Carbon Emission Allowances Research Articles

Carbon emission allowance, global climate risk, and agricultural futures: An extreme spillover analysis in China

The aim of this paper is to investigate the extreme spillover effects between the newly launched China's national carbon emission allowance and agricultural futures markets with the influence of global climate risk. The results show that, first, under both normal and extreme market (climate risk) conditions, the three edible oil futures, i.e., soybean oil, palm oil and rapeseed oil, are strong spillover senders to other agricultural futures. Second, under low and normal climate risk states, climate transition risk is a spillover transmitter, while under high climate risk states, climate physical risk becomes a spillover sender. In addition, the network analysis shows that climate risk is an important information receiving and disseminating node, and large changes in it can lead to increased systemic risk across the network. Finally, China's carbon emission allowance market is always a spillover receiver across different market (climate risk) conditions, and can be used as an appropriate hedging instrument for climate risk and agricultural futures. These findings have valuable implications for both policymakers and agricultural investors.

Contemporaneous and lagged spillovers between agriculture, crude oil, carbon emission allowance, and climate change

In this paper, we examine the contemporaneous and lagged spillovers among the agricultural, crude oil, carbon emission allowance, and climate change markets. Adopting the R2 decomposed connectedness approach, our empirical analysis reveals several key findings. First, the overall total connectedness index (TCI) dynamics have been mainly dominated by contemporaneous effects. Second, there are heterogeneous spillover effects among agricultural markets. Specially, corn, soybean meal, and wheat are the major risk transmitters to this system, while barley, cocoa, and lean hog are the main risk receivers of shocks. Third, we also find that climate change has significant spillovers to other markets.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Carbon emission reduction dynamic decision-making in the electricity supply chain with different carbon emission allowance principles

To address the impacts of typical principles for allocating initial carbon emission allowance, namely, the grandfathering and benchmarking principles on long-term emission reduction strategies, this paper presents four differential game models for an electricity supply chain consisting of a single electricity supplier and a single electricity retailer. The equilibrium solutions for different models are obtained and comparatively analyzed. The results show that the power supplier exhibits greater emission reduction efforts and, therefore, that the retailer has higher profits under the benchmarking principle than under the grandfathering principle. Moreover, under the two principles, the level of the power supplier’s emission reduction efforts is related to the decision-making modes of the electricity supply chain. Under specific conditions, the power supplier’s profit is also higher under the benchmarking principle than under the grandfathering principle. In addition, this study extends the models to investigate the constrained differential game models and obtains the game equilibrium solutions, which can be proved as saddle point equilibria.

Extreme time-frequency connectedness between energy sector markets and financial markets

The renewable energy market has developed into a distinct asset class that has garnered significant attention from investors worldwide. This study investigates the connectedness between the S&P 500 and various energy sectors, including the Green Bond (Green Bond), S&P GSCI Carbon Emission Allowances Index (GSCI EUA), S&P GSCI Biofuel (GSCI Biofuel), and S&P Global Clean Energy Indices (Global Clean Energy). Combining the quantile connectedness approach of Ando et al. (2022) and the spillover index developed by Chatziantoniou et al. (2022), we investigate the quantile time-frequency connectedness among these markets. Our findings reveal that the impact of tail risk spreading to the energy sector during extremely challenging market conditions is significantly amplified. Most tail risk spillovers between the energy sectors involve short-term risk spillovers, as seen by analyzing the frequency domain. The impact of tail risk spillover is stronger during extreme situations, showing a short-term asymmetry. Global Clean Energy and the S&P 500 play important roles in spreading risk to other sectors, known as tail risk spillover. They add to the overall risk that affects the network system. On the other hand, other energy sectors (GSCI EUA and GSCI Biofuel) as well as green bonds act as net recipients by receiving the risk rather than spreading it further, potentially providing portfolio diversification. It is essential to understand how these key players influence risk in the financial system. Utilizing a variety of techniques to assess portfolio performance shows the importance of understanding the return transmission mechanism in the process of developing a portfolio and suggests that Clean Energy, given its relative independence, may make an excellent diversification investment.

