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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

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]

Policy synergy is a unique way for the government to establish a policy network and a crucial way for cities to expand their carbon reduction channels. Using urban data from China, this paper empirically investigates how environmental governance policy synergy (EGPS) affects carbon emissions. In comparison to non-pilot cities, the environmental information disclosure (EID) policy and low-carbon city (LC) policy play an inhibitory role in carbon emissions. Furthermore, EGPS has a stronger effect and is heterogeneous by region. Through the use of technical advancement and industrial structure optimisation, EGPS lowers carbon emissions, and this effect of EGPS grows over time in tandem with the level of environmental pollution in the cities. Our paper proposes policy implications in order to improve the overall effect of environmental governance policy and help achieve the dual carbon targets. [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]

This paper proposes a novel aviation CO 2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO 2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO 2 emissions and mitigate the effects of global warming. • An aviation CO 2 emissions prediction model is proposed considering both microscopic flight dynamics and macroscopic traffic demand. • A more efficient market-based CO 2 emission reduction framework is developed based on a two-phase carbon trading model by means of taxation and cap-and-trade. • Case study shows that the proposed method has a more optimal incentive for CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

• We study the relationship between GVC embedding and carbon emission density. • A mediation effect model was established to test the mechanism. • There is a U-shaped relationship between urban GVC embedding and carbon emission density. To achieve its carbon peak and neutrality plan, whether China's global value chain (GVC) embedding has a carbon emission reduction effect is a question worthy of attention. This paper measures the GVC embedding degree of 218 Chinese cities from 2000 to 2016, and constructs a panel data model to investigate the impact and its mechanism of GVC embedding on carbon emission intensity. This study finds that there is a significant U-shaped relationship between GVC embedding and carbon emission intensity, and a series of robustness test results support this conclusion. Export scale, energy consumption structure and technological innovation play a mediation role in the impact path of GVC embedding on carbon emission intensity. Based on this, this paper proposes that while participating in GVC, China needs to actively utilize the emission reduction effect of GVC, promote the transformation of export mode to high quality, reduce the use and dependence on high-carbon resources such as coal, and encourage companies to innovate. [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards (RPS) and carbon emission trading (CET) system are the top-level system design of China's energy low-carbon transformation, which have great strategic significance for realizing the "carbon peak in 2030 and carbon neutrality in 2060". At present, China's tradable green certificates market and CET market are still in the initial stage. The implementation single system may have diverse trials and errors in its initial stage, and the market activity and transaction frequency are low. Therefore, this paper aims to explore whether the synergistic effect between the two systems can effectively promote China's energy low-carbon transformation. This paper establishes the system dynamic model of the synergistic effect between the RPS and CET in China's electricity market to reveal the influence mechanism and the synergistic effect. The results show that: (1) There is a positive synergistic effect between the RPS and CET, which will change with the institutional parameters. (2) The simultaneous implementation of the RPS and CET can effectively stimulate market vitality and promote the development of the renewable energy industry. (3) Higher quota and lower benchmark price can optimize power source structure, and improve social welfare and institutional performance. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) emissions and climate change risk have become constraints on global economic sustainable development. Environmental regulation (ER) is a key method for achieving synergy in CO 2 and pollution reduction in China. This paper is the first study to explore the effects of ER on CO 2 emissions by exploiting the National Environmental Protection 11th Five-Year Plan (NEP11-FYP). The implementation of the NEP11-FYP significantly decreases CO 2 emissions by 19.73% in key environmental cities relative to other cities. Heterogeneity analyses suggest that this negative impact is larger in western cities and cities with more pressure to reduce CO 2 emissions. Scale effects, structural effects, and technical effects might be three potential influencing channels through which ER contributes to the CO 2 reduction effect. In addition, the results of the spatial externality of the NEP11-FYP demonstrate a positive spillover effect in neighboring cities within a distance of 300 km and a negative spillover effect in cities more than 500 km away. Our empirical findings provide policy implications for implementing low-carbon transition strategies and reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

