CO2 Emission Quotas Research Articles

Allocation of CO2 emission quotas for industrial production in Industry 4.0: Efficiency and equity

The method of carbon emission quota (CEQ) allocation based on data envelopment analysis (DEA) can allocate quotas without impacting efficiency improvements in industrial sector. A key element in effectively reducing carbon emissions is ensuring accurate efficiency measurement and equitable allocation of CEQ in the Industry 4.0 era. This paper divides industrial system into two stages: production (which more accurately reflects carbon emissions in production) and trading (which involves in the commercial system). Carbon emission efficiency in the production stage is calculated with a DEA model, with a high degree of accuracy. Given that higher efficiency values correspond with stronger capacity to bear the costs of carbon reduction, a CEQ allocation model is proposed which considers the contribution degree of efficiency. This model is applied to China’s industrial production system. The results show that a CEQ allocation scheme that focuses only on the production stage is more equitable than schemes which treat the industrial system as a black box. In allocation schemes which focus only on the production stage, the central region of China is least able to bear the cost of carbon reduction and receives the most CEQ, and the opposite is true in the eastern region of China. The proposed method not only measures the carbon emission efficiency of industrial production more accurately, but also considers the balanced development of different regions when allocating carbon emission rights. The CEQ allocation scheme based on our model can confidently meet the needs of sustainable development in the era of Industry 4.0.

Legal and regulatory framework of environmental initiatives in the Czech Republic as a potential model for green policy implementation in Eastern Europe

EU countries, in particular the Czech Republic, are developing structural mechanisms to influence the development of their economic model in the environmental sphere. The study of the Czech Republic’s experience in implementing environmental reforms is relevant for understanding their impact on the country’s economic sustainability and fulfilment of international obligations. The purpose of the study was to analyse the regulatory framework and results of the reforms implemented in the environmental policy of the Czech Republic and their impact on the country’s further development. The results were obtained using special legal methods, such as terminological, formal legal and special legal methods. Identification of the main environmental problems, reformed economic sectors and the intensity of human activity’s impact on nature, determination of stakeholders in the changes and identification of the problems solved allowed us to conclude that the measures taken were rational and varied in their effectiveness. The analysis of the environmental and regulatory framework of environmental policy in the Czech Republic and the adopted environmental legislation, such as CO2 emission quotas, rational use of natural raw materials, waste management, green taxes, benefits and subsidies for the functioning of markets for environmental goods and services, showed the undeniable advantages of an environmentally oriented economic model over the one focused on profit. The study found that the application of such measures helps to reduce the negative impact on the environment, improve the quality of life of the population and ensure sustainable economic development. Incentives for companies that implement environmentally friendly technologies stimulate innovation and reduce production costs. Legislation plays a significant role in this, in particular, the Law of the Czech Republic No. 165/2012 Sb. The results of the study can be used by researchers from around the world who study the implementation of new methods of environmental policy

Economic feasibility assessment of coal-biomass co-firing power generation technology

Biomass co-firing technology is a potential solution for low-carbon transition of coal-fired power plants. Existing techno-economic analyses of this technology fail to consider indirect benefits, such as avoided losses from technology retrofitting, and the heterogeneity across regions. To address these limitations, we developed five types of techno-economic indicators to analyze six possible future scenarios for the application of this technology in 29 provinces of China, based on regional heterogeneous data. The results show, first, generally, the higher the local coal price, the local on-grid electricity price, the biomass feed-in tariff subsidy, and the carbon trading price, the greater the incentives and benefits for biomass co-firing. Second, bio-electricity tariff subsidies greatly increase the incentives for technology retrofitting, with 26 of the 29 provinces analyzed having sufficient incentives for retrofits in this scenario. Third, biomass co-firing technology has more revenue potential when emission quota is limited than when it's not. Our results indicate that a timely tightening of the CO2 emission quotas of the coal power sector will stimulate technological retrofits even without additional policy incentives, while more plants retrofitted will achieve positive returns with additional incentives.

Allocating China's 2025 CO2 emission burden shares to 340 prefecture cities: methods and findings.

