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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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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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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]

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies. [ABSTRACT FROM AUTHOR]

To promote carbon neutrality, carbon compensation in mountaineering tourism destinations must be studied from the perspectives of both carbon sources and carbon sinks. This study established a carbon neutral calculation framework for the Qomolangma mountaineering tourism area using the "bottom-up" tourism carbon footprint analysis model and a carbon sink calculation model. Three types of data were obtained through field surveys, Internet searches, and literature review and used for the subsequent quantitative analysis. The results showed that: (1) in 2019, the case site had a carbon surplus, with a total 3.87E + 06 kg in carbon emissions and a total 3.22E + 09 kg in carbon sequestration capacity; (2) among the carbon sources, tourist catering accounted for 45.78% of the total, which was the largest share, followed by transportation at 39.83%; the carbon footprint of waste was the smallest, accounting for only 0.66%; and (3) according to the current carbon price in China, the Qomolangma mountaineering tourism area should have theoretically been compensated with 1.15E + 07 CNY (equivalent to 1.67E + 06 USD) in 2019. Finally, based on the above research conclusions, suggestions were proposed to further improve the nature reserve's carbon balance and plan for the future. [ABSTRACT FROM AUTHOR]

China's green credit has mostly been invested in new energy areas with positive environmental externalities in recent years, while coal power enterprises have been neglected. This paper constructs a tripartite evolutionary game model among government, coal power enterprises, and banks to clarify the key factors and mechanisms for coal power enterprises undergoing green transformation. The research results show that: Firstly, to realize the spontaneous green transformation of coal power enterprises, spontaneous profitability must be achieved before the removal of policy incentives, which is reflected in the continuous increase in electricity price, carbon emission trading price, and decrease in green transformation cost. Secondly, the green credit adjustment factor cannot determine whether a company chooses to undertake green transition, but it provides a valuable window of green transition for companies. When the relative benefits of green transformation projects are greater than the relative costs, coal power enterprises will undergo green transformation spontaneously. Thirdly, lower green credit interest rates are not better. An optimal green credit interest rate exists, that allows coal power enterprises to obtain the longest transition window in which to achieve spontaneous profitability for green transition projects. [ABSTRACT FROM AUTHOR]

The advancement of electricity market reform highlights the need for China's photovoltaic (PV) industry to enter the stage of market competition. Under the carbon neutrality, what impacts electricity market reform has on China's PV industry is an important issue that needs to be considered. This paper analyzes the driving mechanism of the marketed on-grid price and constructs a system framework for the internal connection within the PV industry under the background of carbon neutrality. Then, two system dynamics (SD) models are built to perform the numerical simulation and analyze the evolution process. The results indicate that during the market competition stage, (i) the on-grid price will be stable at about 0.07 yuan/kWh by 2060; (ii) China's PV industry will go through three stages in the future: the first stage is the "rush to install" period for carbon peaking before 2030, followed by the "sluggish installation" period from 2030 to 2038. The third stage is the outbreak period of "PV + multi-scenarios."; (iii) under three scenarios by 2060, the proportion of PV power generation will account for 46%, 39%, and 32%, respectively, which are higher than those under the benchmark on-grid price; (iv) it is necessary for the government and enterprises to continue increasing support for research and development (R&D). Finally, we put forward corresponding policy recommendations. [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]

With the launch of a unified national carbon market in China, its synergistic effects on promoting carbon emission reduction in the power industry with renewable energy subsidies are noteworthy. The traditional view is that the total amount of carbon emissions is fixed under the carbon trading policy and the renewable energy subsidy policy under the total amount control will no longer play an additional role in reducing emissions, and would even lead to excessive carbon market quotas, distorting carbon market prices. This paper establishes a general equilibrium analysis framework for national carbon trading and examines the parallel emission reduction effects of renewable energy subsidies in the power industry and the national carbon market and their policy synergy paths. The results show that: ① The national carbon market and renewable energy subsidy policy for the power industry would both bring about CO2 emission reduction in the whole industry. ② The carbon market that only covers the power industry would produce a ‘carbon leakage’ effect, which would lead to an increase in carbon emissions in non-power industries. On this basis, continuing the renewable energy subsidies may alleviate the ‘carbon leakage’ by means of the transfer of production factors among industries or lead to excessive carbon market quotas by ‘greening’ the power production structure. ③ Considering two sources of renewable energy subsidies: the one-time tax on residents (resident income tax) and the renewable energy electricity price surcharge (electricity consumption tax), the emission reduction effect of the former resource is better than that of the latter resource, and the latter may even cause ‘cross-border damage.’ ④ The renewable energy subsidies would lead to a decrease in the carbon market transaction price. The FIT is better than the FIP, and it is necessary to increase the total amount of constraints to effectively restrain the decline of carbon trade prices. As the carbon market expands and improves, renewable energy subsidies can be gradually reduced or even eliminated to avoid their impact on the carbon market price. [ABSTRACT FROM AUTHOR]

