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In recent years, the issue of regional economics and carbon emissions has become a research hotspot in the cross field of economy, environment and ecology. This paper selects the regional economics and carbon emissions related literature collected in the Web of Science (WOS) database as the basis, and uses the bibliometric software Citespace and VOSviewer to visually analyze the time distribution, organization, author and keywords in this research field. This paper provides a more systematic analysis of how different regions of China could achieve carbon emission objectives, from the aspects of regional industrial transformation, energy consumption structure, policy implementation and regional coordinated development. The keywords with high frequency are carbon emissions, economic growth and energy consumption, etc. The research hotspots can be divided into structural decomposition analysis, low-carbon industry transformation path, policy framework and energy efficiency, etc. The results show that future research should strengthen multidisciplinary cross-integration in different universities and institutions. However, based on in-depth analysis, the key factors which affect regional carbon emissions are regional policy implementation, changes in industrial structures, optimization of energy consumption structure and carbon trade market mechanism. Finally, we suggest that institutions and scholars should conduct adequate interdisciplinary and cross-industry cooperation; industrial sector development should consider local endowment; there should greater use of clean energy to optimize the energy consumption structure; and an increase in R&D carbon capture and sequestration technology. [ABSTRACT FROM AUTHOR]

The synergistic relationship between water, energy, and food faces increasing challenges. Green finance as a policy tool promotes high-quality and efficient development of water, energy, and food subsystems. However, whether it can improve the synergistic relationship of "water–energy–food" (WEF) still needs to be studied. Using the panel data of 38 prefecture-level cities in the Yangtze River Delta (YRD) from 2013 to 2021, and network DEA and panel regression models, we study the impact of green finance on the level of synergy of "water–energy–food". The results of the study show that green finance can promote the synergy level of "water–energy–food" in the Yangtze River Delta city cluster, which was first inhibited and then promoted from 2013 to 2021. The impacts of green finance on the efficiency of the three subsystems are also characterized by a "U" shape. However, linear impacts show differences, with green finance improving the efficiency of the water subsystem but not the energy and food subsystems. The implementation of China's national strategies empowers green finance. The digital strategy, the "dual carbon" strategy, and the new urbanization strategy have increased the effectiveness of green finance in contributing to the level of water–energy–food synergy. Regional heterogeneity analysis shows that the promotion effect of green finance mainly exists in non-resource-based cities, non-main grain-producing areas, and non-cities along the South-to-North water diversion route and large cities. Finally, this paper puts forward relevant policy recommendations. [ABSTRACT FROM AUTHOR]

Energy and environmental studies (E&E) have faced a significant turning point due to the lack of reliability of the existing models, as well as the lack of policy governance. Most papers in E&E have adapted data envelope analysis due to its popularity, which is a result of its structure of having multiple inputs and outputs. However, due to its crucial weakness in statistical reliability, diverse new methodologies to gain better reliability have been developed, such as difference-in-difference and computational general equilibrium models, but they are still do not popular because the world has not shown significant progress in the abatement of carbon emissions. This comes not only from the lack of appropriate, precise research models, but also from a worldwide lack of governance. Most countries advocate for the necessity of E&E policies, yet their policies alone are not enough for sustainable performance, due to the lack of reliability and/or weakness of public–private partnerships. This Special Issue shall examine all of these new challenges to the methodologies, as well as the implications and suggestions arising from their empirical results. [ABSTRACT FROM AUTHOR]

Foundation industries, encompassing metals, ceramics, cement, paper, chemicals, and glass, play a vital role in driving industrial economies. Despite their pivotal role, a comprehensive understanding of shared processes and their impact on resource utilisation remains elusive. This study employs a novel approach, leveraging an adapted Dependency Structure Matrix (DSM), to unveil the core processes commonly utilised among these industries. These processes are then evaluated based on their influence on energy consumption and CO2 emission. The investigation revealed 18 common processes categorised by their processing principles, their expected outcomes, and the equipment used. Remarkably, these processes emerge as significant contributors to both energy consumption and CO2 emissions. Notably, pyroprocessing emerged as a prevalent practice in five out of the six sectors, while the production of dried products and crushers and mills were the most frequently encountered outcomes and equipment used, respectively. This paper discusses the implications of these findings for foundation industries, emphasising potential areas for enhancing manufacturing operations to reduce environmental damage and facilitate knowledge transfer among the various sectors. Furthermore, the study identifies shared abatement options that can be collectively implemented across industries to achieve more substantial reductions in environmental footprint. By identifying and prioritising the most impactful processes in foundation industries, this study provides a strategic footing for advancing sustainable and efficient manufacturing practices within these critical sectors. [ABSTRACT FROM AUTHOR]

Within the sustainability framework, technological innovation's impact is acknowledged. However, the environmental implications of institutional innovation, a crucial component of the innovation system, remain unclear, necessitating further research. This paper focuses on financial innovation as a representative of institutional innovation, exploring its relationship with carbon emissions. Utilizing panel data from 30 Chinese provinces spanning 2011 to 2022, we establish a spatial Durbin model and a mediating effects model to delve into the intricate relationships among financial innovation, industrial structure upgrading, and carbon emissions. Our findings reveal that: (1) Financial innovation significantly contributes to the upgrading of industrial structures both locally and in neighboring regions; (2) Both financial innovation and industrial structure upgrading effectively mitigate carbon emissions, with the latter playing a mediating role; (3) All three studied factors exhibit spatial clustering effects; (4) The suppressive effect of financial innovation on carbon emissions exhibits a notable spatial spillover. Compared to recent studies, this work innovatively explores the mediating impact mechanism of financial innovation suppressing carbon emissions, particularly demonstrating the spatial spillover characteristics of the mediating effect among the three variables. As China is a major carbon emitter and emerging economy, these insights offer valuable insights for global carbon governance. [ABSTRACT FROM AUTHOR]

