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Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market. [ABSTRACT FROM AUTHOR]

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

Compared to cast-in-situ construction, prefabricated construction reduces the generation of construction waste; however, construction waste cannot be entirely eliminated through prefabrication and can be generated at each stage of its life cycle. This paper generates a system dynamics model, comprising two causal-loop diagrams and one stock-flow diagram, to simulate the effectiveness of policies on decarbonizing prefabricated concrete building waste. The model can predict the annual and total construction waste, along with the associated carbon emissions, under different policy scenarios in China by 2030. This paper focuses on prefabricated concrete buildings in China, with specific attention to the wastage of concrete, bricks, mortar, and metal. In single-factor scenario simulations, increasing landfilling fee is the most effective policy for waste decarbonization, potentially reducing carbon emissions by 31%. Increasing prefabrication rate and enhancing regulation degree at the design phase of prefabrication can directly reduce prefabricated building waste generation, subsequently resulting in an 18% and 7% carbon emissions reduction. Multi-factor scenario simulations demonstrate that the simultaneous application of all four policies is most effective, achieving a 44% reduction in carbon emissions. This research establishes a theoretical framework for prefabricated concrete building waste management and provides the Chinese government with practical insights on optimizing policies for waste decarbonization in the dynamic landscape of prefabrication. • This paper develops the prefabricated building waste generation and disposal systems. • The model predicts the construction waste and associated carbon emissions by 2030. • Raising landfill fee is an effective policy for decarbonizing construction waste by 31.06%. • Prefabrication rate reaching 100% can reduce 18.04% of carbon emissions from waste. • Applying all policies is most effective, achieving a 44.34% carbon emissions reduction. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

Population aging has indeed emerged as a significant challenge for China. Using data from four waves of the China Family Panel Studies, this paper explores the impact of population aging on household carbon emissions and its underlying mechanisms. Results show that population aging significantly decreases per capita household carbon emissions, and this conclusion is robust with respect to sample selection, measurement of primary independent variable, and data selection. Population aging influences household carbon emissions through negative consumption effects and positive composition effects, with the former playing a dominant role. Notably, population aging increases per capita household carbon emissions in items such as medicine, heating, and healthcare, while decreasing emissions in most other items. Furthermore, empirical evidence suggests impact heterogeneity across hukou, income level, and the health status of household head. The insights presented in this paper are crucial for advancing our understanding of sustainable development. • Population aging decreases per capita household carbon emissions. • It is achieved through negative consumption effects and positive composition effects. • The consumption effects play a dominant role. • Evidence indicates impact heterogeneity across, hukou, income level, and the health status of household head. [ABSTRACT FROM AUTHOR]

Carbon trading is widely used as a carbon finance instrument to promote green transformation. This paper focuses on carbon trading's impact on the bond spread for high-carbon enterprises. Using variation in carbon trading access due to pilot policies in China, we find that bonds issued by high-carbon enterprises participating in the carbon trading pilots have lower spreads. Furthermore, the results suggest that carbon trading reduces bond spreads by encouraging high-carbon enterprises to improve their environmental performance. Another channel is the information asymmetry between bondholders and enterprises. More active carbon markets provide investors with more information about enterprises' motivations for reducing carbon emissions, leading to a greater reduction in bond spreads. In addition, the impact of carbon trading is more significant in local SOEs, high-innovation enterprises, and longer-term bonds. Our research helps us understand the importance of carbon trading in achieving a low-carbon economy. [ABSTRACT FROM AUTHOR]

• An improved singular spectrum decomposition is proposed. • High complexity components are secondarily decomposed to reduce its complexity. • An improved prediction model of optimization algorithm is proposed. • Intelligent weighting strategy is introduced to overcome traditional weighting problem. • A combined prediction model for carbon emission is proposed. Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. [ABSTRACT FROM AUTHOR]

With the increasing severity of global warming, the goal of carbon sequestration and emission reduction in various industries has become an important responsibility for sustainable development. As an important part of carbon sink, the improvement of net carbon efficiency in the plantations industry will make a great contribution to the realization of the "double carbon" target in agriculture. However, there are large spatial and temporal differences in the net carbon efficiency of the plantations sector in various provinces, and there is still a large scope for increasing carbon sink and reducing carbon emission. In this paper, we have analyzed spatial and temporal trends in the net carbon sink efficiency of the plantations' production processes in 31 provinces in China (excluding Hong Kong, Macao and Taiwan) from 2000 to 2020. On this basis, we have analyzed the influencing factors of net carbon efficiency in the plantations' production process through an extended Kaya constant equation. Through the geographically and temporally weighted regression model, we explore the spatio-temporal heterogeneity of the influencing factors, and further analyze the driving and restraining factors of net carbon efficiency in plantations' production process, in order to propose appropriate countermeasures. The study shows that: (1) The net carbon efficiency of plantations sector in China increase between 2000 and 2020, and the development of net carbon efficiency is uneven across regions. (2) The geographic center of net carbon efficiency of China's plantations industry shift from the northeast to the southwest and is consistent with the migration direction of agricultural inputs. (3) The influencing factors of net carbon efficiency in the plantations sector show large regional differences, and can be specifically divided into four major types of zones. In this regard, this paper provides more appropriate countermeasure suggestions for increasing carbon sink and reducing carbon emission in each zone, with a view to achieving the goal of double carbon in agriculture with practical significance. • Net carbon efficiency is a new objective indicator to measure the carbon sink level of cropland. • Cultivated land carbon sink capacity is closely related to agricultural inputs. • Regional differences in the factors influencing net carbon efficiency. [ABSTRACT FROM AUTHOR]

China has committed to peak carbon emissions by 2030 and become carbon neutral by 2060. Hebei, as a major energy province in China, has more challenging carbon peaking and carbon neutrality targets. Based on the extended STIRPAT model, this paper evaluates the time to peak carbon and post-peak carbon trend changes in Hebei province under different scenarios from 2020 to 2060. Based on the support vector machine model with particle swarm optimization, this paper evaluates the carbon neutral pathways under different scenarios in Hebei province from 2020 to 2060 in terms of forests, croplands, wetlands, grasslands and Carbon Capture, Utilization and Storage (CCUS). The results show that a monotonically flat trend without inflection point, a single-inflection-point plateau-period trend and a multi-inflection-point inverted U-wave trend occur after carbon peaks. Hebei Province can achieve carbon neutrality by 2060 under the policy scenario and the enhanced scenario, and the enhanced scenario is better. Comparing with existing studies, the innovation of this paper is to design different trends of changes after carbon peak, combining carbon absorption and carbon emission, and providing more diversified development paths for carbon neutral strategies through a more integrated perspective and multi-scenario comprehensive analysis. Therefore, In the future, Hebei Province should establish a platform for carbon emissions monitoring and carbon footprint assessment, and set up an early warning and assessment mechanism for carbon peaking, so as to prevent a rebound trend in carbon emissions after peaking. At the same time, it should introduce and develop carbon capture and conversion technologies and promote the low-carbon transformation of the industrial and energy sectors, thereby forcing the early realization of peak carbon. Pay attention to the role of forestry resources in carbon neutrality, increase carbon sink resources such as forests and wetlands, promote the development of CCUS, and enhance the implementation of the policy. [ABSTRACT FROM AUTHOR]