Carbon trading market is a central policy tool for promoting cleaner enterprises' energy consumption and investment behaviors. It is crucial for enterprises to establish a green and low-carbon development model by clarifying carbon trading impact mechanism on enterprise behaviors and the interactive relationship between entities in the carbon market. China's contract-based carbon market is still in the development stage, the impact of the mainstream allowance mechanism and government regulation on enterprises' energy behaviors has yet to be determined. In view of this, this paper describes mathematical mechanisms of the influence of carbon prices and carbon quotas on enterprises' energy consumption and investment, and the evolutionary game model of government-enterprise behaviors is constructed, the evolution process and appropriate strategies of enterprises' energy behaviors in the development of the carbon market are discussed. This paper uses real data of China carbon market to simulate the model, it is found that strategies such as relaxation of carbon quotas and carbon subsidies dynamization are needed during the initial development of the market. In the mature stage of the carbon market, strategies are needed to increase the proportion of government carbon revenue and reduce its regulatory cost. The higher the initial willingness of government-enterprise, the better the effect of strategies and the constraints of public willingness. The model and results have theoretical implications for enterprises' energy transformation and government regulation, and provide a reference for the development of China's carbon market. • Constructed benefit-cost analytical framework for enterprises' energy behaviors. • Described mechanisms of carbon trading affecting enterprises' energy behaviors. • Constructed government-enterprise behaviors model by the evolutionary game theory. • Found new conclusions of enterprises' energy behaviors under the carbon market. [ABSTRACT FROM AUTHOR]

China's carbon emission trading policy plays a crucial role in achieving both its "3060" dual carbon objectives and the United Nations Sustainable Development Goal 13 (SDG 13) on climate action. The policy's effectiveness in reducing pollution and mitigating carbon emissions holds significant importance. This paper investigated whether China's carbon emission trading policy affects pollution reduction (PM 2.5 and SO 2) and carbon mitigation (CO 2) in pilot regions, using panel data from 30 provinces and municipalities in China from 2005 to 2019 and employing a multi-period difference-in-differences (DID) model. Furthermore, it analyzed the heterogeneity of carbon market mechanisms and regional variations. Finally, it examined the governance pathways for pollution reduction and carbon mitigation from a holistic perspective. The results indicate that: (1) China's carbon emission trading policy has reduced CO 2 emissions by 18% and SO 2 emissions by 36% in pilot areas, with an immediate impact on the "carbon mitigation" effect, while the "pollution reduction" effect exhibits a time lag. (2) Higher carbon trading prices lead to stronger "carbon mitigation" effect, and larger carbon market scales are associated with greater "pollution reduction" effects on PM 2.5. Governance effects on pollution reduction and carbon mitigation vary among pilot regions: Carbon markets of Beijing, Chongqing, Shanghai, and Tianjin show significant governance effects in both "pollution reduction" and "carbon mitigation", whereas Guangdong's carbon market exhibits only a "pollution reduction" effect, and Hubei's carbon market demonstrates only a "carbon mitigation" effect. (3) Currently, China's carbon emission trading policy achieves pollution reduction and carbon mitigation through "process management" and "end-of-pipe treatment". This study could provide empirical insights and policy implications for pollution reduction and carbon mitigation, as well as for the development of China's carbon emission trading market. • China's carbon emission trading (CET) policy promotes pollution reduction and carbon mitigation. • Higher carbon trading prices lead to stronger "carbon mitigation" effect. • Larger carbon market scales bring greater "pollution reduction" effects on PM 2.5. • Regional heterogeneity exists on pollution reduction and carbon mitigation. [ABSTRACT FROM AUTHOR]