At present, net-zero emissions have become a widely accepted goal globally. For a giant carbon emitter like China, especially after just experiencing a high-energy consumption, high-emission, and low-efficiency extensive economic model, achieving the global net-zero emissions target by the middle of this century is particularly important. The implementation of environmental regulation policies is one of the inevitable choices for achieving carbon peak and carbon neutrality. Existing theoretical analysis shows that environmental regulation acts on pollution emissions through cost effects and technological innovation, but relevant studies mostly focus on macro effects and ignore the impact of enterprise heterogeneity. This study calculates the carbon emission data of listed enterprises in China from 2012 to 2021 and examines the impact of environmental regulation policies on the carbon emission intensity of enterprises and its transmission mechanism from both theoretical and empirical perspectives. At the same time, the heterogeneity effect of resource-based industry and non-resource-based industry is considered. The research results show that China's environmental supervision has been increasing year by year, which can reduce the carbon emission intensity of enterprises by improving the level of environmental disclosure of enterprises, environmental management concepts, and resource allocation efficiency and accelerating the establishment of environmental systems of enterprises, but the effect of technological innovation has not been highlighted. Further heterogeneity also indicates that environmental regulation is more conducive to reducing the carbon emission intensity of non-resource-based enterprises, small enterprises, and non-state-owned enterprises. The conclusions of this paper provide a precise direction for the implementation of environmental regulation policies in China and the world. [ABSTRACT FROM AUTHOR]

One of the solutions to achieve the goal of net-zero emissions by 2050 is to try to reduce the carbon emission by using the carbon tax or carbon credit (carbon right). This paper examines the impact of carbon taxes and carbon credit costs on the cement industry, focusing on ESG indicators and corporate profits. Utilizing Activity-Based Costing and the Theory of Constraints, a production decision model is developed and analyzed using mathematical programming. The paper categorizes carbon tax models into continuous and discontinuous progressive tax rates, taking into account potential government policies like emission tax exemptions and carbon trading. It finds that reducing emission caps is more effective than increasing carbon tax rates in curbing emissions. These insights can assist governments in policy formulation and provide a reference framework for establishing carbon tax systems. [ABSTRACT FROM AUTHOR]

From the perspective of the industry chain, this paper uses the life cycle assessment (LCA) method to divide the swine industry into six production stages: feed crop cultivation, feed crop transportation and processing, intestinal fermentation, manure management, energy consumption in pig farming, and slaughtering and processing. Using the LCA method, the carbon emissions from the swine industry are measured from 2001 to 2020 for the whole country and 31 provincial regions. Based on the measurement results, this paper analyzes the dynamic evolution of carbon emissions from the national swine industry during the study period. Meanwhile, the spatial divergence in carbon emissions from the swine industry and the share of carbon emissions from each production stage were further analyzed by combining different provincial regions and production stages. Afterward, this paper uses the Logarithmic Mean Divisia Index (LMDI) model to decompose the influencing factors of carbon emissions at the national and provincial levels, and in each production stage. It is found that (1) The dynamic evolution of China's swine industry carbon emissions from 2001 to 2020 roughly follows a trend of "slow growth—sharp decline—fluctuating rise—fluctuating decline." The fluctuations are influenced by multiple factors, including the industry structure, agricultural policy, and farming scale. The primary driver for the increase in carbon emissions from the swine industry is the growth in demand for pork consumption, leading to the rise in swine supply. (2) In terms of spatial divergence at the provincial level, the regional differences in carbon emissions from the swine industry are significant, the total carbon emissions and unit carbon emissions of Jiangsu, Anhui, and Henan are higher than the national average. (3) In the production stages of the swine industry, feed crop cultivation and manure management are the primary sources of carbon emissions, associated with factors such as substantial feed consumption, crop production patterns, and backward manure management practices. (4) Regarding influencing factors, production efficiency, industry structure, and urbanization level have inhibiting effects on carbon emissions in the swine industry. Economic development and population scale have promoting effects. Production efficiency is the most significant inhibiting factor, and economic development is the most significant promoting factor. Finally, suggestions are made to curb carbon emissions in China's swine industry, including strengthening environmental control, formulating long-term plans for carbon emission reduction, delineating key areas and demonstration bases for carbon emission reduction, enhancing expertise in fertilizer application and manure treatment, and improving agricultural machinery and equipment. [ABSTRACT FROM AUTHOR]