• Constructed an operation architecture of the coupling of energy supply and demand. • Established the comprehensive demand response mechanism based on time-sharing energy prices. • Established the minimum carbon emission cost economic scheduling model as an objective function. • The scheduling method could achieve the goal of low-carbon economic dispatch. Aiming at the problems of low energy utilization and environmental pollution in China's livestock and poultry breeding industry under the carbon peaking and carbon neutralization goals, this paper proposes an optimal scheduling model for multi-energy coupling integrated energy system with ladder-type carbon trading mechanism and demand response. First, based on the construction of a reward and punishment model with ladder-type carbon trading mechanism, this paper explores the potential interaction between the coupling ability of multiple heterogeneous energy sources and carbon emissions in consideration of the impact of carbon capture equipment on carbon emissions. Second, in view of the diversity and complexity of demands for agricultural energy supply, the energy supply side equipment model and the comprehensive demand response model of electricity, heat, and gas are established, and a low-carbon economic scheduling model of integrated agricultural energy system is proposed, aiming at minimizing the total operation cost of the system. Finally, the model is solved by CPLEX, and the coupling system of a 6-node power distribution network, a 6-node gas network, and a 4-node heat supply network is used to verify the effectiveness of the proposed model and method and optimize the operation of the integrated agricultural energy system. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

The support of digital technology is urgently needed to promote low-carbon development and green economic and social transformation and to realize the goal of "carbon neutrality" early. This paper utilizes the "Golden Tax III" project for the digital transformation of China's tax collection and administration and empirically confirms that digital tax collection and administration significantly reduces carbon emissions based on the panel data of 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2010 to 2020, i.e., digital technology plays a positive role in carbon emission reduction. The mechanism test shows that digital technology indirectly affects carbon emissions through industrial structure, technological innovation, and tax structure. Heterogeneity analysis shows that digital technology has a significant inhibitory effect on carbon emissions in the northern region, non-Yangtze River region, provincial administrative areas with heavy local government debt, and a large proportion of tax revenue from the secondary industry and tax revenue from state-owned enterprises. Under the requirement of high-quality and green development of the economy, this paper is of great theoretical and practical significance for using digital technology to ensure the governance functions of tax, help low-carbon development, and reach the goal of "carbon neutrality" at an early date. • This study assesses the impact of digital technology on carbon emissions from the government's perspective. • This study enriches the mechanism by which digital technology affects carbon emissions. • This study provides suggestions to promote the digitization of government affairs and carbon emission reduction. [ABSTRACT FROM AUTHOR]

Under the goal of "carbon peaking" and "carbon neutrality" in China, it is significant to explore whether emission reduction commitments contribute to carbon risk premium. Therefore, based on a sample of Chinese listed companies that publicly disclosed questionnaires in the CDP from 2009 to 2021, this paper empirically examines the relationship between emission reduction commitments and carbon risk premium and their formation mechanism. The results show that emission reduction commitments can effectively hedge the carbon risk premium, which pass several robustness tests. Specifically, the more comprehensive and ambitious the commitment is, the greater the suppression of the carbon risk premium. The mechanism test shows that disclosing emission reduction commitment information can reduce carbon risk by improving investor confidence and attracting political resources. Differences in enterprise emission levels, regional environmental regulation intensity and green financial development result in different risk-hedging effects of emission reduction commitments. Moreover, the greater the substantial pressure from stakeholders, the greater the tendency for firms to set up emission reduction commitments. Finally, the heterogeneity analysis reveals that confidence level of commitments has a significant impact on risk premium. Specifically, enterprises with SBTi validation, higher CDP ratings and higher target completion are more helpful in curbing carbon risk premium. Our findings contribute to corporate commitment to climate change and complement the carbon risk premium hypothesis. [Display omitted] The possible highlights of this study are as follows. • With the help of forward-looking information on emission reduction commitments, we re-examine the conclusion of the carbon risk premium and find that emission reduction commitments can effectively hedge the risk premium, enriching and developing the carbon risk premium hypothesis • This paper brings commitment levels and carbon emission levels into a unified analytical framework to study the correspondence between emission reduction commitments made under pressure to reduce emissions and the carbon risk premium, providing micro evidence for solving the mystery of the carbon risk premium. • Measure emission reduction commitment indicators from various angles, making the data on emission reduction commitments more objective and scientific. [ABSTRACT FROM AUTHOR]

Existing studies on emission accounting in global value chains (GVCs) have not focused on the relational nature of GVC activities manifested as the interaction of intermediate goods between enterprises. This paper proposes a relational GVC emissions accounting framework based on an inter-country input-output (ICIO) model distinguishing between domestic-owned enterprises (DOEs) and foreign-invested enterprises (FIEs), which is classified as trade-based, investment-based and hybrid-based emissions. Using AMNE-ICIO tables from 2005 to 2016, this paper traces emissions and carbon intensity in relational GVC activities at the global, economic, and industry levels. We find the following: (1) Investment-based emissions accounted for about 94.15% of the increase in relational GVC emissions, of which more than half comes from the interactive activities between DOEs and FIEs. (2) For the United States, high-income economies, and high-tech manufacturing, investment-based emissions were higher than trade-based emissions, and the reverse was true for upper-middle and lower-middle income economies and China. (3) Although relational GVC carbon intensity was high, the trend of decline was significant. (4) Compared to trade activities, investment-based activities were less carbon intensity in China and upper-middle income economies due to the interaction of production between FIEs and DOEs. The findings add new insights into the coordination of global emissions reductions and the allocation of emissions responsibility. • Emphasize the interdependence between enterprises in cross-border production. • Combine GVC linkages with carbon emissions and carbon intensity. • Investment-based emissions are the main factor of relational GVC emissions growth. • The carbon intensity of relational GVC decreased significantly. • Developing countries can access green spillover through investment-based GVC links. [ABSTRACT FROM AUTHOR]