Peak emission is an important policy/scheme for all the countries to respond greenhouse gas mitigation. The key is how to distribute the emission burden shares to its sub-regions. This study aims to develop a prefecture city leveled CO2 emission allocation model by integrating multi-indicators method and benchmark method so that China's 2025 (end year of 14th Five-Year Plan, FYP) CO2 emission burdens can be allocated to its prefecture cities and provinces. Results show that China's total CO2 emission will reach 12 billion tons in 2025. The majority of such emission will occur in the east China due to its more developed economy and dense population. Cities with high emissions are usually allocated more emission quotas, such as Shanghai, Tianjin, Chongqing, Tangshan, Yulin, Suzhou, and Ningbo. The top five provinces with higher CO2 emission quotas are traditionally high-emission and energy-intensive provinces, including Shandong, Jiangsu, Inner Mongolia, Henan, and Hebei. The national CO2 emission intensity will decrease by 69.35% in 2025 compared to the 2005 level. The CO2 emission intensity reduction rates among the 340 Chinese cities is found to be fluctuating significantly from 16 to 74% during the 14th FYP. Finally, policy recommendations are raised for mitigating city level CO2 emissions by considering the local realities.

An innovative provincial CO2 emission quota allocation scheme for Chinese low-carbon transition

China inaugurated its nationwide emissions trading scheme (ETS) in 2021 as a key policy instrument to fulfill its carbon-peak and carbon-neutrality commitments. The allocation scheme of Chinese ETS is based on grandfathering and benchmarking and has been argued as economically inefficient. This paper proposes an alternative emission quota allocation scheme, a prospective zero-sum gain data envelopment analysis (pZSG-DEA) approach, to efficiently allocate CO2 emission quotas among market participants. We examined the potential economic gains and losses of implementing this scheme in China. Results showed that the proposed quota allocation can potentially lower the country's carbon intensity in 2020 by an additional 6.96 % compared to the 2005 level. The gains and losses vary substantially across 30 provinces. Beijing, Chongqing, and Zhejiang, were identified as potential quota receivers because increasing their emission quotas could lead to large economic gains; while Liaoning, Hebei, and Guizhou could be major quota givers as their emission reduction is associated with low economic losses. This study could be informative for the design of a high-efficiency emission quota allocation scheme for China's nationally unified emission trading market.

How does carbon emission price stimulate enterprises' total factor productivity? Insights from China's emission trading scheme pilots

Carbon emission price of Emissions Trading Scheme (ETS) reflects firms' marginal cost of emission, while no previous literature discusses whether it is consistent with the Porter hypothesis or not. Based on a rounded and unique fir-level longitudinal dataset with the information of total factor productivity (TFP) and the economic variables from 2009 to 2019, this paper employs the time-varying difference-in-differences (TDID) model and conducts a post-evaluation of the carbon emission price of ETS pilots on firms' TFP for the first time. The results show that there exists a persistent and significant positive causal relationship between the carbon emission price and firms' TFP. According to a rough estimate, the Chinese firms' TFP should increase by approximately 22.73% if it reaches the same carbon emission price as EU ETS. Our empirical results have passed the placebo test as well as the common trend test, and is mainly consistent with the PSM-DID approach, which can further eliminate the possible selection bias. Furthermore, the influence of the carbon emission price is more evident among the state-owned firms located in the eastern China. While the spillover effect is not significant, which means the price effect is merely effective on target industries. Most importantly, the mechanism analysis shows that it is an effective way to improve firms' TFP by investing more on R&D patents and funds, as well as promoting the status of R&D executive. Based on our findings, we suggest that: 1) the policymakers should restrict CO2 emission quotas so as to guarantee the carbon emission price with a certain upward trend appropriately; 2) researchers should further explore the ways to activate the market-oriented environmental regulation tools from the perspective of the carbon emission price for better designing a unified carbon ETS market.