• A CGE model elaborately depicting six CCS technologies was developed. • Impacts of four CCS incentive scenarios on decarbonization pathways were simulated. • CCS can sequester 960 ∼ 1,604 MtCO 2 annually in China by 2060 with policy incentives. • CCS incentives can reduce China's future primary energy demand by 7.7 ∼ 17.4 %. • CCS incentives can increase GDP by 5.64 ∼ 6.59 % compared to no policy scenario. Carbon capture and storage (CCS) technology, considered as a pivotal tool in mitigating climate change within the fossil energy system, particularly in China, has experienced slower development than expected. The exploration of direct incentive policies to facilitate its growth remains relatively underdeveloped. This study developed a hybrid dynamic computable general equilibrium (CGE) model to simulate the substantial impacts of CCS incentive policies on China within the context of carbon neutrality target. Two potential incentive policies, carbon emission trading system (ETS) and 45Q tax credit, were simulated, with different sectoral coverage. The results indicate that CCS technologies can reduce carbon emissions by 960 ∼ 1,604 MtCO 2 annually by 2060 through the strategic implementation of these incentive policies. The 45Q tax credit demonstrates its effectiveness in promoting early-stage research and development (R&D) and demonstration of CCS, while the ETS policy facilitates the commercial development of CCS in the later stage of development. By 2060, the implementation of CCS incentive policies could potentially result in 7.7 ∼ 17.4 % reduction in China's primary energy consumption, 71.2 ∼ 82.7 % decrease in the carbon price of ETS and 5.64 ∼ 6.59 % increase in the GDP compared with the no-policy scenario. In addition, the sectoral output in various sectors and the welfare of urban and rural households also increase. This paper provides an important reference for the realization of China's carbon neutrality goal and the model framework can be applied to other countries. [ABSTRACT FROM AUTHOR]

This paper studies the carbon price fluctuation in China through the adaptive Fourier decomposition (AFD). Apart from the transient time-frequency distribution of the original AFD model, we also reconstruct the mono-components of this model to obtain the components in different time-frequency scales. Our empirical results based on the carbon price in Hubei Province demonstrate that there are three periods when the price fluctuates dramatically, mainly affected by the governmental policies about carbon emission and the development of clean energies, as well as the outbreak of COVID-19. Furthermore, the fluctuations of the price in the three identified periods are reflected in different scales. The comparison of the decomposition results and those of EMD and VMD shows that the AFD performs best in absorbing the price's useful information extracted through all these methods. [ABSTRACT FROM AUTHOR]

Carbon market is the most effective market tool for carbon emission reduction. China, the largest carbon emitter in the world, established the national carbon market in 2021, covering over 2000 key units in the power sector. Therefore, the forecasting of carbon price has profound implications for environmental and energy policies. In this paper, two traditional econometric model and three kinds of machine learning (ML) algorithms are used to predict carbon returns in the Chinese carbon trading market based on carefully selected predictors. Among all forecasting models, the Random Forest (RF) has the best forecasting performance, followed by some GARCH models using various factors. Compared with the traditional benchmark of ARMA model, the BP neural network (BP) and the GA improved BP method (GA-BP) are less competitive in predicting carbon returns in China because of their large forecasting errors. According to the optimal model, social attention to the carbon market, the international crude oil returns and the overall performance of the stock market are the most important predictors. The findings are robust to the change in the sample set. Overall, the ML approach shows an advantage in forecasting carbon returns, but the selection of predictors is also important. • Machine learning and econometric models are used to forecast carbon returns in China. • The selection of predictors is based on economic analysis. • The Random Forest (RF) outperforms the other ML and econometric methods. • The energy market is extremely important for predicting carbon returns. • Social concerns and stock market performance improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

Numerous studies have discussed the economic impacts of the COVID-19 pandemic in recent years. However, the effectiveness and trade-offs of diverse countermeasures still need to be investigated, particularly under the long-term goal of low-carbon transition, which is crucial for understanding the potential impacts of the future public health emergency (PHE) related economic crisis. Given that China still faces big pressures from the potential PHE and carbon neutrality, this paper assesses the effectiveness of policy instruments in restoring the economy and advancing green development after the PHE using the Dynamic Stochastic General Equilibrium framework. Our findings reveal that the PHE imposes more constraints on the economy because of the decrease in productivity on the supply side and in consumption on the demand side. Compared to the other counterparts, the mixed stimulus can overcome the adverse impacts of the PHE while contributing to carbon reduction. Furthermore, all types of low-carbon policies investigated in this study can contribute to carbon reduction at the expense of economic growth. Meanwhile, the carbon tax realizes the target of reducing emissions with the smallest negative impact on economic growth. Thus, we suggest adopting the carbon tax policy as the most effective low-carbon measure to address uncertainties associated with the PHE. • This study explores pathways for advancing economic recovery and green development after the public health emergency. • We develop a scenario-based dynamic stochastic general equilibrium (DSGE) model. • We characterize public health emergency shocks from a supply and demand perspective. • The model introduces different carbon abatement mechanisms. [ABSTRACT FROM AUTHOR]