This paper aims to study the significance of China's Industrial Structure Rationalization (ISR) in reducing carbon emissions. To this end, this study uses panel data from 30 Chinese province from 2000 to 2019 to measure the Industrial Structure Rationalization of each province by calculating the Thiel index, base on which a nonlinear autoregressive lagged distribution (NARDL-PMG) model is established to explore the impacts of industrial structure rationalization on carbon emissions. The results show that: (1) There still exist significant regional differences between Eastern and Central-Western regions in industrial structure as illustrated by the ISR index. The industrial structure in Eastern region is more rational; (2) In the long run, the carbon emission in China will drop when the industrial structure becomes more rational; (3) However, in the short run, the carbon emission in China will increase when the industrial structure becomes more rational. Besides the findings above, the major difference between this paper and the extant researches is that this paper pays attention to the relationship between industrial structure rationalization and environmental quality by investigating the asymmetric influences of industrial structure rationalization index's positive and negative shocks on carbon emission, which lays solid foundations for government aiming to set more detailed policies. • Focus on the impact of rationalization of industrial structure on CO2 emissions. • Focused on the linear relationship between industrial structure and carbon emissions. • Attempts to analyze three regional pairs from the East, Central and West regions. • Provide a reference for developing countries to achieve sustainable development. [ABSTRACT FROM AUTHOR]

Forest carbon sink project (FCSP) has received growing attention for its outstanding advantages in carbon emission reduction, environment protection, and achieving the carbon neutrality objective. Using a government-social capital cooperation model, FCSP helps to revitalize the ecological and economic benefits of the forest with standard and efficient management. However, the management and operation barriers of FCSP implementation are complex, multiple and indefinite in the long run, which requires a comprehensive barrier evaluation framework. Hence, this paper is devoted to constructing a barrier evaluation framework for FCSP implementation in China with an integrated multi-criteria decision-making (MCDM) method. To this end, critical barriers are identified and the criteria system is constructed with a two-stage systematic literature review and interaction with the experts. Later, an applicable and comprehensive barrier evaluation framework is built, wherein the interval type-2 fuzzy set (IT2FS) is used to process fuzziness and uncertain information, the best-worst method (BWM) is adopted to determine the criteria weight, and the Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) is introduced for comprehensive barrier evaluation. The criteria weighting results indicate that FCSP implementation encounters challenges taking into account institutional and governance barriers (criteria weight: 0.3592), management barriers (0.2384), economic and market barriers (0.1637), knowledge barriers (0.0747), technical and infrastructure barriers (0.1639), C1 "Lack of clear leadership and policies" and C5 "Forest management performance" were determined the most significant criteria, A5 and A4 respectively performed the lowest and the highest barrier level. In sensitivity analysis, the ranking results were stable when the criteria changed, and Spearman's rank correlations of B2, B4, and B5 are significant. The results were validated by a comparative analysis with other methods with the help of Spearman's rank correlation coefficient, which reflects accepted high correlations (Spearman's pro values of BWM-IT2F-PROMETHEE II (Gaussian rule), BWM-IT2F-TOPSIS, and BWM-TFN-VIKOR are 1.00, 1.00, 1.00). The findings of the paper help the decision makers to develop more rational strategies and successfully implement the FCSP. [ABSTRACT FROM AUTHOR]

Big data industry development and carbon dioxide emissions reduction are the crucial driver and objective functions of economic growth, respectively. Whether the two types of corresponding policies can achieve the "win-win" effect is the key to green development transformation in the future. However, little attention has been paid to the influence of big data industry development on carbon dioxide emissions. Based on the quasi-natural experiment in China's national big data comprehensive experimental area, the paper identifies the causal effect between big data industry development and carbon dioxide emissions. Specifically, the panel data of 282 cities in China spanning from 2010 to 2019 are used in regression analysis. The study reveals that the big data industry development has a substantial impact in reducing carbon dioxide emissions. Meanwhile, the findings are confirmed by a set of rigorous robustness checks. The heterogeneity analyses suggest that the effect of inhibiting carbon dioxide emissions due to the big data industry development is more significant in eastern & central China, and large-sized cities. In addition, the big data industry reduces carbon dioxide emissions through the effects of industrial structure optimization, technological innovation, and resource allocation. Our findings contribute to the nexus between big data industry development and carbon dioxide emissions. In light of these findings, the paper recommends that governments integrate economic growth and environmental protection policies by promoting big data industry development. • The paper provides a novel insight into the CO 2 emission from the perspective of big data industry. • Based on quasi-natural experiment, the paper has identified the causal effect between the big data industry development and CO 2 emissions with the Difference-in-Difference (DID) methodology. • Big data industry development effectively stimulate the reduction of CO 2 emissions. [ABSTRACT FROM AUTHOR]

China's industry is facing a carbon lock-in (CLI) dilemma. Based on the complexity and dynamics of CLI, exploring diversified unlocking paths is an important way to achieve emission reduction goals. This paper innovatively applies system dynamics (SD) to the study of CLI prediction and unlocking path, and systematically constructs an industrial CLI SD model, trying to break the positive feedback mechanism of CLI through multiple causality and feedback chains. This paper simulates and predicts the changing trend of the CLI level of China's industry and 8 major subsectors from 2020 to 2050. At the same time, the random forest algorithm is used to select the regulatory variables of policy analysis. Compared with the previous research methods, the accuracy and objectivity of the selection of regulatory variables are improved, so as to test the effectiveness of the unlocking path under each scenario. The results show that (1) the whole industry faces a serious CLI problem and is still in a deep lock-in state. From the perspective of subsectors, the light, electromechanical, and textile industries have achieved unlocking; the oil and extractive industries are in a moderately lock-in state; the chemical, steel, and power industries are in a deep lock-in state; and the CLI situation is serious. (2) The results of the random forest algorithm show that R&D investment, the proportion of fixed asset investments in the energy industry, car sales, energy structure, and carbon emission industry structure are the most significant factors affecting CLI in each subsystem. (3) Compared with the existing economic development model, the technological, institutional, social, and comprehensive unlocking scenarios can all enable industry to achieve carbon peak and carbon unlocking in advance. However, the effect of each scenario is as follows: comprehensive unlocking scenario > technological unlocking scenario > institutional unlocking scenario > social unlocking scenario. (4) The unlocking times of the oil, chemical, steel, and power industries are relatively late in each scenario. To realize carbon unlocking in industry, these four areas should be focused on in the future. This study helps policymakers formulate reasonable policies to accelerate industrial carbon unlocking and promote the implementation of the "dual carbon" strategy. • The whole industry faces a serious carbon lock-in problem. • The technological, institutional, social and comprehensive unlocking scenarios can all achieve carbon unlocking in advance. • The comprehensive unlocking scenario has the best effect, and the social unlocking scenario has the worst effect. • The oil, chemical, steel and power industries should be focused on in the future. [ABSTRACT FROM AUTHOR]

