carbon emission trading policy and regional economic green transformation is established through the combination of DID and mediation models, and the impact and mechanism of carbon emission trading policy on regional economic green transformation are demonstrated. This establishes the relationship and offers a new theoretical perspective for resolving global economic and energy climate problems. Third, in a practical sense, by analyzing the effect of the heterogeneity of the original resource endowment in different regions on the impact of carbon trading policies on the green transition, this study provides a reference for regional governments around the world to improve the policy mechanism of carbon emission trading, and provides diversified solutions for promoting the global economy's green transition and solving the global climate problem.
The remainder of this work is organized as follows: a literature review is performed in Section 2, model development is demonstrated in Section 3, and variable selection and descriptive statistics are performed in Section 4. The empirical results are analyzed in Section 5. More discussion is given in Section 6. Section 7 gives the final results and recommendations.
LITERATURE REVIEW
Research on the elements that influence the green transformation and its measurement
The gradual transition of the traditional production mode and development path of high levels of pollution and energy use into the low-carbon, clean, and sustainable development mode and path is referred to as the ‘green transformation’ of the regional economy. Its core objective is to reduce the pressure on the environment while ensuring economic growth, and promote the effective use of resources and enhance the natural environment. The implementation of regional economic green transformation involves many aspects, mainly including but not limited to the following aspects: energy structure adjustment, industrial structure optimization, resource utilization efficiency improvement, urban planning and construction, environmental governance, and protection (Wang et al. 2023c). The green transformation of the regional economy will help build a strong foundation for future economic prosperity, encourage sustainable development, safeguard the environment, improve people's quality of life, and more. Although there is a substantial body of studies on the subject of green transformation, there is no unified standard to measure green transformation. To date, three categories have generally been used to evaluate green transformation. The first is to build an index system that can reflect the results of carbon-free economic change. In terms of calculation methods, most of the above studies use the entropy method and analytic hierarchy process (AHP) to assign weights to indicators so as to carry out the comprehensive evaluation (Pradhan & Mahapatra 2022; Vardhan et al. 2022). The second type obtains the performance level of economic low-carbon transition through the efficiency measurement model, so as to find the development track and turning point of economic low-carbon transition. In the selection of efficiency measurement methods, scholars mostly adopt non-parametric frontier efficiency analysis methods that are free from model assumptions, namely data envelopment analysis (DEA), Super-SBM, and other methods (Wang 2022). The third option is to utilize a single indicator to assess the extent of the low-carbon transition. Commonly used indicators include regional or industry-level carbon productivity, carbon intensity, carbon emissions, or calculate the decoupling index to judge the degree of low-carbon transition (Corbier & Gonand 2023; Wang et al. 2023a). In terms of research methodology selection, the existing measurement methods can provide certain references for the selection of methods to evaluate the transition to a green economy. This study will improve the efficiency of the regional economy's green transformation.
The influencing factors of economic green transformation are closely related to economic growth rate, energy structure, and technical level. Domestic and international researchers have investigated the elements driving carbon emissions reduction and shift to a low-carbon economy from the perspectives of financial and social support, natural basis, policy and technology, and industrial organization (Bian et al. 2023). Most existing research focuses on discussing the impact of factors such as Environment, Social and Governance (ESG) (Liu & Zhang 2023), policies and regulations (Hang 2022), power design (Hasibuan et al. 2022), energy environment (Kuriqi & Jurasz 2022), and finance (Khan et al. 2022) on the green development of the regional economy. Among them, the influencing factors of industrial low-carbon transformation have received more attention, and according to relevant researchers both at home and abroad, the key elements impacting China's green development include energy construction, energy-saving technology, financial framework, and economic growth rate (Tan et al. 2023). The discussion of other external factors includes environmental regulation and FDI, technological innovation mode selection, local government competition behavior, environmental regulation and clean technology innovation (Feng et al. 2021; Hong et al. 2022). Because the heart of economic green transformation is the reduction of carbon emissions and carbon intensity, the influencing elements of economic green transformation are comparable to the drivers of lowering carbon emissions (Yu et al. 2022a).
From the existing research, the green transformation of the regional economy has become the key to solving energy and climate problems and promoting high-quality global economic growth. However, in the existing research, the concept of economic green transformation has not been uniformly defined and measured. There is insufficient presentation and analysis of the measurement results of regional green transformation, which simply equates regional economic green transformation with carbon emission reduction. Most studies only consider the role of policies and regulations, energy environment, power design, technological innovation, local government competition, industrial upgrading, finance, ESG and other factors in regional economic green transformation. Few studies have innovatively analyzed the direct and mediating effects of multiple factors from the perspective of emissions trading policies. In addition, further discussion of the findings is insufficient, especially the robustness and heterogeneity of the results. This study complements existing research. This has important implications for interdisciplinary research, solutions to energy and climate issues, and high-quality growth of the world economy.