Promoting the trading of blue carbon generated by marine ranching (MR) is essential for China to achieve its carbon peaking and carbon neutrality goals. As blue carbon trading develops, it remains uncertain whether it can promote the transformation of marine ranching firm (MRF) from traditional fishery aquaculture-type MR (FAMR) to more eco-friendly conservation-type MR (CMR). This paper constructs a game model involving MRF and the government to explore the impact of blue carbon trading on the transformation of MRF' production modes by considering carbon asset renting, carbon cap-and-trade, government subsidies, and green investment, which is of great significance in managing coastal marine and aquatic organisms, as well as promoting sustainable development of the marine economy. Research shows that the price and demand of FAMR products are solely influenced by carbon cap-and-trade. The introduction of carbon rents will increase the incentive for MRF to choose CMR, and this incentive is further enhanced when government subsidies are provided. Concurrently, green investment and MRF's profit have an inverted-U relationship. Both government subsidies and blue carbon trading will increase blue carbon. Although this policy will also increase MRF's carbon emissions, the carbon surplus generated by MRF will be higher than without subsidies. • A game model considering MRF, and government was developed. • Carbon asset renting can lead MRF to choose greener CMR production mode. • MRF is more willing to provide green investments when subsidy is offered. • MRF's green investment volume has an inverted-U relationship with profits. • Government subsidies and blue carbon trading can increase carbon surpluses. [ABSTRACT FROM AUTHOR]

In order to probe collaborative environmental governance (CEG) in China's "1 + N" policy framework for carbon peak and carbon neutrality, this paper applies the Actor-network theory (ANT) as an analytical framework to explore carbon reduction policy coordination. We then model ANT's relations with an evolutionary game theory (EGT) model to examine the evolutionarily stable strategies in CO 2 emission reduction by various actors including the central government, local governments, commercial banks, and polluting enterprises. Our objective is to examine policy coordination as the key enabler for governing success via an ANT-based mechanism design in China's institutional context. Combining the strength of ANT with a four-party EGT model, we investigate how to improve policy coordination to mobilize the actor-network for promoting CEG. Simulations are conducted to examine the effectiveness of policy coordination involving multiple policy instruments for controlling carbon emissions. The central government fulfills leadership in translating the interests of other actors and shaping the desired collaborative network. Without proper coordination, environmental policies may counteract each other. Government transfers and subsidies have direct impacts, but in the long run enhanced competitiveness and profitability of enterprises represent the intrinsic key to CEG success. From a methodology perspective, policy simulation in an EGT framework represents a novel addition to the research toolbox that expands ANT. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed. • Deep learning by combined CNN-LSTM networks for carbon price forecasting. • Hyperparameter tuning of deep learning models for improving forecast accuracy. • CNN-LSTM models outperform single CNN models and single LSTM models. • Performance metric of deep learning for abrupt changes in carbon price variation. [ABSTRACT FROM AUTHOR]

To effectively solve the environmental pollution caused by greenhouse gases, countries around the world have successively set up carbon emission trading markets in recent years. At present, China's carbon trading market is only in its infancy, making various mechanisms and policies insufficient. As the core issue of carbon trading market, the fluctuations of carbon trading price are related to the investment decisions of market participants, the formulation of enterprise production and operation plans, and the realization of global emission reduction targets. Therefore, it is of great practical significance to study the influencing factors of carbon price fluctuations and to predict future carbon prices. In this paper, we take the carbon trading week data of the Beijing carbon trading market in China from May to September 2020 as the research object. Firstly, the gray correlation technique is employed to measure the rationality of the selected factors affecting carbon price fluctuations. Secondly, through the principal component analysis method to analyze the various influencing factors, it is found that energy prices and macroeconomic development are the main factors affecting carbon prices, and weather conditions will also cause carbon price fluctuations. Then, four different models are proposed to predict carbon trading prices, and the forecast results are evaluated through the performance evaluation index system. The results show that the GA-ELM model has the best prognosis effect. Finally, according to the analysis results, it provides a useful theoretical reference for carbon market decision makers and participants. [ABSTRACT FROM AUTHOR]

