Carbon Emission Quota Allocation Research Articles

The Impact of Carbon Emission Quota Allocation Regulations on the Investment of Low-Carbon Technology in Electric Power Industry Under Peak-Valley Price Policy

This article focuses on carbon emission quota allocation regulations and their impact on investments in low-carbon technology within the electric power industry, as well as other enterprises’ decisions and profits under peak-valley price policy and cap-and-trade mechanism. An electricity supply-chain model is constructed in a Stackelberg game, which comprises an electricity-generating enterprise (leader) and an electricity retailer (follower). The backward-induction method is used to obtain the investment in low-carbon technology and electricity price under three scenarios of carbon emission quota allocation regulations: no regulation, grandfathering allocation, and benchmarking allocation. The main results are: first, benchmarking allocation always results in higher investment in low-carbon technology, but generates greater total carbon emissions than grandfathering allocation under most conditions; and second, under benchmarking allocation, as the unit carbon emission quota increases, total carbon emissions first increase and then decrease. Therefore, benchmarking allocation is the better regulation for the low-carbon technology investment. Moreover, benchmarking allocation is also the better regulation for total carbon emissions with a higher unit carbon quota or a higher carbon price.

How does carbon emission trading scheme affect enterprise market value? A roadmap towards natural resources sustainability

China has committed to establishing a unified national carbon emissions market to achieve carbon reduction goals. The Carbon Emission Trading Scheme (CETS) internalizes emission reduction costs, which may affect enterprise market value (EMV), but this has seldom received attention. Therefore, based on A-share listed enterprises data from 2009 to 2019, this study not only focuses on investigating the direct and heterogeneous impact of CETS on EMV but also identifies the effective market mechanism design by constructing a unified research framework including carbon emission quota allocation schemes, penalty mechanisms, and EMV performance. The empirical results are as follows: (1) The CETS can significantly enhance the EMV of covered enterprises, indicating that China's CETS supports the Porter hypothesis. Compared with state-owned and small enterprises, the CETS has a more significant effect on promoting the EMV of non-state-owned and large enterprises. (2) The mixed carbon quota allocation scheme (the combination of free and auction schemes) is more effective in improving EMV than the pure free quota allocation system. Meanwhile, the mixed penalties combining economic and non-economic measures can effectively encourage CETS-covered enterprises to participate in carbon trading activities, thus enhancing overall EMV. (3) The results of the mediation effect test indicate that the mixed schemes improve EMV by promoting low-carbon technology innovation, and mixed carbon quota allocation schemes have a more significant effect through this channel. These findings have important reference value for improving EMV under the CTEs, achieving the carbon peak and carbon neutrality goals, and promoting sustainable economic development.

A study on the two-level allocation of carbon emission quotas in China at the provincial level.

Carbon emission reduction is an essential means to achieve the "double carbon goal," and the scientific and reasonable allocation of carbon emission quotas (CEQ) is the basis for promoting carbon emission reduction. In this study, the first level was based on the entropy TOPSIS scores of provinces under the principles of fairness, efficiency, sustainability, and feasibility and used the K-mean clustering method to cluster the 30 provinces and allocate the CEQ to each zone group; the second level consolidated the impacts of the four principles and the marginal abatement costs of CO2 to allocate CEQ to the provinces within the zone group. Finally, each province's initial spatial balance of CEQ (ISBQ) is classified and evaluated. The study shows that the most quotas are for Guangdong, Zhejiang, and Inner Mongolia, and the least for Ningxia, Shanxi, and Guizhou. This study compares the results of CEQ allocation with the current carbon emission scale and finds that 11 provinces, such as Shandong and Hebei, show a deficit in future carbon emission space, and 19 provinces, such as Hainan and Beijing, show a surplus in carbon emission space. Given each province's different emission reduction tasks and pressures, differentiated emission control policies are the key to achieving China's "2030 target".