Research on the relationship between carbon emission trading policy and green transformation
The typical nature of public ecological resources and the negative externality of environmental problems are important factors behind the lack of endogenous impetus for regional economic green transformation. By combing through the existing studies, it is found that energy structure and urbanization level are important influencing factors for regional green transformation (Chen et al. 2023; Gilmore et al. 2023). Energy structure refers to the composition and proportion of various primary and secondary energy sources in total energy production or total energy consumption. Energy structure is an important content of energy system engineering research, which directly affects the final energy use of various departments of the national economy and reflects people's living standards. The optimization of the energy structure is conducive to the realization of sustainable development goals that are compatible with economic and environmental benefits. Urbanization refers to the process of transforming the rural populations into the urban populations (Lin & Zhang 2023). The level of urbanization is an important indicator of the economic development of a country or region, as well as an important indicator of the degree of social organization and management of a country or region. The level of urbanization usually reflects the process and degree of population agglomeration in cities. When the population gathers in some specific areas, it will certainly have an impact on the energy structure and the green transformation of the economy in the region (Sun et al. 2023). Existing studies have paid some attention to the relationship between energy structure, urbanization level, and energy environment, but few have explored the relationship between energy structure and urbanization and regional economic green development. Grodzicki & Jankiewicz (2022), Wang et al. (2023b), and Zheng et al. (2023) explored the role of the energy mix, industrial structure, and labor on economic complexity and ecological footprint. In the context of carbon neutrality, Wang et al. (2023d) explored the important relationship between energy structure transformation and cleaner production and government regulation. Kartal et al. (2023) further analyzed the impact of different types of energy on carbon emissions from the perspective of nuclear and renewable energy generation. Pande et al. (2023) used the Google Earth Engine platform to study the intersecting effects of urbanization and land cover change on urban climate and agriculture in Aurangabad, India. Wu et al. (2023) analyzed the relationship between land use, urbanization and ecosystem service value in China, and explored whether there was spatial and temporal heterogeneity. Warsame et al. (2023) analyzed Somalia's sustainable environment, arguing that conflict, urbanization, and globalization play important roles in environmental degradation and emissions. Wang et al. (2022) analyzed the impact of urbanization, trade openness and other factors on the energy environment.
Government policy intervention has become an effective way to promote the green transformation of the regional economy (Su et al. 2022). As an important market mechanism to promote carbon emission reduction, carbon emission trading policy can further promote the green development of the economy, so as to better cope with the global energy and climate issues. Emissions trading policies require companies to obtain carbon credits from government agencies and then trade carbon permits on the market (Chang et al. 2023). It is a ‘government-created, market-operated’ institutional innovation, the main purpose of which is to control the total amount of carbon emissions, and then reduce the quantitative emission of greenhouse gases at a lower cost. Under the threat of global warming and energy security, carbon emission trading has become a research topic of energy and climate economics. There is little discussion on the impact of carbon emission trading policy on energy structure and urbanization level. Yu & Zhang (2021) use the differential method to investigate the impact of carbon emission trading policies on industrial and regional carbon emissions, and evaluate the low-carbon innovation role of carbon trading. Some existing studies have used spatial econometric models (Li et al. 2023) and recursive dynamic CGE model (Cui & Wang 2023) to analyze the effect of carbon emission trading policies. Wang et al. (2019) and Fu et al. (2021) used dummy variables to indicate whether or not to implement carbon trading, and then used DDD or PSM-DID methods to study the impact of carbon trading policies on carbon market emission levels, collaborative emission reduction, technological progress, and employment. It can be seen that the existing research mainly focuses on the impact of carbon emission trading policies on some aspects of green development. There are few studies that specifically consider the impact of carbon emission trading policies on energy structure and urbanization level. Perhaps because the concept of regional green transition has not received much attention until recently, few studies have considered energy structure and urbanization level as the mediating variables in the study of emissions trading policies and regional green transition. So if the research looks at some aspects of carbon trading policies and regional green transitions, there will be more to look at. Based on the above theoretical demonstration, this paper proposes the following hypotheses.
Hypothesis 1 Carbon emission trading policy can promote the green transformation of the regional economy.
Hypothesis 2 The carbon emission trading policy promotes the green transformation of the regional economy through the optimization of energy structure.
Hypothesis 3 The carbon emission trading policy promotes the green transformation of the regional economy through the improvement of urbanization level.
Although energy structure and urbanization level may play intermediary roles as independent mediating variables, in the process of promoting the green transformation of the regional economy, the two are difficult to separate theoretically and practically, which is proved by the close connection between them (Chen et al. 2018; Chang et al. 2023). The optimization of energy structure will improve the level of urbanization to a certain extent, and the improvement of urbanization level will in turn optimize the energy structure. Can carbon emission trading policies promote energy structure optimization and urbanization? Can the synergy of energy structure and urbanization promote the green transformation of the regional economy? Based on the above theoretical demonstration, considering the possible linkage effect of these two mechanisms, and further introducing the interaction term between energy structure and urbanization level, this study proposes hypothesis 4.
Hypothesis 4 The carbon emission trading policy promotes the green transformation of the regional economy by adjusting the interaction between energy structure and urbanization level.
Literature gaps
The path of the carbon emission trading policy effect on green transformation.
MODEL CONSTRUCTION
In this part, the Super-SBM, DID and mediation model are proposed. Compared with the traditional DEA model, undesired output Super-SBM solves the problem of relaxation, and also solves the problem that when there are a large number of input and output indicators, the efficiency value of multiple DMU often reaches the maximum value 1, and the efficiency of effective DMU cannot be compared. The measurement results are more accurate. DID is often used to study the effect of policy evaluation. Compared with other methods, it is easier to understand and apply. At the same time, the endogeneity problem can be avoided to a great extent, that is, the interaction effect between independent variables and dependent variables can be effectively controlled. The mediation model means that when the influence of independent variables on dependent variables is considered, if the dependent variable is affected by the influence variable , then M is called the intermediary variable. The biggest advantage of the mediation model is to uncover the black box between the independent variable and the dependent variable on the basis of the known relationship between the independent variable and the dependent variable, and find out the mechanism of their action.
