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The existence of structural change features can lead to the failure of traditional econometric methods. This research uses the Bai-Perron test to diagnose the structural change point of the price series in China's pilot carbon market. It uses an impulse response function to analyze the interaction between carbon prices and energy prices, stock price indices, power industry index, and similar asset prices. The results show structural change points in all five carbon markets during the operation period, and the timing of these structural change points relates to an economic situation and compliance period. In addition, the impact mechanisms of the stock price index, similar asset price, and power industry index on the price of China's pilot carbon market change significantly before and after the structural change points, but the impact of energy price does not change except for the Hubei carbon market. [ABSTRACT FROM AUTHOR]

As China's carbon market continues to develop, its close connection with the financial and energy markets is becoming increasingly apparent. A systematic study of the spillover effects between markets is important, as it can help prevent excessive fluctuations in carbon prices. With this in mind, this study proposes a time‐varying parameter vector autoregression with Lanne–Nyberg decomposition extended joint connectedness approach to analyze quantitatively the spillover effects in the "carbon–energy–financial" system. Empirical results show that a bidirectional spillover effect exists among markets. Not only does the carbon market have the most pronounced return (volatility) linkages with the natural gas (clean energy) market, but the information connected with the energy markets is also more closely linked than with the financial markets. We also find that market fluctuations, caused by the China–US trade conflict and the COVID‐19 pandemic, have increased spillovers in the system. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS) is popular to abate anthropogenic emissions throughout the world. Previous researchers focused on evaluating ETS policy effect, but ETS design is usually neglected because ETS is already mostly sophisticated worldwide. This is not the case in China, as the Chinese nationwide ETS (CNETS) came into effect in July 2021. Implemented for a brief period, the CNETS lacks implementation details and thus may not achieve mitigation targets cost-effectively. In this paper, we attempt to narrow the research gap by comprehensively designing the CNETS. Our research framework is based on a dynamic recursive computable general equilibrium (CGE) model. The CGE model results show that the appropriate CNETS should include the coverage of the electricity generation and manufacturing sectors, higher carbon price (175 C N Y / t C O 2 ), quota allocation based on the carbon intensity in the previous year, higher quota decline factor (2%) and time-decreasing free quota ratio. Although we have only designed the Chinese ETS in this paper, the research framework may become a paradigm of designing appropriate ETS. [ABSTRACT FROM AUTHOR]

In this paper, we select the China Carbon Market Price Index, which reflects the overall price changes in China's carbon market (CCM), and employs the TVP- VAR-BK model to examine the risk spillover effects between the carbon market and high-carbon-emission industries in China from a frequency domain viewpoint. Employing the nonparametric quantile Granger causality test, it delves further into the effects of economic policy uncertainty (EPU) in China on the degree of risk spillovers between the carbon market and high-carbon- emission industries. There are significant risk spillover effects between the carbon market and high-carbon-emission industries. During the short term, the carbon market affects the cement industry more than the electric power and steel industries. However, the carbon market is affected by the volatility of the high-carbon-emission industries over the long term. In addition, the effect of EPU on the magnitude of risk spillovers between the carbon market and high- carbon-emission industries is nonsignificant at extreme quartiles and significant at the middle quartile level, which is typically asymmetric. [ABSTRACT FROM AUTHOR]

China is planning to introduce carbon tax policy to control the carbon emissions of the country better and achieve the "3060 goals", but there is still widespread discussion about how to introduce it and how to combine it with cap and trade. China has already established a national carbon emission trading market; however, there is also disagreement on whether to impose the carbon tax on companies and projects that have been included in scope of cap and trade. This paper adopts the research method of experimental economics to study the effect on social economy and social emission reduction under cap and trade, carbon tax, and carbon tax-carbon trading policies, and analyzes average prices of carbon market under cap and trade and carbon tax-carbon trading policies. The study finds that under the carbon tax-carbon trading policy, carbon emissions cannot be reduced significantly; but the profits of manufacturers will be reduced significantly; meanwhile, this reduction effect is even more severe for high consumption manufacturers; and it will be resulting in a lower average carbon market price under the carbon tax-carbon trading policies than under the cap and trade policy. This paper will provide theoretical suggestions for introducing carbon tax policy into China in the future and make policy recommendations for the better development of China's carbon market. [ABSTRACT FROM AUTHOR]

The integration or segmentation of regional carbon prices implies that barriers may hinder prices from adjusting efficiently and impede linkages between regional carbon markets. Past studies have found that crossregional carbon prices in China tend to diverge, indicating unbalanced regional markets and that carbon cap-and-trade policies may be insufficiently effective. China's carbon trading pilots opened in 2013. Is the integration efficient? This is the research question addressed in this study. This paper uses the carbon prices of four carbon-trading pilot regions in China (i.e. Beijing, Shanghai, Tianjin, and Guangdong) over the period of January 1, 2014, to March 23, 2021, to explore whether prices are correlated across three dimensions: long-term convergence, short-term correlation, and volatility transmission by adopting a vector error correction model with heterogeneous variances. And by using longer-term daily data and empirical tests that can account for the multiple-faceted correlations, this paper provides evidence showing that long-term convergence and risk transfer between the four regional carbon prices exist, which indicates significant regional integration. The results indicate that the price behavior of the Beijing pilot is efficient and that the Shanghai pilot has the lowest volatility. The paper also documents an information transfer pattern. For example, the Beijing pilot is shown to be the leading risk transfer market, whereas the Shanghai pilot is shown to be the market where most risk is transferred. Contrary to findings elsewhere, our main results demonstrate that China's carbon markets exhibit integration efficiency and no imbalance between regions. This may be because the efficiency of China's carbon markets has improved over time. The evidence also implies that if a multi-dimensional correlation has not been considered, it may underestimate the efficiency of regional carbon prices' integration. [ABSTRACT FROM AUTHOR]

