Allocation Of Carbon Emissions Research Articles

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.

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.

Carbon emission allowances and green development efficiency

This study delves into the nexus between the efficiency of carbon emission allocation and the efficiency of green development within the context of innovative city construction. Leveraging data from 283 prefecture-level cities and corresponding provincial-level panel data spanning the period from 2005 to 2019 in mainland China, the research employs the zero-sum gain DEA model to compute provincial carbon emission allocation efficiency and allocate allowances. Furthermore, it utilizes the all-factor non-radial directional distance function and the SBM-DEA model to gauge the green development efficiency of prefecture-level cities. The double-difference method is applied to evaluate the impact of innovative city construction on regional green development efficiency. The results indicate that an imbalance between carbon emission allocation efficiency and emission quotas detrimentally affects urban green development. However, technological progress, factor accumulation, and the institutional environment associated with innovative city building mitigate this negative impact. Heterogeneity analysis reveals a differentiated effect of innovative city building on regional green development efficiency. This paper not only explores the concept of achieving an equitable distribution of carbon emission allowances but also examines how innovative city building influences regional green development. By establishing connections between urban policies, the equitable distribution of carbon emission allowances, and regional green development, it introduces a new perspective for promoting high-quality development at the regional level.

Calculation method of carbon flow distribution in load-intensive regional energy centers

With the development of the economy, people’s demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning.

Allocation of CO2 emission target in China under the “1 + N” policy: Considering natural carbon sinks and wind-solar-hydropower endowments

The CO2 emission control targets (CET) allocation faces the unavoidable issues of “energy equity” and “carbon sink equity”, which are complicated by changing regional characteristics. By combining regional characteristics assessment, energy consumption status in the “1 + N” policy, and Data Envelopment Analysis (DEA) modeling, two schemes for China's net CET allocation in 2020–2030 are implied to reveal the impacts of regional carbon sinks and renewable energy endowments differences on the allocation. The results indicate that: firstly, significant regional differences are inevitably intrinsic within China's renewable energy endowments and carbon sinks to substantially affect the equity of carbon emission allocation in 2020–2030; secondly Provinces with high fossil energy reliance, such as Hebei, Shandong, Shanxi, and Inner Mongolia, are generally facing continuous pressure to reduce CO2 emissions during the 14th FYP and 15th FYP; thirdly, as a regional ecological advantage, carbon sinks are an essential source of additional carbon budgets for provinces, as in Sichuan and Yunnan. As a dominating impact factor of carbon sinks on CET allocation, terrestrial carbon sinks are much higher than ocean carbon sinks with clear provincial attribution; fourthly, in our allocation scheme, the exploitation of renewable energy endowments directly affects the provincial carbon budget and can promote low carbon energy supply; finally, the foundation to build the CET allocation mechanism, China's carbon market needs to be improved. This study helps policymakers understand and balance the equity issues in CET allocation due to regional differences in renewable energy endowments and carbon sinks.

Operation optimization method of integrated energy system in park considering carbon transaction cost

Considering the demand of energy consumption and carbon reduction in industrial parks, combined with the coupling relationship of different energy conversion and the characteristics of equipment operation, the typical equipment in the park is modeled. Based on the carbon emission allocation, the carbon transaction cost is quantified, and the operation optimization method of the integrated energy system(IES) for park is proposed with goal of economy and low carbon. Taking an industrial park as an example, the results of the two scenarios of subsystem discrete operation and integrated energy system optimal operation are analyzed. The results show that the construction of IES in park found on carbon trading and the optimization method have a significant effect on the realization of energy saving and carbon reduction and cost reduction.

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the “high emission with high price” principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission.

