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The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China's national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries. [ABSTRACT FROM AUTHOR]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China's carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020–2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56–0.57% in 2020–2060; this is hard to achieve. In addition, the study finds that China's 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a significant effect. Therefore, the paper believes that the key to carbon neutrality lies in the coexistence of technological innovation and carbon pricing to ensure that we can cope with global warming with the lowest cost and resistance. [ABSTRACT FROM AUTHOR]

Amid China's pursuit of a green and low-carbon transition, corporate social responsibility (CSR) is facing new challenges. Our research delves into the influencing factors and mechanisms for CSR reputation under green and low-carbon transition and provides practical enlightenment for enterprises to achieve sustainable development. This paper constructs a comprehensive index system of CSR from five dimensions (innovation, coordination, sustainability, openness, and sharing), and CSR reputation of China's A-share listed companies is comprehensively estimated by using an entropy method and data from 2013 to 2021. Then, from the perspective of external supervision and internal governance, we discuss the influence factors of CSR reputation, with an emphasis on the impact of public environmental concerns. Finally, the realization mechanism of CSR is further revealed. It is found that public environmental concern and the expansion of the enterprise scale boost the enhancement of CSR reputation. However, a higher proportion of female managers tends to hinder CSR reputation. Furthermore, public environmental concern plays a more prominent role in improving CSR reputation of non-state-owned and eastern enterprises. Additionally, public environmental concern significantly enhances CSR reputation through green technology innovation and executive environmental awareness. This research provides valuable insights for improving CSR reputation and optimizing regulatory compliance and governance practices. [ABSTRACT FROM AUTHOR]

Climate anomalies are affecting the world. How to reduce carbon emissions has become an important issue for governments and academics. Although previous researchers have discussed the factors of carbon emission reduction from environmental regulation, economic development, and industrial structure, limited studies have explored the carbon emission reduction effect of a city's spatial structure. Based on 108 Chinese cities from the Yangtze River Economic Belt between 2003 and 2017, this paper examines the impact of the city cluster policy on city carbon emissions using the difference-in-differences (DID) method. We find that: (1) The city cluster policy has significantly reduced the cities' carbon emissions by 7.4%. Furthermore, after a series of robust and endogenous tests, such as parallel trend and PSM-DID, the core conclusion still remains. (2) We further identify possible economic channels through this effect, and find that city cluster policy would increase city productivity, city technological innovation, and industrial structure optimization. The conclusions of this paper have important practical significance for China to achieve carbon neutrality and facilitate future deep decarbonization. [ABSTRACT FROM AUTHOR]

The launch of the national carbon emissions trading (CET) market has resulted in a closer relationship between China's CET market and its electricity market, making it easy for risks to transfer between markets. This paper utilizes data from China's CET market and electric power companies between 2017 and 2023 to construct the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Barun'ik and Křehl'ik, and a minimum spanning tree model. The comparison is made before and after the launch of the national CET market. Subsequently, this paper examines the market spillover effects, as well as the static and dynamic properties of network structures, considering both the time domain and frequency-domain perspectives. The research findings suggest the following: (1) There is a strong risk spillover effect between China's CET market and the stock prices of electric power companies; (2) There is asymmetry in the paired spillover effects between carbon trading pilot markets and the national CET market, and differences exist in the impact of risk spillovers from power companies between the two; (3) The results of the MST model indicate that the risk contagion efficiency is higher in the regional CET pilot stage compared to the national CET market launch stage, with significant changes occurring in key nodes before and after the launch of the national CET market; (4) Both the dynamic spillover index and the standardized tree length results demonstrate that crisis events can worsen the risk contagion between markets. Besides offering a theoretical foundation and empirical evidence for the development of China's CET and electricity markets, the findings of this paper can provide recommendations for financial market participants as well. [ABSTRACT FROM AUTHOR]

China is one of the world's largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China's carbon emissions by constructing a system dynamics model of China's carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China's carbon emissions. The contribution rates of the five factors to China's carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China's carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China's carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small. [ABSTRACT FROM AUTHOR]

With the proposal of China's "carbon peak, carbon neutral" strategy, the increasing awareness of low carbon production among consumers, and the government's introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China's low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises' low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the increase in carbon price has a positive effect on the increase in enterprise profit; the increase of carbon emission has a negative effect on the increase of enterprise profit and the high carbon price will intensify this effect. In addition, changing the intensity of government subsidies to consumers will lead to the change of enterprise carbon emission strategy. The study of this paper provides a certain reference for the government to reasonably formulate carbon trading prices and consumer low-carbon subsidies. In addition, considering consumer low-carbon preferences is also conducive to promoting green production practices of enterprises, thus promoting the realization of the carbon neutral strategy. [ABSTRACT FROM AUTHOR]