Super-SBM model














DID model
In Formula (1), represents Green transformation, i represents province and city, and t represents year;
represents a constant term,
represents whether the province or city is affected by carbon emission trading policy,
represents the municipality or province that has adopted carbon emission trading policies,
represents the municipality or area that has not adopted carbon emission trading policies,
represents the dummy parameter of carbon emission trading policy's implementation; if
, then
, indicates the period following the carbon emission trading policy's adoption as a dummy value, where
,
, represents the time dummy variable before the implementation of carbon emission trading policy.
is the control variable,
indicates the provinces' and regions' fixed effect,
depicts the fixed effect of time, and
symbolizes the phrase for random error.
Mediation model

Overall, the benefits and complementarity of the three models discussed above are evident. The Super-SBM model, which includes undesired output, accurately measures the efficiency of regional economic green transformation. The combination of the DID and the mediation model can effectively investigate the influence and mechanism of the policy variable of carbon emission trading policy on the dependent variable of regional economic green transformation, enriching and confirming the research results.
VARIABLE SELECTION AND DESCRIPTIVE STATISTICS
This research explored the impact and mechanism of carbon emission trading policy on the green transformation of the regional economy. The green transition was taken as the explained variable and measured by GTFP. Carbon emission trading policy is taken as the explanatory variable. With the level of economic development, the degree of opening up to the outside world, energy consumption and fixed assets stock as control variables, the per capita GDP value, the percentage of foreign direct investment in GDP (), the regional energy consumption value and the total regional fixed assets value were respectively expressed. Specific research is carried out with the value of energy structure, the value of urbanization level and the value interaction term of the two as the intermediary variables, and the share of coal use in total energy consumption (
), the percentage of urban population (
), and the value interaction term between the above two factors are measured respectively. Table 1 shows the selection and measurement of each indicator.
Index selection and measurement
Variable type . | Variable name . | Variable symbol . |
---|---|---|
Explained variable | Green transformation | Green |
Intermediate variable | Energy structure | lnENS |
Urbanization level | URL | |
Energy structure*Urbanization level | lnENS*URL | |
Control variable | Level of economic development | lnPGDP |
Degree of opening up | OPEN | |
Population size | lnPOP | |
Energy consumption | lnENC | |
Stock of fixed assets | lnFCS |
Variable type . | Variable name . | Variable symbol . |
---|---|---|
Explained variable | Green transformation | Green |
Intermediate variable | Energy structure | lnENS |
Urbanization level | URL | |
Energy structure*Urbanization level | lnENS*URL | |
Control variable | Level of economic development | lnPGDP |
Degree of opening up | OPEN | |
Population size | lnPOP | |
Energy consumption | lnENC | |
Stock of fixed assets | lnFCS |
Note: Green is measured by green total factor productivity, lnENS is the share of coal consumption in total energy consumption (%), URL is the proportion of urban population to total population (%), lnENS*URL is the interaction between energy structure and urbanization level (%), lnPGDP is the logarithm of regional GDP per capita, OPEN is the foreign direct investment as a percentage of GDP (%), lnPOP is the annual average population logarithm of the region, lnENC is the regional energy consumption logarithm, and lnFCS is the logarithm of regional fixed asset.
This research pays more attention to the level of green transformation in various provinces and regions in China. Therefore, the study sample covers panel data from 30 provinces and cities in China (except Hong Kong, Macau, Taiwan, and Tibet) between 2005 and 2018. The data come from the National Bureau of Statistics of China, China Energy Statistical Yearbook, China Environment Statistical Yearbook, China Urban Statistical Yearbook, etc.




Green transformation measurement index
Indicators . | Category . | Measurement . |
---|---|---|
Input indicator | Labor input | Number of employed persons (10 thousand people) |
Capital input | Fixed capital stock (100 million yuan) | |
Energy efficiency | Energy consumption (10,000 tons of standard coal) | |
Output indicator | Desired output | Gross regional product (100 million yuan) |
Undesired output | ![]() | |
![]() |
Indicators . | Category . | Measurement . |
---|---|---|
Input indicator | Labor input | Number of employed persons (10 thousand people) |
Capital input | Fixed capital stock (100 million yuan) | |
Energy efficiency | Energy consumption (10,000 tons of standard coal) | |
Output indicator | Desired output | Gross regional product (100 million yuan) |
Undesired output | ![]() | |
![]() |
Green total factor productivity trend chart of 30 provinces and cities in China.
Variable description statistics
Variable . | Sample size . | Mean . | Standard deviation . | Minimum . | Maximum . |
---|---|---|---|---|---|
Green | 420 | 1.466 | 0.846 | 0.310 | 4.760 |
lnENS | 418 | 0.950 | 0.403 | 0.0380 | 2.423 |
URL | 390 | 0.534 | 0.136 | 0.268 | 0.938 |
lnPGDP | 420 | 9.079 | 0.991 | 6.230 | 11.08 |
OPEN | 420 | 14.92 | 23.30 | 0.01000 | 111.3 |
lnPOP | 420 | 8.178 | 0.749 | 6.300 | 9.420 |
lnENC | 420 | 9.271 | 0.705 | 6.710 | 10.61 |
lnFCS | 420 | 10.06 | 0.938 | 7.360 | 11.97 |
Variable . | Sample size . | Mean . | Standard deviation . | Minimum . | Maximum . |
---|---|---|---|---|---|
Green | 420 | 1.466 | 0.846 | 0.310 | 4.760 |
lnENS | 418 | 0.950 | 0.403 | 0.0380 | 2.423 |
URL | 390 | 0.534 | 0.136 | 0.268 | 0.938 |
lnPGDP | 420 | 9.079 | 0.991 | 6.230 | 11.08 |
OPEN | 420 | 14.92 | 23.30 | 0.01000 | 111.3 |
lnPOP | 420 | 8.178 | 0.749 | 6.300 | 9.420 |
lnENC | 420 | 9.271 | 0.705 | 6.710 | 10.61 |
lnFCS | 420 | 10.06 | 0.938 | 7.360 | 11.97 |
Note: Green is measured by green total factor productivity, lnENS is the share of coal consumption in total energy consumption (%), URL is the proportion of urban population to total population (%), lnENS*URL is the interaction between energy structure and urbanization level (%), lnPGDP is the logarithm of regional GDP per capita, OPEN is the foreign direct investment as a percentage of GDP (%), lnPOP is the annual average population logarithm of the region, lnENC is the regional energy consumption logarithm, and lnFCS is the logarithm of regional fixed asset.