Global warming, mainly caused by rising carbon dioxide (CO 2) emissions in the atmosphere, has led to severe effects worldwide. Hence, reducing carbon emissions has become an urgent need to mitigate the adverse effects. To do so, accurately forecasting CO 2 emissions is crucial for policymakers in formulating climate and energy policies. Existing CO 2 emission forecast models rely on various longitudinal economy, energy or environment data collected at the same periods, e.g., yearly, as indicators. However, these data usually come from diverse sources with different sampling frequencies, e.g., monthly vs. daily. Therefore, in this paper, we introduce for the first time a Mixed-frequency data Sampling Grey system Model (MSGM) that can exploit the potential information in high-frequency sampling (HF) data of economic and social indicators to effectively forecast annual CO 2 emissions. In MSGM, (1) we directly model low-frequency CO 2 emissions and HF economic-energy influence indicators without undertaking same-frequency conversion, thereby avoiding potential information loss and enhancing prediction accuracy; (2) we introduce polynomial weight functions to address the frequency mismatch, and its parameters are obtained through search optimization algorithms; (3) unlike existing grey system models, the whitening equation of MSGM adopts a reduced-order form to address the exponential explosion issue caused by HF data accumulation; (4) the time and cumulative terms are also introduced separately to explore the effects of temporal development and historical data on future CO 2 emissions. Extensive Monte-Carlo simulations show that MSGM exhibits high accuracy on both training and testing datasets and outperforms many other state-of-the-art rival techniques like MIDAS, AR-MIDAS, AMTGM, MLRM, BPNN, SVR, ARIMA and TVGBM. We further demonstrate the potential of MSGM to forecast China's CO 2 emissions under three distinct scenarios. Out-of-sample forecasting results indicate that only under a tight-contraction scenario does the growth rate of CO 2 emissions decrease. To the best of our knowledge, the MSGM is the first technique that can exploit different frequency data to effectively forecast CO 2 emissions. [Display omitted] • A novel grey system forecast model based on mixed-sampling frequency is proposed. • Introducing more higher frequency data from economic and social indicators. • Selecting popular local search algorithms to find optimal unstructured parameters. • The CO 2 emission forecast for 2022–2026 is carried out under three distinct scenarios. [ABSTRACT FROM AUTHOR]

The current research investigating the effectiveness of policy-driven transformation in resource-based cities has focused mainly on city-level impacts and has overlooked firm-level evidence. This paper filled this gap by focusing on its impact on firm-level carbon emissions. Drawing on the quasi-natural experiment of China's reform of resource-based city transformation, we utilize a difference-in-differences (DID) model based on nationwide firm-level tax survey data from 2009 to 2016. The findings indicate a noteworthy reduction in firms' carbon emissions due to the reform, particularly in growing-type resource-based cities and within the manufacturing industry. The empirical results provide evidence that the implementation of the "Plan" has effectively enhanced the level of urban resilience. Moreover, the findings demonstrate stronger effects on large firms, publicly owned firms, and firms in municipal districts. • Resource-based city transformation is effective at mitigating firm carbon emissions and enhancing urban resilience. • Different development stages exhibit heterogeneity in the effectiveness of firm emissions reductions. • Sector- and firm-specific characteristics play a significant role in reduction in firms' carbon emissions. [ABSTRACT FROM AUTHOR]

As major strategic development areas in China, the provinces around the Yangtze River are facing the pressure of large aggregate CO 2 emissions and high per capita CO 2 emissions. The study on the pathway of reducing CO 2 emissions and increasing carbon sequestration will help China better clarify the ways realize the goals of carbon peak and carbon neutrality as soon as possible. In this paper, the extended STIRPAT model and OLS regression were used to establish a prediction model to estimate the CO 2 emissions during 2010–2060 under different scenarios. Meanwhile, the InVEST model was used to estimate the carbon sequestration, and the comparison with the CO 2 emissions was analyzed. The results show that Qinghai and Yunnan have achieved the goal of carbon neutrality in 2010. Under the low-carbon and energy-saving scenario and the baseline scenario, all provinces within the study area are anticipated to attain their emissions peak by 2030 or earlier, and the CO 2 emissions peak value of the whole study area is 3111 Mt and 3199 Mt, respectively. While under the high emissions scenario, only Chongqing and Qinghai will reach the emissions peak in 2030, and the whole study area will reach the emissions peak in 2040, with peak value of 3470 Mt. Under the three scenarios, the whole study area cannot achieve carbon neutrality before 2060. Only Sichuan and Hubei can achieve carbon neutrality in 2050 and 2060 under the low-carbon and energy-saving scenario. All provinces and municipalities in the lower reaches of the Yangtze River are facing huge pressure of emissions reduction. The research reveals that the goals of carbon peak and carbon neutrality should be achieved by developing clean energy, transforming industrial structure, increasing carbon sequestration and improving carbon emissions trading system. [Display omitted] • The CO 2 emissions showed a pattern of increase during 2010–2020. • The whole study area will achieve carbon peak by 2040 or earlier. • The whole study area cannot achieve carbon neutrality before 2060. • The realization pathway of carbon peak and neutrality was proposed. [ABSTRACT FROM AUTHOR]

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial–Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN's superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model's improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

China and Korea have planned a substantial power grid interconnection project, driven by a shared commitment to achieve their respective policy goals. This paper analyzes the economic and environmental impacts of this cross-border power grid interconnection and presents a comprehensive financial analysis of the project. We formulate a power system model that includes a planned submarine HVDC transmission line between two countries and study various scenarios by using hourly data. Our results demonstrate that the interconnection line operates with a remarkably high utilization rate, ranging from 98.90 % to 99.95 % across all analyzed scenarios. This high utilization rate suggests that the project holds significant economic potential, with estimated net present values ranging from $1,786 million to $3,019 million at a discount rate of 7 %. Furthermore, from the sensitivity analysis on carbon price, discount rate, and transmission capacity, we show how the financial outcome of the project is affected by the change in these parameters. Finally, we discuss the consequence of imposing an additional constraint on the carbon emission to our model and then, minimizing the overall carbon emissions subject to keeping the overall cost without the interconnection. In both variations, we show that the two countries can achieve their policy goals without increasing carbon emission. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