In the context of China's carbon peak and carbon neutrality goals (the "dual carbon" goals), balancing economic development, environmental protection, and low carbon is a high priority. Therefore, exploring the impact of digital finance on carbon dioxide (CO 2) emissions is crucial. In this paper, we select the panel data of 241 cities in China from 2011 to 2020 and use the double logarithm model after verifying model forms to explore the impact of digital finance on urban CO 2 emissions. The results show a significant negative correlation between the level of digital finance and the intensity of CO 2 emissions. Heterogeneity analysis results show that the eastern region exhibits the greatest inhibitory effect, while the western region exhibits the lowest. The three sub-dimensions of digital finance can all reduce carbon emissions, but the usage depth dimension has the greatest reduction effect, while the coverage breadth dimension is slightly lower. Through mediating effect tests from two dimensions, we find that digital finance can achieve CO 2 emission reduction by promoting scientific and technological innovation. These findings have implications for understanding the carbon reduction effectiveness of digital finance and achieving the dual carbon goals. [ABSTRACT FROM AUTHOR]

Climate change induced by carbon emissions has received extensive global attention. However, with the ever-increasing environmental attention from governments, whether firms, as main contributors to carbon emissions, have responded to the government's call to achieve carbon abatement target is uncertain. To answer this question, we manually collected urban government work reports from 2007-2016 and extracted environment-related textual content to quantify government environmental attention (GEA). Employing firms' carbon emission data from National Tax Survey Database (NTSD), this paper explores the impact of GEA on firms' carbon emissions governance. We observe that the increase in GEA cuts firms' carbon emissions. The results remain solid after using the instrumental variable method to alleviate endogeneity problems. The mechanism analysis reveals that GEA reduces firms' carbon emissions via three environmental-related decision-making behaviors of government: environmental regulations, fiscal expenditure and subsidy policies, and energy structure transformation strategies. Heterogeneity analysis shows that GEA notably reduces firms' carbon emissions from burning coal and oil energy. Moreover, firms in eastern-north, central, and western regions are more susceptible to GEA, while the impact is insignificant for firms in eastern regions. Furthermore, the carbon reduction performance is more pronounced for enterprises in capital-intensive, technology-intensive, and high polluting industries. This study provides a reference for policymakers to ensure the enforcement of carbon emissions governance of firms and to reach carbon abatement targets. [ABSTRACT FROM AUTHOR]

[Display omitted] Waste-to-Energy (WtE) technology is the most effective solution for managing non-recyclable wastes through mass burning and energy recovery. Owing to the significant volumes of plastics in China's industrial solid wastes (ISW), a large amount of greenhouse gases (GHG) is generated during the incineration process. Therefore, monitoring GHG emissions from WtE facilities is essential. Owing to the lack of suitable accounting models and characterized fossil carbon fraction (FCF) data, current studies use default values provided by the Intergovernmental Panel on Climate Change's (IPCC), which increases calculation inaccuracies. Therefore, this study established an improved method to accurately account for carbon emissions during solid waste incineration by firstly using radiocarbon dating by accelerator mass spectrometry (AMS) technique to determine the FCF of the solid waste components in China. Monte Carlo analysis was employed to perform the sensitivity analysis, and the results indicated that there was a significant deviation between the measured value and IPCC's default values of FCF, 3.2, 32.48, 93.39, 93.76, 90.49, and 93.8 % for paper, cotton, synthetic textiles, artificial rubber, artificial leather, and plastics, respectively. By replacing coal with ISW in a 2 × 110 t/h circulating fluidized bed boilers, 9.251 × 104 t CO 2 -eq emissions were reduced, and the carbon emission factor reached 0.56 t CO 2 -eq/t waste. This study complements the research gap fossil carbon data of wastes in the IPCC guidelines and provides a more accurate and effective way to calculate carbon emissions during ISW incineration treatment. [ABSTRACT FROM AUTHOR]

This paper uses panel data on renewable energy in China. Thus, the current analysis fills the gaps by examining the relationship between green technology innovation and renewable energy and CO 2 emissions from 1990 to 2018. The current study examines all concerns connected to panel data analysis with advanced panel estimators, such as cross-sectional dependence, stationarity, variation in slope parameters, and structural break. Our findings show that test results reveal that green technology innovation and renewable energy have a negative and considerable influence on CO2 emissions in the long run. Moreover, the short-run relationship of green technology innovation, on the other hand, is not significant—as evidenced by the findings of robustness tests such as AMG and CCEMG. Other empirical results revealed that GTI, REN and NEN significantly reduced local CO 2 emission, while POP and PI had no obvious reduction effects on CO 2 emissions. Different GTIs had the same spatial "symbiotic effects" on CO 2 emission reduction in the short term, showing positive spatial spillover reduction effects. Lastly, we conclude that the use of green technology innovation has positive externalities. Some policies to assist green innovation technologies and renewable energy resources have been suggested for the Chinese government to enact. • This paper uses panel data on renewable energy in China. • The current study examines all concerns connected to panel data analysis with advanced panel estimators. • The short-run relationship of green technology innovation is not significant. • We conclude that the use of green technology innovation has positive externalities. [ABSTRACT FROM AUTHOR]