The phenomenon of China’s carbon accounting policies and practices

Purpose This study aims to explore China’s carbon accounting policies and practices to enhance its carbon accounting and trading practices and achieve carbon neutrality. Design/methodology/approach This study adopts a phenomenological methodology to explore carbon accounting by setting the scope of the phenomenon, establishing research methods, conducting interviews with 30 participants from 14 emitting entities and four accounting firms and establishing methods for data analysis. Five themes were identified using a five-step coding method. Findings The results show no uniformity in the accounting treatment of carbon trading; however, common characteristics exist, and they have generally adopted simplified and conservative accounting methods under China’s emission trading scheme. Their common characteristics are that the granted allowances are not recognised as assets by most emitting entities, emissions allowance assets are measured using the historical cost method, most emitting entities only conduct accounting when there are actual cash flows or outflows with regard to carbon emissions allowance trading, and only a few emitting entities disclose carbon trading information in their financial statements or corporate social responsibility reports. Originality/value This study highlights accountants’ crucial role in carbon activities by guiding emitting entities’ low-carbon initiatives and culture and transforming managers’ mindsets. It also introduces the China Accounting Standards Committee-based accounting methods for allowances and obligations, aiding carbon disclosure quality and sustainability. These findings also inform the carbon accounting standards that may enhance corporate accountability and address climate change.

Carbon emission constraint: A catalyst for the shift from real economy to financialization?

AbstractThe goals of carbon peak and carbon neutrality have gained significant attention in China. Carbon‐intensive corporates are tasked with decoupling economic growth from carbon emissions. This research examines their potential for financialization based on the compulsory fulfillment requirement of carbon emission allowances and the emission‐free characteristics of financial assets. This research finds that carbon emission policy significantly promotes corporates to allocate more capital toward business operations in the real economy. It leads to an increase in capacity utilization efficiency and profitability for the main business. The findings support that the policy has a positive impact on the real economy.

China’s carbon emission allowance prices forecasting and option designing in uncertain environment

China’s carbon emission allowance prices forecasting and option designing in uncertain environment

Carbon emission allowances and Nordic electricity markets: Linkages and hedging analysis

Unlike previous studies which tend to consider only return linkages between European emission allowances and electricity markets, this paper examines return and volatility linkages between the two markets and importantly conducts a hedging analysis. Using a dynamic conditional correlation model and daily price data covering European emission allowances and Nord Pool electricity markets from 28th January 2009 to 22th February 2022, which comprise the second and third phases of the European emission allowances trading system, the main results are summarized as follows: Firstly, volatility and returns flow from the Nordic electricity market to the European emission market, with no evidence of a feedback effect, suggesting that rapid changes in demand for electricity may have forced producers to ramp up carbon-intensive facilities. Secondly, the conditional dynamic correlation between the two markets is weak but varies with time, and its sensitivity to crisis periods is noted. Thirdly, the hedging analysis indicates that Nordic electricity market participants can lower their downside risk by including carbon emission allowances in their portfolios, as evidenced by the effectiveness of hedging returns of the Nordic electricity markets with European allowances, irrespective of the COVID-19 sub-periods.

Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Cooperative operation for multiple virtual power plants considering energy-carbon trading: A Nash bargaining model

With the development of the energy and carbon markets, it has become a trend for multiple virtual power plants (MVPP) that aggregate distributed resources from different regions to participate in market trading through cooperative operation. In order to study the energy-carbon interactions between VPPs and the supply-demand interactions for MVPP and users, this paper introduces a load aggregator (LA) and establishes a cooperative alliance consisting of MVPP and LA. Firstly, a carbon emission allowance allocation and transaction mechanism for MVPP is proposed based on the modified Shapley value method, which considers the impact of energy interactions within the alliance on the carbon emissions. Then, a cooperation model for the MVPP-LA alliance is established based on Nash bargaining theory, and it is decomposed into two subproblems of the maximal alliance benefits and the optimal benefit distribution. In terms of benefit distribution, this paper modifies the bargaining mechanism based on the contribution of each VPP to the alliance benefit. A dynamic penalty factor-based alternating direction method of multipliers (DP-ADMM) is used to solve the two subproblems in a distributed manner. Finally, the validity of the proposed trading mechanism, allocation method and solution algorithm are verified through several cases.