Frequent forest fires severely damage forest ecosystems. One potential big issue, however, is the fact that the substantial release of greenhouse gases (GHGs) from biomass combustion significantly impacts the global climate system and carbon balance. This study aims to assess the emissions of major carbon-containing gases from forest fires in mainland China between 2011 and 2021. Information on wildfires was obtained with Google Earth Engine (GEE) through the integration of MODIS products and Global Forest Above Ground Biomass (AGB) produced by the Climate Change Initiative Biomass (CCI BIOMASS) Programme. Utilizing the combustible load model and consideration of carbon emission factors in different vegetation, from 2011 to 2022, mainland China consumed an estimated 35.55 Tg C (3.23 Tg C yr−1) of above-ground combustible matter, resulting in 46.94 Tg (4.27 Tg yr−1) of carbon-containing gas emissions. Among these emissions, 42.16 Tg (3.83 Tg yr−1), 4.58 Tg (0.42 Tg yr−1), and 0.20 Tg (0.02 Tg yr−1) were attributed to CO 2 , CO, and CH 4. In general, carbon emissions from forest fires in mainland China exhibited a declining pattern with fluctuations. This indicates a substantial decrease in average annual emissions of CO 2 and CH 4 by 48.38% and 88.87%, respectively, compared to the previous century (1990s). Additionally, there was an average annual decrease in biomass consumption of approximately 35.4%. When analyzing the results on regional scales using linear regression and normalization methods, it was found that 81%–84% of provinces and regions in mainland China showed a decline in forest fire carbon emissions. Nevertheless, several areas in the northwest region displayed a noticeable rising trend. Pixel analysis is utilized in this paper to perform a spatial analysis of carbon emissions and fire locations associated with forestry fires in mainland China. This analysis clarifies the impact of combustible load distribution across different fire intensities on the interannual variability of carbon emissions from wildfires, thereby offering fire management authorities valuable insights. • Estimated carbon-containing gas emissions from wildfires in Mainland China at a pixel level in 2011–2021. • Compared 11-year average annual wildfire emissions with data from the previous century. • Explored the interrelationships among wildfire points, burnt area, net primary productivity, and fuel load. • Analyzed emission trends of major carbon gases from wildfires in Mainland China. [ABSTRACT FROM AUTHOR]

Exploring the key influencing factors (KIFs) of Urban Low-Carbon Innovation (ULCI) from urban characteristics is essential for devising customized promotion strategies. However, existing studies are hampered by methodological limitations that lead to an inability to effectively discern KIFs among urban characteristics or unravel complex, non-linear relationships, and interaction effects. To address these gaps, this paper adopts a synthetic approach based on interpretable Machine Learning (ML). Firstly, the influencing factors are identified through the Delphi method and a systematic literature review. Subsequently, three single AI models (KNN, SVR, and CART) and three ensemble models (RF, XGBoost, and LBGM) are employed to fit the data. Finally, the SHapley Additive exPlanations (SHAP) algorithm is integrated to identify the KIFs and disentangle their impact effects. The findings indicate that (1) 41 influencing factors are identified, from which 10 KIFs, such as Expenditure on Research, Carbon Emissions, Local General Budgetary Revenue, and Education Expenditure, are determined, (2) the developed interpretable ML model tailored for ULCI's KIFs analysis demonstrates high precision and effectively capturing non-linear relationships (R2 = 0.841, RMSE = 0.591, MAE = 0.463), and (3) the global impact, interactive effects, and individual sample impact of the KIFs are explained, and two categories of KIFs dominated by positive and negative influences are revealed respectively. Results of the KIFs identification can provide policy-makers with insight for designing ULCI enhancement paths and consequently promote emission mitigation in China. • 41 influencing factors of ULCI is identified from urban characteristics. • An interpretable ML model is developed to quantitatively explore KIFs. • 10 KIFs are determined and the comprehensive impact, interactive effects, and individual sample impact of the them are explained. [ABSTRACT FROM AUTHOR]

Abstract With the development of China's economy, the use of fossil energy has become more and more, resulting in increasing carbon emissions. CO 2 emissions have caused global warming, threatening humans and creatures on Earth. In order to effectively suppress the growth of carbon emissions, it is necessary to analyze the influencing factors of carbon emissions and apply them to predict carbon emissions. This paper presents sixteen potential influencing factors and uses grey relational analysis to identify the factors that have a strong correlation with carbon emissions. The principal component analysis (PCA) is used to extract the four principal components, which reduce the redundancy of the input data. The long short-term memory (LSTM) method is established to predict carbon emissions in China. We use back propagation neural network (BPNN) and Gaussian process regression (GPR) to compare LSTM method. The simulation results show that the prediction accuracy of carbon emissions based on LSTM is better than that of BPNN and GPR, indicating the effectiveness of PCA and LSTM in prediction of carbon emissions. Finally, this paper provides the theoretical basis for China to reduce carbon emissions by studying prediction of carbon emissions. Highlights • Grey correlation analysis is applied to identify factors associated with carbon emissions. • Principal components analysis is used to simplify input variables and obtain the main factors affecting carbon emissions. • The long short-term memory model is used for the first time to predict carbon emissions. • The direction of reducing carbon emissions in China is given by analyzing the prediction results. [ABSTRACT FROM AUTHOR]

The failure of the global food system to produce food in a sustainable way needs to change urgently, and a solid push to harmonize low-carbon food production with stable supply security is the right direction in which modern agriculture must evolve, but it is more difficult for large food-producing countries, and finding the right practices or systems to help policymakers or producers address this dilemma is critical. This requires political will, increased financing and top-level design. Digital inclusive finance (DIFI) embodies the financial concept of a community of human destiny, and it is worth examining whether this more inclusive and comprehensive financial service can shoulder the "responsibility" of "ensuring food security and reducing carbon emissions" in agriculture. This paper takes China's grain production system as an example, measures the coordinated development level of food production ensuring supply and reducing carbon emissions, and then analyzes the overall and regional characteristics of it. It is found that the coordinated development trend of China's food production ensuring supply and reducing carbon emissions is relatively stable during 2011–2020, fluctuating slightly above and below 0.9, and the central region has the highest level of coordinated development. The empirical results show that DIFI has led to a 4.3 percentage point increase in the level of coordinated development of food production ensuring supply and reducing carbon emissions during the decade, showing a stronger spatial spillover effect on neighboring regions than the local effect. At the same time, in the research of regional division in China, the impact of DIFI on the level of coordinated development of food production ensuring supply and reducing carbon emissions varies across regions, with typical uneven characteristics, and the promotion effect is more significant in the central and northern regions of China. Overall, this paper provides a simple tool for policy makers or producers to understand the balance between guaranteed output and low-carbon development of agricultural production activities. This paper will provide a methodological basis for assessing and standardizing low-carbon agricultural practices, innovating financial development models and systems, and promoting sustainable agriculture. [Display omitted] • Measuring coordinated development level of ensuring food supply and low-carbon. • Digital inclusive finance had increased the coordinated development level by 4.3%. • Digital inclusive finance also had a significant spatial spillover effect. • The role of digital inclusive finance showed a typical non-equilibrium feature. • The study's results could help to sustainable agriculture in agricultural country. [ABSTRACT FROM AUTHOR]