Against the backdrop of carbon trading, the Chinese Certified Emissions Reduction (CCER) scheme is one of the most important measures of China to reduce carbon emissions. We study a supply chain consisting of a CCER supplier and a manufacturer that buys CCER quotas from the supplier. The supplier is a non-emissions-control enterprise engaged in clean energy projects, while the manufacturer is classified into three types: a clean, a relatively clean, and an unclean firm. We analyse the impacts of the price and offset rate of CCER quotas on firms' emissions reductions. We find that when the price of CCER quotas is lower than the price of Chinese Emissions Allowances (CEAs): (1) for both clean and unclean manufacturers, with increasing CCER offset rate (quota price), technical emissions reduction decreases (increases). (2) The incentive for the relatively clean manufacturer to reduce emissions is not affected by other extraneous factors. When the prices of CCER quotas and CEAs are uncertain, we find that (1) the technology abatement decisions of the three types of manufacturer are consistent and the corresponding CCER suppliers price their CCER quotas in the same way. (2) When the proportion of CCER quotas purchased by the manufacturer increases, the unit technical emissions reduction increases. Confirming that introduction of the CCER scheme can reduce carbon emissions, our findings provide theoretical guidance for suppliers to make pricing decisions on CCER quotas and for manufacturers to make technical emissions reduction decisions. [ABSTRACT FROM AUTHOR]

China has the world's largest carbon market in terms of greenhouse gas emissions, but its system needs to be improved and enhanced. In comparison, the European carbon market stands as the most mature and well-developed carbon market globally. Carbon trading prices, serving as a barometer for the carbon market, are significantly influenced by investor behavior. Therefore, it is necessary to analyze the characteristics of carbon trading prices in both China and Europe, considering the impact of investor trading intervals and psychological expected returns. This study utilizes the Zipf method to characterize the dynamic behavior of carbon trading prices between China and Europe, conducting a comparative analysis. The results show distinctive asymmetry in the behavior of carbon trading prices in both markets. In the Chinese market, when τ < 277, the absolute deviation da(τ, ε) value gradually changes but consistently indicates a bullish trend. However, when τ ≥ 277, the da(τ, ε) value surges rapidly, reflecting a pronounced bullish sentiment among investors toward carbon trading prices in China. In the European market, within the sample period, regardless of variations in τ and ε, the da(τ, ε) value shows a linear upward trend, indicating a significant overall bullishness in prices. This suggests a higher probability of long-term bullishness in carbon trading prices. Investors' investment time scale (τ) and expected returns (ε) both influence the behavior of carbon trading prices in both China and Europe. Generally, a longer τ implies a higher probability of bullishness. As for ε, higher values lead to more extreme judgments on price movements, resulting in greater distortion in carbon trading prices. Short-term investors (τ<1 month) anticipate extreme fluctuations, exhibiting random behavior when ε < 0.15 and converging rapidly to extreme values of 1 or 0 when ε ≥ 0.15. Long-term investors (τ>quarter) are less biased, expressing a bullish outlook on both Chinese and European carbon prices. With increasing ε, the probability of bullishness either increases or decreases rapidly until reaching the saturation point. Once saturated, there is no further distortion in carbon price behavior. Furthermore, the Chinese carbon market displays a positive trend in carbon trading prices and a higher probability of long-term bullishness. For the European market, lower expected returns contribute to considerable carbon trading price fluctuations, exacerbating risk and uncertainty. The results of this study contribute to understanding the diverse trading behaviors in Chinese and European carbon markets and provide guidance for avoiding extreme volatility in carbon trading prices. [ABSTRACT FROM AUTHOR]

The Chinese government has declared a determination at the 75th United Nations General Assembly that China will improve its independent contribution and adopt more powerful measures to peak the carbon emissions before 2030. However, such strict implementation of carbon reduction policies is bound to bring the cost of sacrificing economic development. In such a context, this paper tries to use shadow price to measure the average social cost of emission reduction, marginal abatement cost to depict the pressure to reduce carbon emissions based on non-radial distance function, and provides an optimal scheme for provincial emission reduction to minimize the national cost of emission reduction based on variable-coefficient model. Results show that: First, the average value of shadow price is 15.91 and varies widely among regions, which means on average reducing one unit of carbon emissions will sacrifice 15.914 yuan RMB of economic output, and there exists possibility of carbon transactions across regions; Second, on the one hand, marginal abatement cost of carbon emission for most regions presents an upward tendency over time, which means greater economic costs have to be sacrificed with economic development in the future; On the other hand, marginal abatement cost is much higher in regions with high economic level than that in the economically undeveloped areas, which indicates reducing carbon emissions is becoming increasingly difficult and would pay more economical cost in economically developed regions; Third, the optional allocation scheme of CO2 reduction derived from this research is better than administrative ways of Grandfathering and Benchmarking in terms of minimizing emission reduction cost. Results of this paper indicate that larger carbon trading market can be implemented in China to economically fulfill the commitment of peaking carbon emissions. • This paper provides a scheme to minimize the carbon emission reduction cost. • Shadow price and marginal cost of carbon emission are estimated. • More comprehensive carbon trading market can be implemented in China. [ABSTRACT FROM AUTHOR]