Analysis and Optimization Suggestions on Allowance Allocation Methods of the Power Industry in the Carbon Market

The initial allocation of carbon emission allowances is an important component of the carbon market. An equitable, scientific, and operational quota allocation method and quota management system are the cornerstones for ensuring the healthy operation of the carbon market. Owing to the high emissions, simple process, and good data foundation, the pilot and national carbon market in China have initially included the power industry in allowance management and introduced a common and differentiated quota allocation method. In this study, we compared the allowance allocation methods for the power industry and summarized the methods for key issues such as unit classification, correction factor, product measurement, and quota carry-over. We observed that there were concerns, such as lagging issuance time, lack of carry-over provisions, lack of regulatory mechanisms, and imperfect methods, in the first performance cycle of the national carbon market quota allocation. We improved the allowance allocation system for the power industry in the carbon market from the methodological and management perspectives, including establishing a total allowance constraint, clarifying quota carry-over provisions, improving the quota verification method for co-firing units, optimizing correction factors' selection, and introducing a compensatory distribution system in a timely manner.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b1∈[0.73,0.8]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

Carbon emission allocation policy making in liner shipping: A novel approach toward equitable and efficient maritime sustainability

Maritime shipping, an integral component of global trade, accounts for nearly 3% of global carbon dioxide emissions and faces significant challenges in mitigating its carbon footprints. This study addresses the shortcomings of current carbon emission surcharges levied by shipping companies and the deficiencies of allocation methods utilized by the government, such as the inequity of uniform carbon emission charges across diverse origin–destination (od) pairs and the reliance on potentially inaccurate self-reported emissions data to assign carbon emission allowances. We propose a novel approach to carbon emission allocation per twenty-foot equivalent unit (TEU) container per od pair in liner shipping from the perspective of the government, aiming to enhance government oversight and minimize social costs. Leveraging a bi-level programming model, our research navigates the complex interplay between government regulatory objectives and shipping companies’ operational decisions, identifying a socially optimal carbon allocation policy. Our approach introduces three innovative carbon allocation methods that advance beyond the traditional metric that typically focuses solely on emissions and cargo volume. Our first two innovative methods integrate shipping distances into the carbon allocation process, specifically considering both the actual traveling distance and the shortest sailing distance. Furthermore, we propose the third innovative “harmonic distance,” which is a convex combination of the actual traveling distance and the shortest sailing distance. This measure effectively reflects both the operational realities and equity concerns associated with shipping services. Our findings reveal the effectiveness of utilizing the shortest sailing distance for carbon allocation per TEU per od pair, offering a straightforward, route-independent, and equitable solution. This approach diverges from current industrial practices, favoring a fairer emission allocation basis and balancing the interests of governments, shipping companies, and consumers. Through comprehensive experiments, we demonstrate the superiority of this method in minimizing total social costs and providing a viable path toward environmental sustainability and economic efficiency in liner shipping.

Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network

Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.

The role of renewable energy and carbon dioxide emissions on the ESG market in European Union

AbstractIn view of global climate problems, public interest in the environment has recently evolved over decision economics. Accordingly, this study assesses the impact of carbon emission allowances (CEA), information technology (IT), renewable energy generation (REG), and carbon dioxide (CO2) on the environmental, social, governance (ESG) in the European Union (EU) by applying quantile‐based models from January 2, 2019 to February 29, 2024. The outcomes demonstrate that CEA and IT have an increasing effect on the ESG with the moderating effect of economic policy uncertainty (EPU). REG has a declining effect on the ESG, while the EPU moderates the effect and makes the effect increasing across higher quantiles.