Carbon emission policy aims to restrict carbon emissions from energy-intensive companies. It encourages target companies to take green innovation but brings fulfillment pressure. Policy prospects for a low-carbon economy are widely discussed in current studies while insufficient attention has been paid to the effect of the policy on the operating activities of target companies. Using the data of Chinese A-share listed companies from 2007 to 2020, this paper explores the effect of carbon emission policy on the financial performance of target companies. The main empirical results prove the significant positive effect of the policy on the financial performance of target companies and reject the interference of earnings management and survivor bias. Further analysis indicates that target companies significantly reduce the selling, general and administrative (SG&A) expenses and non-operating expenses. The trade of carbon quotas between target companies is allowed and it is regarded as non-operating activity. Results of non-operating revenues and expenses prove that target companies tend to actively reduce carbon emissions but the action does not exceed the policy expectation. The increasing efficiency of asset utilization represents that target companies have taken relevant measures to deal with overcapacity. This paper confirms the positive policy responses of target companies. Policy implications are proposed from the perspective of coverage expansion and market incentive mechanisms to realize the full potential of target companies for carbon emission reduction. [ABSTRACT FROM AUTHOR]

With the aggravation of carbon emissions and global warming, the probability of various natural disasters and environmental risks will increase significantly. Therefore, carbon emission reduction and low-carbon development have become the focus of environmental economic research. This paper uses the information about the opening of high-speed railway (HSR) in China to comprehensively explore the effect of infrastructure construction on urban carbon total factor productivity (CTFP) in China, which is calculated by the novel non-parametric method named biennial non-radial Luenberger productivity indicator (BLPI). Our results reveal that in China, an extra HSR line significantly improves the CTFP of local cities by 0.63%, while the CTFP of related surrounding cities increases by 6.3%. We confirm the robustness of our empirical findings and perform the mechanism analysis from several aspects. The mechanism analysis shows that the HSR improves the CTFP of local cities mainly via industrial upgrading and technological progress, and improves the CTFP of surrounding cities via industrial agglomeration and factor spillover. Besides, the impact of the HSR is more pronounced for the core cities, large cities, and innovative cities. Our results suggest that the HSR makes cities more carbon efficient, which is a non-negligible advantage of infrastructure construction in reducing environmental risks. • Governments around the world have been committed to reducing external environmental risks by adopting urbanization. • This paper analyzes the spillover effect of the onening of HSR on CTFP in China. • This paper provides a novel framework, which uses the LPI combined with the BNDDF to measure CTFP. • An extra HSR line significantly improves the CTFP of local cities by 0.63% and surrounding cities by 6.3%. • The mechanism analysis reveals four channels, including industrial upgrading, technological progress, industrial agglomeration, and factor spillover. [ABSTRACT FROM AUTHOR]

The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

Green finance is a financial innovation that directs capital allocation towards green industries. How will this affect regional carbon emissions? Based on the data on green finance and carbon emissions of 30 provinces in China from 2008 to 2019, this paper employs a multivariate linear regression model and a spatial econometric model to empirically study the impact of green finance on carbon emissions. The results find that (1) green finance significantly reduces provincial carbon emissions, specifically through promoting a cleaner energy structure and an advanced industrial structure, as well as improving the quality of green innovation. (2) Heterogeneity exists between regions. In eastern regions and areas characterized by high levels of marketization and minimal fiscal pressure, the role of green finance in promoting provincial carbon emission reduction is more pronounced. (3) Spatial econometric analysis found that green finance has a significant spatial spillover effect on carbon reduction in adjacent regions. Specifically, the flow of green resources and green ideas between provinces facilitates the formation of regional radiation, while green competition generates a siphon effect on neighboring regions. These findings provide some meaningful policy implications to achieve China's dual-carbon goal by promoting green finance. • Green finance has significantly reduced China's provincial carbon emissions. • The upgrading of energy structure and industrial structure is a positive channel to reduce carbon emissions. • Green innovation strengthens the carbon reduction effect of green finance, but reveals the potential "greenwashing". • Green finance has a significant spatial spillover effect on carbon reduction in adjacent regions. • Policymakers should develop flexible, dynamic and coordinated green finance development policies. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Understanding the relationship between land-use patterns and regional carbon storage, as well as predicting future land-use changes for regional carbon sink and emission management, are of immense significance. This study utilized land-use data from 1990, 2000, 2010, and 2020, as well as the InVEST model, to evaluate the spatiotemporal evolution of carbon storage in the Sanjiangyuan area over the past three decades. Furthermore, predictions for carbon storage in 2035 were presented using the PLUS model. The findings revealed the following key results: (1) the land types in the Sanjiangyuan area are mainly low cover grassland, medium cover grassland and unused land, among which unused land decreased significantly from 1990 to 2020, and the area of low cover grassland and wetland increased, which is the main reason for the increase of carbon storage. (2) Climatic-environmental and social-economic factors jointly influenced the land-use change in the Sanjiangyuan area. Except for the three types of grassland, the expansion of other land types was mainly influenced by climatic and environmental factors. (3) During 1990–2020, carbon storage in the source region of Sanjiangyuan area showed an overall upward trend, with a total increase of 39.97 × 107 t, and land-use change had a positive potential impact on carbon storage as a whole. (4) Under the natural change scenario, both carbon storage and regional carbon density increased in the simulation of 2035, and the potential impact of land-use change on carbon storage is positive. On this basis, the paper puts some suggestions forward to improve the carbon storage capacity of the Sanjiangyuan area in the future. This study provides valuable scientific insights for land-use management decision-making and promotes sustainable development of carbon storage functions in this region. • Total carbon storage increased between 1990 and 2020. • The simulation predicts higher carbon storage in 2035 than in 2020. • Ecological protection policies have a crucial contribution to improving carbon storage. • Regulating regional Socioeconomic activities is an effective measure. [ABSTRACT FROM AUTHOR]

