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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]

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]

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]

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]

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]

Emission trading scheme (ETS) is considered as a tool to reduce emissions at a low cost, which can effectively promote the carbon peak and carbon neutrality. As the main participants in the carbon emission trading (CET) market, the behavior and strategies for governments and different types of enterprises affect the final emission reduction performance. Taking the national carbon market as an example, this paper constructs a tripartite evolutionary game model including governments and power generation enterprises with different emission reduction capacity to analyze the possible game situations in the development process of the CET market. The results show that with the development of the CET market, the governments and power generation enterprises will have different game results. In order to achieve the optimal stable equilibrium and maximize carbon emission reduction, governments should improve regulatory efficiency and formulate appropriate incentive strategies for excess emission reduction and punishment strategies for insufficient emission reduction. In addition, the initial willingness will not affect the final strategy choice of the three parties. Reasonable free carbon quota ratio, lower technology emission reduction cost and higher carbon price can stimulate the enthusiasm of governments and enterprises to reduce emissions. The above results provide a reference for coordinating the relationships between governments and power generation enterprises with different emission reduction capacity in the construction of the CET market in the future. [ABSTRACT FROM AUTHOR]

The coverage of Emission Trading Scheme (ETS) has been widely discussed. Based on the plans of the various stages of the National Development and Reform Commission in China, we explore the different impacts of different plans by applying recursive dynamic computable general equilibrium. What we found is different. Covering more industries, carbon prices will be lower so that the emission reduction motivation of each covering enterprises will be reduced. However, the resource attributes of carbon emission allowance will gradually emerge. In general, we found that the changes in the sectoral coverage will bring about the changes in the two effects in ETS market: carbon price effect and the allocation effect. The interaction of the two effects has led to a positive impact of both low-coverage and high-coverage on reducing emissions and improving the energy efficiency. This paper suggests that just covering three enterprises that are easy-to-MRV (monitoring, reporting, and verification) is enough for emissions reduction: cement, chemical and electric power, which can also achieve high emission reduction efficiency and can effectively reduce supervision cost. • A dynamic recursive CGE model is established in this paper. • There are two effects in ETS market: carbon price effect and the allocation effect. • Reduction efficiency is an inverted U-shaped curve to the changes of coverage. • Cement, chemical and electricity are encouraged to participate in ETS. [ABSTRACT FROM AUTHOR]

Motivated by reducing carbon emissions, carbon trading market have been opened to promote environmental protection. Accurate carbon trading volume and price forecasts have far-reaching implications for environmental and energy policy formulation. As the country with the most massive carbon emissions in the world, China's carbon price trends, carbon trading volume trends, and policy orientation are of great significance. As matters stand, there are few studies on carbon price and trading volume forecasts, and no research paper has covered carbon price and trading volume forecasts for all carbon trading markets in China. In order to provide a relatively complete reference for the carbon price and trading volume in China, this paper uses six machine learning models to predict the daily carbon price and trading volume of eight carbon markets in China, including Beijing, Shenzhen, Guangdong, Hubei, Shanghai, Fujian, Tianjin, Chongqing. The prediction models include extreme gradient boosting, random forest, kernel-based nonlinear extension of the Arps decline model optimized by grey wolf optimizer (GWO-KNEA), support vector machine optimized by particle swarm optimizer, support vector machine optimized by fruit fly optimizer and simulated annealing algorithm, and radial basis function neural network (RBFNN). Moreover, an advanced data denoising method, i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is used in the models to smooth the raw data. Because the eight carbon trading markets have different carbon price and trading volume distribution characteristics in time series, by comparing the prediction results, more suitable models for the specific carbon market can be obtained. The prediction results show that CEEMDAN-GWO-KNEA and CEEMDAN-RBFNN are more competitive in predicting carbon prices and trading volume in China because of their best performance in many datasets. The average accuracy of CEEMDAN-RBFNN and CEMDAN-GWO-KNEA in carbon price prediction is 98.40% and 97.89%, respectively. The predictive stability of each model in different application scenarios is also discussed. The results show that high prediction accuracy does not mean better prediction stability, but CEEMDAN-RBFNN and CEMDAN-GWO-KNEA can still guarantee high stability in most datasets. [ABSTRACT FROM AUTHOR]

China is the world's largest developing country and a carbon dioxide emitter. A functional carbon market can effectively reduce carbon emissions. This paper uses the Markov chain Monte Carlo-stochastic volatility model and the wavelet multi-resolution model to analyze the volatility of price returns and the dynamic characteristics of price fluctuations in the carbon pilot markets in Hubei, Shanghai, and Shenzhen. The price movements in these markets are compared to the emissions trading system of the European Union (EU-ETS). The results show that there is a volatility clustering in the price of carbon trading in Hubei, Shanghai, Shenzhen and the EU-ETS. China's carbon pilot markets have a deficiency in terms of volatility stability, as does the EU-ETS. From a long-term perspective, China's carbon market lacks a detailed development plan, which is vital because the construction of the market system is not yet optimal. From a short-term view, China's carbon market is not active and the participants' attitude toward risk is extremely sensitive. • MCMC-SV and wavelet multi-resolution model applied to carbon price volatility. • Three major Chinese markets are compared to the EU-ETS. • All markets evidence volatility clustering. • China's carbon market lacks a detailed development plan for the long term. • Short-term, China's carbon market must become more active and offer more products. [ABSTRACT FROM AUTHOR]

Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting. • A novel hybrid forecasting architecture is proposed for carbon price forecasting. • A two-stage feature selection method is proposed to gain optimal input variables. • A multi-objective optimization algorithm is used to improve the prediction results. • The developed model can perform better using both EU ETS and China data. [ABSTRACT FROM AUTHOR]