The shadow price of carbon emissions can measure the marginal output effect under the carbon emission regulation and is also one of the key indicators to construct a low-carbon development path for production units. Currently, international research on shadow price is focused on the industrial and energy sectors. However, in the context of carbon peaking and carbon neutrality targets in China, the use of shadow price to study the cost of reducing emissions from agricultural production, especially forestry fruit industries, is significant. In this paper, we use a parametric approach to construct the quadratic ambient directional distance function. Using the input–output data of peach, we then calculate the environmental technical efficiency and shadow price of carbon emissions from peach production in Guangxi, Jiangsu, Shandong, and Sichuan provinces, and further estimate the values of green output in each province. The results show that (i) the environmental technology efficiency of peach production in Jiangsu province, located in the coastal plain area of eastern China, is the highest among the four provinces, while that in Guangxi province, located in the hilly area of southeast China, is the lowest. (ii) Guangxi province has the smallest carbon shadow price of peach production among the four provinces, while Sichuan province, located in the mountainous area of southwest China, has the largest. (iii) The green output value of the peach production in Jiangsu province is the highest among the four provinces, and that in Guangxi province is the lowest. In order to effectively reduce carbon emissions in peach production without affecting economic benefits, the paper puts forward the following suggestions: for peach-producing areas in the southeast hills of China, it is necessary to increase the application of green environmental technology while reducing the input of production factors in peach production. For peach-producing areas in the northern plains of China, the input of production factors should be reduced. It is not easy for peach-producing areas in the southwest mountains of China to reduce the input of production factors while increasing the application of green technologies. Finally, for peach-producing areas in China's eastern coastal plain, the implementation of environmental regulations for peach production should be gradual. [ABSTRACT FROM AUTHOR]

Low-carbon policies are essential for facilitating manufacturing industries' low-carbon transformation and achieving carbon neutrality in China. However, recent studies usually apply proxy variables to quantify policies, while composite indices of policy intensity measured by objectives and instruments focus more on the national level. It is deficient in direct and comprehensive quantification for low-carbon policies. Hence, having extended the meaning of policy intensity, this paper constructs a low-carbon policy intensity index quantified by policy level, objective and instrument via phrase-oriented NLP algorithm and text-based prompt learning. This process is based on the low-carbon policy inventory we built for China's manufacturing industries containing 7282 national-, provincial- and prefecture-level policies over 2007–2022. Lastly, we organize the dataset in two formats (.dta and.xlsx) for multidiscipline researchers. Apart from the inventory and intensity for each policy, the policy intensity is also aggregated to national-, provincial- and prefecture-level with sub-intensity for four objectives and three instruments. This dataset has potential uses for future studies by merging with macro and micro data related to low-carbon performances. [ABSTRACT FROM AUTHOR]

Carbon price prediction is of great importance to regulators and participants in the carbon trading market. It is the basis for developing policies related to the carbon trading market and stabilizing that market. Considering the numerous factors that influence carbon prices in China, dimensionality reduction is needed to improve the prediction accuracy and efficiency. However, the traditional dimensionality reduction methods fail to fully consider the role of influencing factors, which has certain limitations. In this paper, a new dimensionality reduction method, namely scaled principal component analysis (s-PCA), is employed to improve the prediction accuracy of carbon prices. Firstly, a factor library that influence carbon prices is constructed from three perspectives: technical indicators, financial indicators and commodities indicators. Then, the s-PCA method is used to reduce the dimensionality of factors influencing carbon price. Next, two different methods are used to predict carbon prices, including traditional regression method and Long Short-Term Memory (LSTM) method. Finally, the economic value of the s-PCA method is examined by constructing investment portfolios. The empirical results of the Hubei Emissions Exchange show that the s-PCA model outperforms other competing models both in- and out-of-sample. In addition, the LSTM model could improve the performance of the s-PCA model in carbon price prediction. From a market timing perspective, investors can achieve a greater return and a larger Sharpe ratio using the s-PCA method than using other comparative methods and buy-and-hold strategy. Therefore, the s-PCA method is effective and robust in predicting carbon price. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon reduction, China has been piloting the Carbon Emission Trading System (CETS) since 2013. As the economy faces a downward trend, it is significant to explore the impact of CETS on business development. There is still debate in academia about whether this policy can boost the level of business development. This paper, based on all A-share data of listed companies in China from 2009 to 2018, uses the Difference in Differences (DID) model to verify the impact of CETS on the input of capital and labor factors and the level of technology in enterprises and discusses the industry heterogeneity of this impact in detail. Placebo tests, propensity score matching, and triple differences ensure the robustness of the conclusions. In further research, this paper decomposes the policy effect of CETS. It regresses the impact of carbon quota prices and carbon market trading scale on business development. The final conclusion is that CETS has a positive impact on the input of capital and labor in enterprises and a negative impact on the level of technology. After distinguishing industries, this conclusion shows differences according to different characteristics of high emissions and low emissions. In addition, the increase in carbon quota prices hinders business development, while the scale of carbon market trading shows an inverted "U" relationship with business development. The article provides meaningful policy references for China and countries in the early stages of CETS construction. [ABSTRACT FROM AUTHOR]