China is building a society of common prosperity by enlarging its middle-income and affluent groups. Following current unsustainable consumption patterns, rising affluence may lead to additional carbon leakage through imports of goods and services for consumption in China. In this paper, we quantify outsourced carbon emissions that accompany the expansion of China's affluent group and identify its socio-economic driving factors. The result shows that China outsourced 151Mt of carbon emissions to fulfill household consumption in 2012, where the affluent group, 22% of the population, was responsible for almost half. In 2017, affluent group expansion induced an additional 44Mt of emissions, which is 1.3 times that of the entire household, as the shrinking low-income group partly offset. Affluent group's consumption scale, the predominant factor, contributed 120% to the increment. However, outsourcing from China had only <1/10 the marginal carbon intensity compared to high-intensity countries, helping them grow economies at a lower emission. • Dynamic change in income distribution and its global carbon implication are analyzed. • 67% of China's household outsourced CO 2 are attributed to the affluent group. • Domestic and international consumption drove the most outsourced carbon increment. • China's outsourcing had 1/4 the marginal carbon intensity of high-intensity nations. [ABSTRACT FROM AUTHOR]

Innovative emission reduction technologies combined with optimized processes can effectively reduce the environmental pressure in the iron and steel industry (ISI). Identifying and comparatively analyzing the carbon emissions (CE) from steel productions is conducive to achieving China's ambitious carbon reduction targets. In this paper, we investigated two innovative routes: the shaft furnace-induction furnace-basic oxygen furnace route (SF-IF-BOF) and shaft furnace-electric smelting furnace-basic oxygen furnace route (SF-ESF-BOF), alongside two traditional routes: the blast furnace-BOF route (BF-BOF) and SF-electric arc furnace route (SF-ESF). The analysis was based on a functional unit of 1000 kg of crude steel (CS). By establishing the mass and energy balance models and decision criteria, 12 typical smelting scenarios for these routes were analyzed and compared in terms of materials consumption and CE. The results showed that increasing the proportion of scrap was beneficial for reducing CE. Under the condition of a 50% scrap ratio, the CE of the SF-IF-BOF, SF-ESF-BOF, BF-BOF, and SF-EAF routes were 768, 823, 946, and 870 kg, respectively. The SF-IF-BOF route displayed the most significant advantage of CE reduction. In addition, by using 100% zero-carbon electricity substitution, the SF-EAF route exhibited the greatest CE reduction effect of 41% (from 870 to 510 kg/t). While deploying 100% biomass fuels to replace coal, the CE of the BF-BOF route reduced by 80%. Finally, after introducing biomass fuels and zero-carbon electricity, the CE of the SF-IF-BOF route was minimized to a significantly low 13 kg/t (from 768), nearing zero CE. This research is expected to provide a novel solution for further CE reduction in the future of ISI, contributing to environmental sustainability and carbon reduction goals. • Suggestions for solutions to reduce CO2 emissions in the steel industry are provided. • Emissions of the SF-IF-BOF and SF-ESF-BOF compared to the BF-BOF and DRI-EAF routes. • DRI is considered to be generated using COG zero reforming process. • Using zero-C electricity and biomass can reduce CO2 emissions for the steel industry. [ABSTRACT FROM AUTHOR]

As the integrated gas-electricity system (IEGS) grows within the context of carbon abatement, natural gas shippers (NGSs) face a twofold challenge: perceiving the dynamic behavior of the gas system swiftly while considering the revenue and costs of IEGS. To address these challenges, a novel Integrated Electric-Gas Dispatch (IEGD) model is introduced in this paper. Firstly, the Box-Jenkins-based NGPN rapid transient flow model is proposed. Secondly, an IEGD model with optimization targets focusing on IEGS cost and NGSs revenue is introduced. Finally, the proposed natural gas flow model is validated through its application to an inter-provincial natural gas pipeline network and a regional-level network in China's central and western regions. A comparison with commercial software indicates that the proposed model offers additional advantages, with a total relative average error of 0.57% and 0.36%, respectively, and a computation speed of 62.3 times and 72.4 faster. When the IEGD model is applied to an IEGS, promising outcomes are revealed by two distinct scenarios: carbon emissions within the IEGS can be reduced by as much as 32.64% per day, considering carbon taxes. Furthermore, through intensifying the collaborative integration of gas and electricity systems, overall carbon emissions can be lowered by up to 48.19%, and NGSs revenue can be boosted by as much as 11.2%. • A novel rapid natural gas network transient flow model is proposed. • Natural gas transient flow is first converted to the Box-Jenkins system. • The network topology can be established by the proposed model conveniently. • Natural gas shipper revenue and integrated system cost are considered as objective. • Overall carbon emissions can be lowered by up to 48.19%. [ABSTRACT FROM AUTHOR]

Inequality and climate change are major challenges for sustainable development, but a dilemma between the objectives has been widely observed. Based on the panel data of China Household Finance Survey (CHFS), inequality in carbon emissions across income groups is witnessed, which provides basics for the study. From a quasi-natural experiment, an increase of 9.9% in household per capita carbon emissions (HCEs) of the impoverished group is found after the Targeted Poverty Alleviation policies (TPA) in China, providing evidence for the enhancement of poverty alleviation measures on equal right for household carbon emissions. Specifically, dual mechanism of transfer payments and employment assistance acting as direct and indirect strategies to enhance family wealth are examined as the pathways to carbon equality. Further, a decline in household carbon emission intensity in the impoverished group is also observed after the treatment, which implies the accelerating role of TPA in low-carbon lifestyle formulation for the impoverished families. The findings of this paper confirm the positive effect of TPA policies on carbon equality for households, and also refute the "equality-pollution dilemma", which provide insights for policy makers in formulating equitable decarbonization policies and allocating the carbon budget. • Poverty alleviation policies have reduced household carbon emission inequality. • Transfer payments and employment assistance act as mechanisms of policy effect. • Household carbon emission intensity is decreased by the policies. • Policy effect is more prominent in families suffered from special burdens. [ABSTRACT FROM AUTHOR]