Modeling robust bi-level BCC production planning problem with uncertain carbon emission mechanism

This paper studies the biomass–coal co-firing (BCC) production planning problem under the carbon emission quota allocation (CEQA) mechanism. However, carbon emission parameters are uncertain due to many factors, such as the type of power units and coal quality. To address this challenge issue, this paper proposes a new globalized robust bi-level optimization model, where the uncertain parameters are characterized by a pair of uncertainty sets. In the proposed model, the upper government as the leader decides the CEQA mechanism, while the lower power plants as the followers develop the production planning according to the given CEQA mechanism. Moreover, based on the Lagrange duality theory and Karush–Kuhn–Tucker (KKT) conditions, the proposed model is equivalently converted into a computationally tractable single-level model. Finally, a practical case study in Shandong Province demonstrates that compared with the nominal bi-level and robust bi-level models, the proposed optimization model can not only effectively resist parameter uncertainty, but also be less conservative.

Prediction and balanced allocation of thermal power carbon emissions from a provincial perspective of China.

Carbon control in the thermal power generation industry is crucial for achieving the overall carbon peak target. How to predict, evaluate, and balance the allocation of inter provincial carbon emissions has a significant impact on the decision-making of reasonable allocation of inter provincial carbon emissions in the target year. Therefore, this paper uses Monte Carlo-ARIMA-BP neural network and ZSG-DEA model to conduct temporal trend prediction and carbon emission quota allocation research. We propose the "intra provincial and inter provincial" framework for carbon emissions trading in thermal power plants, which aims to break through the barriers in carbon emission rights exchange among provinces. The conclusions are as follows: (1) the growth trend of carbon emissions from thermal power is gradually slowing down and is expected to peak before 2030. (2) Inner Mongolia, Jiangsu, and Shandong have high input-output efficiency, and are all the main output provinces for carbon emission quota allocation. After being adjusted using the ZSG-DEA model, they can still be at the forefront of efficiency. (3) The "intra provincial and inter provincial" framework for carbon emissions trading can effectively predict and allocate the carbon emission demand of each province from time and space dimensions, balance the carbon emission rights and interests of each province, and provide forward-looking planning suggestions for inter provincial carbon emission rights exchange.

Study on international carbon emission quota allocation of shipping industry-based on fairness and efficiency

In 2024, the EU intends to include the global shipping industry in the European Union Emission Trading Scheme (EU ETS). Shipping companies will have to pay for the carbon emissions of ships over 5,000 GT on routes between EU and non-EU ports. This paper selects typical shipping companies in the world. Based on the principle of fairness, historical method, baseline method and mixed method are adopted to explore their carbon emission quota allocation. The ZSG-DEA efficiency model is used to evaluate the distribution results and verify the optimal efficiency. The research results show that the mixed method has a high efficiency of allocation. The method predicts that the carbon quota of typical shipping companies in the world will reach the Pareto optimal allocation in 2024 and Maersk has the highest carbon emission quota among the eight typical shipping companies, reaching 32,431,800 tons, followed by MSC and EMC, reaching 8,542,400 tons and 6,809,500 tons, respectively. Based on the results, we can obtain a reasonable allocation of carbon allowances in the EU carbon market according to the proportion of business of shipping companies involved in EU routes. The research is still applicable to the allocation of carbon emmissions in future years. Therefore, this paper provides suggestions for the orderly allocation of carbon quota and carbon trading in the global shipping market.

Research on carbon emission quota allocation scheme under "Double Carbon" target: a case study of industrial sector in Henan Province.

To achieve China's "Double Carbon" target, overall carbon emissions should be effectively controlled, and carbon emission quota (CEQ) allocation is an important tool. This study develops carbon emission prediction, CEQ allocation, and scheme feasibility evaluation models based on the principles of fairness, efficiency, and economy. The purpose is to propose a suitable CEQ allocation scheme for the Industrial Sector in Henan Province (ISHP). The results show that (1) the allocation model combining the technique for order preference by similarity to ideal solution (TOPSIS) and the zero-sum gains DEA (ZSG-DEA) can trade off the fairness and efficiency principles. (2) The reallocation scheme has an environmental Gini coefficient of 0.393 (< 0.4), which maximizes efficiency while lowering the abatement costs by 126.268 billion yuan, making it an ideal scheme that considers multiple principles. (3) CEQ should be reduced in 7 subsectors of ISHP while increasing in 33 others. Carbon emissions from these 7 subsectors are high, and CEQ should be reduced in accordance with the fairness principle. Even if their abatement costs are high and CEQ rises according to the efficiency principle, the increase is much smaller than the decrease. The findings are useful for optimizing the CEQ allocation under the "Double Carbon" target.