Changes in international markets and strict environmental regulations have challenged Chinese energy companies to reconcile sustainable and low-carbon development. Therefore, this paper focuses on 296 energy firms in China from 2011 to 2021 and uses the Difference-in-Differences (DID) method to explore the impact of China's environmental protection tax (EPT) reform on the dual dividend of energy firms from the perspective of resilience and CO 2 emissions, and fully discusses the role of government subsidies and corporate social financing. This research finds that China's EPT reform can improve the resilience of energy firms while reducing CO 2 emissions, and thus bringing a double dividend to energy firms. At the same time, government subsidies can enhance the decarbonization effect of EPT reform, but excessive subsidies can reduce the resilience of firms. In addition, the EPT reform can expand the scale of social financing for energy firms, helping them to realize a double dividend. The study's findings have important implications for the long-term low-carbon development of energy firms and provide a theoretical reference for China's EPT reform. [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]

Single-rate pipeline prices have resulted in low competitive flexibility and have led to high costs and consumption in China's refined oil logistics system. This study aims to propose a framework for multi-product pipelines supplementary pricing strategy that creates a win-win situation for both shippers and pipeline carriers while quantifying economic, energy and environmental performance. Based on the objective of cost minimization, a lower limit constraint on pipeline transportation quantity is added, and a discount coefficient is introduced for pipeline prices, thus improving the transportation optimization model. Moreover, this paper develops an implementation approach for a supplementary pricing strategy that can ensure that the discounted price is not lower than the sum of cost and minimum profit. This framework usability is confirmed using a Chinese oil company as an example. The results show that the optimal scheme can increase pipeline revenue by ¥79.8 million while reduce shipper's logistics costs by ¥1.68 million during the practice cycle. Meanwhile, this work reduces energy consumption by 1.37 tce, CO 2 emission by 444 tons, SO 2 emission by 5.65 tons, and NO X emission by 7.07 million tons for the logistics system. Compared with past studies, this study is more economical or more in line with market-oriented behavior. • A framework for 3E performance improvement of multi-product pipelines is proposed. • The improved transportation optimization model is used as a pricing tool. • A supplementary strategy implementation approach is defined. • Total economic benefit to shippers and pipeline carriers is ¥9.66 million. • The energy and environmental benefits of the logistics system are quantified. [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]

[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]

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [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]

Urban residents' travel is an important component of carbon emissions and is becoming a hot topic in discussions on how to achieve carbon neutrality by 2060. Existing studies mainly focus on low-carbon travel behaviours and intentions independently, ignoring the inconsistency between the two. This paper uses a bivariate probit model based on questionnaire survey data to explore the consistency between urban residents' intentions and behaviour. The results show that sociodemographic factors, TPB (theory of planned behaviour) factors, and situational factors all significantly impact the inconsistency. This paper provides careful analysis, and based on the results and analysis, proposes targeted suggestions to enhance the public intention of low-carbon travel and proper policies to improve low-carbon travel in cities. [ABSTRACT FROM AUTHOR]

