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

China's provincial long-term carbon emissions reduction planning considering fairness, efficiency and gradualness: A multi-objective programming approach

This study investigates the imperative challenge of achieving carbon neutrality in China by implementing effective strategies for carbon emissions. While existing studies on carbon emissions reduction often focus on efficiency or fairness in isolation, few provide a comprehensive framework that integrates efficiency, fairness, and gradualness-key principles for achieving carbon neutrality in a diverse and dynamic context like China. To address this gap, the study presents an innovative multi-objective optimization model meticulously designed for long-term provincial carbon emissions reduction planning. Prioritizing these three principles, this model serves as a powerful tool in guiding policy formulation. The paper introduces the MNSGA-III, a novel multi-objective genetic algorithm with distinctive features like initial solution generation, dedicated crossover, and mutation operations, offering a refined method to navigate this complex landscape. The study further provides cost estimates for achieving carbon neutrality under optimistic, neutral, and pessimistic scenarios, illuminating the financial implications. Additionally, it underscores the urgent need for ambitious mitigation strategies aligned with the IPCC's guidelines to limit global warming to 1.5 °C with 50 % certainty. These findings provide policymakers scientifically robust insights for effective carbon emissions reduction planning.

Biological-chemical conversion process design and machine learning-related life cycle assessment: Bio-lubricant production in a real case study of South Korea

This study explores the production of poly alpha olefin (PAO) from biomass as an environmentally friendly alternative to fossil fuel-based methods, aiming to reduce greenhouse gas (GHG) emissions. The primary goal is to design a process for converting 2,000 metric tons of biomass into PAO daily, integrating biological and chemical pathways. Environmental impact is assessed through a life cycle assessment (LCA), comparing this biomass-based method with traditional fossil fuel-derived processes. Key findings include the successful production of 458 metric tons of PAO, with the LCA revealing a 34.8% reduction in GHG emissions (9.88 kg CO2-eq./kg of PAO) compared to fossil fuel-based PAO. Sensitivity analyses on the oligomerization yield (60-70%, base case at 65%) and the recycle ratio of glucose in the bioprocess for octanoic acid production show significant environmental benefits when exceeding a 55% recycle ratio. Additionally, an energy scenario analysis predicts the impact of shifting to renewable energy by 2030. In a scenario where all electric utilities are renewable (RE100 scenario), GHG emissions are estimated at 13.07 kg CO2-eq./kg of PAO, further emphasizing the environmental advantage of biomass-based PAO. This study, through its integration of biological and chemical processes and comprehensive LCA, provides critical insights into the potential of biomass-based materials for reducing GHG emissions, making a substantial contribution to future research in high-value material production from renewable resources.

A burden-sharing model shaping the embodied carbon emission and considering regions' efforts to reduce emissions in China's power sector

To achieve "carbon peaking and carbon neutrality", reasonable burden-sharing for emission reductions among key players is critical. And reasonable burden-sharing requires scientific clarification of their actual emissions and responsibilities, and full accounting of efforts to reduce emissions. Therefore, this paper focuses on China's power industry and proposes a burden-sharing model that reflects regions' embodied carbon emissions and mitigation efforts. Moreover, considering the economic spillover effects of the power industry in different regions to re-define China's sub-regional emission reduction targets. Results show a significant difference in the flow of electricity embodied carbon among China's regions. The carbon embodied in the power industry increased by 948 million tonnes between 2012 and 2017, mainly due to changes in the structure of final demand and production. Furthermore, implementing the Emissions Trading System (ETS) has effectively curbed carbon emissions in seven pilot provinces and cities, namely Beijing, Tianjin, Shanghai, Guangdong, Hubei, Chongqing, and Fujian, especially Beijing. Spillover effects significantly impact the economic growth of provinces, and some provinces that have enjoyed the benefits of spillover effects should take more responsibility for emission reductions. In summary, each region has different allowances and it is suggested that differentiated policies should be implemented to promote regionally-ordered low-carbon development and achieve the dual-carbon goal.