Cities are the main spaces to study a low-carbon economy. This paper introduces basic concepts covering "tolerance of planning carbon emissions," "inevitable carbon emissions" and "excessive carbon emissions," then discusses the problem with optimal city carbon emissions under the condition of tolerance. Theoretical analysis and scenario analysis were employed. First, according to optimal allocation theory of resources, we discuss how optimal society carbon emissions take place. Then, the deviation of optimal city carbon emissions was explored and analyzed, which was mostly caused by the missing price of carbon emissions allowance, excessive carbon emissions, and so on. Third, we analyzed optimal city carbon emissions under different conditions, in which different types of tolerance were included. Results show that theoretical investigations on optimal city carbon emissions provide relative ideas for determining control index and optimizing control countermeasures. Policy implications include improving the control index of city carbon emissions through environment enhancement, setting a reasonable control index of city carbon emissions under the consideration of the relationship between city economy and its environment, and establishing trading allowance and compensation mechanism of city carbon emissions, etc. [ABSTRACT FROM AUTHOR]

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations. [ABSTRACT FROM AUTHOR]

The launch of the national carbon emissions trading (CET) market has resulted in a closer relationship between China's CET market and its electricity market, making it easy for risks to transfer between markets. This paper utilizes data from China's CET market and electric power companies between 2017 and 2023 to construct the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Barun'ik and Křehl'ik, and a minimum spanning tree model. The comparison is made before and after the launch of the national CET market. Subsequently, this paper examines the market spillover effects, as well as the static and dynamic properties of network structures, considering both the time domain and frequency-domain perspectives. The research findings suggest the following: (1) There is a strong risk spillover effect between China's CET market and the stock prices of electric power companies; (2) There is asymmetry in the paired spillover effects between carbon trading pilot markets and the national CET market, and differences exist in the impact of risk spillovers from power companies between the two; (3) The results of the MST model indicate that the risk contagion efficiency is higher in the regional CET pilot stage compared to the national CET market launch stage, with significant changes occurring in key nodes before and after the launch of the national CET market; (4) Both the dynamic spillover index and the standardized tree length results demonstrate that crisis events can worsen the risk contagion between markets. Besides offering a theoretical foundation and empirical evidence for the development of China's CET and electricity markets, the findings of this paper can provide recommendations for financial market participants as well. [ABSTRACT FROM AUTHOR]

Many countries have launched the carbon emission trading scheme (ETS), attempting to limit carbon emissions through total amount control, and thus accelerating the improvement of emission reduction technology. However, the role of the ETS in emission reduction technical change (ERTC) is still controversial. To clarify the role of the ETS in ERTC, this paper firstly proposes an endogenous growth equilibrium model to theoretically analyze how the ETS induces ERTC, and extracts the corresponding influencing factors. Then, the emission reduction technology is further decomposed into the source, process and end-of-pipe emission reduction technologies by the whole process decomposition method. Last, taking China's carbon pilot market as a sample, we use the time-varying difference-in-difference method to empirically estimate the impacts of the ETS on ERTC. Results show that: (1) the ETS is conducive to ERTC, but whether the effect is significant is depends on the technology accumulation in the early stage, research efforts, success probability of technology research, and carbon trading effect; (2) China's ETS significantly improved the process emission reduction technology, but has no obvious effects on the source and end-of-pipe emission reduction technology. (3) China's ETS has heterogeneous impacts on technical change concerning different regions. Accordingly, three suggestions are provided, including laying out new technologies for energy conservation and emission reduction, expanding the industry coverage of the ETS, and correcting the distortion of carbon trading price. [ABSTRACT FROM AUTHOR]

Carbon capture, utilization, and storage (CCUS) technology is a vital technical path to realize the low-carbon transformation of the coal power industry as well as a key means to ensure the safe and stable operation of the future power system. However, the current development path of coupling coal power and CCUS technology is not clear, and there is room for the optimization of the corresponding policy mechanism. This paper quantitatively estimates the contribution of CCUS application in the coal power industry to China's low-carbon energy transition strategy, describes the future development path of coal power's CCUS industrialization from the spatio-temporal dimension, and further constructs a comprehensive supporting policy system framework. The results show that the development of coupling coal power and CCUS technology is technically and economically feasible, which can not only achieve deep decarbon-ization of the power system and bring significant output and employment dividends in the coal power industry, but also effectively control the average carbon price level of society. In terms of the temporal dimension, the development path of coal power's CCUS industrialization in the future should be divided into three stages: technology research, development, and demonstration (2021--2030), large-scale application (2030--2040), and commercial development (2040--2060). In terms of the spatial dimension, the technically and economically feasible regions for cluster development are the Ordos Basin, the Junggar Basin, the Bohai Bay Basin, etc. Constructing a comprehensive policy support system framework incorporating ' macro support, fiscal and tax finance, market mechanism, technology research, development, and demonstration, as well as management system' is conducive to realizing a large-scale CCUS application in the coal power industry. In the stage of technology research, development, and demonstration of coal power's CCUS application, more definitive incentive policies such as storage subsidies, compensation for power generation hours, and feed-in tariff subsidies should be implemented. When the project cost drops to a certain extent, there should be a gradual transition to market-based policy tools such as carbon trading and green electricity trading mechanisms, and green finance should also be promoted. Based on the relevant conclusions, some targeted policy suggestions are put forward to provide references for promoting the green and low-carbon transformation of China's coal power industry. [ABSTRACT FROM AUTHOR]

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing's pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China's carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China. [ABSTRACT FROM AUTHOR]