Analysis on China's provincial carbon emission quota allocation based on bankruptcy game

A reasonable allocation of carbon emission quotas is essential to improving the national unified carbon trading market. A bankruptcy game model is developed for allocating carbon emission quotas to Chinese provinces based on bankruptcy theory and the Nash bargaining game. The model takes into account the disparities that exist among these provinces. The allocation scheme under the carbon peak target can be obtained by using the projection pursuit model and the subject satisfaction function. The findings reveal that certain eastern provinces bear a greater responsibility in terms of historical carbon emissions. Furthermore, the rampant transfer of carbon emissions from energy-rich to energy-demanding regions has exacerbated the imbalance in regional development. Interestingly, the carbon emission demands are fulfilled in some provinces situated in the coastal regions of the southwest and southeast, such as Beijing, Tianjin, and Fujian. However, certain provinces in the northeast express the lowest levels of satisfaction with their carbon quota allocation, necessitating a greater burden of carbon emission reduction in the future. The research offers significant implications for regional development and carbon reduction efforts.

A novel city-level carbon emission quota allocation method for carbon peak and neutrality targets

A novel city-level carbon emission quota allocation method for carbon peak and neutrality targets

Regional allocation of carbon emission quotas in China under the total control target

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading

In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method.

A Method for Allocation of Carbon Emission Quotas to Provincial-Level Industries in China Based on DEA

At present, China implements a quota-based trading mechanism to achieve carbon emission reduction, in which the allocation of carbon emission quotas among different provinces is short of considering the influence of unbalanced provincial development. Heterogeneity among the provincial-level three major industries, namely, agriculture, manufacturing and mining, and service industries, is a case in point. To address this insufficiency, this paper proposes a novel parallel data envelopment analysis (DEA) based method for carbon emission quota allocation. The method models each province as a decision-making unit (DMU) and the provincial-level three major industries as parallel sub-decision-making units (SDMUs). A distinguished feature of the method is that it makes explicit tradeoffs between efficiency and equality considerations for policymakers in allocating the carbon quotas among three heterogeneous provincial-level major industries. The empirical results show that the proposed method effectively improves the overall provincial gross domestic product (GDP) potentials through the reallocation of carbon quotas among industries while the equality level is not worse off. This work is helpful for policymakers to achieve a long-term emission reduction target and provides suggestions for improving the initial allocation mechanism of a national carbon trading market.

Optimizing carbon emission forecast for modelling China's 2030 provincial carbon emission quota allocation

Rational allocation of carbon quotas is the fundamental premise for the orderly operation of carbon markets. To achieve the set target of carbon peak by 2030, there is an urgent need to establish China's 2030 provincial carbon quota allocation scheme. Although some proposed schemes have been formulated, there are problems with the methods used for carbon emission forecasting and evaluating the rationality of a proposed allocation scheme. This study aimed to optimize carbon emission forecast by incorporating terrestrial carbon sinks into the mechanism for building China's 2030 provincial carbon emission quota allocation schemes. Aquila Optimizer's Double Support Vector Regression (AO-based TWSVR) that has the advantages in solving problems associated with small sample size, nonlinear and high-dimensional pattern recognition with fast training speed and insensitivity to noise was adopted to predict the net carbon emission. The results show that the application of AO-based TWSVR model allows satisfactory forecast of the net carbon emission in China for the period from 2021 to 2035. This allowed terrestrial carbon sequestration being incorporated into the mechanism to formulate China's 2030 provincial carbon quota allocation schemes. Comparison of the three provincial carbon quota allocation schemes using social network analysis suggests that the equity-based carbon quota allocation scheme is more suitable for China's national conditions compared to the efficiency-based scheme and the combined principle-based scheme. The findings obtained from this study have implications for optimizing the scheme of China's 2030 provincial carbon quota allocation.

