Carbon trading, as the critical market mechanism to achieve greenhouse gas emotion reduction, have received more attention around the world. The target that China establish and improve Carbon Emissions Trading Exchange (CCET) in recent year is to help the achievement of “peak carbon dioxide emissions”, “carbon neutrality”. However, Considering the characteristics of nonlinearness, strong fluctuation of carbon price, the accuracy of data prediction reduced significantly. Based on China Carbon Emissions Trading Exchange (CCET) data from July 2021 to March 2025, this paper build a multi-factor hybrid forecasting model that integrates VMD-CEEMDAN decomposition with GARCH-MIDAS and CNN-BiLSTM-Attention model throughout the ARCH and Sample Entropy as testing methods. Meanwhile, this model introduce influence factor including Macro Economy, Similar Products, Energy Structure and Climate Environment. Combine the random forest method with the MIDAS approach to select and integrate multi-frequency influencing factors, and evaluate model performance using indicators such as MAE, MSE, and RMSE. The experimental results show that the proposed model is better than the other model in accuracy and stability. The indicators including MAE, MSE and RMSE all are less than other comparison models.

To achieve more precise and regionally adaptive emission control, this study develops a dual-control framework that simultaneously constrains both total carbon emissions and pollutant concentration levels. Regional environmental heterogeneity is incorporated into the dispatch of generating units to balance emission reduction and operational efficiency. Based on this concept, a regional carbon emission allowance allocation model is constructed by integrating ecological pollutant concentration thresholds. A multi-source Gaussian plume dispersion model is further developed to characterize the spatial and temporal distribution of pollutants from coal-fired power units. These pollutant concentration constraints are embedded into an environmental–economic dispatch model of a coupled electricity–hydrogen–carbon system supported by hybrid storage. By optimizing resource use and minimizing environmental damage at the energy-supply stage, the proposed model provides a low-carbon foundation for the entire industrial production cycle. This approach aligns with the sustainable development paradigm by integrating precision environmental management with circular economy principles. Simulation results reveal that incorporating pollutant concentration control can effectively reduce localized environmental pressure while maintaining overall system economy, highlighting the importance of region-specific environmental capacity in enhancing the overall environmental friendliness of the industrial chain.

Amid the global energy transition and expanding carbon markets, accurately forecasting carbon emission allowance (CEA) prices is crucial for effective policymaking and risk management. Existing models struggle to capture the non-stationary dynamics of carbon prices, which are driven by external shocks and inherent multi-scale periodicities. To address this, we propose a multivariate forecasting framework based on Autoformer. By leveraging the superior nonlinear forecasting capabilities of Autoformer and constructing multi-dimensional input feature vectors, the proposed method effectively captures latent periodic patterns embedded in time series data of CEA. Rigorous empirical evaluations demonstrate that our method significantly outperforms baselines.

Wetland ecosystems have suffered serious damage. To increase the incentive to protect wetlands, the government can allocate certain carbon emission allowances to environmental organizations that protect wetlands. Common wetland governance modes include restoring water quantity, diversifying vegetation and controlling invasive species. In order to derive the applicable range of various wetland governance modes, this article constructs three differential game models and compares and analyzes the equilibrium results obtained by the models. Finally, the research shows that if the additional reputation gained by restoring water quantity per unit is small, the government can achieve the maximum benefit by choosing the restoring water quantity mode. If the additional reputation gained by restoring water quantity per unit is large, the government can achieve the maximum benefit by choosing the diversifying vegetation mode. Due to the existence of carbon trading, environmental organizations will take wetland ecosystem protection measures. If the additional reputation gained by restoring water quantity per unit is small and the revenue gained by governing wetlands per unit is large, the environmental organizations can achieve the maximum benefit by choosing the controlling invasive species mode. Otherwise, the environmental organizations can achieve the maximum benefit by choosing the diversifying vegetation mode.