A profound analysis of China's research achievements in the realm of carbon emissions holds the potential to furnish insightful references for analogous endeavors and inquiries in other nations. Employing the CiteSpace tool, this paper identifies five major focal points in Chinese scholars' research on carbon emissions: carbon emission computation and prediction, influencing factors of carbon emissions, carbon footprint, carbon emission efficiency, and differential analysis of carbon emissions. Subsequently, this article systematically scrutinizes and dissects the outcomes of Chinese scholars' endeavors in the aforementioned five focal points, culminating in recommending China's forthcoming research on carbon emissions. (1) The research findings reveal a diversified evolution in the methods employed for calculating and predicting carbon emissions in China. However, due to the limited exploration of delineating carbon emission boundaries, instances of overlap and deviation in carbon emission quantification have emerged. (2) Factors influencing carbon emissions can be categorized into five major classes: economic, demographic, energy-related, policy-driven, and others. Yet, studies investigating industry-specific influencing factors remain relatively scarce. (3) Overcoming challenges associated with cross-boundary measurements, comprehensive effects, and policy applications is imperative in carbon footprint research. (4) Significantly disparate levels of carbon emission efficiency prevail across distinct regions or industries, with intricacies characterizing the influencing factors and a notable dearth of micro-level investigations. (5) The analysis of carbon emission differentials primarily encompasses regional disparities, industrial differentials, and temporal variations, lacking sustained tracking studies on the nuances of carbon emission disparities. [ABSTRACT FROM AUTHOR]

High-carbon emission industries are the most important source of carbon emissions in the Zhejiang Province. Due to the differences in the development level of various industries, it is necessary to adjust the carbon emission reduction strategies of various industries. As the first ecological province in China, the promotion of carbon emission reduction in high-carbon industries in the Zhejiang Province plays an important leading role in the development of low-carbon economy in other industries and other provinces in China. Taking eight high-carbon industries in Zhejiang Province as the research object, this paper uses the LMDI factor decomposition model to deconstruct the influencing factors and effects of carbon emissions in eight industries in the Zhejiang Province from 2010 to 2021. On this basis, the Tapio decoupling model is applied to study the reasons and driving factors of the decoupling between economic growth and carbon emissions. The results showed that: (1) During the study period, the total carbon emissions of eight industries in the Zhejiang Province increased by 24,312,200 t, showing an overall upward trend. (2) The effect of economic growth and population size led to the rapid growth of carbon emissions in eight industries in the Zhejiang Province, and the effect of energy intensity on carbon emission reduction was the most significant; the effect of industry structure presented a trend of first promoting and then inhibiting, and the effect of carbon emission coefficient always inhibited carbon emissions. (3) The population size has restricted decoupling efforts; energy intensity has the greatest impact on the realization of industry decoupling; energy structure and industry structure decoupling efforts are small; the carbon emission coefficient has always influenced decoupling efforts. This research paper will provide suggestions and policies for the development of low-carbon economy in Zhejiang Province. [ABSTRACT FROM AUTHOR]

As an important means of farmland policy, whether land transfer can promote agricultural green transformation is worthy of further study; however, related research is relatively rare. Based on the inter-provincial panel data from 2005 to 2020, this paper examines the influence of land transfer on agricultural green transformation and its underlying mechanism by using a two-way fixed effect model and an intermediary effect model. This study reveals significant findings as follows: (1) Land transfer substantially promotes agricultural green transformation. (2) Energy consumption is a major contributor to the growth of agricultural carbon emissions; however, land transfer can mitigate this by reducing energy consumption. (3) Land transfer can promote agricultural green transformation by fostering agricultural technology progress. (4) Further analysis reveals that land transfer in economically developed areas and the southeastern side of the "Hu-Huanyong Line" significantly enhances agricultural green transformation. Based on these findings, this paper suggests promoting land transfer while considering regional differences. Additionally, attention should be directed towards reducing energy consumption and encouraging agricultural technology's progress. [ABSTRACT FROM AUTHOR]

Giving full play to carbon emission reduction of green credits is essential to achieve carbon neutrality. According to low-carbon pilot policies and the condition of industrial transfer, this paper first sorts those provinces into different research zones. The zones are as follows: (Ⅰ) the first and second batch of low-carbon municipalities and the first batch of pilot provinces (L1) and other provinces (L2) and (Ⅱ) strong industry transfer-out zone (STR), weak industry transfer-out zone (WTR), and industrial transfer-in area (TIR). Then, we employ a dynamic panel data model and systematic GMM (SYS-GMM) approach to empirically test the impact of green credit and nongreen credit on carbon emissions. Further, this paper analyzes how to coordinate two types of credits to achieve carbon neutrality. The results show that, first, at the national level, the nexus of green credit and carbon emissions with an inverted U-shaped curve and the current impact of green credit is still in the first half of the inverted U-shaped stage. The achievement of carbon neutrality is associated with the ratio structure of green credit to nongreen credit and the scale of green credit. Second, the achievement of carbon neutrality is with regional heterogeneity. The achievement of carbon neutrality is associated with the scale of green credit in L2 and TIR, but also with the ratio structure of nongreen credit to green credit in L2 and STR. However, the carbon neutralization effects with green credit are insignificant in L1 WTR. Finally, based on those conclusions, this paper puts forwards some suggestions to provide references for the policy formulation of green credits and carbon neutrality. [ABSTRACT FROM AUTHOR]

