On the free allocation of emission rights under endogenous lobby formation

Under the background of low-carbon economic development, carbon emission trading, as an important institutional arrangement, can effectively coordinate the role of emission reduction constraints, price signals and resource allocation. However, there are still some problems in the current market operation, such as wide and large amount of emission data, wide range of price transmission and long chain, and lagging regulation. In this paper, how to realize the dynamic regulation of carbon emission trading in carbon emission market is studied. With the help of computer science, an intelligent computing support system based on data layer, modeling layer, decision layer and interaction layer is constructed. Based on the system, the carbon emission monitoring and prediction model, the carbon quota market price change prediction model, and the dynamic adjustment optimization model of carbon emission trading using deep reinforcement learning method were established. Finally, the closed-loop system of "monitoring-prediction-decision-feedback" is obtained. Finally, the system is simulated and the effectiveness of different control strategies is compared. The results show that the proposed DRL strategy outperforms the benchmark strategy and MILP strategy in terms of net income, volatility control, and response efficiency. In the typical 30-day simulation window, the cumulative net income reaches 7.52 million yuan, the carbon price volatility rate drops to 8.7%, the compliance deviation rate drops to 3.2%, and the average adjustment response duration is shortened to 6 minutes. The study suggests that embedding intelligent computing in the adjustment process of the carbon emission rights market helps improve the accuracy, forward-looking nature, and coordination of market operation. Povzetek: Študija obravnava uporabo tehnologije navidezne resničnosti pri športnem pouku. Združuje računalniški vid, 3D rekonstrukcijo skeleta, semantično prepoznavanje gibov in virtualno interakcijo. Rezultati kažejo boljšo vizualizacijo gibov, hitrejšo povratno informacijo in večjo interaktivnost. Kljub temu ostajajo izzivi pri večuporabniških scenarijih, udobju opreme, stabilnosti sistema in stroških uvajanja v šolski učni praksi.

In recent years, driven by emission reduction targets, an increasing number of manufacturers producing similar products have been compelled to seek emission reduction cooperation even while competing in the market, a phenomenon that has attracted growing attention in recent studies. Based on the carbon cap-and-trade mechanism, this study develops a noncooperative-cooperative biform game model to examine the optimal decisions of technologically complementary manufacturers engaging in emission reduction cooperation under price competition. The model describes alliance profits using a characteristic function and applies the Shapley value for profit allocation, while equilibrium outcomes are derived through noncooperative game analysis. The research results show that the high carbon emission reduction investment coefficient will inhibit the carbon abatement development level of manufacturers and reduce their profits. Without a carbon cap-and-trade mechanism, competitive manufacturers lack incentives for technological collaboration. An increase in carbon trading prices significantly promotes emission reductions and profit growth, whereas the effect of government-allocated initial carbon allowances remains limited. Moreover, the improvement of manufacturers' technological conversion capability can improve the level of carbon abatement development and enhance their profitability under price competition. In general, this study provides theoretical and practical guidance for the cooperative emission reduction of competitive enterprises under the carbon emission rights trading mechanism.

Research on the allocation of carbon emission rights in China for 2026—2030 from a digital and intelligent perspective

Global warming threatens sustainable human development, and carbon emission rights trading (CERT) has emerged as a key market-based tool for reducing emissions. Yet evidence on how CERT affects corporate green innovation—especially high-quality, substantive innovation—remains mixed and fragmented. Using unbalanced panel data on Chinese A-share listed firms from 2007 to 2016 and applying fixed-effect, DID, and PSM-DID models, this study examines the impact of China’s CERT pilot policy on quota-managed firms’ green innovation. The results show that the policy primarily stimulates substantive green innovation, reflected in green invention patents, with limited influence on strategic, low-novelty patents. Its effects are stronger for firms in central and western pilot regions, in non-high-tech industries, and at more mature stages of development, and differ between firms that anticipated regulation and those brought under quota management unexpectedly. Overall, the findings indicate that a well-designed carbon trading mechanism can reallocate resources to incentivize high-quality green innovation, offering micro-level support for Coasian market-based approaches to environmental externalities and informing the further development of China’s national carbon market.

How the dual pilot policy impacts corporate low-carbon transition: Evidence from energy use rights and carbon emission rights trading in China

Continuous allocation and compensation of carbon emission rights considering expert credibility and uncertainty — A case study of China

Under the backdrop of the rapid development of global carbon pricing mechanisms, carbon emission rights price prediction has become a key tool for optimizing the allocation of emission reduction resources and guiding enterprises' low-carbon transformation. The Long Short-Term Memory (LSTM) model's powerful ability to handle time series data has played a crucial role in this field. This paper focuses on the application of LSTM and its composite models in the field of carbon price prediction, systematically reviewing the typical data processing methods, model types, and model composite methods involved in existing research through case analysis. This paper evaluates LSTM and its composite models from three aspects: applicable scenarios, interpretability, and prediction accuracy. The research shows that LSTM is suitable for long-term trend prediction but has poor interpretability. The composite model of LSTM and econometric models, such as the Autoregressive Integrated Moving Average (ARIMA) model, is suitable for short-term prediction and has higher interpretability. The multi-factor LSTM model has a higher computational cost but is more suitable for scenarios with higher accuracy requirements than the single-factor LSTM model. Finally, this paper finds that the datasets used in current research generally only consider easily quantifiable market factors. In the future, more in-depth research on the factors influencing carbon prices can be conducted and incorporated into the model.