Multi-criteria carbon emission allowance allocation for liner shipping companies in China: An integrated approach based on composite indicators and zero-sum gains DEA

The carbon emission trading system is an effective policy tool to realise carbon emission reduction. However, environmental regulation will inevitably affect economic and social operation, how to realize the coordination between pro-moting environmental protection and stabilising employment is a question that needs to be answered urgently. Based on the micro data of listed companies and the macro data at the city level from 2010 to 2021, this paper uses multi-period DID to study the impact of the carbon emissions trading system on labor de-mand and its mechanism. It is found that the carbon emission trading system reduces the labor demand of enterprises but increases the employment of cities, and the overall employment effect is positive. The mechanism test shows that the carbon emission allowance trading system causes the reduction of enter-prise labor demand mainly through the factor substitution effect, skills, and there is a different effect of the policy effect. Further research shows that the carbon emission right trading system make the flow of labor from polluting in-dustries to non-polluting industries and from non-pilot areas to pilot areas. Therefore, the policy effect is inconsistent between micro and macro effects. The findings of this paper provide a useful reference for improving the carbon trading system and safeguarding employment

Digital twin-driven dynamic coordinated allocation of urban pollutant and carbon emission allowances for individual vehicles

Bidirectional allocation method of provincial carbon emission allowances under China's 2030 carbon peak target: From equity and efficiency perspective

Economic dispatch strategy of electricity–gas integrated energy system based on paid carbon emission allowance allocation

An optimal multi-scale ensemble transformer for carbon emission allowance price prediction based on time series patching and two-stage stabilization

The role of carbon sinks and renewable energy in the allocation of China's provincial carbon emissions allowance: From the perspectives of equity, efficiency, and continuity

This study evaluates the economic feasibility of flexible, renewable ammonia production in Italy through a comprehensive sensitivity analysis of the levelized cost of ammonia (LCOA). Ammonia is produced through Haber–Bosch synthesis from green hydrogen and nitrogen coming from alkaline electrolysis and cryogenic air separation, respectively. The analysis examines the impact of key parameters such as renewable source peak power, Haber–Bosch reactor flexibility, energy mix, electrochemical and hydrogen storage, on the final production cost. The location considered for the PV and wind power output is Southern Italy. The results show that a wind-driven system with minimal battery storage and a flexibility factor (ratio between the minimum operating capacity and the nominal capacity of the plant) of 20% offers the most cost-effective solution, but production is scaled down to 64 tpd. With the 2030 cost structure, battery storage offers better integration with wind systems and flexible operation, even at low levels of turndown. For different combinations of process design choices and flexibility, the optimal LCOA for a green ammonia production is approximately 0.59 USD/kgNH3 in 2050. This cost of production could be competitive with grey ammonia, provided that a carbon emission allowance of USD 0.12/kgCO2 is applied.

ABSTRACTThis study develops a theoretical model to link carbon emission allowance (CEA) prices to oil implied volatility. The model identifies two channels: an explicit channel where rising CEA prices increase production costs, inventory, and option hedging demand while reducing speculating demand, leading to a negative price effect; and an implicit channel where higher CEA prices signal future oil price increases, boosting option hedging demand and futures speculating demand resulting in a positive price effect. These dynamics create a U‐shaped relationship between CEA prices and implied volatility. Empirical analysis in Chinese markets confirms this U‐shaped relationship and the Granger causality of CEA prices. The findings from the seven trial markets suggest that the U‐shape is primarily driven by the hedging demand of company headquarters in Beijing and Shanghai. Additionally, we find that CEA prices influence expected volatility and option demand, with a U‐shaped effect on expected volatility and no impact on unexpected volatility. Higher CEA prices also increase futures speculation demand while leaving futures hedging demand unchanged. Furthermore, this study reveals that CEA prices Granger‐cause West Texas Intermediate futures volatility and the aggregate effect of CEA prices on oil implied volatility reflects the combined impact of hedging and speculating demands in the option and futures markets and international oil volatility.