Robot usage has become an intrinsic requirement to drive the intelligence transformation of the manufacturing industry, providing a key driver for carbon emission reduction in China's manufacturing industry. This paper examines the carbon emission reduction effect of robot usage at three levels: theoretical, empirical, and decomposition. On the basis of the pollution emission data of segments of China's manufacturing industry from 2006 to 2020, the paper takes matching robot stock and incremental data from the International Federation of Robotics statistics, and applies the instrumental variable method to identify and estimate the role played by industry robot stock and incremental improvement in reducing pollution emissions. The empirical results show that the usage of robots does significantly reduce the carbon emissions of the manufacturing industry in China. Further, mechanism testing revealed that robot use reduces corporate pollution emissions mainly through channels such as R&D investment and manual substitution. The research provides microscopic evidence for objectively assessing the impact of robot use on environmental pollution emissions from the perspective of promoting robot applications, and suggests policy recommendations for reducing pollution emissions from China's manufacturing sector, which can help achieve harmonious economic and environmental development. [ABSTRACT FROM AUTHOR]

Adhering to green and low-carbon development is the only way to achieve sustainable economic development in China. High-speed railway (HSR) has the advantages of low energy consumption and low pollution, which is representative of the high-quality development of China's transportation industry. Based on the panel data of 285 cities in China from 2004 to 2017, this paper explores the impact of HSR development on CO2 emissions using a difference-in-difference model. The results show that the opening of HSR can effectively reduce the level of urban CO2 emissions. After the endogeneity test and the robustness test, this core conclusion is still valid. The mechanism test confirms that the opening of HSR can reduce the level of urban carbon emissions by promoting the upgrading of industrial structures and improving the level of green technology innovation. Based on empirical results, this paper proposes that we should make rational use of the positive externalities of the HSR network to promote green development, intensive development, and innovative development. [ABSTRACT FROM AUTHOR]

China is rich in biomass resources, taking straw as an example, the amount of straw in China is 735 million tons in 2021. However, at the level of resource use, biomass resources have the practical difficulties of being widely distributed and difficult to achieve large-scale application. By collecting large amounts of biomass and generating electricity using gasification technology, we can effectively increase the resource utilization of biomass and also improve China's energy security. By using a life cycle assessment (LCA) approach, this paper conducted a life cycle assessment with local biomass gasification power generation data in China and found that the LCA greenhouse gas emissions of biomass gasification power generation technology is 8.68 t CO2 e/104 kWh and the LCA cost is 674 USD/104 kWh. Biomass gasification power generation technology has a 14.7% reduction in whole life carbon emissions compared to coal power generation technology. This paper finds that gas-fired power generation processes result in the largest carbon emissions. In terms of economics, this paper finds that natural gas brings the most additional costs as an external heat source. [ABSTRACT FROM AUTHOR]

The regional demand for electric power is influenced by a variety of factors, such as fluctuations in business cycles, dynamic linkages among regional development, and climate change. The valid quantification of the impacts of these factors on the demand for electric power poses significant challenges. Existing methods often fall short of capturing the inherent complexities. This paper addresses these limitations by proposing a framework, which integrates machine-learning techniques into regional electricity demand forecasting. Regional electricity generation firms could then leverage the power of machine learning and improve the accuracy and robustness of electric power forecasting. In this paper, we conduct extensive numerical experiments using an actual dataset from a large utility firm and other public data sources. The analysis indicates that the support vector regression model (the SVR model) has high accuracy in predicting the demand. The results show that socio-economic development is the major driver of growth in electricity demand, while weather variability is a key contributor to the seasonal fluctuations in electricity use. Furthermore, linkages among regional development and the status of development of the green economy become increasingly important influencing factors. The proposed forecasting approach helps the regional electricity generation firms reduce a large amount of carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

From the perspective of energy consumption optimization, this paper studies the impact of green finance on carbon emissions in China. Firstly, based on the theoretical perspective, this paper explores the mechanism and path by which green finance influences carbon emissions, and analyzes the role of energy consumption in this process. Then, this paper utilizes the STIRPAT model, chain multiple mediation effect model and panel threshold model to empirically analyze the influence of green finance on carbon emissions, using provincial data from China from 2005 to 2019. The results are as follows: (1) Green finance significantly reduces carbon emissions. After accounting for potential endogeneity, this conclusion is still valid. The heterogeneity test reveals that the inhibitory effect is more remarkable in northern regions, high-carbon emission regions and energy-rich regions. (2) The results of the bootstrap test reveal that at the national level, green finance decreases carbon emissions through three paths: green technological innovation, ecological evolution of the industrial structure and green technological innovation facilitating ecological evolution of the industrial structure. Furthermore, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance reduces carbon emissions only through green technological innovation. (3) There is a nonlinear relationship between green finance and carbon emissions. Specifically, regardless of energy intensity or energy consumption structure, only when it is below the threshold can green finance significantly inhibit carbon emissions. Thus, realizing energy consumption optimization is an effective way to ensure the carbon emission reduction effect of green finance. [ABSTRACT FROM AUTHOR]

Based on the data from China's provincial panel from 2008 to 2019, this paper explores the impact of rural industrial integration (RII) on agriculture carbon emissions (ACE). It is found that RII has significantly inhibited ACE. The 1% increase in RII led to a 2.133% reduction in ACE; the RII can realize the goal of green agriculture by accelerating urbanization, thus inhibiting ACE. The labor structure has a significant positive moderating effect on agricultural carbon emission reduction in the process of RII. In the threshold analysis, it is found that the rural labor structure has a single threshold nonlinear effect, and the effect of RII on inhibited ACE is more pronounced when the level of labor structure is more than the threshold of 0.829. This conclusion not only contributes to understanding the relationship between the RII, urbanization, labor structure, and carbon emissions, but also provides substantial support for further promoting the implementation of the rural revitalization strategy and achieving the dual-carbon goal. [ABSTRACT FROM AUTHOR]