Synergistic allocation of carbon emission and energy quotas: A balancing act under carbon peaking constraints

Carbon emissions quotas (CEQ) and energy quotas (EQ) have been recognized as effective policy tools for promoting energy conservation and emission reduction. However, existing methods have considerable limitations in meeting the phased requirements of carbon peaking constraints and synergistically allocating EQ and CEQ. Therefore, we propose a synergistic allocation method for CEQ and EQ to balance economic development and environmental sustainability under carbon peak constraints. Using 30 provinces in China as case studies, a dual-layer allocation model was constructed by integrating an indicator method with an improved ZSG-DEA model to achieve the coordinated management and target control of CEQ and EQ. Reaching energy and carbon emission targets under peak constraints, this approach avoids excessive reductions in regional quota usage rights, and promotes equitable and efficient interprovincial quota distribution. A high correlation was noted between CEQ and EQ among the provinces, highlighting the necessity for synergistic quota management. Energy-intensive regions and cleaner western regions require opposite quotaadjustment strategies, resulting in quota surpluses and deficits across the provinces. Reductions in energy-intensive provinces such as Hebei, Inner Mongolia, Shanxi, and Xinjiang should be maintained within 20%, whereas cleaner western provinces are expected to achieve quota surpluses. This differentiated adjustment strategy helps to narrow the interprovincial quota gap and promotes regional coordination. Under peak carbon targets, economic growth requires the combined efforts of a quota allocation mechanism, efficient use strategies, and economic structural transformation. These findings can inform multiscale environmental resource quota allocation, contributing to carbon peaks and development goals.

Impacts of climate change risk and economic policy uncertainty on carbon prices: Configuration analysis from a complex system perspective

Despite a vast amount of research examining the antecedent variables of carbon prices, the impact of climate change risk on carbon prices has been rarely explored. This study adopted a complex system perspective and applied NCA and fsQCA models to analyze regional climate physical risk (CPR) and climate transition risk (CTR) from a complex system perspective. The results suggested that the primary factors affecting the Hubei carbon price included coal prices, market regulation, and national carbon market prices, and carbon prices could be higher in response to higher levels of economic policy uncertainty (EPU) combined with other factors. By comparing the changes of independent variables before and after the opening of the national carbon market, it was found that lower CPR and CTR were conducive to higher carbon prices; the former has received early attention but the latter has been neglected. The findings from this study provided theoretical and practical insight to inform government regulation of carbon prices and decision-making for carbon market stakeholders. The government should pay attention to the impacts of climate change risks and EPU on carbon prices because these factors could significantly undermine the incentive and economic effectiveness of the carbon market.

Call for multi-policy approach: Synergistic effects of emissions trading scheme and energy efficiency policies

Limited research exists on the synergistic effects of carbon emissions trading and energy efficiency policies despite their significance in achieving global carbon neutrality objectives. This study examines the synergistic effects of carbon emissions trading and energy efficiency policies on aspects of the environment, energy, and economy. Results show that the synergistic effect leads to an additional reduction of 1.2% in carbon emissions, along with a decrease of 4.2% in economic losses. Despite challenges like increased energy external dependency and carbon leakage, the synergistic effect shows a positive externality between policies, reducing the carbon intensity and marginal emission mitigation costs. Furthermore, these synergistic effects yield positive consequences for social welfare, particularly benefiting rural households and fostering equitable distribution of carbon mitigation benefits across societal groups. These findings underscore the importance of considering policy synergies between carbon emissions trading and energy efficiency policies to ensure the total effect of climate change mitigation strategies.