EMPIRICAL TEST AND ANALYSIS
In this part, this research uses the DID and mediation model to analyze the impact of carbon emission trading policy on the green transformation of the regional economy.
Analysis of the results of difference-in-differences
Secondly, this research carried out baseline regression. The results of the basal regression are shown in Table 4. Benchmark regression results preliminarily confirm that carbon emission trading policies can affect the level of green transition, improve production efficiency and improve environmental benefits. Columns (1) and (2) discussed the cases where no control was added and the cases where control variables were added respectively. Both models were analyzed under the condition of fixed time. Columns (3) and (4) discussed the cases without providing control variables and with applying control variables, and both models were analyzed under the condition of fixed region. Columns (5) and (6) discussed the cases where no control was added and the cases where control variables were added respectively. Both models were analyzed under the conditions of fixed time and region. The fact that the coefficient is highly positive at the 1% significance level shows that the carbon emission trading policy has greatly aided the transition to a green economy. Hypothesis H1 was verified.
Baseline regression result
Variable . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
Did | 1.019*** (0.1412) | 0.467*** (0.1077) | 0.931*** (0.0773) | 0.198** (0.0714) | 0.567*** (0.0613) | 0.291*** (0.0676) |
lnPGDP | 1.040*** (0.0957) | 1.670*** (0.1497) | 1.503*** (0.2345) | |||
OPEN | −0.00511** (0.0017) | −0.00902*** (0.0021) | −0.00762*** (0.0021) | |||
lnPOP | −0.342*** (0.0965) | 0.691 (0.3872) | 0.597 (0.3478) | |||
lnENC | −0.186* (0.0830) | −1.256*** (0.1501) | −0.861*** (0.1591) | |||
lnFCS | −0.624*** (0.0427) | −0.378*** (0.0919) | −0.409*** (0.1233) | |||
Term of constant | 1.164*** (0.1383) | 2.637*** (0.4642) | −4.162 (3.1183) | 1.510*** (0.0797) | −5.344 (3.5789) | |
Fixed time | Yes | Yes | Yes | Yes | ||
Fixed by province | Yes | Yes | Yes | Yes | ||
Adj. R2 | 0.1970 | 0.982 5 | 0.975 7 | 0.9230 | 0.9136 | 0.9347 |
Sample size | 420 | 420 | 420 | 420 | 420 | 420 |
Variable . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
Did | 1.019*** (0.1412) | 0.467*** (0.1077) | 0.931*** (0.0773) | 0.198** (0.0714) | 0.567*** (0.0613) | 0.291*** (0.0676) |
lnPGDP | 1.040*** (0.0957) | 1.670*** (0.1497) | 1.503*** (0.2345) | |||
OPEN | −0.00511** (0.0017) | −0.00902*** (0.0021) | −0.00762*** (0.0021) | |||
lnPOP | −0.342*** (0.0965) | 0.691 (0.3872) | 0.597 (0.3478) | |||
lnENC | −0.186* (0.0830) | −1.256*** (0.1501) | −0.861*** (0.1591) | |||
lnFCS | −0.624*** (0.0427) | −0.378*** (0.0919) | −0.409*** (0.1233) | |||
Term of constant | 1.164*** (0.1383) | 2.637*** (0.4642) | −4.162 (3.1183) | 1.510*** (0.0797) | −5.344 (3.5789) | |
Fixed time | Yes | Yes | Yes | Yes | ||
Fixed by province | Yes | Yes | Yes | Yes | ||
Adj. R2 | 0.1970 | 0.982 5 | 0.975 7 | 0.9230 | 0.9136 | 0.9347 |
Sample size | 420 | 420 | 420 | 420 | 420 | 420 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. lnPGDP is the logarithm of regional GDP per capita, OPEN is the foreign direct investment as a percentage of GDP (%), lnPOP is the annual average population logarithm of the region, lnENC is the regional energy consumption logarithm, and lnFCS is the logarithm of regional fixed asset.The robust standard error is shown in parentheses.
Analysis of the results of the mediation model
In this part, the research uses the mediation model to explore the transmission process between the carbon emission trading policy and the green transition. Energy structure, urbanization level and the interaction between urbanization and energy structure were selected as the intermediary variables to analyze the mechanism of carbon emission trading policy on the green transformation of the regional economy. Firstly, according to the results obtained from formula (1) of the DID, the impact of carbon emission trading policy on the green transformation of the regional economy is analyzed. Then, based on the study of the results of DID, the paper explores the mechanism of the impact of carbon emission trading policy on the green transformation of the regional economy. That is, if the results of the DID show that the coefficient is significant, formula (2) of the mediation model is used to test the impact of carbon emissions trading policies on the intermediary variables. If the resulting coefficient is still significant, the Mediation Model formula (3) is used to test the intermediary effect results.
