Carbon Trading Pilots Research Articles

Assessing the Effectiveness of Market-Oriented Environmental Policies on CO2 Emissions from Household Consumption: Evidence from a Quasi-Natural Experiment in Carbon Trading Pilots

Assessing the Effectiveness of Market-Oriented Environmental Policies on CO2 Emissions from Household Consumption: Evidence from a Quasi-Natural Experiment in Carbon Trading Pilots

Assessment of the Impact of China's Carbon Emissions Trading Pilot Policy on the Export Product Quality

To achieve the "dual carbon" goals, China has established a carbon emissions trading market to reduce corporate carbon emissions through market mechanisms. The effect of China's carbon trading pilot programme on export product quality is examined in this study. The analysis employs the quasi-natural experiment provided by this policy, focusing on the quality of exported goods. From 2009 to 2016, panel data from 31 provinces and municipalities created a difference-in-differences (DID) model. This model allows for a comprehensive examination of the impact at the city, industry, and destination country levels. The results show that the quality of exported goods is much improved by the adoption of the carbon trading pilot programme. Additionally, the examination of heterogeneity shows that the influence of the policy is greatest in central cities, with less noticeable effects in the eastern and western areas. The report offers suggestions for advancing carbon trading policies at its conclusion, including the adoption of customized strategies that take into account the distinctive qualities of certain geographic areas. These recommendations are intended to maximise the effectiveness of the carbon trading system and support China's broader environmental and economic objectives.

Carbon trading pilot and low-carbon development of China’s textile industry

To explore whether a carbon trading pilot (CTP) can promote the low-carbon development of China's textile industry (CTI), this paper used a difference-in-difference model to study the impact of CTP on the carbon emissions in CTI and further analysed its regional heterogeneity. The research results showed that the impact coefficient of CTP on the carbon emissions in CTI was significantly negative, indicating that CTP could reduce the carbon emissions in CTI. Robustness tests such as the Placebo test, replacing the explained variable and the estimation method and excluding the interference of other policies verified the robustness of the research results. The dynamic regression results revealed the carbon reduction effect of CTP became stronger as the time of its implementation increased. At the regional level, CTP could also significantly lower the carbon emissions in the textile industry both in the eastern and western regions, and the reduction effect in the western region was greater than that in the eastern.

The Impact of Carbon Emissions Trading on Corporate Environmental Performance--A Hybrid Study Based on PSM-DID and Dual Machine Learning

In the context of "dual carbon", the green economy has gradually become the mainstream trend of economic development. As the main force of social production, the environmental governance behaviour of enterprises is crucial to China's economic transformation. Based on the panel data of Chinese A-share listed companies from 2011 to 2021, this study empirically examines, analyses the mechanism of action, and analyses the heterogeneity of enterprises using the PSM-DID model and the dual machine learning DML model. It is found that the carbon trading pilot policy significantly and positively affects firms' environmental performance, especially for non-state-owned enterprises, non-high-tech and manufacturing industries. In terms of spatial distribution, enterprises in the eastern and central regions are more significantly affected by the carbon trading pilot policy. In addition, the study also finds that executives' green awareness plays a partial mediating role in carbon trading's influence on firms' environmental performance through mediation effect analysis. Finally, the article puts forward recommendations for the government, enterprises and executives to promote the further application and effect of the carbon trading pilot policy in the enhancement of corporate environmental performance.

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

The Impact of Environmental Information Disclosure in the “Carbon Trading Pilot” Project on the Financial Performance of Listed Enterprises in China

The Impact of Environmental Information Disclosure in the “Carbon Trading Pilot” Project on the Financial Performance of Listed Enterprises in China

China’s carbon trading pilot policy, economic stability, and high-quality economic development

The purpose of this study is to examine how economic development and stability are affected by carbon trading pilot programs. Using panel data from 31 Chinese provinces and autonomous regions between 2005 and 2021, two hypotheses were tested using the DID model. The findings indicate that (1) the carbon trading market pilot program has an immediate effect on economic stability. (2) High-quality economic development is positively and significantly impacted by the carbon trading market pilot program. Regional heterogeneity exists in the effects of carbon trading pilot programs on high-quality development and economic stability. The relationship between carbon trading pilot programs and economic development is not conclusive, despite the growing number of these policies. Given this, additional investigation into this connection is required. Understanding the results of carbon trading pilot programs can be used to gauge how successful these initiatives are. This research adds to the body of knowledge regarding the impact of the carbon trading pilot programs. It then makes policy recommendations that may serve as a guide for future “double carbon” research.