A Carbon Emission Allowance Bargaining Model for Energy Transactions Among Prosumers

A Carbon Emission Allowance Bargaining Model for Energy Transactions Among Prosumers

Equilibrium bifurcation and extreme risk in the EU carbon futures market

Considering the long-term memory and volatility clustering of the European Union (EU) Carbon Emission Allowances (EUA) futures returns, based on the economy–energy–environment system perspective and the assumption of investors' heterogeneity, this study proposes a joint modeling approach combining the fractionally integrated generalized autoregressive conditional heteroscedasticity model (FIGARCH) and the stochastic cusp catastrophe model (SCC) to examine the equilibrium bifurcations and extreme risks in the EU carbon futures market. The relevant results are threefold. (1) The SCC model has good fitting effect and interpretability, and is an effective method for investigating catastrophe reactions under time-varying volatility conditions. (2) In the EUA futures market, chartists are mainly affected by short-term price and trading volume changes, which leads to the emergence of equilibrium bifurcations, while fundamentalists make investment decisions based on the economy, the energy market, and market supply–demand, which affects the asymmetry of equilibrium bifurcations. (3) Using the catastrophe criterion (i.e., Cardan's discriminant of the equilibrium surface equation), we identify148 equilibrium bifurcation time points in the EUA futures market from December 3, 2009 to September 16, 2020, most of which are concentrated in two upward periods with an average scale of extreme risks is about 32.51 %. Our analysis provides theoretical support for regulatory authorities to stabilize the carbon futures market and build a collaborative extreme risk management framework covering energy and macroeconomics, also proposing suggestions for traders to effectively prevent extreme risks.

Differential learning model for carbon emission allowance prices prediction based on adaptive decomposition and joint feature contribution

Currently, how to effectively control carbon emissions is a global concern. Accurate carbon price prediction can help us more effectively control carbon emissions and reduce environmental pollution. Due to the complexity of carbon valence, this study proposes an improved decomposition ensemble prediction model. Firstly, a novel signal decomposition algorithm is designed to adaptively determine the number of decomposition modes for carbon price sequences, obtaining a set of intrinsic mode functions (IMFs). Secondly, a creative method for assessing the contribution of feature components is developed. It utilizes a genetic algorithm-based mutual-information method to calculate the contribution of each IMFs to the original sequence, classifying all feature components into high and low contribution components. Then, this study developed a differential learning method that partitions the contribution of feature components and applies targeted learning and integration using temporal convolutional network with different structures. Compared to traditional forecasting frameworks, this framework improves prediction performance while reducing structural complexity and computational costs. Empirical results demonstrate the superiority and robustness of the proposed model with the outcomes of this study present the performances that the proposed model outperforms the others with Guangzhou's mean absolute percentage error of 3.30313%, which are beneficial for governments and enterprises to predict adverse signals in the carbon market in a more timely manner, and to take measures in advance to maintain the stability of carbon emissions.

How to control China's total amount of carbon emissions? An analysis of provincial allowance demands

Establishing a total amount control system for carbon emissions is crucial for China to achieve its carbon peak and neutrality targets. Given the heterogeneity among provinces, it is essential to consider provincial demands for carbon emission allowances (CEA) when allocating China's total amount target of carbon emissions. By identifying provincial preferences for different allocation criteria (equality, responsibility, capability, and efficiency), this study proposed a CEA allocation scheme that contributes to achieving China's 2030 carbon emission target with the least compromise among provinces. The results show that emission reduction efforts vary widely across provinces under any single-criterion scheme. Preferences for allocation criteria diverge among provinces, with a notable preference towards responsibility and equality. However, adhering to provincial expectations for CEA could jeopardize the attainment of national emission reduction target. The least-compromise allocation scheme integrates various criteria while weighting them differently: equality (0.42), responsibility (0.28), efficiency (0.16), and capability (0.14). Considering the varying pressure for emission reduction among provinces, it is vital to formulate differentiated policies and measures.