Table 5 shows the mediating effects of energy structure as a mediating variable. The model demonstrates that the impact of energy structure on green transformation is significantly negative at the confidence level of 0.01, supporting the aforementioned hypothesis that improving energy structure is the only way to significantly increase the efficiency of green transformation. The energy structure, which is the fundamental predictor of the usage of energy, is the key to increasing the degree of green conversion. The model goes on to analyze the impact of carbon emission trading policy on the green transformation and discovers that, at a confidence level of 0.01, carbon emission trading policy has a significant influence on regional energy structure, which is consistent with the projected assumption. On the whole, the energy structure is impacted by the carbon emission trading policy, which has an impact on the effectiveness of green transformation. Both the carbon emission trading policy and the energy structure are incorporated into the model in order to further examine if there is an intermediate influence of the energy structure. The findings show that even after controlling for the indirect effect of the energy framework, the influence of carbon emission trading policy on the green transition is still significant at the confidence level of 0.01, and the estimated coefficient is only slightly lower than the basic regression coefficient. With an adjusted intermediate impact of 0.061, more evidence is offered to support the presence of the intermediary effect. After correcting for the intermediate influence of energy structure, the total impact of carbon emission trading policy on conversion is 0.506.
Testing of indirect effects of energy structure
Variable . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
Did | 0.567*** (0.0613) | −0.176*** (0.0428) | 0.506*** (0.0610) | |
lnENS | −0.462*** (0.0767) | −0.338*** (0.0721) | ||
Term of constant | 1.510*** (0.0797) | 1.000*** (0.0557) | 1.931*** (0.1147) | 1.849*** (0.1060) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.8143 | 0.9034 | 0.9182 |
Sample size | 420 | 418 | 418 | 418 |
Variable . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
Did | 0.567*** (0.0613) | −0.176*** (0.0428) | 0.506*** (0.0610) | |
lnENS | −0.462*** (0.0767) | −0.338*** (0.0721) | ||
Term of constant | 1.510*** (0.0797) | 1.000*** (0.0557) | 1.931*** (0.1147) | 1.849*** (0.1060) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.8143 | 0.9034 | 0.9182 |
Sample size | 420 | 418 | 418 | 418 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. lnENC is the regional energy consumption logarithm. The robust standard error is shown in parentheses.).
Table 6 lists the mediating effect results of urbanization level as a mediating variable. The model supports the prior hypothesis that the efficiency of regional economic green transformation will be significantly improved when the urbanization rate is reduced. The study shows that the impact of urbanization level on green transformation is significantly negative when the trust level is 0.01. The key to improving the effectiveness of green transformation is to pay attention to the level of urbanization, and the carbon emission trading policy is the primary determinant. Further examining the impact of carbon emission trading policy on urbanization level, the model finds that at 0.01 confidence level, carbon emission trading policy significantly affects urbanization level, supporting the prediction. In general, carbon emission trading policies affect the efficiency of green transition by affecting the level of urbanization. At the same time, urbanization level and carbon emission trading policy are included in the model to further investigate whether there is an intermediate impact on urbanization level. The results show that even considering the indirect effect of urbanization level, the effect of carbon emission trading policy on GTFP is still significant at the confidence level of 0.01. Moreover, the estimated coefficient is only slightly higher than the basic regression coefficient. The intermediation effect is further proved, and the corrected intermediation effect is 0.013. After adjusting the intermediate effect of urbanization level, the overall impact of the scheme on environmental transformation is 0.580.
Testing of indirect effects of urbanization level
Variable name . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | 0.567*** (0.0613) | −0.0272*** (0.0046) | 0.580*** (0.0680) | |
URL | −1.943*** (0.7855) | −0.0296 (0.7493) | ||
Term of constant | 1.510*** (0.0797) | 0.391*** (0.0060) | 2.226*** (0.3225) | 1.527*** (0.3048) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.9820 | 0.8931 | 0.9115 |
Sample size | 420 | 390 | 390 | 390 |
Variable name . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | 0.567*** (0.0613) | −0.0272*** (0.0046) | 0.580*** (0.0680) | |
URL | −1.943*** (0.7855) | −0.0296 (0.7493) | ||
Term of constant | 1.510*** (0.0797) | 0.391*** (0.0060) | 2.226*** (0.3225) | 1.527*** (0.3048) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.9820 | 0.8931 | 0.9115 |
Sample size | 420 | 390 | 390 | 390 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. URL is the proportion of urban population to total population (%). The robust standard error is shown in parentheses.
The first two tests mainly examine the mediating effect of energy structure and urbanization level as independent mediating variables. However, in the process of promoting the green transformation of the regional economy, the two are difficult to separate theoretically and practically, which is proved by the close connection between them. The optimization of the energy structure will improve the level of urbanization to a certain extent, and the improvement of the level of urbanization will in turn optimize the energy structure. Can carbon emission trading policies promote the optimization of energy structure and promote urbanization? In order to test the linkage effect of the two mechanisms, the interaction term between energy structure and urbanization level is further introduced.
Table 7 focuses on the intermediary effect results of the interaction between energy structure and urbanization level. The model shows that when the confidence level is 0.01, the interaction term between energy structure and urbanization level has a significantly negative impact on the green transformation of the regional economy, indicating that only by reducing the interaction term between energy structure and urbanization level can the efficiency of green transformation be significantly improved, which is consistent with previous theories. How carbon emission trading policies affect the relationship between energy structure and urbanization level needs further research. At the confidence level of 0.01, the model results show that the carbon emission trading policy has a significant impact on the energy structure, which is consistent with the expected hypothesis. In general, carbon emission trading policies affect the efficiency of regional economic green transformation through the interaction between energy structure and urbanization level. In order to further test whether the interaction term between energy structure and urbanization level has an intermediary effect, this paper also adds the carbon emission trading policy and the interaction term between energy structure and urbanization level into the model. The results show that even after controlling the indirect influence of the interaction term between energy structure and urbanization level, the impact of carbon emission trading policy on green transition is still significant at the confidence level of 0.01. The revised intermediary effect is 1.400, which further supports the existence of the intermediary effect. The calculated coefficient is also slightly larger than the basic regression coefficient. After adjusting the initial effect of the interaction term between energy structure and urbanization level, the total effect of carbon emission trading policy on regional economic green transformation is −0.833.