The Effect of Carbon Trading Pilot Policy on Resource Allocation Efficiency: A Multiple Mediating Effect Model of Development, Innovation, and Investment

This study extends the existing research on carbon trading policies from the perspective of mediating effects. Based on the difference-in-differences method, this study helps to understand the relationship between China’s carbon trading policies and resource allocation efficiency. The study finds that carbon trading policy promotes the optimization of capital allocation efficiency but does not promote the optimization of labor allocation efficiency. This conclusion has passed a series of robustness tests. Moreover, our analysis shows that carbon trading policies can influence resource allocation efficiency through per capita GDP, foreign direct investment, and innovation levels using multiple mediating models. Factors such as market size, the number of emission entities, and the behavior of market participants affect the resource allocation efficiency in the carbon trading process. Finally, the spatial spillover effect of the carbon trading policy is verified. This paper provides empirical evidence and policy implications for achieving the dual carbon goal and sustainable development.

Carbon Abatement Effect of Low-Carbon City Pilot Policy: Insights from a Time-Varying DID Model

The low-carbon city pilot (LCCP) policy constitutes a crucial element in China’s endeavors to attain its carbon peak target. Thoroughly analyzing the carbon abatement effects and mechanisms of the LCCP policy is essential for supporting China’s goals of achieving low-carbon sustainability. We use panel data from 1997 to 2019, covering 249 Chinese cities, for a quasi-natural experiment. This study assesses the effect of the LCCP project on urban carbon emission intensity (CEI) using a difference-in-difference (DID) model. Furthermore, the article conducts analyses on policy coordination effects, impact mechanisms, and spatial spillover effects. Our research findings indicate that the LCCP policy substantially decreases urban CEI, a result that withstands a battery of robustness tests. The carbon abatement effect of LCCP policies is dynamic and typically endures for three years. Subsequent investigations unveil a cooperative impact between the LCCP policy and the carbon trading pilot policy. The decrease of urban CEI under the LCCP policy is primarily attributed to command and market tools, although the efficacy of voluntary tools remains unrealized. From the standpoint of adjacent nonpilot cities, our conclusions do not endorse the presence of spatial spillover effect for the LCCP project. The insights derived from our findings offer crucial guidance for policymakers exploring low-carbon models and pathways.

The impact of carbon emission trading on the financing constraints of high-emission enterprises: Evidence from China

Using Difference-in-Differences (DID) and Propensity Score Matching combined with Difference-in-Differences-in-Differences (PSM-DDD) methodologies, this paper examines the impact of China's carbon trading pilot policy on enterprise financing. We find that the high-emission enterprises in pilot regions are less financially constrained, especially for state-owned enterprises. The carbon trading policy may motivate high-emission enterprises to reduce carbon emissions through green technology innovation, thereby ameliorating their financing environment. Our findings suggest the economic significance of carbon emission trading in enterprise financing.

Research on the spatial spillover effect of carbon trading market development on regional emission reduction

This paper introduces the current situation of carbon trading market in China and the effect of carbon emission reduction in each region. Theoretically, it expounds the influence mechanism and spatial spillover way of carbon trading market on regional carbon emission. Next, we use the data of 30 provinces in China from 2006 to 2019 to build a continuous spatial difference in difference model (SDM-DID) to empirically study the spatial spillover effect of carbon trading market on regional emission reduction. The results are as follows: First, the implementation of the pilot policy of carbon emission trading has significantly promoted the carbon emission reduction in the pilot areas. From the perspective of impact mechanism, the implementation of carbon trading pilot policy promotes carbon emission reduction in pilot areas by promoting clean transformation of energy consumption structure, improving technology absorption capacity and stimulating development of low-carbon technologies. From the perspective of spatial spillover effect, China’s regional carbon emissions have significant spatial spillover effect, and carbon emissions trading has spatial spillover effect. From the perspective of spatial spillover, the carbon emission trading market promotes the carbon emission reduction in neighboring areas by promoting the clean transformation of energy structure in neighboring areas, improving technology absorption capacity and promoting technological progress.