The estimation of carbon emission reduction potential in existing regions often faces the problem of missing data, so scenario analysis based estimation research is carried out. Under the constraints of emission standards, three emission limit scenarios are set: maintaining, ultra-low, and tightening. Based on the SBM model, a carbon emission reduction potential index model is constructed using the full factor carbon emission efficiency measurement method. Build a model that considers the impact of industrial output value and estimate carbon emission rights from 2018 to 2030. After analysis and calculation of allocation weights, experiments show that carbon emission performance is less than 0.05, efficiency is improved, weight is about 4.64%, and industrial carbon emissions contribute nearly zero.

This paper takes China's first batch of carbon emission trading pilot cities (Shanghai, Beijing, Tianjin, Chongqing) as the research objects. Based on the carbon emission and nitrogen oxide emission data from 2011 to 2023, it comprehensively uses the Regression Discontinuity Design (RDD) and the Generalized Difference-in-Differences Model (RDD-DID) to systematically evaluate the environmental governance effects and pollutant emission reduction effects of the carbon emission trading system. The study finds that after the implementation of the "Guiding Opinions on Further Promoting the Pilot Work of Paid Use and Trading of Emission Rights" in 2015 in Shanghai, the coefficient of the policy treatment term is significantly negative (estimated value -0.572, p<0.01), indicating that the carbon emission trading system has significantly reduced nitrogen oxide emissions, and control variables such as economic development level (GDP), population density, and foreign direct investment (FDI) have a positive synergistic effect on emission reduction. However, due to the relatively mature carbon trading system in Beijing, the policy incentive effect is not significant. The RDD-DID model extended to the four municipalities directly under the Central Government shows that the overall emission reduction effect of the policy is significant (YPost coefficient -4.45, p<0.01), but the interaction term coefficient between the activity of carbon emission trading and the policy is significantly positive (0.238, p<0.01), revealing that resource crowding, side effects of technical substitution, and market mechanism fragmentation may weaken the synergistic governance effect of multiple pollutants. The study suggests integrating the market rules of carbon emission rights and emission permits, strengthening the local environmental assessment mechanism, and optimizing the differentiated pricing strategy to improve the synergistic efficiency of environmental policies and provide theoretical support for the construction of a national unified environmental rights market.

ABSTRACT Progressive emissions trading systems have gradually become a key incentive-based policy tool for China to address energy saving and emission reduction issues in the context of resource and environmental constraints. In this study, we used China’s 2007 sulphur dioxide (SO2) emissions trading pilot as a quasi-natural experiment, based on panel data of Chinese cities at the prefecture level and higher from 2003 to 2021. Additionally, we employed the propensity score matching and difference-in-differences (PSM-DID) method to assess the pollution control and synergistic haze-reduction effects of the emissions trading policy in the pilot area. The results showed that the emissions trading pilot policy had a remarkable environmental policy effect on SO2 emission reduction in the short term, but the reduction effects were not effectively exerted in the medium and long term. Further heterogeneity analysis showed that the pollution-control effect of the emission rights trading policy was greater on cities with stronger enforcement than on cities with weaker enforcement. Moreover, the ‘primary market first’ mode had a greater reduction effect than the ‘secondary market first’ mode. These results indicate that the policy effect on cities with a higher marketization degree is significantly stronger than that on cities with a lower one.

This study explores the impact of Chinas pilot carbon emission trading scheme (ETS) on corporate green innovation using a difference-in-differences approach, with data from A-share listed companies spanning 2010-2021. It examines whether the policy drives green patent applications and the mediating role of public attention. Results indicate that the pilot ETS significantly promotes corporate green innovation, with a stronger effect on state-controlled enterprises. Public attention partially mediates this relationship, as the policy enhances public scrutiny, which in turn stimulates firms green innovation. These findings highlight the effectiveness of market-oriented environmental policies and the role of non-market forces in advancing sustainable development. Based on the research findings, it is suggested to promote enterprises to accelerate green transformation, so as to help achieve carbon emission reduction and sustainable development goals.