Carbon neutrality is urgent as rapidly emerging economies aggravate their share of global energy demand. In China, the energy structure is dominated by fossil fuels, but it varies significantly across provinces. As an indicator of carbon neutrality, previous studies of decoupling between carbon dioxide emissions and economic growth focused at the national and sector levels in China. However, they overlook the role of industrial structure in decoupling at the provincial level. In this light, the following paper focuses on Qinghai Province, analyzing decoupling and its influencing factors for achieving carbon neutrality from an industrial structure perspective over 1990–2021. It uses the Tapio decoupling model to evaluate decoupling states and the Logarithmic Mean Divisia Index decomposition to evaluate the influencing factors. A Data Envelopment Analysis model of super-efficiency Slacks-Based Measure is used to evaluate the decarbonization efficiency. The study finds that the overall trend shifted from weak to strong decoupling. Strong decoupling dominated the primary industry while weak decoupling dominated the secondary and tertiary industries. Economic growth negatively impacted overall decoupling, while population had a marginal effect. Energy structure and intensity generally promoted decoupling. Additionally, the overall mean efficiency of decarbonization was 0.95, led by the tertiary industry. The paper concludes by discussing policy implications. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions while controlling air pollution is a dual challenge for China. However, few studies have analyzed whether there is a synergy between the two. In view of this, this paper takes the urban agglomeration in Central Henan as an example, uses multi-source remote sensing and panel data from 2000 to 2022 and analyzes the spatiotemporal evolution patterns and synergistic benefits of air pollution and carbon emissions based on the spatial distribution direction analysis model, coupling coordination degree model and multi-scale geographic weighting model. The results indicate the following: (1) Carbon emissions show a growing trend, but the difference in the carbon emissions of different cities is relatively large, showing the characteristics of "one center and two zones" in space. Air pollution shows a trend of first increasing and then decreasing. (2) The synergistic benefits have been continuously enhanced, and the overall unbalanced state has gradually become coordinated. There is no obvious aggregation feature. (3) The impact of socioeconomic factors on the synergistic benefit is obviously stronger than that of natural ecological factors, among which the total energy consumption, population density and industrial structure are the leading factors of the synergistic benefit of carbon emissions and air pollution. This study offers valuable insights for green development, high-quality growth and collaborative environmental governance within the Central Henan urban agglomeration. [ABSTRACT FROM AUTHOR]

Energy poverty and climate change are global challenges. While the Belt and Road Initiative (BRI) has fostered energy cooperation and alleviated energy poverty in BRI countries, its impact on climate change requires careful examination. This study investigates the impact of energy poverty alleviation on carbon emissions in BRI countries and analyzes the underlying mechanisms. In particular, this study constructs an integrated energy poverty assessment framework that includes three dimensions of energy accessibility, cleanliness, and affordability and utilizes the entropy-TOPSIS method to assess the status of energy poverty in 70 BRI countries. Subsequently, an empirical analysis using the Gini index as an instrumental variable was conducted to explore the impact of energy poverty alleviation on carbon emissions and the specific mechanisms. It is found that alleviating energy poverty in BRI countries will lead to an increase in per capita carbon emissions. However, improving energy cleanliness is effective in reducing per capita carbon emissions, while improving energy affordability has no significant impact on carbon emissions. This study shows that energy poverty alleviation and carbon emission reduction are two non-conflicting sustainable development goals. BRI countries can synergize energy poverty alleviation and carbon emissions reduction by focusing on clean energy development and providing energy subsidies. [ABSTRACT FROM AUTHOR]

As the strategic task of China's modernization, the implementation of new-type urbanization has an important impact on carbon emissions from the construction industry. To fill the gap in considering the spatial correlation and threshold characteristics of new-type urbanization on carbon emissions from the construction industry, this paper constructs a comprehensive evaluation indicator of new-type urbanization, and the spatial economic model and the threshold regression model are adopted to analyze the panel data of 30 provinces in China from 2002 to 2020. The results indicate that (1) carbon emissions from China's construction industry exhibit a significant positive spatial correlation, with more than half of provinces distributed as H-H and L-L types. (2) New-type urbanization has significant positive direct and indirect effects on carbon emissions in the construction industry; the labor efficiency, energy intensity, and development level of the construction industry and trade openness also have a significant spillover effect on carbon emissions from the construction industry. (3) At this stage, new-type urbanization exhibits a threshold effect on carbon emissions from the construction industry due to the different levels of development and energy intensity of the construction industry. After crossing the threshold value, the promotion effect of new-type urbanization on carbon emissions from the construction industry gradually increases. This paper provides a reference for promoting carbon emission reduction in the construction industry in the process of new-type urbanization. [ABSTRACT FROM AUTHOR]