Price Mechanism, Government Constraints and Carbon Trading Pilot Policy for Emission Reduction

Price Mechanism, Government Constraints and Carbon Trading Pilot Policy for Emission Reduction

The Impact of Carbon Emissions Trading on the Total Factor Productivity of China’s Electric Power Enterprises—An Empirical Analysis Based on the Differences-in-Differences Model

Based on the panel data of China’s listed electric power enterprises, this paper adopts the differences-in-differences model to empirically analyze the pilot policy of carbon emissions trading’s impact on the total factor productivity of power enterprises in 2013. The study finds that the carbon trading pilot policy has a significant positive effect on the total factor productivity of power companies, and the two possible impact mechanisms are external cost compensation and additional income, and internal low-carbon technology innovation and resource allocation optimization. The conclusions above have been further confirmed by the parallel trend test and robustness test. The heterogeneity analysis demonstrates that there are differences in the regression results between state-owned enterprises and nonstate-owned enterprises. The possible reason is that state-owned enterprises are more likely to be affected by the carbon emissions trading system, and their asset-heavy model puts greater pressure on carbon emission reduction. Therefore, their demand for low-carbon technology innovation is more urgent; areas with stricter carbon emission verification are more sensitive to the implementation of carbon trading, and a reasonable increase in carbon verification can make the carbon trading market more effective. Based on the research results, this paper proposes to speed up the improvement of the national carbon trading market system, enhance the diversity and richness of the main market, improve the liquidity of the carbon trading market, broaden financing channels for electric power enterprises, and improve the carbon market supervision mechanism.

Testing mechanisms through which China's ETS promotes a low-carbon transition

China regards an emission trading scheme (ETS) as the most promising approach for reducing its carbon dioxide emissions. However, there are still disagreements about the effectiveness of China's ETS (CETS). To settle this issue, we test the impact of CETS on key mechanisms driving a low-carbon transition. We use the difference-in-difference method to analyze data for coal-power plants participating in China's carbon trading pilot. This involves the use of two variables based on life-cycle indicators, namely ‘energy return on energy invested’ (EROI) and ‘energy return on carbon invested’ (EROC). We obtain three main findings: (1) a mediating effect of supply-side reform is significant; (2) this effect is stronger for more intense technological innovation and government intervention; (3) the influence of CETS on a low-carbon transition is independent of the development of clean energy. Overall, we conclude that the scope of CETS should be extended to other emitters to fully unleash its potential for reducing carbon emissions.

Can the environmental trading enhance corporate green innovation efficiency?

This paper examines how environmental trading affects corporate green innovation efficiency. Based on data from 2010 to 2022, this paper adopts carbon trading pilots policy as a quasi-natural experiment and employs a difference-in-difference model to explore the effects and underlying mechanisms. The findings indicate that implementing a carbon trading pilots policy can effectively improve green innovation efficiency, and the impact is achieved by alleviating financing constraints and stimulating the number of green innovation applications. The findings help to adapt environmental policies, leading to increased green innovation efficiency, resulting in a win-win situation for economic growth and environmental conservation.

Power play in carbon trading market: How status of executives with R&D background incentives companies’ low-carbon innovation

Utilizing the phased initiation of carbon trading pilot projects in eight provinces and municipalities in China from 2013 to 2016 as a quasi-natural experiment, this study matches micro-level data on low-carbon patents and financial information of publicly listed companies from 2009 to 2019. Employing a staggered difference-in-differences framework, it assesses the impact and mechanisms through which the distribution of executive power, among those with R&D backgrounds, influences firms' low-carbon innovation capabilities against the backdrop of a carbon emission trading scheme. The status of executives with R&D backgrounds is characterized by their relative ranking within the team, as denoted in the companies' financial statements. Findings indicate that the carbon emission trading significantly fosters the output of low-carbon patents by firms, with executives possessing R&D backgrounds further enhancing the level of low-carbon innovation. This effect is more pronounced in privately-owned and small to medium-sized enterprises. Further analysis suggests that increasing R&D investment and personnel ratios, correcting managerial overconfidence and myopia, and enhancing corporate attention to green strategies are channels through which R&D-oriented executives lead to an improvement in firms' low-carbon innovation capabilities. The paper proposes government tax incentives and funds for R&D staff, stricter ESG standards for executive accountability, and regulations mandating green goals in company planning.