Carbon emission quota allocation of high energy consumption industries in undeveloped areas – A case study of Inner Mongolia Autonomous Region

With the approaching of the target year of carbon peak, all world countries have gradually strengthened carbon emission reduction actions. However, most of the undeveloped countries or regions do not belong to the Annex I countries in the Kyoto Protocol, and the development of carbon emission trading mechanisms is relatively slow. Therefore, setting emission quota standards for industries in underdeveloped regions is necessary. Considering the principles of fairness, efficiency, and sustainability, we establish a “multi-objective information entropy allocation model” (i.e., IEMMA method) for underdeveloped areas. Take China's Inner Mongolia Autonomous Region as an example, and the allocation results show that: (1) The “historical emissions”, “carbon emission intensity”, “cumulative emissions”, “energy structure”, and “energy intensity” are assigned higher weights. Such industries will carry a higher responsibility for emissions reductions, they should gradually transfer emission quotas to industries with smaller historical emissions to increase enthusiasm for reducing emissions. (2) Compared with the historical emissions allocation scheme, the IEMMA model can increase the emission limits for industries with large historical emissions. For industries with relatively small emissions, their emission quotas can be appropriately relaxed to promote the sustainable development of the industry. (3) Under the allocation scheme of the IEMMA model, the difference in the reduction of carbon emission intensity of different industries is small. Carbon emission intensity becomes a relative emission reduction indicator, which reflects the fairness of allocation while maintaining economic development. (4) The “quota gap” formed by the allocation plan of the IEMMA model is large, which is conducive to promoting the active emission reduction of high-emission enterprises. Larger “allowance gaps” are conducive to increasing the activity of the carbon trading market. In summary, the IEMMA model is conducive to achieving long-term emission reduction goals in underdeveloped regions and is more suitable for the allocation of quotas to high energy-consuming industries.

Research on Carbon Emission Quota of Railway in China from the Perspective of Equity and Efficiency

Under the constraint of total carbon emissions, the allocation of carbon emission quotas of 18 railway bureaus in China is conducted to the realization of carbon emission reduction targets of China’s railway transportation industry. This paper proposes a carbon emission quota model for China’s railway industry from the perspective of equity and efficiency and innovatively undertakes research on the allocation of carbon emission quotas for railway administrations. This paper constructs an econometric model to analyze the impact of various influencing factors on China’s railway operation carbon emission and predicts the total carbon emission of China’s railway operation from 2021 to 2030 by scenario analysis method. From the perspective of equity and efficiency, apply the entropy method to give weight to historical responsibility, egalitarianism, and efficiency principle to obtain the initial allocation value of the carbon emission quota of the operator’s 18 regional railway bureau groups; the ZSG-DEA model is used to obtain the optimal allocation. The results show that railway passenger turnover, freight turnover, vehicle structure, and per capita GDP have a promoting effect on railway carbon emission, and the proportion of clean energy has an inhibitory effect on carbon emission. There is a gap between the distribution results under the single principle and the comprehensive distribution results; the combination of both can more effectively promote the development of the railway industry. From the perspective of equity and efficiency, the carbon emission quota of 18 railway bureau groups in China is high in the east and low in the west. Among them, the Shanghai railway bureau obtains the most carbon emission quota, while the Qinghai–Tibet railway bureau obtains the least carbon emission quota. The research results provide a reference for the railway bureau to coordinate emission reduction and the construction of the railway transport carbon emission market.

Allocation of carbon emission quotas for China's provincial public buildings based on principles of equity and efficiency

In accordance with China's commitments to reduce greenhouse gas emissions under the Paris Agreement, it is important to establish a fair and effective carbon emission allocation mechanism; this would help to achieve China's 2030 “carbon peak” target. Here, the fixed cost allocation model (FCAM) was used to allocate carbon emission quotas to public buildings in China in 2030, based on the principles of equity and efficiency. By comparing these provincial allocation results with those obtained using zero-sum data envelope analysis (ZSG-DEA), optimal carbon emission quotas were proposed for the public building sectors of thirty Chinese provinces. The results revealed large differences, with the public buildings of Guangdong having the highest quota (11.39%) and those of Xinjiang having the lowest (0.08%). Additionally, Heilongjiang, Inner Mongolia, and Liaoning were revealed to be under high pressure with regard to the reduction of emissions from their public buildings; Heilongjiang was predicted to require the highest reduction, exhibiting a need to reduce 34 million tons of emissions. These findings will help provide new ideas for relevant policy makers to formulate fair and effective carbon allowance allocation plans.