Fossil fuel usage has a great impact on the environment and global climate. Promoting new energy vehicles (NEVs) is essential for green and low-carbon transportation and supporting sustainable development. Lithium-ion power batteries (LIPBs) are crucial energy-storage components in NEVs, directly influencing their performance and safety. Therefore, exploring LIPB reliability technologies has become a vital research area. This paper aims to comprehensively summarize the progress in LIPB reliability research. First, we analyze existing reliability studies on LIPB components and common estimation methods. Second, we review the state-estimation methods used for accurate battery monitoring. Third, we summarize the commonly used optimization methods in fault diagnosis and lifetime prediction. Fourth, we conduct a bibliometric analysis. Finally, we identify potential challenges for future LIPB research. Through our literature review, we find that: (1) model-based and data-driven approaches are currently more commonly used in state-estimation methods; (2) neural networks and deep learning are the most prevalent methods in fault diagnosis and lifetime prediction; (3) bibliometric analysis indicates a high interest in LIPB reliability technology in China compared to other countries; (4) this research needs further development in overall system reliability, research on real-world usage scenarios, and advanced simulation and modeling techniques. [ABSTRACT FROM AUTHOR]

China's economic development is characterized by openness, and trade and investment are important engines for promoting economic development. China's economy is now in a transitional period, during which excessive carbon emission reduction would inevitably hinder economic development. In this context, improving carbon emission efficiency is an effective way to achieve sustainable development. This paper deals with the relationships among foreign direct investment, technological innovation and carbon emission efficiency. Our research findings include the following. First, carbon efficiency shows regional differences. East China has the highest mean value of carbon emission efficiency, followed by central China and west China over the sample period. Second, FDI exerts both direct and indirect impacts on carbon emission efficiency through technological innovation, which confirms the intermediate effect of technological innovation. Finally, sub-sample analysis indicates that the impact of FDI and technological innovation on carbon emission efficiency show regional heterogeneity. According to these findings, we offer policy recommendations as follows. The government should stimulate independent innovation, promote technological progress in renewable energy and green energy, and attract environmentally friendly foreign investment to improve carbon emission efficiency and boost green development. [ABSTRACT FROM AUTHOR]

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China's logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry. [ABSTRACT FROM AUTHOR]

A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world's greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 and carbon neutrality by 2060. Carbon-reduction AI applications promote the green economy. However, there is no comprehensive explanation of how AI affects carbon emissions. Based on panel data for 270 Chinese cities from 2011 to 2017, this study uses the Bartik method to quantify data on manufacturing firms and robots in China and demonstrates the effect of AI on carbon emissions. The results of the study indicate that (1) artificial intelligence has a significant inhibitory effect on carbon emission intensity; (2) the carbon emission reduction effect of AI is more significant in super- and megacities, large cities, and cities with better infrastructure and advanced technology, whereas it is not significant in small and medium cities, and cities with poor infrastructure and low technology level; (3) artificial intelligence reduces carbon emissions through optimizing industrial structure, enhancing information infrastructure, and improving green technology innovation. In order to achieve carbon peaking and carbon neutrality as quickly as possible during economic development, China should make greater efforts to apply AI in production and life, infrastructure construction, energy conservation, and emission reduction, particularly in developed cities. [ABSTRACT FROM AUTHOR]

To cope with huge carbon emission pressure, China has implemented a carbon emissions trading pilot policy that aims to provide reasonable suggestions for the smooth operation of the national carbon market. This paper selects the provincial panel data in China from 2005 to 2019 and uses the propensity score matching-difference in difference (PSM-DID) method to evaluate the carbon emission policy's reduction effect. Based on carbon emissions (CE) and carbon emission intensity (CI), provinces and cities are divided into four regions, and each region is verified by spatial difference analysis. Furthermore, the mediating effects of carbon emission reduction through the dual aspects of technological progress and industry structure are also discussed. Results verified that, (1) under the carbon emission trading policy, regional carbon emissions and carbon emission intensity are both significantly reduced. (2) Technological progress helps to reduce carbon emissions, while industrial structure shows no obvious contribution. (3) The four regions all show ideal emission reduction effects, of which the High CE-High CI region shows the best, but is greatly restricted by techniques. The industrial structure of the High CE-Low CI region needs to be further optimized for carbon reduction. In the Low CE-High CI region, the carbon emissions brought by economic development fail to effectively improve per capita GDP. The Low CE-Low CI region contributes greatly to carbon emission reduction with technical advantages. [ABSTRACT FROM AUTHOR]