China has issued a series of biodiesel-related policies to alleviate energy shortage anxiety and reduce carbon emissions. However, no other provinces or cities in China widely utilize biodiesel except Shanghai. This article firstly conducted a comprehensive estimation of the potential botanical biodiesel capacity in China. Then, we examined why biodiesel advancement in China was stagnated by comparing biodiesel-related policies in pioneer countries such as Malaysia and Indonesia. We found the highest mix ratio will be ∼13 % if all potential botanical oils are added to petrochemical diesel and an addition of 3 % blending ratio can be achieved if waste cooking oils are effectively utilized. We concluded that despite the encouragement from policies, insufficient materials and lack of downstream consumptions may be the major reasons for the sluggish development of biodiesel in China. If microalgae biodiesel production technology is overcome in the future, China can compulsorily mix biodiesel on a large scale. This paper provided a comprehensive review of biodiesel-related policies in Malaysia, Indonesia and China, and figured out the key problems that hinder the promotion of biodiesel, helping to understand the dilemma and drive the potential development of biodiesel in China. • Potential biodiesel capacities were estimated among various provinces in China. • The highest biodiesel mix ratio will be ∼13 % if adding all potential botanical oils. • The southern China is more likely to prioritize blending biodiesel than the north. • Biodiesel-related policies in Malaysia, Indonesia and China were fully reviewed. • Inadequate materials and lacking downstream consumption hindered biodiesel growth. [ABSTRACT FROM AUTHOR]

The map of radiative cooling potential shows that the radiative cooling power can reach up to 100 W/m2 for its application in different climatic zones. However, the effect of building deployment on the city-scale application of radiative cooling is generally ignored in majority of existing studies, which overestimates the cooling benefit. This paper investigates the cooling benefit of implementing radiative cooling on a city-scale by considering building deployment. To determine the effect of building deployment on the radiative cooling potential, the city of Xi'an in China is modeled using COMSOL and verified with real weather data. Then, the thermal response of buildings with radiative cooling envelopes is discussed in detail. Finally, carbon emission reduction is derived at the city-scale by applying radiative cooling considering building deployment. The results show that the estimated radiative cooling power is approximately decreased by 14.7% for the ideal broadband surface and 47.1% for the ideal selective surface on the selected day by considering building deployment on a city-scale. Furthermore, carbon emission reductions of 0.52 gCO 2 /(m2⋅h) and 0.16 gCO 2 /(m2⋅h) can be achieved from the roof and walls on the selected day by applying radiative cooling material on both the roof and walls. This study can provide guiding significance for the large-scale application of radiative cooling in cities. [ABSTRACT FROM AUTHOR]

The fundamental way for China to achieve carbon peak and carbon neutrality is to continuously improve the penetration of renewable energy (RE) in the power system and establish an electricity mix dominated by RE power. According to China's 14th Five-Year Plan, the proportion of non-fossil energy power generation should reach about 39% by 2025. However, the impact of the increase in share of RE power on carbon emissions of the electricity sector remains to be estimated. To this end, the purpose of this paper is to capture the nonlinear characteristics of RE on electricity-related carbon emissions by using panel smooth transition regression (PSTR) model and provincial panel data covering the period 1991–2020 under the background of provincial RE quota system. The estimation results indicate that, (i) in the four transition variables of PSTR model such as the installed capacity of power generation, substation equipment capacity, total electricity consumption and total power generation, the nexus between RE deployment and electricity-related carbon emissions was detected as nonlinear. (ii) The heterogeneous RE carbon abatement effect is reflected in its increasing penetration in provincial power systems. (iii) Carbon abatement performance of RE generation decreases in the negative range with the increase of transition variables. (iv) The estimation coefficient of RE is positive at the higher value of the transition variable in the PSTR model of carbon emissions per capita. As a result, the RE quota with the best emission abatement performance should be designed based on the provincial power resource endowment and the medium and long-term expected electricity consumption. [ABSTRACT FROM AUTHOR]

Co-combustion of semi-coke and coal is a prospective technique for large-scale disposal or utilization of the solid waste. In this paper, the co-combustion behaviors and kinetics of semi-coke and coal were studied by thermogravimetric analysis with a focus on the influence of blending method and synergy. The results indicated that the semi-coke combustion was improved by blending with coal, and the interaction between fuels tended to promote the co-combustion. The ignition and burnout temperatures were positively correlated with the carbon content of the mixture, while the comprehensive combustion index and stability index were negatively correlated with it. The layer blending and partition blending method seemed to show stronger promotion than uniform blending method, and the layer blending method with coal in the upper layer was recommended due to the better combustion behavior. The promotion of coal on semi-coke combustion was less affected in devolatilization stage but weakened by the decrease of O 2 concentration in char burnout stage. The apparent activation energy (E a) of the mixture decreased with the increasing faction of coal. The layer blending method with coal in the upper layer had the lowest E a in devolatilization stage and relatively high E a in char burnout stage. The work can give some new insights into waste co-disposal strategy and carbon emission reduction in China. [Display omitted] [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]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [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]