The proposal of "double carbon" goal increases the pressure of power structure transformation. This paper sets up two scenarios according to the timing progress of realizing the "double carbon" goal and explores the transformation planning schemes of China's power structure. The conclusions are as follows: (1) Technological progress and policy support will greatly reduce the levelized cost of electricity (LCOE) of onshore wind power, offshore wind power, photovoltaic power, and photothermal power. The rapid rise in carbon price will lead to the LCOE of coal power in 2060 rising to 2 CNY/kWh. (2) The power consumption of the whole society in the baseline scenario can reach 17,000 TWh in 2060. In the acceleration scenario, this value may triple that in 2020 to 21,550 TWh. (3) The acceleration scenario will pay more newly added power costs and coal power stranded scale than the baseline scenario but can achieve carbon peak and negative emissions earlier. (4) More attention should be paid to the flexible level of power system, improve the allocation proportion and requirements of new energy storage on the power supply side, help the steady exit of coal power, and ensure the safety of low-carbon transformation of power structure. [ABSTRACT FROM AUTHOR]

With the proposal of China's "carbon peak, carbon neutral" strategy, the increasing awareness of low carbon production among consumers, and the government's introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China's low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises' low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the increase in carbon price has a positive effect on the increase in enterprise profit; the increase of carbon emission has a negative effect on the increase of enterprise profit and the high carbon price will intensify this effect. In addition, changing the intensity of government subsidies to consumers will lead to the change of enterprise carbon emission strategy. The study of this paper provides a certain reference for the government to reasonably formulate carbon trading prices and consumer low-carbon subsidies. In addition, considering consumer low-carbon preferences is also conducive to promoting green production practices of enterprises, thus promoting the realization of the carbon neutral strategy. [ABSTRACT FROM AUTHOR]

To combat climate change, the Chinese government has implemented a package of policies to support the development of the biogas power generation industry. However, the promotion of biogas power generation technology in China is relatively slow. Therefore, it is of practical significance to study the promotion of biogas power generation technology against the background of policy support. In order to study the effect of policy on the promotion of biogas power generation technology, a system dynamics model is constructed in this paper. The results show that under the feed-in tariff subsidy policy, biogas power generation technology can be well promoted because it has good economic and environmental effects. In addition, if the biogas power generation technology is considered to participate in carbon emission trading, the carbon price also has a positive impact on the promotion of biogas power generation technology because it increases the perceived economic value of biogas power generation projects. Finally, this study can also provide reference value for the promotion of biogas power generation technology in other areas. [ABSTRACT FROM AUTHOR]

Given the spatial heterogeneity of the social-economic situations across different regions in China, the decomposition of emission reduction targets should be designed according to the actual characteristics of the industrial economy. There is concern about the loss of industrial competitiveness and leakage of CO2 emissions if just seven pilot carbon markets operate independently, so the national carbon market of the power sector was established in 2021. In this study, a China two-region CGE model including Guangdong (GD) and the rest of China (ROC) is built on an analysis of the long-term effects of CO2 prices in industrial sectors at the target 2030 peak. Based on this model, we constructed one business-as-usual scenario and six comparison carbon tax scenarios to quantify the CO2 cost impact for a wide range of manufacturing sectors and identify specific economic activities that face relatively high CO2 costs between the two regions. Based on the China two-region CGE model, the risks of leakage and competitiveness distortions in these potentially exposed sectors are qualitatively assessed. The results show that chemical, nonferrous metal, and machinery are GD's competitive sectors, and agriculture, food, textile, paper, cement, construction, and service belong to ROC's competitive industry. Both GD and ROC need to further unify carbon pricing policies at the same time to effectively coordinate the carbon intensity reduction target and industrial development, which is 2.6% and 3.2% of the severe GDP loss compared with BaU when implementing carbon tax policy. The results can support the setting of the carbon tax and industrial competitiveness promotion policy and with a strong reference to support the provincial emission reduction path. [ABSTRACT FROM AUTHOR]

The open carbon trading market is an important means to reduce carbon emissions, develop a low-carbon economy, and promote environmental protection. Accurate carbon price projections have far-reaching implications for economic and environmental policymaking. In this paper, a new combination model of carbon price prediction is proposed, which can effectively improve the accuracy of prediction. First, adaptive variational mode decomposition (AVMD) was used to decompose the original carbon price sequence into several less complex intrinsic mode functions (IMF). Secondly, the partial autocorrelation function (PACF) is used to determine the input variables for each IMF forecasting model. Finally, the extreme learning machine (ELM) optimized by the dynamic adaptive inertial factor particle swarm optimization (DAIFPSO) algorithm was used for prediction. The proposed combination model is applied to the carbon price prediction of some carbon markets in China (Beijing, Guangdong, Hubei) and compared with the other 11 models. Finally, it is found that the model has the best performance. The results show that the model is effective and stable. Therefore, this model can be used as an effective tool for government and carbon market participants to predict carbon prices and provide a reference for their decision-making. [ABSTRACT FROM AUTHOR]

Since the implementation of China's clean heating plan in 2017, the progress of natural gas replacing coal for heating is still not ideal. With the proposal of the goal of carbon neutralization and the impact of international war, the price, supply, and demand of natural gas have attracted attention. In case of gas shortage, reasonable schemes are required to be allocated to different industries and categories. Therefore, this research divides the gas field into four levels according to the social utility, and establishes the multi-objective distribution optimization model considering the social and economic benefits. The solution of the model is based on genetic algorithm and realized on MATLAB. Multi-objective optimization strategies and several constraints are used to make the algorithm's process of the search is continuously moving closer to the Pareto optimal front. Taking Beijing as an example, two scenarios are set to simulate and optimize, so as to verify the optimization effect of the model. Better quantitative results are created after reallocation of natural gas. The ideal scenario realizes 5.64% more social benefits than 2019 through additional allocation of 117.288 million cubic meters of reserve gas. The constraint scenario creates 0.67% more social benefits with only 0.07% decrease of the economic benefits. The conclusions show that allocating more gas to three main industries can improve the optimization effect and it is necessary to expand the supply of natural gas reserve volume. This paper provides a new perspective of natural gas allocation category and priority, providing theoretical and practical ideas for the planning of natural gas security emergency plan and the development of gas sales plan. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price. [ABSTRACT FROM AUTHOR]