China's industrial sector generally relies on electricity as its main source of energy, and industrial production can be affected if there are problems with the electricity supply. In order to deal with the uncertain electricity supply and achieve the "dual carbon" target, the industrial sector needs to take effective measures to enhance carbon total factor productivity (CTFP). We use the biennial Luenberger productivity index (BLPI) to try to provide strategies for low-carbon industrial development in China. The results indicate that the overall CTFP of China's industrial sector showed an increasing trend from 2006 to 2019. Technology change was the main contributor to the change in CTFP, but fluctuations in efficiency change remained a challenge. Differences were observed between the light industry sector (LIS) and the heavy industry sector (HIS) in terms of changes in CTFP, with LIS showing more stable changes and HIS experiencing larger fluctuations. Most sub-sectors showed increased CTFP during the sample period. R&D investment and R&D personnel have a positive impact on CTFP, while energy structure is found to hinder CTFP. According to the research results of this study, we offer the corresponding policy implications. This study is the first to explore the balance between low-carbon goals and industrial production from the perspective of improving CTFP, providing a new viewpoint on the contributions of technological innovation to solving socio-economic issues. [ABSTRACT FROM AUTHOR]

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023. [ABSTRACT FROM AUTHOR]

Improving the level of cooperative governance of open innovation projects is a key bridge and powerful starting point for promoting the full integration and coordinated development of different market systems. Against the background of the synergy of the electricity–carbon market, this study explores how a company chooses a cooperative governance mechanism and the implementation effects of governance mechanisms. The results, based on the sample data from 175 OIPs of companies in China, show that the collaborative dependence of a company promotes the implementation of relational governance; however, it has no significant effect on contract control. Meanwhile, both relational governance and contract control have significant positive effects on knowledge transfer and cooperative performance. Moreover, knowledge transfer plays a mediating role in relational governance, contract control, and cooperative performance. The conclusion enriches the understanding of cooperative governance mechanisms, which has important implications for management research and the practice of open innovation projects. It also has certain practical significance for helping the power industry to achieve the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

The manufacturing industry is the pillar industry of China's economy and a major carbon emitter, and its carbon emission reduction efforts directly determine whether the country's carbon emission reduction target can be successfully met. In the context of the goals of the carbon peak and carbon neutrality policy, we examine the impact of manufacturing structure optimization on carbon emissions from 2003 to 2020 through a spatial econometric model, taking the old industrial centers in Northeast China as an example. We then apply a machine learning model to simulate manufacturing carbon emissions during the carbon peak stage and identify the optimal path for carbon emission reduction, which is important for promoting manufacturing carbon emission reduction in Northeast China. Since the goal of low-carbon economic development has gradually replaced the goal of maximizing economic efficiency in recent years, manufacturing structure optimization has come to focus on energy saving and emission reduction. Therefore, we define manufacturing structure optimization from the dual perspective of technology and energy consumption to broaden the existing research perspective. The results show the following: (1) The overall trend in manufacturing structure optimization in Northeast China is steadily improving, and the level of manufacturing structure optimization from the technology perspective is higher than that from the energy consumption perspective. (2) Manufacturing structure optimization and manufacturing carbon emissions in Northeast China both show a positive spatial correlation. Manufacturing structure optimization in Northeast China can effectively promote carbon emission reduction, and it also has a spatial spillover effect. (3) The carbon emission reduction effect of manufacturing structure optimization from the energy consumption perspective is better than that from the technology perspective, and the carbon emission reduction effect under the institutional innovation scenario is better than that under the baseline scenario and the technological innovation scenario. Focusing on manufacturing structure optimization from both technology and energy consumption perspectives, as well as continuously improving technological innovation and institutional innovation, can help to achieve manufacturing carbon emission reduction in Northeast China. [ABSTRACT FROM AUTHOR]

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China's regional carbon price. [ABSTRACT FROM AUTHOR]