• More reliable modeling assumptions and methods are proposed. • Price regulation may increase industrial output in China. • The reform can lead to a cleaner power mix and better economic structure. • The reform can be of great help to alleviate the fiscal deficit. • Impact mechanisms through cost linkages in different situations are put forward. China promised to achieve carbon neutrality in 2060. However, electricity prices are still regulated, which may affect the effect of carbon neutrality, especially in China, because 70% of China's electricity comes from thermal power generation. In this paper, a dynamic recursive computable general equilibrium model (CEEEA/CGE model) is applied to simulate the electricity market-oriented reform under the target of carbon neutrality from 2020 to 2060. The results show that the target may increase the demand for labor and capital to cope with increasing energy costs, and the reform may reduce GDP. However, power reform can significantly optimize the energy and economic structures and alleviate the fiscal deficit. Moreover, this paper finds and concludes the mechanism causing these effects (such as factor substitution, price advantage, government revenue, and structure), which provides policymakers with a new perspective of power reform. [ABSTRACT FROM AUTHOR]

With the development of low-carbon energy, the use of thermal and hydropower units for regulation in the power system dominated by new energy (PSDNE) is decreasing. Electric buses (EBs) are expected to become the primary dispatching objects in this context. Accurately evaluation EB's response capability (RC) is vital for the scheduling operation of the PSDNE. However, current evaluation methods for EB's RC are relatively crude, lacking detailed and quantifiable measures. This paper proposes a novel multi-dimensional evaluation method to provide the aforementioned RCs. The EB operational schedule revise model considering traffic and temperature is proposed to obtain more accurate EB charging load by revise arrival charging time and remaining battery energy. The proposed EB charging model considering carbon emissions contributes to revealing the impact of various charging modes, such as low-carbon and electricity price, on the Eb's RC. The EB multi-dimensional evaluation model is established to obtain quantified and detailed RCs to support the operation of NEDPS. These capabilities include fast response within various periods, sustained response over extended durations, and remaining RC after executing scheduling instructions. Finally, using the actual data from an EB station of a city in China, the effectiveness of the proposed evaluation model is verified. [Display omitted] • The impact mechanisms of traffic and temperature on the EB's RC are revealed. • The accuracy of the revised EB's operating schedule is greatly enhanced. • The impact mechanisms of carbon emissions reduction on the EB's RC are revealed. • The EB's RC under different charging modes is quantitatively evaluated. • The fast RC, sustained RC, and remaining RC of the EB are quantitatively evaluated. [ABSTRACT FROM AUTHOR]

Under the background that China will achieve the goal of carbon neutrality by 2060, analyzing the impact of carbon neutral target constraints on the welfare of urban and rural residents is an important part of the design of a carbon emission reduction policy mechanism, in this paper, the dynamic recursive CGE(Computable general equilibrium) model is used to simulate the changes in welfare level, income consumption, and CO2 emissions of residents and their urban-rural heterogeneity under the policy of imposing a carbon tax on the fossil energy industry from 2022 to 2060. The results show that: compared with the BAU scenario, by 2060, first the income and consumption of residents will decrease slightly. Second, the target constraint of carbon neutrality makes the equivalent variation and compensate variation of urban and rural residents' welfare decrease slightly, and the equivalent variation and compensate variation of urban residents decrease more than those of rural residents. The following conclusions are drawn: first, the carbon-neutral target constraint makes the welfare of urban and rural residents decline slightly, but it will not have a strong impact on residents' income and consumption. Second, this constraint has a greater impact on the income, consumption and savings of urban residents than those of rural residents. [Display omitted] • A five-layer nested dynamic recursive CGE model is established to analyze residents' welfare. • Carbon taxes are incorporated into production decisions as a policy cost for energy companies. • The heterogeneity of the impact of carbon tax policy constraints on the welfare of urban and rural residents was analyzed. • The government needs to improve the intensive and diversified energy supply in rural areas to reduce the urban-rural gap. [ABSTRACT FROM AUTHOR]