Industrial chain structure optimization and digital transformation are crucial factors for achieving carbon emission reduction goals. Based on China's non-competitive input-output tables, this paper constructed industrial chain panel data using the structural path analysis method. We explored the influence of industrial chain division (ICD) on CO 2 emission intensity through panel fixed effects model, and examined the moderating effect of digitization. The research found that: (1) The ICD can significantly reduce CO 2 emission intensity, the results are robust after some endogeneity tests. (2) The emission reduction effect brought by ICD will be more obvious in high-stage, high-energy consumption, and high-intelligence industrial chains. There is still room to improve the emission reduction brought by the division network effect. (3) Digitization plays a positive moderating role on this effect. Furthermore, digital manufacturing and digital services exhibit heterogeneity in various types of industrial chains. (4) The ICD can reduce carbon emission intensity through pathways such as intelligent transformation, technological innovation, and energy efficiency, and digitization has positively moderated these impact pathways. Based on research results, targeted policy suggestions are provided for optimizing industrial chain structure and promoting digital transformation. • This paper constructed industrial chain carbon emission intensity panel data. • This paper assessed the industrial chain digitization by digital intermediate input. • This paper constructed industrial chain synergistic division (ICSD) index. • This paper explored the influence of ICSD on CO 2 emission intensity. • Digitization plays a positive moderating role on carbon emission reduction effect. [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]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

China's economy and environment urgently require a green recovery as COVID-19's consequences expand over time, and the platform economy is a practical means of pursuing this goal. By employing the Generalized Divisia Index Method (GDIM), this paper aims to analyze the impact of platform economy on carbon emissions in China during the period 2013–2020. Overall, the platform economy has increased carbon emissions, but there was a decrease in carbon emissions in the platform economy between 2014 and 2016. The scale factors of platform economy are the primary contributors to the increase in China's overall emissions and most provincial carbon emissions, while the carbon intensity of platform economy factors contributes most to the decrease in carbon emissions. In particular, the carbon intensity of platform economy factors promoted the most cumulative carbon emissions in Jiangsu, Heilongjiang, Yunnan, Qinghai, and Ningxia between 2013 and 2020, and the energy intensity of platform economy factors reduced most of the cumulative carbon emissions of Heilongjiang, Yunnan, Qinghai, and Xinjiang during the same period. From the perspective of the heterogeneity of platform economic development, the main contributors of carbon mitigation from high-platform economic provinces are intensity effects. However, the platform economic structure of low-platform economic provinces significantly reduces carbon emissions. In addition, we also found significant differences in the factors influencing emissions in different groups under the influence of the pandemic. Finally, we provide some valuable references for China's platform economic development to achieve "carbon neutrality" targets. [ABSTRACT FROM AUTHOR]

• The influences of the geometric parameters of the reactor on the SCPP-HPCR are investigated. • A formula is proposed to calculate the CO 2 emissions reduction of the system. • The optimal operating conditions of the SCPP-HPCR is analyzed. • 2.38 kg of CH 4 is degraded and 375.52 kg of CO 2 is reduced through the system one day. The increase of 1.09 °C in global temperature resulted in significant catastrophes because of the extreme climate. The climate change rate can be slowed down by reducing the levels of CH 4 and CO 2 in the atmosphere. The solar chimney power plant integrated with a honeycomb photocatalytic reactor (SCPP-HPCR) uses the photocatalytic technology to remove atmospheric CH 4. In addition, CO 2 emissions can be reduced from coal fired power plant due to the generating capacity of the system. In this paper, the influence of the geometric parameters of the reactor and turbine on the overall performance of the SCPP-HPCR is studied by numerical simulation. The obtained results showed that the flow resistance inside the system was mostly caused by the HPCR at low turbine speed, and primarily by the turbine at high turbine speed. Reducing the pore diameter of the reactor could improve the photocatalytic performance of the SCPP-HPCR more than increasing the turbine speed. The SCPP-HPCR having a pore diameter of 3 mm, porosity of 0.85, and constant turbine speed of 180 rpm, built in Qianyanzhou, China, could remove 2.38 kg of CH 4 and reduce 375.52 kg of CO 2 in one day. [ABSTRACT FROM AUTHOR]