Testing of indirect effects of energy structure*urbanization level
Variable name . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | 0.567*** (0.0613) | −0.183** (0.0246) | −1.154*** (0.1321) | −0.833*** (0.1345) |
URL*lnENS | 0.428*** (0.0663) | |||
Term of constant | 1.510*** (0.0797) | 0.390*** (0.0319) | 1.932*** (0.0998) | 1.841*** (0.955) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.9110 | 0.8073 | 0.9204 |
Sample size | 420 | 389 | 389 | 389 |
Variable name . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | 0.567*** (0.0613) | −0.183** (0.0246) | −1.154*** (0.1321) | −0.833*** (0.1345) |
URL*lnENS | 0.428*** (0.0663) | |||
Term of constant | 1.510*** (0.0797) | 0.390*** (0.0319) | 1.932*** (0.0998) | 1.841*** (0.955) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.9136 | 0.9110 | 0.8073 | 0.9204 |
Sample size | 420 | 389 | 389 | 389 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. lnENS*URL is the interaction between energy structure and urbanization level (%). The robust standard error is shown in parentheses.
MORE DISCUSSION
In this section, the study of this research conducted more discussions, including robustness testing and heterogeneity analysis, to make the research results more robust and rich. This has a significant impact on interdisciplinary research, addressing energy and climate issues, and high-quality growth of the world economy.
Test for robustness
To further ensure the robustness of the results, robustness tests were conducted, including sensitivity analysis and placebo testing. The inspection analysis is as follows.
For sensitivity analysis, this study conducted sensitivity analysis on the key parameters of dependent variables and control variables, respectively. The specific practices are as follows. First, the sensitivity analysis of dependent variables is carried out. The dependent variable in this study is the green transformation of the regional economy, and the green total factor productivity is measured. Based on the existing research practice, this study uses total factor productivity (TFP) to replace GTFP to carry out sensitivity analysis. Table 8 focuses on the analysis results. Columns (1) and (2) represent the results without and with control variables added respectively, and the coefficients are not significant, indicating that the results of the original model are robust. Second, the sensitivity analysis of control variables is carried out. By increasing the sample size, control variables are increased. In this study, total factor carbon emission efficiency was added to the model as a control variable for analysis. Columns (3) and (4) in Table 8 list the analysis results. Columns (3) and (4), respectively, represent the results without and with control variables added. The coefficients of the differenced terms are still significant, indicating that the results of the original model are robust. Combined with the above two sensitivity analyses, the robustness and reliability of the research results are verified.
Baseline regression result
Variable . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | −0.0397 (0.0714) | 0.0324 (0.0907) | 0.567*** (0.0613) | 0.179*** (0.0538) |
lnPGDP | −0.330 (0.3146) | 1.400*** (0.1991) | ||
OPEN | 0.00239 (0.0028) | −0.0034* (0.0017) | ||
lnPOP | −0.0603 (0.4666) | 0.654** (0.2805) | ||
lnENC | 0.296 (0.2134) | −0.749 *** (0.1342) | ||
lnFCS | 0.0199 (0.1655) | −0.481*** (0.1010) | ||
Term of constant | 2.391*** (0.0928) | 2.846 (4.8010) | 1.510*** (0.0797) | −0.412 (3.0567) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.8586 | 0.8582 | 0.9136 | 0.9564 |
Sample size | 420 | 420 | 420 | 420 |
Variable . | (1) . | (2) . | (3) . | (4) . |
---|---|---|---|---|
did | −0.0397 (0.0714) | 0.0324 (0.0907) | 0.567*** (0.0613) | 0.179*** (0.0538) |
lnPGDP | −0.330 (0.3146) | 1.400*** (0.1991) | ||
OPEN | 0.00239 (0.0028) | −0.0034* (0.0017) | ||
lnPOP | −0.0603 (0.4666) | 0.654** (0.2805) | ||
lnENC | 0.296 (0.2134) | −0.749 *** (0.1342) | ||
lnFCS | 0.0199 (0.1655) | −0.481*** (0.1010) | ||
Term of constant | 2.391*** (0.0928) | 2.846 (4.8010) | 1.510*** (0.0797) | −0.412 (3.0567) |
Fixed time | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes |
Adj. R2 | 0.8586 | 0.8582 | 0.9136 | 0.9564 |
Sample size | 420 | 420 | 420 | 420 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. lnPGDP is the logarithm of regional GDP per capita, OPEN is the foreign direct investment as a percentage of GDP (%), lnPOP is the annual average population logarithm of the region, lnENC is the regional energy consumption logarithm, lnFCS is the logarithm of regional fixed asset. The robust standard error is shown in parentheses.
Analysis of the heterogeneity
The economic foundation, industrial structure, level of urbanization, and population density vary across the pilot cities. Will regional disparities have an impact on how carbon emission trading policy affects the green transformation of the regional economy? In order to deeply analyze the role of carbon emission trading policies in the green transformation of the regional economy, and further explore the interference of heterogeneity and effects of factors such as the level of original economic development, foreign direct investment, population size, energy consumption, and fixed asset stock in the region, the interaction term between the core explanatory variable and the control variable was introduced in baseline regression. Specifically, it includes the interaction terms between carbon emission trading policies and economic development level, foreign direct investment level, population size, energy consumption, and fixed asset stock, and then tests the interaction coefficient to detect the heterogeneity of control variable effects in different regions, in order to provide a better reference for other countries around the world to carry out regional economic green transformation.