Analyzing the spatiotemporal pattern of the decoupling degree between carbon metabolism and economic development in village and town units

In the context of green and sustainable development and rural revitalization, analysis of the relationship between economic development and the evolution of carbon metabolism is of great significance for China's future transformation of development models. This study analyzed the spatial characteristics and spatiotemporal evolution pattern of the decoupling status between carbon metabolism and economic development of Laiwu during two periods from 2001 to 2018 at the village and town unit scales by using the Tapio decoupling model. The results showed that the growth rate of carbon metabolism from 2001 to 2009 was significantly higher than that from 2009 to 2018. The spatial heterogeneity of the decoupling states between economic development and carbon metabolism from 2009 to 2018 was significantly stronger than that from 2001 to 2009 in two units. From 2001 to 2018, the development trend gradually trended towards spatial imbalance. The decoupling status between villages and towns had a high degree of consistency from 2001 to 2009 and inconsistency from 2009 to 2018. From 2001 to 2009, the decoupling status of about 78% of villages was consistent with that of towns. Moreover, from 2009 to 2018, the consistency reduced to 32.2%, and the decoupling status of about 48% of villages was weaker than that of towns. According to the reclassification results of different decoupling state change types, from 2001 to 2018, about 52.2% of the villages had a decoupling state evolution type of eco-deteriorated economic development, which is an unsatisfactory development trend in a short time. Moreover, about 12.1% of the villages had a decoupling state evolution type of eco-improved economic development, which is a satisfactory development trend.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (-7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

Study on regional carbon quota allocation at provincial level in China from the perspective of carbon peak

This paper constructs a carbon quota allocation index that takes into account equity, efficiency and ecological construction, and calculates carbon emissions and energy consumption data in important periods based on the expected carbon emission targets and economic and social development indicators of the Chinese government. Based on the calculated carbon emissions, the zero-sum game data Envelopment model (ZSG-DEA) is used to discuss the initial allocation of regional quotas and the optimal carbon quota scheme. The results show that:(1) there is a large gap between the optimal carbon quota and the initial carbon quota allocation in Shandong, Guangdong, Jiangsu and other provinces in 2025, and the implementation of emission reduction measures should be accelerated. (2) By 2030, the final allocation of Beijing, Tianjin, Inner Mongolia, Qinghai, Shanghai, Ningxia, Liaoning and Xinjiang will be positive. The provinces with negative final allocation should carry out the work of carbon peak as soon as possible to avoid increasing the pressure of emission reduction in the future. (3) The central region faces greater pressure of emission reduction, while the western region can accept the transfer of carbon emissions from other regions over time. The research conclusions have important policy implications for establishing a fair and effective carbon quota allocation mechanism, achieving the national total carbon emission control target, stimulating the vitality of the unified carbon market, and promoting regional coordinated emission reduction.

Comparing the EU and Chinese carbon trading market operations and their spillover effects

A carbon trading market (CTM) policy for trading carbon dioxide emission rights as a commodity was created to reduce greenhouse gas emissions. CTMs operate differently in different countries and regions, and their interactions deserve an in-depth study. This study focused on the world's largest CTM, the European Union (EU), and the CTM of China, largest carbon-emitting country. First, we evaluate the liquidity and volatility of the two CTMs. Subsequently, the VAR model is used to explore the mean spillover effect between the two markets and the BEKK-GARCH model is used to explore the volatility spillover effect between the two markets. The study concludes that: (1) The liquidity of China's CTM is better than that of the EU's CTM. (2) Both the EU and Chinese CTMs are unstable, but the volatility of the Chinese CTM is lower than that of the EU CTM. (3) Price changes in the EU and Hubei CTMs have a mutual influence. (4) There are interactions between the market fluctuations of the EU CTM and the Shanghai CTM and those of the EU CTM and the Hubei CTM. The results of this study have implications for the construction and development of CTMs in the EU and China.