Coal-to-oil (CTL) combined with carbon capture, utilization and storage (CCUS) can significantly reduce the CO2 emissions generated in the production process to achieve clean coal utilization. Taking CTL enterprises as sources and deep saline aquifers and oil fields as sinks, this paper establishes a source–sink matching model, which is combined with a trinomial tree real-option model of carbon price fluctuation, and evaluates the investment decisions of CTL. The results show that 36 pipelines with an average transportation distance of 319.13 km and predominantly small diameters must be constructed for CO2 capture and storage combined with enhanced oil recovery (EOR). Under the current carbon price, 83.33% of enterprises can invest immediately when adopting EOR; when utilizing storage in a deep saline aquifer (DSF), even with a 50% subsidy and a decrease in costs due to the learning rate, enterprises still need to execute the deferred option investment. Government subsidies and technological advances can greatly increase the value of investment. The critical carbon price of CTL-CCUS projects is sensitive to government subsidies, technological advances, and CO2 transportation distances. Therefore, China should reasonably guide the development of the carbon market and give play to the role of the carbon market in emission reduction incentives. In addition, the Chinese government can provide direct financial support for the CTL-CCUS project to increase the enthusiasm of CTL enterprises for CCUS transformation and promote technological progress. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) plays an important role in achieving the dual-carbon goal, and improving EE is thus indispensable. This paper evaluates the impact of carbon emission trading policy (CETP) on EE based on a difference-in-difference (DID) method, using 16-year data of 30 provinces and cities from 2005 to 2020. Conclusions are as follows: (1) CETP significantly promotes EE, and this conclusion still appears valid after robustness tests. (2) The positive impact of CETP on EE is more effective in regions of high foreign direct investment (FDI) and high government intervention (GOVI). (3) The positive impact of CETP on EE is through impact mechanisms of energy structure adjustment (ESA), green innovation (GI), and industrial structure upgrading (ISU). The findings in this paper may enrich current research in CETP and offer more pragmatic suggestions for policy advancement as well. [ABSTRACT FROM AUTHOR]

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The carbon quota allocation rules of China's pilot carbon emissions trading (CET) regions are various, which mainly include benchmarking, historical carbon intensity reduction and auctioning. When the allocation rules change, it is unresolved how to achieve the optimal product prices and effectively allocate the carbon emissions reduction profits of CET-covered enterprises in the cooperative supply chain. Thus, this paper uses the Stackelberg game, Nash equilibrium and the Shapley value based on cost modification to investigate these issues. The results indicate that: (1) The increasing carbon prices can always improve the retail prices only under the auctioning rule. Meanwhile, the growing low-carbon awareness of consumer cannot be always conducive to improving the wholesale and retail prices, and the similar product prices of non-CET-covered enterprises have greater impact on the wholesale prices than that on the retail prices. (2) Only under the free carbon quota allocation rules, can the optimal wholesale and retail prices under the Stackelberg game be always higher than those under the Nash equilibrium. Meanwhile, the auctioning rule can better reduce carbon emissions than the free allocation rules. (3) Improving carbon emissions reduction contribution and emission reduction costs can be conducive to increasing the carbon emission reduction profits of the supplier and retailer, while the impact of carbon emission reduction contribution on improving the carbon emission reduction profits is not always greater than that of the carbon emission reduction costs. [ABSTRACT FROM AUTHOR]

With ecological civilization becoming an increasingly high profile topic, terms such as carbon neutrality and carbon markets have entered the research landscape. Carbon prices are the core of carbon markets. After analyzing the eight carbon trading markets in China, this paper finds that carbon prices are poorly balanced, i.e., they vary widely between pilots. Regional carbon prices show a discrete distribution, and regions with better carbon market development do not have a spatial demonstration effect on neighboring regions. Therefore, in the initial stage of China's unified carbon trading market, the mandatory control threshold amount should be lowered to allow more enterprises can participate in emission reduction. Reliance on nonclean energy should be weakened, investment in environmental protection and emission reduction should be strengthened, the proportion of carbon offset in forestry should be increased, and a carbon tax should be gradually introduced. The paper's overall aim is to bridge regional barriers and narrow differences to achieve a smooth transition from pilot regions to national and global carbon markets. [ABSTRACT FROM AUTHOR]

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China's carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China's carbon intensity showed the spatial agglomeration and temporal "inertia" from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China. [ABSTRACT FROM AUTHOR]

Energy-consuming rights trading (ECRT) policy represents a critical policy instrument for China striving to achieve its “dual carbon” objectives, captivating significant attention for its potential to reduce pollution and carbon emissions. This study utilizes panel data from 290 Chinese cities spanning 2010 to 2021, leveraging the ECRT policy as a quasi-natural experiment. Employing Difference-in-Differences (DID) and Propensity Score Matching-Difference-in-Differences (PSM-DID) methodologies, we assess the effect of the ECRT policy on urban pollution and carbon reduction levels. The findings indicate: 1) Relative to non-demonstration cities, the ECRT policy significantly enhances pollution and carbon reduction levels in demonstration cities; this conclusion remains robust after rigorous testing. 2) Heterogeneity analysis indicates that the policy’s effect on pollution and carbon reduction is more significant in the central and western regions, and particularly evident in key and resource-based cities. 3) Mechanism tests demonstrate that the policy facilitates urban pollution and carbon reduction by cultivating green technological innovation and industrial structure upgrading. Therefore, to further advance the ECRT policy, it is necessary to expand the breadth, depth, and flexibility of policy implementation, while also optimizing environmental regulations to fully leverage the system’s potential in enhancing urban pollution and carbon emissions. [ABSTRACT FROM AUTHOR]