Table 9 highlights the results of heterogeneity analysis. Columns (1) and (5) in Table 9 report the research results of the interaction items between carbon emission trading policy and economic development level, and the interaction items between carbon emission trading policy and fixed assets stock on the impact of regional economic green transformation. The study found that the coefficients of these two interaction terms are both positive and not significant. Furthermore, it explains that the impact of carbon emission trading policies on the green transformation of the regional economy will not exhibit heterogeneity due to differences in the development level of the original economy and the stock of fixed assets in the region.
Heterogeneity analysis
Variable . | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
did | 0.634 (1.3790) | 1.377*** (0.1177) | 3.387*** (0.8895) | 4.666*** (1.4060) | 0.197 (1.0091) |
did*lnPGDP | 0.0309 (0.1398) | ||||
did*OPEN | −0.0130*** (0.0027) | ||||
did*lnPOP | −0.301** (0.1089) | ||||
did*lnENC | −0.397** (0.1496) | ||||
did*lnFCS | 0.0710 (0.0919) | ||||
Term of constant | 1.385*** (0.1325) | 1.385*** (0.1333) | 1.385*** (0.1331) | 1.385*** (0.1324) | 1.385*** (0.1281) |
Fixed time | Yes | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes | Yes |
Adj. R2 | 0.2717 | 0.3132 | 0.2854 | 0.2840 | 0.2730 |
Sample size | 420 | 420 | 420 | 420 | 420 |
Variable . | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
did | 0.634 (1.3790) | 1.377*** (0.1177) | 3.387*** (0.8895) | 4.666*** (1.4060) | 0.197 (1.0091) |
did*lnPGDP | 0.0309 (0.1398) | ||||
did*OPEN | −0.0130*** (0.0027) | ||||
did*lnPOP | −0.301** (0.1089) | ||||
did*lnENC | −0.397** (0.1496) | ||||
did*lnFCS | 0.0710 (0.0919) | ||||
Term of constant | 1.385*** (0.1325) | 1.385*** (0.1333) | 1.385*** (0.1331) | 1.385*** (0.1324) | 1.385*** (0.1281) |
Fixed time | Yes | Yes | Yes | Yes | Yes |
Fixed by province | Yes | Yes | Yes | Yes | Yes |
Adj. R2 | 0.2717 | 0.3132 | 0.2854 | 0.2840 | 0.2730 |
Sample size | 420 | 420 | 420 | 420 | 420 |
Note: ***, ** and * are significant at the level of 0.01, 0.05 and 0.1 respectively. did *lnPGDP is the interaction term between the logarithm of regional per capita GDP and the carbon emission trading policy; did*OPEN is the interaction term between the percentage (%) of foreign direct investment in GDP and the carbon emission trading policy; did*lnPOP is the interaction term between the regional average population logarithm and the carbon emission trading policy. did*lnENC is the interaction term between regional energy consumption logarithm and carbon emission trading policy, and did*lnFCS is the interaction term between regional fixed asset logarithm and carbon emission trading policy. The robust standard error is shown in parentheses.
Column (2) in Table 9 presents the research results on the impact of the interaction between carbon emission trading policies and foreign direct investment on the green transformation of the regional economy. The research results show that the coefficient is significantly −0.013 at a statistical level of 0.01, confirming the significant impact of foreign direct investment heterogeneity. This indicates that in regions with a relatively high proportion of foreign direct investment, the promotion effect of carbon emission trading policies on the green transformation of regional economies has been suppressed. The main reason may be due to the increase in foreign direct investment and the failure to keep up with environmental regulations in a timely manner, resulting in many pollution projects, known as the ‘pollution paradise’ effect. With the implementation of carbon emission trading policies, the gradual improvement of carbon emission trading markets, and the continuous improvement of carbon emission trading mechanisms, the heterogeneity brought about by foreign direct investment will inevitably gradually shrink.
Column (3) in Table 9 presents the research results on the impact of the interaction between carbon emission trading policies and population size on the green transformation of the regional economy. The research results show that the coefficient is significantly −0.301 at a statistical level of 0.01, the significant effect of population size heterogeneity was confirmed. This indicates that in areas with large populations, the carbon emission trading policy has significantly enhanced the promotion of green transformation of the regional economy. The main reason may be that it is difficult to control when the population size is large, and the effect of environmental regulation is not significant. In addition, the large population provides a large amount of labor for the job market, and the level of foreign direct investment further improves, making it easier to present the ‘pollution paradise’ effect.
Column (4) in Table 9 shows the research results on the impact of the interaction between carbon emission trading policies and energy consumption on the green transformation of the regional economy. The research results show that at a statistical level of 0.01, the coefficient is significantly −0.397, indicating that in regions with relatively small energy consumption, carbon emission trading policies have a more significant effect on promoting regional economic green transformation. The main reason may be that larger energy consumption brings about significant environmental pollution, and energy consumption generates a large amount of carbon. In the case of limited environmental supervision and pollution removal capacity, a substantial increase in energy consumption is not conducive to the green transformation of the regional economy. Therefore, it is necessary to minimize energy consumption and replace traditional energy with clean and environmentally friendly resources.