Since the ecological protection and high-quality development of the Yellow River basin in China have become a major national strategy, reducing carbon emissions has become pivotal. Therefore, based on the relevant data of 53 cities from 2008 to 2021 in seven urban agglomerations in the Yellow River basin, this paper explores the overall situation and spatio-temporal evolution of environmental governance and carbon emissions in the urban agglomerations in the Yellow River basin using the entropy method, ArcGIS, slacks-based measurement models (SBM models), etc. Additionally, this paper quantitatively analyzes the pathways by which environmental governance affects carbon emissions in the urban agglomerations in the Yellow River basin. The results show that carbon emissions increased year on year from 2008 to 2021, the growth rate slowed down gradually and exhibited a downward trend, and the largest amount of carbon was emitted in 2019, at 3495 million tons. Before 2017, the growth rate of carbon emissions showed a trend of increasing year by year, with the largest increase rate being 11.17% in 2010. After that, the growth rate of carbon emissions continued to decrease and entered a stage of fluctuation. The growth rate of carbon emissions in 2020 was the lowest, reaching −5.66%. The environmental governance effect of urban agglomerations in the Yellow River basin exhibits a large gap; the regional difference is obvious, and the overall trend is rising. Environmental governance has a significant negative effect on carbon emissions in urban agglomerations in the Yellow River basin. The cross-terms of environmental governance, the energy consumption structure, industrial structure upgrading, green technological innovation, and foreign direct investment (FDI) have significant negative impacts on carbon emissions, while the indirect impacts on urban agglomerations have shown regional heterogeneity. The goal of reducing carbon emissions in urban agglomerations in the Yellow River basin is being realized gradually. Based on research conclusions, policy suggestions are put forward, hoping to provide ideas for environmental protection and high-quality development of urban agglomerations in the Yellow River basin. [ABSTRACT FROM AUTHOR]

In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China's carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China's carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China's carbon dioxide emissions. The predicted results showed that the peak of China's carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China's low-carbon development and provides a reference for an adjustment plan of energy structure. [ABSTRACT FROM AUTHOR]

Information and communication technology (ICT) has broken the boundaries of traditional geographical space, and thus substantially promoted the development of the logistics industry. However, the effect of ICT on the carbon emissions of the logistics industry (CELI) has not been systematically explained. With Chinese provincial data from 2000 to 2018, this paper uses static and dynamic panel models and three robustness test methods to unveil the nonlinear impact of ICT (taking Internet usage as a proxy) on CELI. Some significant findings are concluded in this paper. (1) The relationship between Internet usage and CELI is inverted U-shaped, with the curve's inflection point emerging in 2015. (2) The formation of the inverted U-shaped relationship is determined by the different roles of total factor productivity (TFP) and the express delivery business of the logistics industry. Specifically, Internet usage plays a positive role in TFP, which reduces carbon emissions but simultaneously increases carbon emissions by promoting the development of express delivery business. (3) The relationship between Internet usage and CELI is an inverted U-shape in the eastern and western regions, while in the central region, the relationship is linear. According to the findings, some policies at country, region and enterprise levels are highlighted in order for policymakers to utilize ICT to achieve reductions in carbon emissions. [ABSTRACT FROM AUTHOR]

Carbon emissions have become a global issue of increasing concern due to their detrimental impact on the environment. Efforts to combat rising emissions have been taken globally. Despite China's commitment to globalization, policymakers have faced challenges in adequately addressing this pressing issue. This paper aims to fill this gap by exploring a specific aspect of international trade, namely service trade. We theoretically analyzed the impact of service trade on carbon emissions and then empirically examined the impact using panel data from 2009 to 2019 of 30 provincial regions in China. Specifically, a non-linear model was used to capture the direct effect, particularly the potential non-linear relationship; a mediating effect model was applied to investigate the indirect effects; and a panel quantile model was adopted to examine the heterogeneity of the impact across different levels of carbon emissions. The research revealed: (1) The impact of service trade on carbon emissions exhibits a non-linear characteristic with a significant inverted U-shaped relationship being evident, indicating that the development of service trade can ultimately contribute to carbon reduction; (2) service trade can directly impact carbon emissions through its scale effect, while mechanism analysis showed that service trade can indirectly affect carbon emissions through its technological and structural effects, with carbon emission reduction also relying on these mechanisms; (3) There is significant heterogeneity in the impact of service trade on carbon emissions across geographic regions and at different levels of carbon emissions in China. [ABSTRACT FROM AUTHOR]

China's power industry is a major energy consumer, with the carbon dioxide (CO2) generated by coal consumption making the power industry one of the key emission sectors. Therefore, it is crucial to explore energy conservation and emissions reduction strategies suitable for China's current situation. Taking a typical cogeneration enterprise in North China as an example, this paper aims to establish a generalized regression prediction model for carbon emissions of coal-fired power plants, which will provide a reference for China to seek strategies for carbon peaking and carbon neutralization in the future. Firstly, in terms of the selection of influencing factors, this paper uses objective index screening methods, simulation means, and the eXtreme Gradient Boosting algorithm (XG-Boost) to analyze the feature importance of various influencing factors. It is concluded that the relevant influencing factors of the boiler feed water system have a strong correlation and characteristic importance with the carbon emissions results of coal-fired power plants. Therefore, this paper proposes to introduce these factors into the regression prediction model as auxiliary variables to more scientifically reflect the carbon emissions results of coal-fired power plants. Secondly, in the aspect of regression prediction model establishment, inspired by the sparrow's foraging behavior and anti-predation behavior, this paper selects the sparrow search algorithm (SSA) with strong optimization ability and fast convergence speed to optimize the super parameters of the long short-term memory network algorithm (LSTM). It is proposed to use the SSA-LSTM algorithm to establish the carbon emissions regression prediction model of coal-fired power plants. The advantage of the SSA-LSTM algorithm is that it can effectively simplify the super parameter selection process of the LSTM algorithm, effectively solve the global optimization problem, prevent the model from falling into overfitting and local optimization, and make the carbon emissions regression prediction model of coal-fired power plants achieve a better fitting effect. By comparing the performance indicators of the model before and after the improvement, it is found that the regression prediction effect of the SSA-LSTM coal-fired power plant carbon emissions regression prediction model, which introduces boiler feed water influencing factors, has been effectively improved. Therefore, the model proposed in this paper can be used to conduct a comprehensive impact factor analysis and regression prediction analysis on the carbon emissions intensity of China's coal-fired power plants, formulate targeted carbon emissions reduction countermeasures, and provide a theoretical basis for energy conservation and emissions reduction of China's coal-fired power plants. [ABSTRACT FROM AUTHOR]