In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan. [ABSTRACT FROM AUTHOR]

The Chinese government's use of fiscal policy, which includes fiscal income and spending, is crucial in its fight against environmental pollution and climate change. Innovation‐driven has arisen as a new alternative to reconcile economic success with environmental conservation in light of tighter resource restrictions and the suggestion of the Chinese High‐quality Development policy. The paper uses an econometric model to examine the connection between institutional resource consumption efficiency and carbon neutrality, using a data set consisting of Chinese inter‐provincial panel data from 2007 to 2020 as a research sample; fiscal decentralization is also discussed as a potential moderating factor. Collaborative innovation positively boosts ecological efficiency and produces geographical spillovers, and the findings suggest so long as the economic distance is considered. When it comes to increasing ecological efficiency, collaborative innovation plays a more important role than financial decentralization alone. Among these is the improvement of carbon neutrality, which is best achieved via the joint promotion of ecological efficiency and industrial organizations. By reducing the intensity of the positive effect of innovative cooperation on ecological efficiency, the moderating effect suggests that fiscal decentralization is a critical factor in this relationship. Promoting China's ecological efficiency necessitates a fair distribution of local budgetary spending. China should also actively encourage the degree of synergy between institutional resource consumption efficiency and associated institutions to improve eco‐efficiency. [ABSTRACT FROM AUTHOR]

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks. [ABSTRACT FROM AUTHOR]

Forecasting carbon prices has important implications for the formulation of environmental and energy policies as well as for the investment and management of workers in related industries. This paper proposes a novel multi-index nonlinear ensemble framework based on carbon price trading data and technical index data, which combines extreme gradient boost (XGBoost), K-means clustering algorithm based on elbow method (KCE), bidirectional long and short-term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU). First, the XGBoost method is used to filter the raw data set to determine the input variables, and they were then divided into groups using the KCE method. Then, the BiLSTM method is used as the predictor for forecasting the sub model. Finally, BiGRU is applied to nonlinear integration. The developed prediction framework is used to predict China's regional carbon price to evaluate its performance. Experimental results show that the prediction performance of the framework in all target carbon markets is better than the benchmark model, and it is an effective tool for carbon market. [ABSTRACT FROM AUTHOR]

The effectiveness of carbon price in emissions trading schemes (ETS) is an important issue in China in the context of its ambitious climate change goals. This paper adopts a staggered difference-in-differences model to estimate whether China's ETS reduced CO2 emissions while maintaining economic growth even under low carbon prices. The results indicate that despite the low carbon prices, the ETS effectively reduced CO2 emissions without undermining the economy. Specifically, an increase of $1 in the carbon price reduced CO2 emissions by 1.69% and increased the per capita GDP by $286. The carbon price primarily achieved emission reduction and promoted economic development through channels, such as technological innovation, foreign direct investment, energy mix, and industrial structure. Carbon leakage to neighboring regions was not evident. Heterogeneity analysis showed that the environmental effects of the carbon price were more pronounced in regions with higher levels of economic development and CO2 emissions. Conversely, the economic effects of the carbon price were more pronounced in regions with lower levels of economic development and CO2 emissions. The carbon price achieved significant economic effects in regions that solely adopted the free allocation mode of emission allowances, while regions that used a combination of free allocation and auctioning experienced substantial emission reduction effects. Despite low carbon prices, China's ETS effectively reduced CO2 emissions without compromising economic growth. This finding provides new empirical evidence for the effectiveness of carbon price as well as decision support for the future promotion of ETS. Technological innovation, FDI, energy mix, and industrial structure are crucial channels through which the carbon price achieves emission reduction and promotes economic development. The absence of carbon leakage to neighboring regions suggests that ETS is an internally effective mechanism for emission control. Heterogeneity analysis showed that regional characteristics and allowance allocation modes can influence the effectiveness of carbon price, thus emphasizing the importance of targeted policy design and appropriate allocation methods. [ABSTRACT FROM AUTHOR]

Carbon emissions from the refining industry are receiving increasing national attention. In view of long-term sustainable development, a carbon pricing mechanism oriented to carbon emission reduction needs to be developed. Currently, the two most common carbon pricing instruments are emission trading system and carbon tax. Therefore, it is important to study the carbon emission problems in the refining industry under emission trading system or carbon tax. Based on the current situation of China's refining industry, this paper constructs an evolutionary game model for backward and advanced refineries to explore which instrument is more effectively applied in the refining industry and identify the effective factors that can promote carbon emission reduction in refineries. According to the numerical results, if the heterogeneity of enterprises is small, the government's implementation of an emission trading system is the most effective measure, while carbon tax can only ensure that the equilibrium strategy solution is (1,1) when the tax rate is high. If the heterogeneity is large, the carbon tax policy will not have any effect, indicating that government implementation of an emission trading system is more effective than the carbon tax. In addition, there is a positive relationship between carbon price, carbon tax, and refineries' agreement to carbon emission reduction. Finally, consumers' preference for low-carbon products, R&D investment level, and R&D spillover effect have nothing to do with carbon emission reduction. Only by reducing refinery heterogeneity and improving the R&D efficiency of backward refineries can all enterprises agree to carbon emission reduction. [ABSTRACT FROM AUTHOR]

