Emission Trading Price Research Articles

A Sustainable Model for Forecasting Carbon Emission Trading Prices

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Analysis of the influencing factors of emission trading price in China

China established pilot carbon markets in 2013. In 2020, it set targets for carbon peaking in 2030 and carbon neutrality by 2050. China’s national carbon market officially commenced operations in 2021. Based on the national market and seven pilot markets, this study established the factors influencing carbon trading prices by examining market participants, macroeconomics, energy prices, carbon prices in other markets, etc. Asymmetrical development among the seven pilot cities, for which the study employed a mixed-effects model, was the primary factor impacting carbon prices. The carbon prices in the pilot cities cannot be extrapolated to the entire country. In the national carbon market, where the study employed a multiple regression lag model, the SSE index was positively correlated with carbon prices, whereas the Dow Jones index had no significant effect on carbon prices in terms of macroeconomics. Coal and natural gas prices were negatively correlated with carbon prices, whereas oil prices were positively correlated with energy prices. The EU market prices have a positive correlation with prices in other markets. The significance of this study is that it covers the largest national Emissions Trading System (ETS) in the world and allows for comparing the characteristics of the Chinese market with those of other ETS markets. Additional studies, including more sectors, should be conducted as China’s ETS coverage increases.

Source tracing and contagion measurement of carbon emission trading price fluctuation in China from the perspective of major emergencies.

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict-financial market pressure and energy market-China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China's carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale.

Complex dynamics of dual oligopoly demand uncertainty under carbon emission trading mechanism

The international community's growing focus on energy conservation and emission reduction has led businesses to prioritize low-carbon operations and the marketing of environmentally friendly products during their production processes. On the one hand, with the reform of carbon emissions trading, carbon emission trading has become a potential profit point for enterprises. On the contrary, the consumer market is influenced by the uncertain external environment. And consumers' environmental awareness increases the uncertainty of product demand. Therefore, the intricate dynamics of dual oligopoly demand uncertainty within the framework of a carbon emission trading mechanism are explored in this scholarly analysis. The best market Porter's generic strategies, including emission reduction investment and product pricing how can carbon quotas, uncertainty in market demand, and consumer environmental preferences improve expected profits? The results show that technology emission reduction costs, carbon emission trading prices and consumer preferences for low-carbon products. It is an important factor that affects the competitive strategy of the two enterprises. The different values of the above factors will change the relative intensity of emission reduction investment and the relative profitability of the two. Complete company statement in a balanced state. This article also investigates the complexity of adjusting the dynamic evolution path of competitive strategies between two organizations. Feedback controllers can effectively suppress unstable equilibrium states and provide effective references for their stable operation.

Green Technology Investment Strategies Under Cap-and-Trade Policy

This article aims to address methods for reducing carbon emissions by investigating the green technology investment behavior of enterprises under cap-and-trade regulation and governments’ cap-and-trade policy design mechanisms. Considering that investments are long-term, we developed a two-period green technology investment model for a manufacturing firm facing carbon-sensitive demand in a cap-and-trade regulated market. Our findings show that the firm's investment strategy depends on trade-offs between the gains from selling low-carbon products and the costs of investing in technology. Such trade-offs are affected by consumers’ carbon emissions sensitivity, the unit carbon emission trading price, and the firm's investment efficiency across two periods. The first-period optimal free allowance allocated to the manufacturer is unique, but the second-period optimal free proportion is not unique as it is decided by the unit carbon emission trading price. In addition, the firm's chosen investment strategy based on the optimal carbon policies can create a mutually beneficial situation for the manufacturer, consumers, and society, which shows that the government-designed carbon policies, including the first-period free emissions allowance and the second-period free proportion, are effective. Our study results can aid manufacturers in making wise, strategic, and operational decisions about green technology investment and provide guidance on carbon quota allocation policies that promote the development of low-carbon economies.