In summary, it can be found that the effect of carbon emission trading policies on the green transformation of the regional economy is influenced by the heterogeneity of three factors: the original level of foreign direct investment, population size, and energy consumption in the region. However, it is not affected by the heterogeneity of economic development level and fixed asset stock. The carbon emission trading policy should play a better role in promoting the green transformation of the regional economy, fully considering the original resource endowment of the region and adapting to local conditions. In this way, it also provides an important reference for other countries around the world to carry out regional economic green transformation.
RESULTS AND RECOMMENDATIONS
Most existing research focuses on discussing the impact of factors such as policies and regulations, power design, energy environment, finance, and ESG on the green development of the regional economy. However, few studies have analyzed the green transformation of regional economies from the perspective of carbon emission trading policies. This study takes the pilot carbon emission trading policy in China as a natural experiment, and uses the data of 30 provinces and cities in China from 2005 to 2018 to build Super-SBM, DID and mediation models to explore the impact of carbon emission trading policy on the green transformation of the regional economy and its mechanism. Super-SBM with non-expected output accurately measures the efficiency of regional economic green transformation. Research on the DID has found that carbon emission trading policies can promote the green transformation of regional economies. The research results of the mediation effect model indicate that carbon emission trading policies affect regional economic green transformation through energy structure, urbanization level, and the interaction between energy structure and urbanization level. Furthermore, the robustness of the model results was verified through more discussions. It was found that the impact of carbon emission trading policies on the green transformation of regional economies exhibits heterogeneity due to differences in the original resource endowments of regions. This study provides a reference for improving the carbon emission trading policy mechanism, and provides diversified solutions for promoting green transformation of the global economy and solving global climate problems. This is of great significance for interdisciplinary research, addressing energy and climate issues, and achieving high-quality growth in the world economy.
The study's findings have the following suggestions. First of all, the main takeaways from this analysis focus on how carbon emission trading policy promotes the green transformation of regional economies. This discovery should aid in boosting the working practices of the area economy's green transition. Although economic development may quickly modify the economic green condition, it is not the sole tool for promoting the green transformation of regional economies. On the whole, the development and strengthening of the scale of carbon emission trading policy has increased relevance in fostering the green transformation of regional economies as a potent weapon for improving well-being. As a result, the government should prioritize boosting funding for the carbon market and emphasizing the demonstration function of pilot programs. Governments should systematically improve the efficiency of carbon markets, for example by expanding industry and geographical scope. We will improve the quota allocation mechanism, trading mechanism and constraint mechanism, develop secondary and tertiary markets, solve investment and financing problems, achieve emission reduction targets at the lowest possible social cost, and promote green, low-carbon and circular development of the regional economy, in order to better promote the green transformation of the global economy to deal with climate change.
Second, the debate on the intermediate channels for supporting regional economy green transformation demonstrates that carbon emission trading policy influences regional economic green transformation through energy structure, urbanization level, and the connection between the two. As a result, we should not only employ the carbon emission trading policy to help the regional economy green transformation in practice; we should also cultivate new growth points by optimizing the role channels. Comprehensive consideration of energy structure and urbanization is an important channel for carbon trading policies to play an effective role. On the one hand, government policy should be oriented on promoting energy structure optimization. The regional energy structure should be optimized through a series of measures such as enhancing clean energy supply, improving energy infrastructure and improving energy efficiency. On the other hand, when urbanization is being implemented, local conditions should be adapted to consider the coordinated development of local endowments and energy security. In addition, it is necessary to consider the synergistic effect of regional green transformation factors, and adopt appropriate policy incentives to promote global regional green development by learning from the successful experience of regions with higher green levels.
Third, from the data measurement results of the sample period, regional disparities exist in the economic green transition, including the heterogeneity of foreign direct investment, population size and energy consumption. For regions with a higher proportion of foreign direct investment, a larger population and a larger energy consumption, carbon trading policy have less of an impact on promoting green transformation. Governments should set different emission reduction targets according to the heterogeneity of regional resource endowments. Addressing global climate change will require significant and profound economic and social restructuring, and leaders must demonstrate firm resolve. While strict emission reduction targets are conducive to improving environmental quality, ignoring the diversity of resource endowment heterogeneity will inevitably have huge economic consequences. Therefore, a reasonable emission reduction target is the key factor in promoting the green transformation of the regional economy, and it is also a guarantee mechanism to maintain the two-way interaction between carbon emission trading and the green transformation of the regional economy, and realize the coexistence of economy and environment. In this way, we can better deal with the global climate problem.
Although this work innovatively analyzes global economic green development and energy climate issues from the perspective of carbon emission trading policies, one drawback is the lack of cross-border comparison. Due to limitations in data availability, this research mainly focuses on evidence from China, but the validity of these conclusions may far exceed the specific situation in China. This requires a more in-depth review of other developing and developed countries and the collection of necessary data. Further research can collect macro data from various countries around the world and compare the results of data research from developed countries with those from developing countries. In addition, China's microeconomic data can also be used to investigate the impact and mechanism of carbon emission trading policies on green transformation at the corporate level (Joltreau & Sommerfeld 2019).
ACKNOWLEDGEMENTS
The National Social Science Foundation of China is funding this study (20BTJ007), Major project of the National Social Science Foundation of China (19ZDA119), the Major Program of National Social, Science Foundation of China (18ZDA125), Study on the effect and Path of Zhejiang's Economic Green Development under the Background of Chinese Modernization (23ZXZJ064) (Zhejiang Social Research Project of Knowledge and Action); Research on Efficiency Measurement and development path Simulation of Regional economic Green Development [First Class Discipline of Zhejiang - A (Zhejiang University of Finance and Economics- Statistics)]. The supercomputing equipment at the University of Science and Technology of China's Supercomputing Center has been used for some of the computations. The authors would like to thank the editors and reviewers for their valuable time giving comments and support.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.