Research on cleaner production decision-making mechanisms for livestock and poultry farmers driven by waste emission rights trading

In order to reduce carbon emission and increase flexibility of thermal power units, a new power system of compressed air energy storage coupled with oxyfuel coal-fired units using CPU waste heat(CFPP+CAES+CPU) was proposed. Using Ebsilon Professial and Aspen Plus software to build a model, the thermodynamic characteristics and economic analysis of the system were carried out. The results show that: In fixed coal consumption mode, the exergic power of CFPP+CAES+CPU unit increased by 0.745% at the lowest and 1.216% at the highest. Exergic damage of exergic power of CFPP+CAES+CPU unit decreased compared with control unit, while exergic efficiency of heat exchanger in CPU increased. The maximum economic mode of CFPP+CAES+CPU unit is that when CO2 and carbon emission rights are sold, the minimum LCOE is 0.374¥/kWh when carbon tax is not considered

In this paper, a novel four-level distributed game decision-making framework of dynamic peer-to-peer (P2P) carbon emission right (CER) sharing is proposed for microgrid clusters (MGCs), which consists of four phases: exploration, selection, equilibrium and correction. Firstly, in the exploration stage, the tradable information is published by microgrids (MGs) and an initial set of tradable options is constructed. Secondly, in the selection stage, bilateral transaction coefficients are calculated according to the deficit and surplus of CERs in the P2P carbon market, and then an optimal sharing matching scheme of CERs is designed to reduce invalid transactions. Thirdly, in the equilibrium stage, a P2P carbon right sharing strategy based on the Stackelberg game is proposed to realize the dynamic change in carbon prices and the flexible conversion of leader-follower roles in P2P carbon trading, which can promote the popularity of carbon market. Fourthly, in the correction stage, a carbon emission right balancer (CERB) is developed to avoid the imbalance of CERs, and then a billing scheme including incentives and penalties is established to achieve the benefit redistribution after P2P carbon trading. In the end, numerical results illustrate the effectiveness of the proposed multi-layer game decision-making framework for P2P carbon right sharing among MGs.

Carbon dioxide (CO₂) emissions pose a significant climate threat, impacting all aspects of human activity and necessitating global collaboration to protect both human and nonhuman species. Transitioning from fossil fuels to renewable energy and enhancing energy efficiency are widely regarded as the most effective strategies for reducing emissions and mitigating global warming. Against this backdrop, we examine CO₂ emissions convergence among 50 Organization of Islamic Countries (OIC) member states, considering the role of economic growth, renewable energy use, and energy intensity. Our analysis employs stochastic, club, and beta convergence methods, alongside system generalized method of moments (GMM) estimation. Four key findings emerge from this analysis. First, accounting for country heterogeneity and cross-sectional dependence, we confirm stochastic convergence in CO₂ emissions among OIC members. Second, there is evidence of club convergence, where emissions cluster into distinct groups. Third, while renewable energy consumption negatively affects emissions pathway, energy intensity positively and directly affects CO₂ emissions’ growth. However, fourth, economic growth increases carbon emissions. These findings have significant policy implications. If emissions do not converge, allocating emission rights through carbon trading could lead to substantial international wealth transfers, influencing global carbon policy. Additionally, countries with similar convergence patterns could adopt common climate policies. At the same time, all nations should prioritize increasing the share of renewable energy in their energy mix to achieve sustainable emission reductions.

Carbon reduction decision-making in the supply chain considering carbon allowances and bidirectional option trading mode of carbon emission rights

Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a “green-carbon” offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the “green-carbon” offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO2 produced by waste incineration is absorbed and reused. Finally, the “green-carbon” offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES.

ABSTRACTCarbon emissions are a significant contributor to global warming. As one of the largest carbon emitters in the world, China is committed to establishing a carbon emission trading market to address the challenges posed by climate change. The carbon price is a fundamental component of the carbon financial market. Accurately predicting it can improve environmental quality, reduce energy demand, and promote economic growth. This study uses price data from the Guangdong carbon market as a case study and employs a hybrid model that integrates Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) networks for carbon price forecasting. The findings indicate that: (1) the CNN–LSTM model exhibits optimal predictive performance when the sliding window is set to a size of 5 on the basis of previous carbon price data. (2) By incorporating significant indicator features from the Guangdong pilot carbon price dataset while maintaining a sliding window size of 5, the model achieves superior predictive accuracy, as evidenced by a Goodness of Fit (R2) of 0.8622 and a mean absolute error (MAE) of 0.0228, resulting in the most favorable comprehensive evaluation index. (3) The integration of one‐dimensional convolutional layers with LSTM layers in the CNN–LSTM model effectively leverages the strengths of CNNs for local feature extraction and the capabilities of LSTMs for modeling time series data. This approach leads to a substantial improvement in predictive performance compared with alternative models such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), and LSTM.

Abstract The condition of “defining property rights” in Coase Theorem is actually the key method to solve the external infringement problems such as air pollution. It changes the public nature of external infringement into private nature. Because the cost of trading private goods is much lower than that of public goods, it will greatly simplify the solution of air pollution and other problems. The primary reason why emissions trading has achieved some success is that emission quotas define the property rights (or responsibilities) among emitters. This turns public externalities into private externalities, and subsequently forms protected and tradable private property rights. As the total amount and quota of emissions still have to be arranged by the emission regulatory authorities, emissions trading has not completely got rid of the government’s intervention. Therefore, this paper suggests that all emission quotas should be given to all residents in the world, and each of them has an equal share of emission quotas, which can be traded in the online global carbon emission market, and emitters can buy emission quotas in the market. In this way, the value of reducing carbon emissions for everyone can be evaluated more accurately, and the deadlock between countries can be broken, so that the cause of reducing carbon emissions can have a great development.