Provincial Allocation of Energy Consumption, Air Pollutant and CO2 Emission Quotas in China: Based on a Weighted Environment ZSG-DEA Model

Air pollutants and CO2 emissions have a common important source, namely energy consumption. Considering fairness and efficiency, the provincial coordinated allocation of energy consumption, air pollutant emission, and carbon emission (EAC) quotas is of great significance to promote provincial development and achieve national energy conservation and emission reduction targets. A weighted environment zero-sum-gains data envelopment analysis (ZSG-DEA) model is constructed to optimize the efficiency of the initial provincial quotas under the fairness principle, so as to realize the fairness and efficiency of allocation. The empirical analysis in 2020 shows that the optimal allocation scheme proposed in this study is better than the national planning scheme in terms of fairness and efficiency, and the optimal scheme based on the initial allocation of priority order of “capacity to pay egalitarianism > historical egalitarianism > population egalitarianism” is the fairest. The optimal allocation scheme in 2025 can achieve absolute fairness. In this scheme, the pressures of energy conservation and emission reduction undertaken by different provinces vary greatly. The implementation of regional coordinated development strategies can narrow this gap and improve the enforceability of this scheme. Combined with the analysis of energy conservation and emission reduction in seven categories and three major national strategic regions, we put forward corresponding measures to provide decision support for China’s energy conservation and emission reduction.

Modeling Intercity CO2 Trading Scenarios in China: Complexity of Urban Networks Integrating Different Spatial Scales

Today, cities account for the majority of global energy consumption and CO2 emissions. The interaction between cities is critical for regional low‐carbon development. At this time, China has turned to the full opening of the carbon trading market as an important policy tool for achieving its ambitious emission reduction goals. This paper attempts to explore the spatial relationships among cities from the perspective of carbon trading. A “multiscenarios across different spatial scales” analysis framework applied to the allocation and trading of CO2 emission quotas among cities in China is proposed. Based on this framework, the carbon trading networks of 182 cities in China are specifically simulated under four different scenarios, with a comparative analysis of the networks’ complexity made from different perspectives. The results of the study are as follows: firstly, significant spatial network correlations exist between the cities’ carbon trading under each scenario. Secondly, on a national scale, there are more inner connections in carbon trading networks, with stronger correlations and robustness, while the degree of connection of intercity carbon trading is higher overall on a regional scale. Thirdly, on a national scale, no obvious core nodes exist in the network, while core nodes do exist in the overall network and in each region under the regional scale. Fourthly, cities in the central region have the highest core position overall. Finally, based on the research results, practical guidance in using the framework for achieving carbon reduction, promoting economic growth, and balancing regional development, as well as the important inspiration for policy formulation, is further discussed.

Optimal allocation of CO2 emission quotas at the city level in Bohai Rim Economic Circle based on multi-objective decision approach.

As the most developed city circle in northern China, allocating CO2 emission quotas at the Bohai Rim Economic Circle (BREC) city level is essential for developing specific abatement policies. Thus, with reflecting multi-principles (fairness, efficiency, sustainability, and feasibility), this paper formulates the CO2 emission quota allocation among cities in BREC in 2030 based on the multi-objective decision approach. We first propose three allocation schemes based on the principles of fairness, efficiency, and sustainability, which are conducted by entropy method, zero-sum gains data envelopment (ZSG-DEA) model, and CO2 sequestration share method, respectively. Then, the CO2 allocation satisfaction is defined and used to measure the feasibility principle which is integrated as the objective function of the multi-objective decision model together with three allocation schemes to obtain the optimal allocation results. The results show that Beijing, Tianjin, Dalian, Shijiazhuang, Yantai, Weifang, and Linyi enjoy the largest CO2 emission quotas, having 1179.94 Mt in total and accounting for 31%. Beijing has the highest quotas, and Laiwu has the lowest emission quotas. Cities with large energy consumption and less CO2 sequestration capacity, such as Tianjin, Handan, and Tangshan, experience a decrease in the emission quota shares from 2017 to 2030, indicating that these cities would undertake large emission reduction obligations. Sensitivity analysis shows that Beijing, Zibo, and Jinan are more sensitive to minimum satisfaction changes, and the total satisfaction experiences an increase first and declines thereafter. Based on the results above, cities with large pressure to reduce CO2 emissions should not only promote economic development but also improve the capacity of CO2 sequestration by enhancing environmental protection to realize emission reduction targets.