Design and operation of renewable energy microgrids under uncertainty towards green deal and minimum carbon emissions

The regulations regarding the Paris Agreement are inevitable to keep the global temperature rise within 2 °C. Moreover, integrating renewable energy-based equipment and adopting new ways of producing energy resources, for example Power to Gas (PtG) technology, becomes essential because of the current environmental, economic, and political concerns. It is vital to supply the growing energy demand with the increasing population. Uncertainty must be considered in the transition phase since parameters regarding the electricity demand, carbon tax policies, and intermittency of renewable energy-based equipment have intermittent nature. A multi-period two-stage stochastic MILP model is proposed in this work where the wind speed, solar irradiance, temperature, power demand, carbon emission trading (CET) price, and CO2 emission limit are considered uncertain parameters. Three stochastic case studies with scenarios including different combinations of the aforementioned uncertain parameters are investigated. Results show that more renewable energy-based equipment with higher rated power values is chosen as the sanctions get stricter. In addition, the optimality of PtG technology is also investigated for a specific location. Implementing the CO2 emission limit as an uncertain parameter instead of including CET price as uncertain results in lower annual CO2 emission rates and higher net present cost values.

Optimal Green Technology Choice for Firms under an Emission Trading Scheme: End-of-Pipe vs. Cleaner Production

Green technology innovation helps to improve both economic and environmental performance simultaneously. How to invest in green technology innovation under emission trading policy is a current issue worthy of attention. However, existing research has not delved into the choices of different green technology innovation models, namely cleaner production technology and end-of-pipe technology, available to firms and governments under the joint implementation of other policies. Thus, this paper studies the optimal model of green technology innovation under emission trading policy and emission tax policy by constructing a two-stage game model suitable for complex decision analysis. The results show that regardless of the value of emission trading price, the optimal green technology innovation choice of the firms is cleaner production technology. Furthermore, the results show that neither conflict nor consistency always exists between governments’ and firms’ choices. When the emission trading price is high, the choice of governments and firms is in conflict; when the emission trading price is low, the choice of the two is consistent, both prefer cleaner production. This study not only enriches the existing research in theory but also provides support for governments to guide the choice of firms and achieve a win–win situation in practice.

Spillovers and connectedness among climate policy uncertainty, energy, green bond and carbon markets: A global perspective

Extreme weather anomalies, energy crisis and environmental degradation have garnered significant attention in the context of sustainable development. This paper analyses the spillovers among climate policy uncertainty (CPU), energy prices, green bond index, and carbon emission trading price with quantile connectedness approach. The results suggest that connectedness among variables is higher at the extreme quantiles than the median quantile, and the connectedness would strengthen during international events, such as the US withdrawal from the Paris Agreement, COVID-19 epidemic, and Russian-Ukraine conflict. Additionally, the dynamic spillover analysis further demonstrates that CPU consistently acts as the risk receiver and passively bears influence from other markets. The constructed theoretical mechanism provides valuable insights for a comprehensive assessment of climate policy, facilitating the orderly functioning of energy, green finance and carbon markets, and sustainable investment. Thus, the paper concludes that the significant spillover from energy, green bond and carbon markets serve as useful indicators for judging and predicting uncertainties related to climate policy.

Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?

This paper proposes a new forecast method of stock price return and volatility by GAVMD-Carbon-BiLSTM. First, based on the data from eight carbon markets in China, the logarithmic rate of return of carbon emission rights price is constructed. Second, the TVP-VAR model is used to investigate the impact effect of carbon emission rights trading, stock price return, and volatility. It is used as a predictor of return and volatility rationality. Third, the genetic algorithm is used to optimize the parameters of the variational mode decomposition. Fourth, the return and volatility of stock price are decomposed into multiple intrinsic modes, effectively reducing the data’s complexity. Finally, GAVMD is combined with BiLSTM, and the logarithmic return of carbon emission trading price is used as input to predict the stock price return and volatility. The results show that carbon emissions trading impacts the rate of return and volatility at different lead times and time points. Therefore, using it to predict stock price return and volatility can improve prediction accuracy. At the same time, combining GAVMD with BiLSTM, the prediction performance of carbon emission trading price logarithmic return rate as input is much better than other machine learning models.