Real-Time Bidding Strategy of Energy Storage in an Energy Market with Carbon Emission Allocation Based on Aumann-Shapley Prices

Real-Time Bidding Strategy of Energy Storage in an Energy Market with Carbon Emission Allocation Based on Aumann-Shapley Prices

A dynamic city-level carbon emission allocation distribution analysis under the constraint of diverse provincial carbon neutral trajectory

A dynamic city-level carbon emission allocation distribution analysis under the constraint of diverse provincial carbon neutral trajectory

An integrated zero-sum game and data envelopment analysis model for efficiency analysis and regional carbon emission allocation

As the global economic environment changes, the concept of green and sustainable development has become the international community’s consensus. As a participant in the worldwide emission reduction plan, China is also under great pressure to reduce carbon emissions. As an example, this study constructs an optimal scheme for regional carbon emission allowance allocation in Hunan Province, China. We first forecast the total carbon emissions in 2030 for each city, state, and major industry in Hunan Province through a scenario and then innovatively use the mass function allocation theory in marketing to sort out the logical ideas of the optimization model. More importantly, we combine the zero-sum game theory to build the zero-sum game-data envelopment analysis model to continuously reallocate the carbon emission allowances of each city and state and major industries in Hunan Province until the optimal allocation scheme is reached. From the perspective of each city and state, the initial carbon emission efficiency level of fourteen cities in Hunan is high, but there are still some differences. From the perspective of industries, the initial efficiency of carbon emission of six major industries in Hunan is lower, and the difference in efficiency values is evident.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

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.

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.

Fine allocation of sectoral carbon emissions at block scale and contribution of functional zones

The rapid development of cities has resulted in a substantial increase in carbon emissions, which has a detrimental impact on the environment. Previous studies had focused on large-scale carbon emissions; however, the carbon emissions from urban functional zones that consist of blocks as basic units have not been explored. In this study, we used Open Street Map (OSM) road and point-of-interest (POI) data to classify Shanghai into different functional zones and investigated their contributions to carbon emissions from various sectors. A top–down method was used to finely allocate the statistical data to different functional zones, and a random forest algorithm was used to investigate the contributions of different features to the regional carbon emissions. Among the 5677 units examined in this study, the majority belonged to residential zones, followed by commercial and industrial zones. The carbon emission contributions of the three zones were 26, 34, and 12%, respectively. With respect to sectors, higher carbon emissions originated from the residential and tertiary sectors, primarily concentrated in the urban centers and western city areas. Secondary and primary sectors followed suit, with the highest concentrations being from urban peripheries. This study can be used to optimize and rationalize the allocation of functional areas; notably, our study presents a novel direction for assigning carbon emissions responsibilities to different urban functions. Furthermore, the method presented in this study can be customized for carrying out analysis for other countries, with the aim of estimating and addressing carbon emissions on a sectoral basis.

Retracted: Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

Retracted: Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

CO2 Emission Allocation for Urban Public Buildings Considering Efficiency and Equity: An Application at the Provincial Level in China

China is currently recognized as the leading global energy consumer and CO2 emitter. A significant amount of carbon emissions can be attributed to urban public buildings. Establishing an equitable and efficient carbon emission allocation mechanism is a crucial step to meeting the ambitious targets in China’s 2030 carbon peak plan. In this study, we estimate the total amount of CO2 emissions from urban public buildings by 2030 and propose a preliminary scheme of carbon quota assignment for each province. By means of applying the zero-sum gains data envelopment analysis (ZSG-DEA) model, the carbon emission quotas allocation of urban public buildings in China’s 30 provinces is proposed, and the corresponding pressure to reduce provincial carbon emissions is analyzed. The results indicate that Qinghai has the lowest carbon emission rate (0.01%) for urban public buildings, while Guangdong has the highest (9.06%). Among the provinces, Jiangsu, Jiangxi, and Tianjin face the least pressure in reducing carbon emissions from urban public buildings. On the other hand, Hebei, Beijing, and Anhui are under great pressure to decrease carbon emissions. Notably, Hebei is predicted to have the highest emission reduction requirement of 95.66 million tons. In terms of pressures on carbon emissions reduction for urban public buildings, Jiangsu, Jiangxi, and Tianjin exhibit the least pressure. Hebei, Beijing, and Anhui are facing intense pressure to decrease carbon emissions. These findings offer policymakers valuable insights into developing a fair and efficient carbon allowance allocation strategy, while also contributing to China’s efforts to mitigate carbon emissions and combat climate change.