Allocation of carbon emission quotas based on global equality perspective.

In a context of global climate change and issues relating to national carbon neutrality, carbon emissions (CE), which are linked with sustainable national development, have become the focus of worldwide competition. We performed geographic information system (GIS) spatial analysis using historical CE data for major countries and world regions for the period 1970-2018 to identify their cumulative and per capita CE. The analysis revealed the characteristics of large cumulative CE in countries that underwent early industrialization, notably the USA, Russia, and Germany and of low per capita emissions in developing countries, notably China and India. We identified 10 mainstream international emission reduction scenarios and compared and analyzed the advantages and disadvantages of different models for measuring CE allowances. We further summarized the characteristics of IPCC scenarios, shared socioeconomic pathway scenarios, and temperature-controlled threshold scenarios along with the CE allowances of China and other major countries under different scenarios. The intended nationally determined contributions (INDCs) commitment mechanism may have an impact on promoting the construction of a new global climate governance order. Considering multiple scenarios and the uncertainty of carbon emissions, choosing a certain carbon budget plan is an important future research direction. Last, we reviewed the current methods of allocating CE rights globally and among major countries and the development trend of CE to provide scientific evidence for the formulation and implementation of future international climate change agreements. In conclusion, we offer solutions for enhancing participation in the global governance of climate change.

Allocation of carbon emission quotas among provinces in China: efficiency, fairness and balanced allocation.

This paper constructs the ZSG-SBM model, comprehensive fairness allocation model, and balance allocation model from the perspectives of efficiency, fairness, and balance. Making use of the actual input-output data of China's provincial economic system in 2019, and the above three models to study the reasonable scheme of China's provincial carbon emission quota allocation in 2019. The results show that ZSG efficiency allocation can significantly improve the carbon emission efficiency of inefficient provinces, and the carbon emissions of inefficient provinces after allocation reach the efficiency frontier. The carbon emission quota of 16 provinces which located in the central and western regions of China with underdeveloped economy and relatively low carbon emission efficiency need to be decreased, while the eastern coastal provinces with more developed economy and high carbon emission efficiency in China should increase their carbon emission quota. On the one hand, comprehensive fairness allocation reduces the carbon emission reduction target constraints of economically underdeveloped provinces; on the other hand, it strengthens the carbon emission reduction target constraints of low-carbon technology backward provinces. As a result, the carbon emission quota of economically developed provinces and provinces with high carbon intensity per unit GDP is reduced in this method. The result of balancing efficiency and fairness lies between ZSG efficiency allocation and comprehensive fairness allocation. In order to alleviate the huge pressure on the emission reduction of provinces with low actual carbon emission efficiency under the ZSG efficiency allocation mode, the Chinese government can gradually increase the weight of ZSG efficiency allocation results, and finally adopt a complete ZSG efficiency allocation scheme in the carbon peak year to realize the transformation of low-carbon economy.

A performance analysis framework for carbon emission quota allocation schemes in China: Perspectives from economics and energy conservation

Carbon emission quota allocation plays a critical role in carbon emission reduction in an efficient and economic manner. However, the scientific assessment of carbon quota allocation schemes and thus making a reasonable decision depending on preference still confuses relevant decision makers. This study proposes a performance analysis framework for carbon quota allocation schemes by using a nonparametric frontier analysis approach from the perspectives of economic growth and energy conservation. Then, we apply this approach to evaluate the relative economic and energy conservation performances of 15 allocation schemes by reallocating the 2015 Chinese provincial carbon emission quotas, each of which is constructed via a combination of equity, grandfathering, efficiency and ability to pay principles. The results show that the proposed allocation scheme that integrates efficiency and the ability to pay principles is the best option, realizing approximately 6.78% outputs increase and 3.01% energy conservation relative to the actual 2015 emission scenario. We also show that the best scheme is linked to total quotas; the scheme considering only the equity principle is superior to others, while the total quotas are reduced by 10%. The study provides not only a theoretical tool, but also empirical evidence for the construction of China's carbon quota allocation scheme.

Allocation of carbon emission quotas in Chinese provinces based on Super-SBM model and ZSG-DEA model

Allocation of carbon emission quotas in Chinese provinces based on Super-SBM model and ZSG-DEA model