Using the multifractal detrended cross-correlation analysis (MF-DCCA) method and the Empirical Mode Decomposition (EMD)-MF-DCCA method, this study quantifies the dynamic interrelation between carbon emission allowance returns in the Chinese and EU markets. The cross-correlation statistics indicate a moderate acceptance of the cross-correlation between the two carbon markets. Applying the MF-DCCA and EMD-MF-DCCA methods to the two markets reveals that their cross-correlation exhibits a power-law nature. Moreover, the apparent persistence of the cross-correlation and notable Hurst index show that the cross-correlation between long-term trends of the returns of the Guangdong and EU carbon emission markets exhibits stronger fractality over the long term, whereas the cross-correlation between the short-term fluctuations of the Hubei and EU carbon emission markets demonstrates stronger fractality. Subsequent investigations show that both fat tails and long memory contribute to the various fractals of the cross-correlation between the returns of the Chinese and EU carbon emission markets, especially for the fractals between the Hubei and EU carbon emission markets. Ultimately, the sliding window analysis demonstrates that national policy, trading activity, and other factors can make the observed multiple fractals more sensitive. The aforementioned findings facilitate an understanding of the current state of the Chinese carbon emission market and inform strategies for its future development.

This study explores the time-varying correlations and quantile spillover connectedness to identify the hedging potential of smart transportation assets for energy markets. The study finds that amid crises like COVID-19 and the Russia-Ukraine conflict, smart transportation indices demonstrate strong safe-haven characteristics against volatility in equity commodity energy and electricity transmission and distribution infrastructure indices. Additionally, the smart transportation, electric vehicle, and drone indices offer limited hedging benefits and safe-haven attributes for carbon emission allowance and natural gas. Furthermore, smart transportation assets are the major spillover transmitters to fossil energy assets across all quantiles. These outcomes hold substantial implications for environmental advocates, investors, and policymakers.

This paper examines carbon emission allowances and pricing mechanisms in the context of climate change, utilizing nonlinear evolution equation theory. Through empirical analysis of European Union EUA option data using the EGARCH model, the study identifies non-normal distribution characteristics in carbon market returns and explores how policy innovations influence price fluctuations. A key contribution is its application of soliton theory to analyze carbon price dynamics. By employing integrable systems like the (1 + 1)-dimensional Boussinesq equation, it aims to develop a mathematical model for carbon price stability. The research calculates the Lax pair for this system and uses Hirota’s bilinear method among other techniques to investigate whether carbon prices can exhibit soliton phenomena with consistent waveforms and amplitudes. This work provides insights into the carbon market’s dynamics and lays a theoretical foundation for better simulation, market behavior prediction, and optimization of climate policies.

The path to carbon neutral shipping: A comparative analysis of low carbon technologies.

ABSTRACT As a critical emerging market for carbon neutrality, the carbon emission allowance market’s risk linkage to energy markets is crucial. By employing the multivariate-quantile conditional autoregressive model, the Granger causality risk test and QVAR-DY, we find significant bilateral downside risk transmission between coal and carbon markets in the short term, while the carbon market exhibits persistent risk spillover effects on clean energy and natural gas markets across both short and long horizons. Analysis of upside risk dynamics demonstrates that climate policy implementations and associated market events generate spillover effects propagate through the carbon market to energy markets. China’s National ETS has strengthened the correlation between the carbon and energy markets. These findings highlight the carbon market’s crucial role in curbing fossil fuel consumption and controlling climate risk. The results of this study are valuable for enterprises, investors, and regulators in enhancing carbon risk management, promoting green investment, and supporting sustainable development.

Effectively constructing a carbon emission trading Market is crucial for China to achieve its goals of carbon peaking and carbon neutrality. However, carbon emission allowance prices are influenced by various factors. This paper employs a Vector Autoregression (VAR) model, using the average transaction price of Beijing Carbon Emission Allowances (BEA) as the research subject. It analyzes related indicators from four aspects: energy prices, macroeconomic conditions, environmental factors, and public sentiment. The results show that among energy price factors, coal and oil prices have a positive impact on BEA prices, while natural gas has a negative impact. Macroeconomic factors initially have a negative impact, followed by fluctuating recovery. Environmental factors, such as the Air Quality Index (AQI) and maximum temperature, initially have a positive impact on BEA prices, followed by fluctuating recovery, while the minimum temperature has the opposite effect. Public sentiment has a significant negative impact on BEA price fluctuations. The empirical results of this study can help investors better understand the changes in the prices of carbon emission allowance, provide references for policymakers, and offer foundations for constructing a national carbon emission trading market that reflects comprehensive and accurate information.

Scenario-driven distributionally robust optimization model for a rural virtual power plant considering flexible energy-carbon-green certificate trading