Assuming responsibility for emission reduction is an important part of participating in the global governance of climate change. Countries with a carbon neutrality target now cover about 75% of global greenhouse gas emissions. Carrying out fairness evaluations of their carbon neutrality targets provide a strong basis for the global carbon inventory. In this paper, the target years of carbon neutrality for major countries and regions under the global target of 2 °C and 1.5 °C are studied using the burden sharing model. On this basis, this paper simulates the possible pathways of carbon neutrality and evaluates the ambition of emission reduction targets in terms of annual emission reduction, per capita emission, and cumulative per capita emission. The results show that, from the perspective of equity, most of the developing countries cannot meet the global goal of 1.5 °C. The United States and the European Union nations could achieve carbon neutrality by 2050 by continuing their annual emission reductions under their existing NDC targets. However, to achieve carbon neutrality by 2060, China's annual emission reduction rate from 2030 to 2050 will be 6.7%, and the annual amount of emission reduction accounts for 51% of the global amount under the 1.5 °C target. Meanwhile, China would become the main force behind global cumulative emissions in the following decades. Thus, the emission reduction pathways China chooses will have an important impact on global climate mitigation progress. Based on the above research conclusions, this paper proposes that developed countries and developing countries should explore bilateral or multilateral cooperation in technology and finance in the field of climate change. Under the great pressure to achieve carbon neutrality, China needs to carry out research on the overall emission reduction pathways as soon as possible and clarify the paths at the scale of sectors, industries, provinces, and time stages. [ABSTRACT FROM AUTHOR]

China has announced a target of achieving carbon peaking by 2030 and carbon neutrality by 2060. Therefore, it is important to assess the economic impacts and emission reduction effects of China's low-carbon policies. In this paper, a multi-agent dynamic stochastic general equilibrium (DSGE) model is established. We analyze the effects of carbon tax and carbon cap-and-trade policies under both deterministic and stochastic conditions, as well as their ability to cope with stochastic shocks. We found that (1) from a deterministic perspective, these two policies have the same effect. Every 1% cut in CO2 emissions will bring a 0.12% output loss, a 0.5% drop in demand for fossil fuels, and a 0.05% rise in demand for renewable energy; (2) from a stochastic perspective, effects of these two policies are different. This is mainly because economic uncertainty does not change the cost of CO2 emissions under a carbon tax policy, but it does change the price of CO2 quotas and the emission reduction behaviors under a carbon cap-and-trade policy; (3) from an economic volatility perspective, both two policies can act as automatic stabilizers. Compared to a carbon tax, a cap-and-trade policy can better ease economic fluctuations. The results of this study provide implications for policy-making. [ABSTRACT FROM AUTHOR]

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In this paper, we develop a multiregional dynamic computable general equilibrium model, in which the technical details of the power sector are enriched by endogenizing nonfossil energy technological change. We then examine the impact of China's carbon peaking and energy transition goals in 2030 on regional economic development. The results show that the carbon pricing policy will have a negative impact on the economy and aggravate the regional economic imbalance but will be conducive to total energy control. The targeted power investment policy promotes economic development and alleviates the regional economic imbalance; further, clean power investment also provides incentives for developing nonfossil energy technologies. It seems to be natural to achieve the carbon peaking and carbon intensity goals in 2030, whereas it will be more challenging to realize the total energy control and clean energy development goals. Targeted power investment adjusting combined with flexible carbon pricing does well in reconciling the attainment of multiple policy goals and the balance of reginal economic development. [ABSTRACT FROM AUTHOR]

Carbon trading mechanism is an effective means to control greenhouse gas emissions. This paper focuses on the low‐carbon economic operation of the integrated energy system under carbon trading mechanism in China. The integrated energy system includes the energy storage, ground source heat pump, and other equipment. The objective of this paper was to minimize the annual total cost of the system considering the carbon trading cost and study the operation modes under different carbon trading prices by commercial optimization software. The simulation results show the operation modes in summer are changed obviously with the increase of the carbon trading prices, while the operation modes in winter basically are not changed with the fluctuation of the carbon trading prices; under the carbon trading mechanism, the integrated energy system can not only reduce the carbon emissions, but also reduce the annual total cost through carbon trading, which shows its advantages and good development prospect. In addition, the setting of the carbon emission quota has a direct impact on the economy of the enterprises. Furthermore, the ground source heat pump and the electric refrigeration units can stabilize the fluctuation of the equipment output caused by the change of natural gas prices in this integrated energy system. [ABSTRACT FROM AUTHOR]

Over the past four decades, China's extensive economic growth mode has led to substantial greenhouse gas emissions, and China has become the world's largest emitter since 2009. In order to alleviate the dual pressures from international climate negotiations and domestic environmental degradation, the Chinese government has pronounced it will reach its emission peak before 2030. However, through analyzing 12 scenarios, we found that it will be very difficult to meet this ambitious goal under the current widely used policies. With the trial implementation of China's carbon emission trading system (ETS), concerns arise over whether national ETS can accelerate the carbon peak process. In this paper, we propose a new proactive data envelopment analysis approach to investigate the impacts of national carbon ETS on carbon peak. Several important results are obtained. For example, we find that carbon ETS has a significant accelerating effect on carbon peak, which effect will advance the carbon peak by one to 2 years, and the corresponding peak values are reduced by 2.71–3 Gt. In addition, the setting of carbon price in the current Chinese pilot carbon market is found to be overly conservative. Last, our estimation on the carbon trading volume indicates that the ETS lacks vitality as the annual average carbon trading volume only represents approximately 4.3% of the total average carbon emissions. Based on these findings, several policy implications are suggested regarding the means by which China can more smoothly peak its carbon emissions before 2030 and implement national carbon ETS. [ABSTRACT FROM AUTHOR]