Two-step allocation of CO2 emission quotas in China based on multi-principles: Going regional to provincial

In order to ensure the implementation of the emission trading scheme (ETS), the allocation of CO2 emission quotas in China’s provinces is of significant importance. Unlike previous studies that allocated CO2 emission quotas directly to Chinese provinces, we propose a two-step allocation scheme of “country to region” and “region to province” to formulate the provincial CO2 emission quotas allocation. In the first step, considering the regional coordination development, the Shapley value method is used to assign the CO2 emission quotas to China’s eastern, central, and western regions by simulating the regional collaborative abatement. In the second step, we use the entropy method to obtain the initial CO2 emission quotas for each province combining the principles of fairness, efficiency, feasibility, and sustainability. Then, a zero-sum gains data envelopment analysis (ZSG-DEA) model is applied to evaluate initial allocation efficiency and reallocate the CO2 emission quotas to realize efficiency optimization within the region. The results show that the eastern region obtains the largest CO2 emission quotas, followed by central and western regions. The energy-abundant and economic-prosperous provinces, such as Shandong, Hebei, Inner Mongolia, and Xinjiang, face great pressures on CO2 reduction. Compared with existing literature, the proposed scheme’s regional allocation results are more balanced, which will narrow the gaps of regional economic development. Since the emission reduction pressure varies in provinces, developing differentiated policies is critical to realizing China’s reduction targets. By considering regional fairness, this paper provides a reference for allocating CO2 emission quota among provinces to improve adaptability to the current conditions of China.

Use of BP Neural Networks to Determine China’s Regional CO2 Emission Quota

China declared a long-term commitment at the United Nations General Assembly (UNGA) in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas (hereafter referred to as quotas) is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model (BP) to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China.

Grid-Air Environment Planning Considering Environmental Factors

This paper aims to realize sustainable grid planning in the medium and long term. For this purpose, the author reviewed the objectives of the energy, environment and economic policies on air environmental protection, discussed the impact of these policies on power system planning and management, and created a grid-air environment planning model considering environmental factors like polluting gas emissions and the AERs. Specifically, the purchase of SO2, NOX and CO2 emission quotas in the power industry was deliberated, the impact of uncertain power demand on grid planning was taken into account, and the grid planning model was established by two-stage mixed integer random programming. Then, the proposed model was applied to prepare the AER supply plan, power generation plan and power expansion plan of the grid in Jilin Province, China, considering different development speeds of clean energy generator sets. The case study reveals that the proposed grid-air environment planning method can satisfy the emission targets of polluting gases under the relevant constraints, and significantly enhance the environmental and economic benefits considering the quota trading. The research results shed new light on optimizing industrial structure and improving energy efficiency.

Carbon emission performance evaluation and allocation in Chinese cities

This paper presents a DEA approach with multiple abatement factors to evaluate CO2 emission performance and allocate CO2 emission quotas in Chinese cities. We first consider the difference of marginal abatement costs among cities, and construct the non-radial directional distance function with multiple abatement factors. The total-factor CO2 emission performance index and its dynamic change index are then proposed to measure CO2 emission performance. Considering equity and efficiency, we develop a composite index by the hybrid method to allocate emissions quota, which considers CO2 emissions as well as CO2 emission performance. Then we conduct an empirical study using inputs and outputs dataset of 71 Chinese cities in 2005–2012. Chinese cities have poor energy efficiency and still have high CO2 emissions. The eastern region outperforms the central region and the western region performs worst, whereas the dynamic CO2 emission performance of the central region has the largest increase. The change of CO2 emission performance is driven mainly by technological advances. As for the CO2 emission allocation, the composite index method encourages cities to reduce emissions and enhance emission performance through carbon trading market. It also motivates cities with high historical emissions to reduce their emissions by improving technology when they have poor CO2 emission performance.

Regional allocation of carbon emissions in China based on zero sum gains data envelopment analysis model

Along with China`s increasing share in global total CO₂ emissions, there is a necessity for China to shoulder large emission-mitigating responsibility. The appropriate allocation of CO₂ emission quotas can build up a solid foundation for future emissions trading. In views of originality, an optimized approach to determine CO₂ emissions allocation efficiency based on the zero sum gains data envelopment analysis (ZSG-DEA) method is proposed. This paper uses a non-radial ZSG-DEA model to allocate CO₂ emissions between different Chinese provinces by 2020 and treats CO₂ as the undesirable output variable. Through the calculation of efficiency allocation amounts of provincial CO₂ emissions, all provinces are on the ZSG-DEA efficiency frontier. The allocation results indicate that the cumulative optimal amounts of CO₂ emissions in 2020 were higher than the actual amounts in 13 provinces, and lower in other 17 provinces, and show that different provinces have to shoulder different mitigation burdens in terms of emission reduction.