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

Optimal strategies for production plan and carbon emission reduction in a hydrogen supply chain under cap-and-trade policy

Hydrogen is now being seen as one of the most promising carbon-free and abundant energy sources on the path to net-zero emissions. However, high transportation, storage, and carbon emission costs in hydrogen supply chains are critical economic barriers that hinder the development of the hydrogen economy. Consequently, hydrogen refuelling stations have to face a trade-off between self-produced and purchased hydrogen quantities while considering transportation and storage costs. Furthermore, hydrogen manufacturers and refuelling stations have to make a trade-off between their hydrogen outputs and carbon emission reduction quantities under a cap-and-trade policy. We develop a differential game model that draws on and extends the literature on hydrogen supply chain design and operation to address this dual dilemma. Therein, state variables include the hydrogen retail price and carbon emission trading price, and control variables involve the hydrogen outputs and carbon emission reduction amounts of the hydrogen manufacturer and refuelling station. We use the feedback control technique to find optimal solutions and implement numerical simulations to validate solutions and the sensitivity of parameters. The results suggest that carbon emission reduction efficiency, hydrogen wholesale price, and carbon trading reference price significantly impact the optimal decisions of production plans and carbon emission reduction. This study may provide theoretical guidance for promoting hydrogen utilisation, cost reduction and low-carbon development.

How do the carbon emission trading prices affect the financing decision of the supply chain considering carbon neutrality?

This paper considers a supply chain formed by a manufacturer with constrained capital and a retailer with sufficient capital. By using the Stackelberg game theory, we discuss the manufacturer's and retailer's optimization decisions for bank financing, zero-interest early payment financing, and in-house factoring financing under both normal and carbon neutrality scenarios. In the carbon neutrality scenario, numerical analysis shows that improving the efficiency of emission reduction drives manufacturers to switch from external to internal financing methods. The impact degree of green sensitivity on the supply chain's profit depends on the carbon emission trading prices. In the framework of green sensitivity of products and emission reduction efficiency, manufacturers' financing decisions are affected by carbon emission trading prices rather than by whether emissions exceed the standards or not. When the price is higher, internal financing is easier to obtain, whereas the space for external financing is constrained.

Low-carbon transformation of ethylene production system through deployment of carbon capture, utilization, storage and renewable energy technologies

Ethylene industry contributes significantly to the world economy, but the conventional steam cracking based production process generates huge amount of CO2 emissions due to massive use of fossil fuels for power and heat supply. Deploying technologies of carbon capture, utilization and storage (CCUS) and renewable energy is urgently necessary to steer the emission-intensive ethylene industry towards carbon neutrality. To attain this goal, this paper proposes a low-carbon ethylene production system with the joint deployment of CCUS, wind turbine, solar heat collector, electric boiler and thermal energy storage technologies. Energy, economic and CO2 generation/reduction performance models of each equipment are developed first. Based on which, a mixed-integer liner programming framework is constructed to find the optimal capacity configuration and coordinated operation scheme of the multiple devices, laying a foundation for the reliable, economical and low-carbon operation of the integrated system. Case studies on a practical ethylene production system demonstrate that the proposed low-carbon retrofit leads to 57.50% CO2 reduction at the expense of 15.92% total annual cost increase. Discussions are then carried out to investigate the effects of different decarbonization routes, the impact of carbon emission trading price, the approach to promote the deployment of CO2 hydrogenation to methanol, and the advantage of taking carbon capture as flexible load, through which, broader insights are provided for the low-carbon transformation of the ethylene industry.

Influencing Factors and Forecast of Carbon Emission Trading Price in Beijing

Influencing Factors and Forecast of Carbon Emission Trading Price in Beijing

Closing the Gap between Carbon Neutrality Targets and Action: Technology Solutions for China's Key Energy-Intensive Sectors.

Facing significant carbon emissions annually, China requires a clear decarbonization strategy to meet its climate targets. This study presents a MESSAGEix-CAEP model to explore Chinese decarbonization pathways and their cost-benefit under two mitigation scenarios by establishing connections between five energy-intensive sectors based on energy and material flows. The results indicated the following: 1) Interaction and feedback between sectors should not be disregarded. The electrification process of the other four sectors was projected to increase electricity production by 206%, resulting in a higher power demand than current forecasts. 2) The marginal abatement cost to achieve carbon neutrality across all five sectors was 2189 CNY/tCO2, notably higher than current Chinese carbon emission trading prices. 3) The cost-benefit analysis indicates that a more ambitious abatement strategy would decrease the marginal abatement cost and result in a higher net carbon abatement benefit. The cumulative net benefit of carbon reduction was 7.8 trillion CNY under ambitious mitigation scenario, 1.3 trillion CNY higher than that under current Chinese mitigation scenario. These findings suggest that policy-makers should focus on the interaction effects of decarbonization pathways between sectors and strengthen their decarbonization efforts to motivate early carbon reduction.