Research and Development (R&D) have significant impacts on carbon emissions, yet the specific data on R&D capital stock and carbon emissions have not been released by Chinese officials, hindering in-depth analysis. In light of this, this study calculates the R&D capital stock of Chinese provinces based on the SNA2008 framework and the BEA method, and estimates the carbon emissions from energy consumption and cement production using the carbon emission factor method. It then examines the carbon emission reduction effects of the quality and quantity of R&D activities at the provincial level. We find that the quality of R&D activities has a significant carbon emission reduction effect, which is stronger in regions with high levels of economic growth and marketization, and this effect strengthens over time. Moreover, mechanism analysis shows that both the quality and quantity of R&D activities reduce carbon emissions by promoting industrial structure upgrading. This paper expands the analytical approach and framework for the carbon reduction effects of R&D activities and offers significant policy and practical implications. [ABSTRACT FROM AUTHOR]

This study explores the intricate dynamics of CO2 emissions stemming from transport within the tourism sector. It aims to unravel the multidimensional aspects of how transport-related tourism contributes to CO2 emissions and to elucidate the complex relationship between regional economic growth and CO2 emissions from transport-related tourism. Our study employed Logarithmic Mean Divisia Index (LMDI) and Panel Vector Autoregression (PVAR) models to analyze data from 30 Chinese provincial regions between 2010 and 2018. The tourism transport-related CO2 emissions were decomposed into four separate driving effects using the LMDI approach. Then, PVAR models were constructed to reveal dynamic interactions between each driving effect and per capita gross regional product (GRP). Our results demonstrate a decrease in both energy structure effect (tourism transport-related CO2 emissions from energy structure) and energy intensity effect (tourism transport-related CO2 emissions from energy intensity) during the pre-COVID decade. Notably, the positive impulse response of energy structure effect to per capita GRP is observed. However, we found no evidence of a cointegrated relationship between energy intensity effect and regional economic growth, although other factors demonstrated connections. These findings echo the necessity to integrate sustainable practices into the tourism transportation business, especially in the area of energy structure, in order to mitigate adverse environmental effects from tourism. This paper disseminates the main drivers of CO2 emissions in the tourism transport sector and their interrelationship with regional economic growth. It not only guides tourism policymakers in targeting efforts to reduce carbon footprints, but also sets a new benchmark for future studies on CO2 emissions. [ABSTRACT FROM AUTHOR]

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm's effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses. [ABSTRACT FROM AUTHOR]

With the introduction of China's dual carbon goals and the rise of the digital economy as a new model of economic development, the role of the digital economy in achieving green growth is garnering increasing attention. This paper constructs a comprehensive digital economy index, utilizing panel data from 30 provinces in China between 2006 and 2017, and employs the System GMM method to examine the comprehensive impact of the digital economy on low-carbon development from the perspectives of "emission reduction" and "efficiency enhancement". The primary findings indicate that the digital economy aids China in meeting its dual carbon goals by reducing carbon emissions (CEs) and increasing carbon emissions' efficiency (CEE). However, this impact varies with different components of the digital economy, and the role of digital finance is limited. This conclusion underscores the necessity of subdividing digital economy indicators. Our conclusions have been substantiated through various robustness checks, including but not limited to the method of distinguishing pure emission reduction from efficiency enhancement. Additionally, our research reveals the dynamic nonlinear effects of the digital economy in promoting emission reduction and efficiency enhancement. Green regulations that exceed a threshold value enhance emission reduction and efficiency, while the impact of sustainable technological innovation may be constrained by changes in policy and market environments. Academically, this study offers a new perspective on the complex relationship between the digital economy and its effectiveness in reducing carbon and enhancing efficiency. From a policy standpoint, it provides insights for China and other countries in advancing energy conservation and emission-reduction initiatives. [ABSTRACT FROM AUTHOR]

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the following research question: how do practitioners researching, working, or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that these calls for more data obscure the fact that there will always be uncertainty, and that we must learn to govern from within it. [ABSTRACT FROM AUTHOR]

Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. At the Paris Conference, Ghana pledged to reduce CO2 emissions by 15% and increase its renewable energy share to 10% of the national energy mix. Ghana's efforts to implement a low-carbon policy were, however, hampered for several reasons. Ghana is Africa's leading and the world's seventh largest gold producer. Mining has contributed significantly to foreign currency acquisition and government revenue. The gold mining sector contributes approximately 95% of Ghana's total mineral revenue. Considering these situations, the fundamental question is how Ghana's mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. We adopted 5-point Likert scale statement questions. The questionnaire survey was conducted among Ghana's four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy for carbon emission reduction, and the acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. We found that a major challenge among companies was the expected cost involved in implementing a low-carbon emission policy. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of low-carbon technology providers, such as solar Photovoltaics installation. [ABSTRACT FROM AUTHOR]