Incentive contract design considering quotas production: A principal-agent perspective

The quality of primary products in a supply chain is determined by the agent and the principal. Simultaneously, there are quota production constraints on the principals. Our discourse centers on the design of incentive contracts for agents within these supply chains. We derived the parameters describing the contract, level of effort, profits for both sides, and the minimum requirements of the principal for the proportion of high-quality primary products. This study compares the decision-making paradigms of agents and principals in various contexts. The results show that decision-making mechanisms are strongly influenced by individual effort costs, various internal and external organizational variables, and the interplay of efforts on both sides. Using numerical experiments, we investigate the effects of different situations between clients and contractors on contracting and effort strategies. The results show that when the agent exerts unilateral effort, reasonable incentive contracts can spur the agent to increase effort and thus increase the proportion of high-quality primary products. In the case of bilateral efforts, a well-calibrated contract design benefits the agent (bearing less risk). For the principal, the expected profit increases in most cases. When considering the quota production, it is necessary to set an appropriate limit on the proportion of high-quality primary products.

Pathways to improve energy efficiency under carbon emission constraints in iron and steel industry: Using EBM, NCA and QCA approaches

Improving environmental performance of energy- and carbon-intensive sectors represented by the iron and steel (IS) industry is of utmost importance to address the challenges of resource depletion and climate change worldwide. This article adopts a global-super-Epsilon-Based Measure (EBM) model with undesirable output for IS energy efficiency estimation, identifies efficiency determinants based on Technology-Organization-Environment (TOE) framework, and analyzes various pathways for efficiency improvement by grouping Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Empirical testing using statistical data of the G20 economies during 2010-2020 demonstrates that: 1) energy efficiency in the IS industry in G20 countries has risen amidst fluctuations, with developed countries performing more efficiently than developing countries; 2) individual factors do not constitute a compulsory condition to achieve high energy efficiency in the IS industry; 3) three different paths to achieve high energy performance are found, that is, technology-structure driven, regulation-economy-technology driven, and regulation-technology-production driven. Heterogenous policy recommendations for efficiency gains in the IS sector of different countries with divergent features are proposed accordingly.

Multi-scenario reduction pathways and decoupling analysis of China's sectoral carbon emissions

To achieve its goal of carbon emissions peak and neutrality, China requires synergistic efforts across all sectors. In this study, three scenarios-baseline, policy, and green low-carbon-were developed to explore the pathways for China's emissions reduction across sectors from 2020 to 2060, and the timing of decoupling economic growth from CO2. The results showed that, under these scenarios, China's carbon emissions peak in 2030, 2026, and 2025, with strong decoupling time, lagged one year behind peak attainment. The agriculture, forestry, livestock, and fishing (AFH) and mining and quarrying (MQ) sectors would be the first to achieve a carbon peak. Under all three scenarios, all of the other sectors-with the exception of electricity, gas, and water production and supply (EGW)-will achieve a carbon peak by 2030. Therefore, policymakers should set carbon peak goals based on sector characteristics and ensure energy security in the process of achieving carbon neutrality.

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.

Coupling and Coordinating Relationship between Agricultural Eco-Efficiency and Food Security System in China

Carbon peaking, carbon neutrality goals and food security are the basis of sustainable development, and exploring the coordination relationship between China's agricultural eco-efficiency and food security system has a major significance for the implementation of relevant strategies. This paper is based on collaboration research on the synergistic relationship between agricultural eco-efficiency and food security systems using methods such as entropy weight method, coupling coordination model, spatial autocorrelation model, etc., revealing the evolution-driven mechanism of the coupling coordination degree. This study found that a higher level of coupling coordination always occurs in those areas with high standard farmland construction and large grain production scale, while economically developed areas appear to have a lower overall coordination level limited by endowment constraints and division of labor in development planning. It shows a positive spatial correlation in terms of geographical distance between agricultural eco-efficiency and food security, and the positive spillover effect gradually increases but is not strong overall. China should combine regional resource endowment and development planning, pay attention to the improvement of large-scale and standardized agricultural production, continue to strengthen the development of clean agricultural production, and achieve food security under the constraints of the carbon peaking and carbon neutrality goals.