Hydrogen will be an important part of China's energy system in the future and an important carrier for energy-using terminals to realize green and low-carbon transformation. It is important to establish a nationwide hydrogen market to promote the healthy and orderly development of the hydrogen industry chain. The core is to form a complete hydrogen price mechanism and play a decisive role in the process of resource allocation by the market. In this paper, we have developed the framework of the 'China Hydrogen Price Index' system by establishing the 'Assessment + Collection' model, which covers four types of hydrogen: hydrogen, clean hydrogen, renewable hydrogen and high-purity hydrogen. The model considers the raw materials required for hydrogen production, fixed equipment, engineering construction costs and carbon prices, and conducts sensitivity analysis on the trends and influencing factors of national and regional hydrogen prices of multiple categories since 2018. The results show that, with respect to the level of hydrogen prices, fossil-energy-rich and renewable-energy-rich areas have more advantages than other regions. The price of raw materials is the main factor of the hydrogen price change, and the utilization hours of renewable energy and hydrogen production equipment have a key influence on the price of renewable hydrogen. Next, by establishing an index update mechanism, improving the standard system and building a trading platform, we can further exert the role of price signals and continue to promote the efficient and smooth expansion of the domestic hydrogen market. [ABSTRACT FROM AUTHOR]

In the context of tax sharing reform and land reform during the 1990s, local governments in China relied heavily on land finance. Local governments have fierce competition in attracting investment, omitting the development of green economy. Based on the data of industrial land sales and carbon dioxide emissions, this study constructed the panel data of 196 cities in China from 2007 to 2017 and analyzed the spatial and temporal evolution characteristics of urban industrial land price distortion and carbon emission intensity. Furthermore, a multiple linear regression model was constructed from the aspects of scale effect to empirically analyze the overall impact and differential impact of urban industrial land price distortion on carbon emission intensity. With the help of the mediating effect model, the mechanism of urban industrial land price distortion on carbon emission intensity was investigated from the perspective of industrial structure upgrading. The results showed that (1) the higher the price distortion of industrial land is, the more detrimental it is to the development of green and low-carbon economy. (2) The regional heterogeneity test showed that the impact of industrial land price distortion on carbon emission intensity is most significant in the central region, medium cities, and cities with low fiscal self-sufficiency rates, respectively. (3) The higher the distortion degree of industrial land price, the greater the restriction on the upgrading of industrial structure, further increasing the carbon emission intensity. This paper provides policy implications for the market-oriented reform of land factors and the realization of the "double carbon" goal. [ABSTRACT FROM AUTHOR]

Financial incentives play a key role in promoting renewable energy investments that can help China achieve the 'dual carbon' goal. The national emissions trading scheme (ETS) and the renewable energy portfolio standard (RPS) are two existing market-based policy instruments that can generate stable expected returns for low-carbon projects. This paper studies the interactive distribution effects of these two market-based instruments. We use the micro-level thermal power plant data to investigate the abatement effects of the national ETS, in which the details show that the existing rate-based ETS will result in higher negative impacts on power units, whose installed capacities are smaller than 400 MW. The interactive distribution effects between the two markets will occur when the permit allocation standards of the national ETS become stricter than the existing ones. Provinces in Eastern China and Northern China will face high pressure on costs in both ETS and RPS markets. When the levels of the permit allocation standards are set as 70% of the existing ones and the carbon price is assumed to be 200 yuan/ton in 2030, the annual market size of the national ETS will be nearly 100 billion yuan, and the annual market size is predicted to be 250 billion yuan. In the existing rate-based national ETS, the China Certified Emission Reduction (CCER) mechanism will have an offsetting effect, which should be taken into serious consideration during the policy-making processes in the future. [ABSTRACT FROM AUTHOR]

Unlike the decarbonization trajectories adopted by developed countries, some research suggests that non-pricing policies should be prioritized over pricing policies to mitigate carbon emissions in developing economies. This paper enriches the current literature on carbon pricing by empirically linking China's carbon pricing and non-pricing policies, using a difference-in-differences approach. The results indicate the effectiveness of the carbon pricing strategy. Furthermore, by introducing an interaction effect model, we find that a higher carbon price will significantly increase the carbon emission abatement effects. In conclusion, the findings highlight the necessity of carbon pricing instruments for decarbonization in developing countries. [ABSTRACT FROM AUTHOR]

Climate change poses an urgent threat, necessitating the implementation of measures to actively reduce carbon emissions. The development of effective carbon emission reduction policies requires accurate estimation of the costs involved. In situations where actual prices of commodities are not available in the market, shadow pricing provides a useful method to calculate relative prices between commodities with and without price information. However, most studies focus on the industry, with few contributions on agricultural sector. This paper estimates the shadow price of carbon emissions in the agricultural sector from a provincial perspective, incorporating the impact of livestock into the calculation of carbon emissions and shadow pricing. Our findings indicate that ignoring livestock may overestimate CSP values. On the whole, the level of carbon shadow price is rising, indicating good green development in China's agricultural sector. The two types of convergence results show that there is sigma convergence and beta convergence in the western and central regions, demonstrating a significant improvement in environmental performance. [ABSTRACT FROM AUTHOR]

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation. [ABSTRACT FROM AUTHOR]

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development. [ABSTRACT FROM AUTHOR]