Towards the target of carbon neutrality, the need for deep decarbonization in iron and steel industry is increasingly recognized. Existing studies rarely mention the effects of collaboration with its source industries for carbon emission reduction in ISI. Furthermore, the fundamental laws of CO 2 emission reduction capacity with cost change of source substitutions for various steel production routes under market economics are unclear. To avoid the blind substitution of clean sources for meeting the urgent requirements for decarbonization in China's steel industry, which may result in increased carbon emissions and uneconomical carbon reduction from a lifecycle perspective. This paper integrates the production and utilization processes of source resources into the steel production process, establishes coupling models of carbon and value flows for four main types of steel production routes, and aims to quantitatively analyze the specific CO 2 emission reduction potential and costs. Three economic market development scenarios from 2021 to 2060 are set, and the marginal carbon prices of various source substitutions are determined. Subsequently, decarbonization pathways with the goal of minimizing production costs are developed. Finally. The future resource demand and the carbon emission reduction contributions of source industries collaborated are predicted. The results show that (1) about 81.7–89.9 % of CO 2 emissions could be reduced in the scenarios; (2) the demands of electric power and hydrogen energy will reach 54.48–511.90 BkWh and 7.47–10.31 Gt in 2060, respectively; and (3) collaboration with source industries could make 18.3–18.3% of CO 2 emission reduction contributions. All in all, this study could provide scientific support for the low-carbon transformation of China's steel industry. • Carbon and value flows of BF-BOF, Scrap-EAF, DRI-EAF, MOE-EAF routes are analyzed. • CO 2 emission reduction potential with marginal carbon price is determined. • Decarbonization paths during 2021–2060 for China's steel industry are developed. • Quantify the contribution of collaborative carbon reduction with source industries. [ABSTRACT FROM AUTHOR]

In recent years, the phenomenon of the urban heat island caused by the rapid development of cities is very serious. To solve the problem of the urban heat island, this study proposed a PPP project consisting of the government (GOVT), photovoltaic investment company (PVIC), and residential customers (RS). Based on an evolutionary game model and combined with current policies and industry regulations in China, the evolution process and stable evolution strategies were studied. The result shows that more government subsidies, higher carbon trading prices, and feed-in tariffs will promote the development of the PPP project. For relatively suitable reference value ranges, the installation tilt angle of the BAPV system is 30°, the photovoltaic grid electricity price is 0.1096∼0.1296 $/kWh, the carbon trading is 8.92∼9.42 $/t. [ABSTRACT FROM AUTHOR]

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Constructing and adopting a county-level panel dataset containing carbon emission (CO 2) and economic information between 2009 and 2017, this paper employs the continuous difference-in-differences (DID) model and is among the first to conduct an evaluation of the CO 2 reduction effect of China's emission trading scheme (ETS) pilot markets from the dual perspectives of price and scale. The empirical results emerge that the increase of transaction price and the expansion of transaction volume in ETS pilots have a persistent and significant influence on CO 2 reduction. Parallel to this, it is found the rising of transaction price in ETS can be effective on CO 2 reduction by improving the energy structure transition, however, optimization of industrial structure and the development of ICT might be the essential channels driven by the expansion of transaction volume. Last, this paper identifies the synergistic effect on different sorts of contaminants and find it is more substantial to those with the gas state. This paper implies that the policymakers should fully excavate the market-oriented environmental regulation tools from transaction price and volume perspective of views for the well achieving the climate ambitions of carbon peak and neutrality. • This article estimates the carbon emission reduction effect in of China's ETS pilots from the dual perspective of price and scale point of view. • The switch of China's carbon ETS regional pilots to national unified one can make the carbon emissions experience a wave of decline about 6.162%. • By estimating the possible synergistic effect on pollution of ETS market pilots, which find it is more significant to pollutants with gas phase. [ABSTRACT FROM AUTHOR]

COVID-19 brings new challenges to addressing climate change and achieving the goal of environmental sustainability. This paper examines how the COVID-19 pandemic affects transmission of returns spillovers among green finance, carbon market and renewable & non-renewable energy in China. We evaluate the dynamic and directional risk connectedness across time and at various frequency bands using Diebold and Yilmaz and Baruník and Křehlík approaches. First, the total spillover effects increase significantly when the COVID-19 outbreak. Second, the carbon price fluctuations are mainly influenced by non-renewable energy. Third, the spillover effect is mainly acted in the short term, meaning that it is strongly influenced by investor sentiment in the short term. Fourth, non-renewable energy and carbon markets are the main net recipients and can be used as hedge assets. Finally, the total spillover is time-varying that is affected by the unexpected crisis. The findings will help policymakers and environmental investors to develop policy measures and portfolios that are appropriate for different frequency domains. • Time-frequency connectedness among green finance, carbon and energy is tested. • Spillovers increase at the onset of the COVID-19 and mainly acted in the short-term. • The carbon price fluctuations are mainly influenced by non-renewable energy. • Non-renewable energy and carbon markets are the major net recipients. • The total spillover is time-varying affected by the unexpected crisis. [ABSTRACT FROM AUTHOR]