Price Mechanism, Government Constraints and Carbon Trading Pilot Policy for Emission Reduction

ABSTRACT Based on the data of 247 cities at the prefecture level in China from 2007 to 2019, this paper analyzes the impact of the carbon emissions trading CET pilot policy on carbon emission reduction from the perspective of the price mechanism and government constraints. The results show that the carbon emissions and carbon intensity in the pilot areas are significantly reduced by adjusting the industrial structure and promoting green technology innovation. In terms of regions, the emission reduction effect of the pilot policy in regions with a high proportion of industry is obviously weaker than that in other regions. The aim of the carbon emission trading policy in China that achieve carbon emission reduction is by coordinating the carbon emission trading price that fail to fulfill this aim independently and the degree of government punishment for enterprises.

When and Under What Conditions Does an Emission Trading Scheme Become Cost Effective?

This paper studies when and under what conditions the actions undertaken by the power plants involved in China’s emission trading scheme (ETS) pilot became cost effective. Based on unique plant-level panel data and the difference-in-differences strategy, we identify that an insignificant initial reduction in cost efficiency occurred at the announcement stage for power plants in the pilot provinces; however, the cost efficiency of the pilot plants increased significantly following formal policy implementation. Additionally, the by-stage treatment effects differed across the pilot provinces due to localized market and non-market variations. Localized conditions of higher marketization, stricter policy enforcement, and lower carbon dependence enhanced this positive effect. The synthetic control results confirmed this variation in the policy effects. The carbon trading pilots resulted in improved efficiency in power plants in Shanghai, Guangdong, and Tianjin during the period 2013–2017, with an associated total cost saving of approximately 29.75 million RMB. To enhance the efficacy of the ETS policy, our findings suggest that the design of the policy should consider localized external factors.

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

Carbon trading risk management and policy making require accurate forecasting of carbon trading prices. Based on the sample of China’s carbon emission trading pilot market, this paper firstly uses the Augmented Dickey–Fuller test and Autoregressive conditional heteroscedasticity model to test the stationarity and autocorrelation of carbon trading price returns, uses the Generalized Autoregressive Conditional Heteroscedasticity family model to analyze the persistence, risk and asymmetry of carbon trading price return fluctuations, and then proposes a hybrid prediction model neural network (generalized autoregressive conditional heteroscedasticity–long short-term memory network) due to the shortcomings of GARCH models in carbon price fluctuation analysis and prediction. The model is used to predict the carbon trading price. The results show that the carbon trading pilots have different degrees of volatility aggregation characteristics and the volatility persistence is long, among which only the Shanghai and Beijing carbon trading markets have risk premiums. The other pilot returns have no correlation with risks, and the fluctuations of carbon trading prices and returns are asymmetrical. The prediction results of different models show that the root mean square error (RMSE) of Hubei, Shenzhen and Shanghai carbon trading pilots based on the GARCH-LSTM model is significantly lower than that of the single GARCH model, and the RMSE values are reduced by 0.0006, 0.2993 and 0.0151, respectively. The RMSE in the three pilot markets improved by 0.0007, 0.3011 and 0.0157, respectively, compared to the standalone LSTM model. At the same time, compared with the single model, the GARCH-LSTM model significantly increased the R^2 value in Hubei (0.2000), Shenzhen (0.7607), Shanghai (0.0542) and Beijing (0.0595). Therefore, compared with other models, the GARCH-LSTM model can significantly improve the prediction accuracy of carbon price and provide a new idea for scientifically predicting the fluctuation of financial time series such as carbon price.

Belated stock returns for green innovation under carbon emissions trading market

Carbon trading is an important market mechanism to achieve carbon neutrality. This study explores the possible impact of carbon markets on the stock market performance of listed companies using data from 2013 to 2022 in China's carbon trading pilot regions. Using the event shock of delayed trading in the Chinese carbon market, we attempt to answer the question of the role of green innovation hidden under the compliance event. Results show that a 1% increase in carbon market turnover leads to an average decrease of CNY 0.123 in the company's stock price. Large-scale companies that have been listed for a short time and have poor green innovation capabilities are more vulnerable to the carbon market. Under delayed trading, firms with high green innovation capability will be profitable. By contrast, profitability is not reflected in low green innovation firms. Companies with low green innovation can reduce stock market performance owing to the carbon market's undersupply situation. Our study reveals the stock performance of different carbon trading entities under delayed trading, providing a realistic basis for firms to choose green innovation while helping to improve carbon trading market dynamics.