The promotion strategies of heat metering and charging in China’s heating region based on complex network evolutionary game model

Abstract Heat metering and charging can help raise residents’ awareness of energy conservation and save heat consumption in buildings. The heat metering and charging in China, however, was promoted insignificantly. To explore the behavior mechanism of heat-supply enterprises implementing heat metering and charging under the background of complex network in China’s heating region, and put forward the promotion strategies of heat metering and charging, this paper uses the heat-supply enterprises as the sample, improves the utility function of heat-supply enterprises by considering their difference aversion preference and altruistic preference at the same time based on the Difference Averse Surplus Maximizer Model. Then, we establish a complex network evolutionary game model among different heat-supply enterprises, and an empirical analysis is conducted to examine the impact of different factor on heat-supply enterprises in China’s heating region to implement heat metering and charging. The conclusions are as follows: (1) With the current heating cost and income, even if the government subsidizes all environmental benefit generated by heat metering and charging to heat-supply enterprises, the heat metering and charging could not be promoted in different scales of networks. (2) Increasing metering heat price, government fine, environmental protection tax and carbon emission trading price have positive effects on the promotion of heat metering and charging in different scales of networks. (3) The value of metering heat price, government fine, environmental protection tax and carbon emission trading price have no significant impacts on the evolutionary speeds of different scales of networks.

Carbon emission reduction policy with privatization in an oligopoly model.

This study constructs a mixed oligopoly model that includes a public enterprise and two private enterprises. Game theory was adopted to explore the effects of carbon emission reduction policies. In addition, this study analyzes the optimal carbon emission trading prices and privatization decisions. The results show that the proportion of state-owned shares and the equity efficiency gap affect the equilibrium results for different carbon emission policies. Privatization increases the profits of public firms but does not necessarily increase social welfare. Different carbon emission reduction policies have different effects on the equilibrium results. Moreover, the emission reduction target is not completely consistent with the maximum social welfare target and should be comprehensively considered. The government can intervene by setting carbon emission trading prices and making privatization decisions. Full and partial privatization may be optimal decisions.

Optimal Financing Decision for Supply Chain under Different Carbon Emission Limitation Policies

With the characteristics of credit enhancement and low cost, supply chain finance is one of the effective ways to solve the "difficult and expensive financing" of small and medium-sized enterprises. In order to achieve the goal of carbon peaking and carbon neutrality as early as possible and to deeply implement the concept of sustainable development, we need to consider the impact of carbon emissions on supply chain financing methods in the study of supply chain finance. The existing carbon emission limitation policies include quantity (carbon cap-and-trade) and price (carbon tax) policies. Therefore, this paper incorporates both policies into the supply chain to consider the policy effects of the two policies being implemented simultaneously. The supply chain consists of a manufacturer with limited capital and a retailer with sufficient capital. Under bank financing, zero-interest early payment financing and in-house factoring financing, this paper simultaneously studies the effects of carbon tax rates and carbon emission trading prices on the supply chain's decision variables and profits by applying the Stackelberg game approach. The numerical simulation results show that the supply chain's profits are lower when emissions exceed standards than its profits when emissions do not exceed standards. Under the high carbon tax rates and high carbon emission trading prices, the optimal financing method is bank financing. Under the low carbon tax rates and low carbon emission trading prices, zero-interest early payment financing and in-house factoring financing are the optimal financing methods. As consumers' green sensitivity of rises, the policy's regulation scope of optimal bank financing is restricted, however, the policy's regulation scope of the other two financing methods is expanded.

Prediction Method of Carbon Emission Trading Price Based on Claim Rights

Prediction Method of Carbon Emission Trading Price Based on Claim Rights

Prediction method of carbon emission trading price based on claim rights

Prediction method of carbon emission trading price based on claim rights