Efficient allocation of CO2 emissions in China: a zero sum gains data envelopment model

The appropriate allocation of CO2 emission quotas can build up a solid foundation for future emissions trading. Building CO2 emissions allocation mechanism has significant practical implications. Based on radial zero sum gains data envelopment (ZSG-DEA) allocation model, this paper uses a non-radial ZSG-DEA model to allocate CO2 emissions between different Chinese provinces. Unlike previous studies treating CO2 as the input variable, we treat CO2 as the undesirable output variable. The modified model can better reflect macro-production process while earlier relevant models treated CO2 emissions as inputs. This paper contributes to the existing resource allocation method and allocates China's provincial CO2 emissions from the view of technical efficiency. The results of our paper reveal that (1) between 2006 and 2010 the cumulative optimal amounts of CO2 emissions were higher than the actual amounts in 12 provinces, and lower in other 18 provinces. (2) Several energy-abundant provinces such as Shanxi and Inter Mongolia need to take more responsibilities in CO2 emissions reduction. (3) After the ZSG-DEA allocation, all provinces' CO2 emissions are on ZSG-DEA frontier, which reflects the overall “Pareto Optimality”.

The Identification of Optimal Co2 Emissions-Trading Strategies Based on an Inexact Two-Stage Chance-Constrained Programming Approach

In this study, an inexact two-stage chance-constrained model was developed for identifying optimal CO2 emissions-trading strategies under uncertainty. The model was formulated through incorporating two-stage and chance-constrained programming approaches within a general interval optimization framework. It can deal with uncertainties expressed as both probability distributions and intervals. A solution method was proposed to solve problems under such uncertainties. The proposed model was then applied to a hypothetical case of power generation planning in a region where emissions trading policy needed to be identified and implemented. Three policy scenarios corresponding to different CO2 emission quotas under emissions trading policies were advanced and analyzed. Interval solutions associated with different electricity generation patterns and CO2 emission quotas were obtained. The results could be used for generating decision alternatives for helping decision makers identify desired policies under environmental, system-availability, and electricity demand constraints.

Non‐Parametric Modelling of CO2Emission Quota

AbstractDutch glasshouse firms are facing the introduction of a system of tradable CO2emission quotas. This paper employs a non‐parametric method for modelling tradable CO2emissions of Dutch glasshouse firms. The method is capable of generating shifts in CO2emissions across the sample of firms. Moreover, changes in volumes of outputs produced and inputs used are computed. Results show that firms using a conventional heating technology will be net purchasers of CO2emissions, whereas firms using more advanced heating technologies will sell part of their emission quota.

Premières simulations de la directive européenne sur les quotas d'émission avec le modèle GEMINI-E3

The article assesses the European Directive on CO2 emission quotas using the GEMINI-E3 model. After summarizing the Directive and briefly describing the model, we present and analyze scenarios for different potential allocation rules. While the European quota mechanism creates a single market for eligible firms, it can also cause distortions vis-à-vis other economic sectors in each country. The arrangement does yield a single carbon price for all eligible firms, but a different carbon price for other sectors — with variations from country to country. The resulting excess costs can be significant.

Politique de l'effet de serre. Une évaluation du plan français de quotas de CO2

Climate Policy. An Assessment of the French Plan of CO2 Quotas. Having in view the implementation of the Kyoto Protocol [1997] limiting greenhouse gas emissions, European Authorities decided to set up a European market of CO2 emission quotas for big emitters of industrial and energy sectors. This market is supposed to be operational since January 2005. Meanwhile, each member state, not the Commission, had to choose the level of the national emissions ceiling for this instrument, the list of plants that will be concerned and the rules for allocating quotas to existing and new facilities. The paper presents an assessment of the French final plan, as accepted in December 2004 by the European Commission after several adjustments of the initial plan. It considers both overall quantities affected and impacts of rules of the game on incentives given to business. As a result, choices made by the French government express a will to protect national industry and energy producers rather than to set appropriate economic incentives for an efficient and strong system in line with Kyoto commitments.