Allocation of carbon emission quotas in China’s provincial power sector based on entropy method and ZSG-DEA

The allocation of carbon emission quotas to five major power generation corporations in China

As the result of climate change and deteriorating global environmental quality, nations are under pressure to reduce their emissions of greenhouse gases per unit of GDP. China has announced that it is aiming not only to reduce carbon emission per unit of GDP, but also to consume increased amounts of non-fossil energy. The carbon emission allowance is a new type of financial asset in each Chinese province and city that also affects individual firms. This paper attempts to examine the allocative efficiency of carbon emission reduction and non-fossil energy consumption by employing a zero sum gains data envelopment analysis (ZSG-DEA) model, given the premise of fixed CO2 emissions as well as non-fossil energy consumption. In making its forecasts, the paper optimizes allocative efficiency in 2020 using 2010 economic and carbon emission data from 30 provinces and cities across China as its baseline. An efficient allocation scheme is achieved for all the provinces and cities using the ZSG-DEA model through five iterative calculations.

How can China achieve its Intended Nationally Determined Contributions by 2030? A multi-criteria allocation of China’s carbon emission allowance

Allocation of carbon emission quotas in Chinese provinces based on equality and efficiency principles

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.

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.

To establish the carbon emission trading scheme and achieve the carbon emission reduction goals in China, it is critical to allocate the carbon emission allowance (CEA). Using the entropy method and the modified fixed cost allocation model (MFCAM), we calculated the CEA and the carbon emission intensity (CEI) reduction targets of 30 Chinese provinces in 2030, from four principles (equity-efficiency-feasibility-sustainability) and three dimensions (economy-society-environment). The results are shown as follows. First, China's total carbon emissions in 2030 calculated in this paper are 17567.9 Mt. Second, on the whole, CEA in China's southeast half of the Hu line is higher than that in the northwest half. Eastern China has a larger final CEA than western China and central China. Third, Henan, Guangdong, Shandong, and Jiangsu are the four provinces with the most CEA, while Gansu, Qinghai, Ningxia, and Hainan are the four regions with the least carbon allowances. Fourth, the regions of Shanxi, Shaanxi, Xinjiang, Ningxia, Inner Mongolia, Guizhou, and Anhui will take on greater responsibility for carbon reduction in the future. On the contrary, the zones of Tianjin, Qinghai, Guangxi, Jilin, Yunnan, and Beijing will be able to sell CEA in the future. Fifth, provinces are divided into three categories from the perspective of CEI reduction. Finally, we put forward relevant policy recommendations based on the conclusions.

China can be regarded as a group of disparate economies, so the responsibilities of reduction have to be decided by considering different development stages over the provinces as well as reaching fairness of allocation. This study analyzed factors that influenced carbon dioxide emission changes due to energy-related consumption of 30 mainland provinces in China from 2005 to 2011, which was to promote carbon emission reduction and allocate carbon emission allowance. First, the Logarithmic Mean Divisia Index (LMDI) technique was adopted to decompose the changes in carbon emissions at the provincial level into five effects that were carbon coefficient, energy structure, energy intensity, economic output and population-scale effect. Next, according to the LMDI decomposition results, the overall contributions of various decomposition factors were calculated and applied to distribute carbon emission allowance over 30 provinces in China in 2020. The total effects of economic output, population-scale effect and energy structure on carbon emissions were positive, whereas the overall effect of energy intensity was negative. The allocation of carbon emission allowance can facilitate decision makers to reconsider the emission reduction targets and some related policies.

Carbon emission allowances and green development efficiency

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

Martin Marietta Corporation

Carbon emission allowance allocation with a mixed mechanism in air passenger transport

Analysis of driving factors and allocation of carbon emission allowance in China

This paper studies the heterogeneous effects of exchange rate and stock market on carbon emission allowance price in four emissions trading scheme pilots in China. We employ a panel quantile regression model, which can describe both individual and distributional heterogeneity. The empirical results illustrate that the effects of explanatory variables on carbon emission allowance price is heterogeneous along the whole quantiles. Specifically, exchange rate has a negative effect on carbon emission allowance price at lower quantiles, while becomes a positive effect at higher quantiles. In addition, a negative effect exists between domestic stock market and carbon emission allowance price, and the intensity decreasing along with the increase of quantile. By contrast, an increasing positive effect is discovered between European stock market and domestic carbon emission allowance prices. Finally, heterogeneous effects on carbon emission allowance price can also be proved in European Union Emission Trading Scheme (EU-ETS).

Carbon trading refers to the buying and selling of carbon emission allowances, stimulating businesses to adopt low-carbon development strategies through price signals. By allocating limited emission allowances to emitters, the carbon market encourages the reduction of carbon emissions to achieve emission reduction targets. Market participants can compensate for emissions exceeding their own quotas by trading carbon emission allowances, thus minimizing the cost of emission reductions. The Chinese carbon market has a large trading volume and numerous development opportunities, but currently, the carbon financial market is not fully open. In comparison to Western countries, China lacks carbon emission derivatives such as futures, options, and swap contracts. The risk and return of carbon emission allowance options are estimated and managed using the B-S-M option pricing model. This paper utilizes data from the Shenzhen Carbon Emission Allowance Exchange as a sample to conduct option pricing practices for carbon emission allowances using the B-S-M model. The aim is to promote research and development in the carbon emission allowance market and contribute to the achievement of emission reduction goals.