China's carbon peaking and carbon neutrality goals present a significant challenge for coal chemical technology, which is critical to securing the energy structure. Combining coal chemical industry technology with new energy is an effective approach to transform the development of the coal chemical industry. This paper proposes and studies a novel coal-to-methanol (CTM) technology of gasification integrated solid oxide electrolysis cell (SOEC). SOEC electrolytic hydrogen production technology is an advanced electrolytic water technology with the advantages of large scale and high efficiency, which is very suitable to be combined with industrial technology and can solve the painful problem of H 2 deficiency in the conventional coal to methanol process. In this study, from mechanistic analysis and model simulations, it is observed that by increasing the SOEC capacity, the novel CTM system can create more methanol at the same coal consumption and simultaneously reduce CO 2 emissions. The novel CTM system can produce up to 2.2 times more methanol and reduce CO 2 emissions by 94% by replacing the water-gas-shift (WGS) process with the SOEC unit. The novel CTM increases energy consumption. In addition, the novel CTM technology will effectively improve the economics of coal to methanol, taking into account the carbon tax. At the methanol price of 2900 RMB/t and SOEC capacity of 250 MW, the economic benefits of novel CTM were 2.1 times greater than CTM technology. • Propose a novel coal-to-methanol technology of gasification integrated electrolysis. • The novel coal-to-methanol technology can produce more methanol and reduce CO 2 emissions. • The novel technology can offer better economics when methanol price is higher than 2900 RMB/t. [ABSTRACT FROM AUTHOR]

It is proposed to develop a novel thermochemical cycle using nuclear energy to decompose H 2 S and CO 2 , thus producing H 2 and CO from acidic gas in petrochemical or coal chemical industries, reducing CO 2 emissions and promoting sustainable energy development. The upper limits and significant energy-consuming steps were determined based on two chemical routes of the thermochemical cycle systems 1 and 2. When the enthalpy of the H 2 S oxidation reaction served as part of the circulating energy input, the values for system 1 and system 2 were 38.7% and 34.3%, respectively. However, if only external energy is considered to satisfy the heat and work required to run the reaction and pump, the thermal efficiency rises to 58.4% and 61.2%, respectively. The life cycle assessment (LCA) was used to investigate the environmental effect of the hydrogen generation process base on the upper thermal efficiency in this study, and the results of the LCA are stated in terms of global warming potential (GWP) and acidification potential (AP). The GWP of systems 1 and 2 was calculated as 2.59 kg CO 2 -eq/kg H 2 , 2.54 kg CO 2 -eq/kg H 2 , The AP of systems 1 and 2 was calculated as 7.48 g SO 2 -eq/kg H 2 and 9.08 g SO 2 -eq/kg H 2 , respectively. This paper assesses the environmental effect of two different hydrogen systems used in manufacturing various subsystems in GWP and AP, and compares them to the life cycle of other hydrogen production pathways. • A novel thermochemical cycle using nuclear energy to split H 2 S-CO 2 is proposed. • Energy-consuming steps and the upper thermal efficiency were determined. • The upper thermal efficiency has been developed to assess environmental impacts. • The LCA integrated China's HTR-PM with the innovative H 2 S-CO 2 process. • GWP and AP in the H 2 S-CO 2 system are compared to other hydrogen methods. [ABSTRACT FROM AUTHOR]

• Identify three key factors impacting carbon neutrality in rMSW management. • Perform LCA comparison of environmental impacts for different treatment technologies. • Comprehensive comparison of rMSW characteristics in China, Germany, Japan, and the USA. • Identify four major challenges hindering low carbon rMSW management in China. • Present a three-stage road-map for "low-, zero-, and negative-carbon" management models. Solid waste management is rapidly developing and undergoing technological transformation in China. However, Waste Classification (WC) methods and the corresponding end-of-pipe technologies have not been fully harmonized, resulting in large volume and complex residual municipal solid waste (rMSW) that poses challenges for waste management. To achieve carbon neutrality in solid waste treatment, a well-designed rMSW management system is necessary. In this paper, we identified the common factors influencing the carbon emissions in solid waste management. Then we presented a comprehensive assessment of four emerging low-carbon rMSW technologies, including their advantages, disadvantages, project examples, and environmental impacts. Finally, with reference to the experiences of Germany, Japan, and the United States, and based on the history and current status of rMSW management in China, we summarized the challenges faced by China and proposed a low-carbon development pathway. This paper aims to offer support for the early achievement of carbon neutrality in solid waste management in China. [Display omitted] [ABSTRACT FROM AUTHOR]