A study linking the two economic and social phenomena of rural labor force migration and energy transition can help analyze the underlying causes of rural "Energy Poverty". However, how off-farm employment affects household low-carbon energy consumption and its potential mechanisms requires further research. Using 1351 sampled rural households from the "Rural Energy, Population Transfer and Well-being" survey in 2018 and 2021 to explore response mechanisms through which off-farm employment can influence low-carbon energy intensity. Utilizing the multivariate regression, Sobel test, and moderating effect test, the results demonstrate that off-farm employment, including short-term and long-term off-farm employment, significantly increases the intensity of low-carbon energy use among rural households. Specifically, long-term off-farm employment tends to have a greater positive contribution to the low-carbon energy intensity than short-term off-farm employment. Furthermore, off-farm employment can affect household low-carbon energy intensity through the total income, and effect of the surrounding people in the off-farm employment process also increases their consumption intensity. The research reveals that the rural energy revolution under the constraints of "Carbon Neutral" and "Carbon Peak" should relate to the off-farm development of rural households to achieve "Precise Energy Poverty Alleviation". [ABSTRACT FROM AUTHOR]

With the strategic goal of "carbon peaking and carbon neutral" in China, new requirements are also put forward for the thermal recovery of heavy oil. In view of the problems of excessive greenhouse gas emission, low steam utilization rate, poor economic efficiency, and limited reservoir application of steam stimulation replacement technology in China, the emerging technologies of medium- and low-temperature thermal fluid, solvent-assisted high-temperature steam injection, solvent-based medium- and low-temperature waterless recovery and in situ electric heating-assisted recovery are discussed in terms of technical principles, technical parameters, experimental/field effects, and technical and economic potential. The technical principles, technical parameters, experimental/field results, and techno-economic potential of low-carbon heavy oil recovery technologies are summarized and future development directions and trends are anticipated. The study's findings indicate that some of the technologies that have been tested in the field, such as HWVP, EMVAPEX, AH-VAPEX, LASER, and ESEIEH, can be developed by relying on the original well groups for production and can reduce greenhouse gas emissions, such as CO2, by about 80% and improve crude oil recovery by 5% to 10%, while the technologies concerned have outstanding effects on increasing oil production rate and lowering upfront capital investment. Some of the technologies that have been tested significantly increase oil production rate, lower initial capital expenditure, and enable solvent recycling, among other things. Among them, COBEEOR and N-SOLV technologies can also lower the amount of asphaltene in the output crude oil, enhance the API of the recovered crude oil, and provide strong economic advantages. CSP, CHSI, and hot water solvent injection were tested in indoor two-dimensional and three-dimensional experiments to validate their feasibility, while CO2, propane, and butane solvents were initially screened and some of the technologies' mechanisms were revealed to lay the groundwork for pilot projects. The executive summary of the research findings will serve as a guide for future low-carbon extraction technology research and development in China. [ABSTRACT FROM AUTHOR]

In China, electricity market reform was first implemented in 2015. At the same time, the national carbon market was built, along with the electricity industry, which was considered a breakthrough. Some key considerations for the future development of China's electricity system include the implementation of demand-side measures in order to adjust the peak-to-valley difference and the economic dispatch of increasing intermittent renewable energy and traditional energy in the process of power marketization with the implementation of a carbon policy. This paper examines the impact of policies on electricity generation by modelling the evolution process of power marketization and the economic dispatch of generation technologies over a sixteen-year period beginning in 2020. We model four potential influencing factors of government policy: (1) the demand response mode; (2) power marketization process; (3) capacity adjustment of thermal power units; and (4) carbon taxes, which vary in terms of their timing and amount. This model assesses the impact of these influencing factors on the competition between electricity generators using a range of output variables, including generation portfolios, electricity prices, capacity factors, CO2 emissions, etc. The results show that the new round of electricity market reforms has had a positive impact on renewable energy generation. The influence of carbon policy is evident in the promotion, transformation and elimination of thermal units, and an indirect increase in renewable energy generation. [ABSTRACT FROM AUTHOR]

The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events. [ABSTRACT FROM AUTHOR]

Based on China's goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry. [ABSTRACT FROM AUTHOR]

Interest in the rapid growth of CO2 emissions, together with the economic performance of various countries continues to attract researchers and practitioners' interest. Alongside, concerns regarding global warming and its effects on human and animal health, and thus sustainable development, escalate. The present study employs the nonlinear autoregressive distributed lag to identify short- and long-run dynamics and the asymmetric nexus between absorptive capacity, and CO2 emissions intensity from 1970 to 2018 in the case of the USA and China. In the short-run, an increase in technology transfer based on human resources increases CO2 emissions in China. Contrarily, the decrease in technology transfer based on infrastructure has an emissions-decreasing effect in China. In the long-run, the effects of an increase in absorptive capacity based on innovation and infrastructure developments provide positive and significant impetus to mitigate the carbon intensity in China and the USA. The results are robust using GHG intensity. Thus, policymakers and researchers have to consider the pivotal role of absorptive capacity in facilitating sustainable development. [ABSTRACT FROM AUTHOR]