Technical lignin (TL) is a solid waste generated in industrial activities including pulp and paper mills and second-generation (2G) biorefineries. After isolation processes, TL is available in large quantities and has favorable compositional characteristics to produce high-performance activated carbons (ACs), such as low amount of residual carbohydrates, high reactivity due to the presence of different functional groups, and high aromatic carbon content. The value of the AC market is projected to reach US$ 7 billion by 2028, led by the water treatment sector. This work provides a brief overview of the status quo in the production of new adsorbents from TL, focusing on biorefineries, together with a bibliometric and patentometric survey. This evidenced a scarcity of publications on this topic, with the primary focus being on chemical modification of TL. A notable increase was observed in the production of ACs for electrochemical applications. Only a few patents were found on the subject, with China predominating technological development in this area. The findings revealed a technical-scientific-economic potential that has been largely unexplored, as no TL-derived AC from 2G biorefineries has yet been produced and applied in water treatment. Hence, there is excellent potential for the wider commercialization of AC produced from TL, representing an excellent opportunity to recycle TL and support the bio-based circular carbon economy, while also contributing to carbon emission neutrality. The marketing of high-performance ACs produced from TL could make biorefineries more profitable and less sensitive to market price fluctuations, while becoming carbon-neutral multiproduct platforms. [Display omitted] • Technical lignin (TL) has untapped technical-scientific-economic potential. • Production of activated carbon is a technological way to add value to TL. • Bibliometrics and patentometrics indicate a scarcity of value-added TL products. • Biorefineries as key contributors to carbon neutral emissions in the energy sector. • New opportunities and challenges identified for excess TL in 2G ethanol plant. [ABSTRACT FROM AUTHOR]

Under the background of "double carbon", ecological water has become one of the significant resources to ensure the healthy and sustainable development of the regional ecological environment. And the ecological water consumption prediction is a prerequisite for optimizing water supply structures. Considering the impact of CO 2 emissions on ecological water consumption, the FGM(1,1) model was firstly used to predict CO 2 emissions considered as an impact factor in this paper. Then, an adjacent accumulative grey multivariate convolution model (AGMC(1,N)) was combined with the Average Weakening Buffer Operator (AWBO) to predict the annual ecological water consumption of 30 provinces (municipalities and autonomous regions) in China. The results show that the AGMC(1,N) model combined with the AWBO has better prediction effect. In addition, in the next decade, only six provinces in the northern region will have a decreasing trend in ecological water consumption. However, the other provinces are showing an upward trend. The ecological water consumption trends deviate from the CO 2 emission trends in 12 provinces. The research results are beneficial for the formulation of the ecological water consumption policies and the achievement of the dual carbon goals. [ABSTRACT FROM AUTHOR]

This paper delineates the impact of digital infrastructure construction (DIC) on carbon emission efficiency (CEE) within the ambit of China's "Broadband China" pilot policy (BCP). A quasi-natural experimental framework reveals direct effects, indirect influences, non-linear relationships, and spatial heterogeneity of DIC on CEE. The research distills several pivotal insights: (1) DIC exerts a markedly positive impact on CEE, as evidenced by comprehensive robustness analyses. (2) The digital service industry agglomeration and green technology innovation act as key mechanisms through which DIC influences CEE. (3) The intensity of BCP exhibits an "inverted U-shaped" relationship with CEE. As the level of CEE increases, the impact of DIC on CEE gradually rises, demonstrating a marginal increasing effect. (4) The spatial spillover effects of DIC on CEE vary with increasing distance, revealing a dichotomy: an "agglomeration shadow" within 200 km of policy pilot cities and a pronounced stimulative effect on CEE within a 200–300 km radius. (5) The efficacy of DIC in enhancing CEE is augmented by proximity to central cities, with the optimal radius of influence for central cities determined to be 200 km, beyond which a diminishing gradient effect is observed, forming a "core-peripheral" spatial pattern. The findings endorse the strategic augmentation of digital infrastructure as a lever for economic and environmental co-benefits, steering towards carbon neutrality and equitable regional development. [ABSTRACT FROM AUTHOR]

Equilibrium capacity expansion is an effective tool to help regulators predict the generation evolution of power systems by analyzing the decision-making of generation companies (Gencos). With the development of carbon markets (CMs) and electricity markets (EMs), Gencos' decisions in these two markets (encompassing investment and bidding decisions related to carbon emission allowances and electricity) are deeply intertwined through market clearing results. However, existing studies mainly focus on equilibrium capacity expansion with fixed carbon prices and usually ignore the realistic clearing process of CMs, which cannot simulate Gencos' coupled decision-making and accurately predict generation evolution under the CMs and EMs. This paper proposes a two-level equilibrium capacity expansion model for power systems which considers the coupled decisions of Gencos between CMs and EMs. At the upper level, multiple Gencos' coupled decision models are proposed based on their interaction with market clearing results. At the lower levels, CMs and EMs are respectively cleared to determine the varying carbon and electricity prices. Furthermore, the developed equilibrium capacity expansion model is reformulated as an equilibrium problem with equilibrium constraints (EPEC), which is solved by a diagonalization-based solution procedure. Finally, a provincial power system of southeast China is used to validate the performance of the proposed framework. The results illustrate that under CMs and EMs, non-fossil energy would account for 81.3% of total capacity and 73.88% of total generation in 2050. Compared to the existing model using fixed carbon prices, the consideration of realistic carbon market clearing will promote the installation of renewable energies and improve social welfare. The proposed model can provide beneficial references for regulators to perceive the evolution of power systems in CMs and EMs. • A two-level equilibrium capacity expansion model for power systems is proposed. • A novel coupled decision model for Gencos in CMs and EMs is developed. • The proposed model is solved by a diagonalization based solution procedure. • The results can guide the transition of power systems under the CMs and EMs. [ABSTRACT FROM AUTHOR]

A carbon reduction labelling scheme is an effective policy tool for motivating enterprises or organizations to reduce their life-cycle-based carbon emissions to benefit the environment. This paper uses system dynamics to model enterprises' compliance with a combined government policy instrument for a carbon reduction labelling scheme, that is, government subsidies and economic sanctions. A case study of paper enterprises in China is provided to demonstrate the application of the proposed model in which two optional technological plans to reduce the enterprises' carbon emissions are examined. The technological plans are categorized as a low-cost and high-cost plan. The simulation results show that both the technological plans are vulnerable to the combined policy making, and the high-cost plan shows superior environmental performance for carbon emissions reduction. However, the enterprises are more inclined to choose the low-cost plan because of superior economic performance. The study offers insights to help enterprises select optimal actions on carbon emissions reduction and to inform government of possible sustainable policy design to promote low-carbon development. The model limitations are discussed as a basis for further improvements in low-carbon development. [ABSTRACT FROM AUTHOR]