• Integrated and flexible carbon capture power plants (IFCCPPs) are analyzed. • A system model is established that connects the IFCCPP, power-to-gas, and wind farms. • A two-stage low-carbon scheduling model is constructed considering demand response. • Reward and punishment ladder-type carbon trading has stricter limits on emissions. • The proposed model has advantages in reducing abandoned wind, emissions and cost. At present, coal-fired thermal power still dominates in China, and as the environmental pollution problem worsens, it is critical to find the optimal combination of coal and new energy to reduce CO 2 emissions and promote new energy consumption. Based on this, this paper considers a reward and punishment ladder-type carbon trading mechanism, firstly, unit transformation of the conventional coal power plant (CPP) on the power side, forming the integrated and flexible carbon capture power plant (IFCCPP), and constructing a coupling model of the IFCCPP with power to gas (P2G) equipment and wind farms(WFs), forming an IFCCPP-P2G-WF system; secondly, introducing a price demand response mechanism on the load side, and establishing a two-stage scheduling model for the IFCCPP-P2G-WF system, with the first stage model aiming at the minimum sum of squared load fluctuations and the second stage model aiming at the minimum operation cost. Finally, the effectiveness of the scheduling model proposed in this paper is validated by comparing and analyzing the scheduling results under eight scenarios. When compared to other models, the proposed model significantly increases wind power consumption, reduces carbon emissions, and lowers operation cost, all of which have advantages. [ABSTRACT FROM AUTHOR]

Electric vehicles play an important role in energy transition and low-carbon development. It is important to evaluate the energy-saving emission reduction and cost-effectiveness of electric vehicles. In this paper, the life cycle carbon emission model and the life cycle cost model are established. Combined with the emission inventory and the calculated life cycle cost, the marginal emission reduction cost model is finally established. The results show that: 1) Electric vehicles have reduced 47 % of life-cycle carbon emissions, which is mainly reflected in the extremely small emissions during the utilization phase, and with little difference in other phases; 2) Although electric vehicles have lower emission costs and vehicle use costs, they still cannot compensate the gap in initial purchase costs. Electric vehicles do not have economic advantages over gasoline vehicles; 3) Under the existing technical conditions, electric vehicles have higher marginal abatement costs. The abatement costs are affected by many factors and have a large potential for decline; 4) When the gasoline price exceeds 9.8 CNY/L (1.372 USD/L), the abatement costs of electric vehicles are negative. In this condition, the development of electric vehicles is most effective in promoting energy saving and emission reduction. [Display omitted] • EV reduces carbon emissions by about 47 % compared to GV. • The life cycle cost of an EV depends on the initial purchase cost. • Use carbon emissions and costs to calculate marginal emission reduction costs. [ABSTRACT FROM AUTHOR]

The outbreak and ongoing evolution of the COVID-19 pandemic have dramatically impacted economic development and CO 2 emissions. China is under simultaneous pressure to recover from the outbreak and meet its carbon reduction targets, and the government is endeavouring to stimulate economic recovery through fiscal and monetary policies. This paper uses a computable general equilibrium model to measure the impact on China's economic recovery and carbon emissions by incorporating the pandemic shock and related economic recovery policies of loan prime rate (LPR) and value-added tax (VAT) reduction. The study found that COVID-19 led to a simultaneous shock on China's supply and demand sides in which GDP dropped by 2.62% and carbon emissions fell by 2.53%, compared to the period prior to COVID-19. Although the LPR and VAT reduction effectively mitigated economic loss, the combined LPR and VAT reduction had a more substantial effect on boosting GDP than the single policies. The VAT cut expands production and was used to overcome supply-side shocks, while lowering LPR mitigates the damage of demand-side shocks. Compared to the VAT reduction policy, reduced LPR has smaller carbon emissions per unit of GDP output. Consequently, we recommend that the government concentrate on a combination of policies to navigate pandemic shocks, as the two economic stimulus policies are confirmed to complement one another in terms of strengths and shortcomings. [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]