This paper presents an energy-sharing market coupled with carbon emission trading to quantify peer-to-peer energy-sharing markets' economic and environmental influences. We establish a bi-level peer-to-peer energy sharing market considering the interaction between energy prosumers and the system operator. An interaction algorithm and generalized Nash game are thus applied in this work. With the simulation results, we find that the daily disutility of provincial systems is reduced by about 7 ¥ when time-of-use electricity rates are applied. This pricing strategy can encourage prosumers to participate in energy-sharing transactions. The subsidies of electricity rates, carbon prices, and renewable energy investments can improve the social welfare of the whole system. Considering the development of power generation technology and emission reduction targets, the change of optimization parameters in the next 30 years is analyzed in the example. It can be concluded that the total CO2 emission in 2050 will be reduced to almost half of that in 2020 in the energy-sharing markets. The subsidy of renewable energy generation and stricter CO2 emission constraints may accelerate the reduction of carbon emissions. [Display omitted] • It is the first time to assess the environmental effect of peer-to-peer energy trading with actual data. • We analyze the peer-to-peer energy trading impacts on the carbon emission amount in China. • We compare the market participants' revenue and CO2 emission considering the different scenarios. • We develop a bi-level coupled emission right trading market to verify whether total emission is reduced. • The development of technologies and various energy structures are considered quantitatively. [ABSTRACT FROM AUTHOR]

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict. • We propose a novel probability forecast model QTCN for carbon prices. • The impact of economic policy uncertainty and geopolitical risks are considered. • The prediction accuracy of QTCN outperforms other competing models. • QTCN-based trading strategies yield higher profits than a point forecast-based one. [ABSTRACT FROM AUTHOR]

Balancing environmental protection and economic development is critical in climate policy-making. The carbon trading policy is an important market-based climate strategy that aids in achieving the national carbon neutrality goals. However, empirical evidence at the industry level has not yet demonstrated whether there is an employment dividend. In this study, we constructed a theoretical framework to analyze the employment effects of carbon trading policies. We quantitatively evaluated the employment effects of policies in the pilot region using the differences-in-differences model. And we considered and explained the adjustment paths of employment dividends from the industry-level onwards. We found a "two-eight split" phenomenon in the employment effects of the carbon trading policy in China—the overall employment level of the region increases while employment in some regulated industries is inspired and it is hindered in others, shifting to other regulated industries and non-regulated regions. The empirical results revealed the following points. (1) After controlling economic and demographic factors, carbon trading policies can bring significant employment dividends to pilot areas. (2) While implementing such policies can improve the employment level of the paper industry, they can also seriously impact the employment development of the petrochemical and building materials industries. (3) Both energy consumption and technology levels effectively dampen the policy's negative employment effects and promote employment in the pilot areas. (4) The high carbon price levels within the range of carbon price fluctuation have inhibited the employment levels of the pilot industries. However, it still promoted the overall employment levels of the pilot areas through the employment transfer effect. As future carbon prices increase, the policy's regional employment dividend may decrease or disappear. Our findings illustrate the employment effect of industry-level carbon trading policies and provide a reference for improving carbon market construction. [ABSTRACT FROM AUTHOR]

Carbon neutrality goal puts forward new requirements for the energy structure of the power industry in China. The impact of electricity marketization reform on energy transition is one key issue that we need to consider. This paper constructs a system framework for the internal connection of the four primary energy sources and two system dynamics (SD) models to analyze the impact of the marketed on-grid price on the energy transition in the power industry. The results show that: (1) Compared with the benchmark on-grid price, the marketed on-grid price can promote the realization of the energy transition goal in the power industry to a certain extent. In 2060, the marketed on-grid price will be stable at about 0.1 yuan/kWh. (2) The energy transition goal can be achieved if the degree of market openness exceeds 60%. However, if coal-based power fully enters the marketization stage, the proportion of renewable energy can even reach more than 90% in 2060. (3) It is difficult to achieve the carbon neutrality goal only by increasing the proportion of renewable energy. Finally, we put forward corresponding policy recommendations. • The electricity marketization reform has an impact on the energy transition. • Driving mechanism of the marketed on-grid price is investigated in the power industry. • Two system dynamics models are built. • The energy transition goal can be achieved if the market openness exceeds 60%. [ABSTRACT FROM AUTHOR]