Long-term multi-sector low-carbon transition pathway planning based on modified-grandfathering emission allocation method: A case study of China

Strict climate goals require a rapid reduction of carbon emissions across society. It is a considerable challenge to design decarbonization pathways considering co-operation in energy and carbon emissions across multiple sectors simultaneously. In order to obtain the decarbonization pathway for the whole society, this paper establishes a long-term multi-sector low-carbon transition pathway planning model. Besides inter-sectoral energy supply and demand, this study fully considers the allocation of carbon emissions quotas among different sectors by modified-grandfathering method, which takes into account both equity and efficiency principles. The decarbonization targets of different sectors, corresponding mitigation pathways and energy consumption can be calculated. China is taken as a case study. Results show that China's total energy consumption peaks at 5 billion tces around 2030, then falls below 4 billion in 2060, with the proportion of primary electricity and other clean energy reaching over 50 percent. Besides, the realisation of carbon neutralisation in China depends heavily on the improvement of carbon capture and storage (CCS) and end-use electrification rate. Moreover, the study of different quota allocation ways shows that the increase in the contribution rate of power system mitigation leads to the reduction of the total low-carbon transition costs, which also aggravates administrative difficulties.

Carbon emission allowance allocation based on a bi-level multi-objective model in maritime shipping

To mitigate carbon emissions from maritime shipping, this study investigates the strategies and performance of allocating carbon emission allowances (CEAs) with the cap-and-trade mechanism among shipping companies. Using a bi-level model solved by the multi-objective particle swarm optimization algorithm based on Pareto solution set, the interactions between decisions of the government and shipping companies are well characterized. The results of the benchmark scenario indicate that all shipping companies’ optimal carbon emissions decrease annually during the planning period, and a larger company usually exhibits a better emission reduction performance. However, economic benefits of shipping companies and the society display an annually declining trend due to additional carbon emission reduction costs. The sensitivity analysis demonstrates that the decreasing carbon prices lead to more emissions, while the optimal total carbon emissions decrease as carbon reduction costs increase. These findings create implications for multiple stakeholders to formulate their own carbon emissions mitigation strategies under the cap-and-trade mechanism.

How does market-oriented allocation of industrial land affect carbon emissions? Evidence from China

Industrial land serves as the fundamental basis for urban economic development and significantly contributes to carbon emissions. Effective market mechanisms are crucial for reducing carbon emissions. As such, investigating the impact of market-oriented allocation of industrial land (MAIL) on carbon emissions and its pathways is of substantial practical importance for global low-carbon development. This study constructs a theoretical framework examining the influence of MAIL on carbon emissions, focusing on 285 Chinese cities from 2003 to 2020. The spatial econometric model is employed to analyze the impact of MAIL on carbon emissions. The results show that: first, from a national perspective, MAIL not only reduces carbon emissions within a region but also in neighboring regions. Higher MAIL leads to more effective carbon emission reductions, which are persistent and hysteresis in time. Path analysis demonstrates that MAIL reduces carbon emissions by promoting industrial upgrading and technological innovation. Second, there are differences in the timeliness of carbon emission reduction effects in cities of different scales and regions. For cities of different scales, the carbon reduction effect of MAIL is more stable in large and medium cities compared to megacities and small cities, but in the short term, MAIL will hinder the industrial upgrading of megacities and thus is not conducive to carbon reduction. For different regional cities, the carbon reduction effect of MAIL is more stable in other regions except northeast region, and in the short term, MAIL will inhibit technological innovation in northeast region, which is not conducive to carbon reduction. Consequently, it is essential not only to design a top-level reform plan for MAIL in China but also to establish differentiated reform policies for MAIL, tailored to the unique characteristics of cities with different scales and regions, to effectively reduce carbon emissions.

Research on Carbon Emission Allocation in Urban Public Buildings in Guangxi

Research on Carbon Emission Allocation in Urban Public Buildings in Guangxi