While artificial intelligence (AI) has had a great impact on the global economy, it has also brought new hope and opportunities for environmental protection. In this context, the authors of this paper collected balanced panel data for 30 Chinese provinces during 2006–2019 and studied the impact of AI development on local carbon emissions by using a two-way fixed-effect model. The results show that AI has significantly lowered carbon emissions. Using a series of robustness tests and instrumental variable (IV) analysis, it was found that the results are still reliable. Furthermore, mechanism analysis revealed that AI mainly reduces carbon emissions by improving energy structure and technological innovation. The lower the dependence on fossil energy, the higher technological innovation becomes, and the better the carbon reduction effect of AI. In addition, the regional heterogeneity test detected that the emission reduction effect of AI is best in the East, followed by the West, and not significant in the Central region. Therefore, to fully exploit the positive effects of AI on carbon emissions, this paper suggests accelerating intelligent transformation, formulating differentiated AI development strategies, promoting the green transformation of energy usage, and strengthening local human capital accumulation. [ABSTRACT FROM AUTHOR]

High-quality introduction and utilization of foreign investment is a new engine for China's further opening up and plays a significant role in urban carbon emission reduction in the context of carbon peaking and carbon neutrality strategies. By constructing a comprehensive assessment index of Foreign Direct Investment (FDI) quality and using panel data from 283 Chinese cities from 2006 to 2019, this paper systematically examines the impact of FDI quality on urban carbon emissions and identifies the mechanism of green technology innovation in order to clarify the correlation among the three. The results show that FDI quality upgrading can significantly reduce urban carbon emissions, which is mainly manifested by FDI management and FDI export capacity to suppress total carbon emissions. The above effect of FDI quality is more significant in cities with a low rationalization degree of industrial structure, high investment in science and technology, strong environmental regulation and high dependence on foreign capital. Green technology innovation is an intermediary mechanism for FDI quality to have a carbon emission reduction effect. Looking at different areas, foreign companies in the eastern regions can promote carbon emission reduction through independent innovation, while the central and western regions essentially rely on imitative innovation; however, there is a delay in the role of innovation quality. Further study suggests that the carbon emission reduction effect of FDI quality has an obvious characteristic of threshold, which will be slightly weakened with the increase in the quantity of imitative innovation but strengthened with the improvement of the quantity of independent innovation and the quality of innovation. The conclusion aims to provide policy implications for China to explore a sustainable pathway that combines promoted opening with carbon reduction targets. [ABSTRACT FROM AUTHOR]

Online grocery shopping is an emerging market. It caused visible growth in last mile delivery services, which has resulted in concerns about its external effects (e.g., pollution, land use, congestion). The study outlined in this paper proposes a grocery delivery concept where goods are transported by train to the customer's nearest station, and then the consumer either picks up the groceries (i.e., click and collect) or the goods are delivered to the customer's home, or to a locker. The focus of this paper is primarily based on the last mile delivery part of the supply chain (i.e., from the train station to the customer). The land use efficiency and emissions of each delivery concept have been evaluated based on the time-area concept and the Handbook Emission Factors for Road Transport (HBEFA 4.1), respectively. This large-scale simulation considers every household in Switzerland with various levels of demand and supermarket network densities. Two machine learning techniques (i.e., random forest and decision tree) have been used to categorise all neighbourhoods within Switzerland based on the best delivery method in terms of emissions and land efficiency. The results show that, depending on the scenarios (e.g., 10 or 100% of the households taking part), home delivery can be better for 76–89% of these communities when compared with a click and collect option based on their land use efficiency. [ABSTRACT FROM AUTHOR]

Climate change mitigation measures are often projected to reduce anthropogenic carbon dioxide concentrations. Yet, it seems there is ample evidence suggesting that we have a limited understanding of the impacts of these measures and their combinations. For example, the Inflation Reduction Act (IRA) enacted in the U.S. in 2022 contains significant provisions, such as the electric vehicle (EV) tax credits, to reduce CO 2 emissions. However, the impact of such provisions is not fully understood across the U.S., particularly in the context of their interactions with other macroeconomic systems. In this paper, we employ an Integrated Assessment Model (IAM), the Global Change Assessment Model (GCAM), to estimate the future CO 2 emissions in the U.S. GCAM is equipped to comprehensively characterize the interactions among different systems, e.g., energy, water, land use, and transportation. Thus, the use of GCAM-USA that has U.S. state-level resolution allows the projection of the impacts and consequences of major provisions in the IRA, i.e., EV tax credits and clean energy incentives. To compare the performance of these incentives and credits, a policy effectiveness index is used to evaluate the strength of the relationship between the achieved total CO 2 emissions and the overarching emission reduction costs. Our results show that the EV tax credits as stipulated in the IRA can only marginally reduce carbon emissions across the U.S. In fact, it may lead to negative impacts in some states. However, simultaneously combining the incentives and tax credits improves performance and outcomes better than the sum of the individual effects of the policies. This demonstrates that the whole is greater than the sum of the parts in this decarbonization approach. Our findings provide insights for policymakers with a recommendation that combining EV tax credits with clean energy incentives magnifies the intended impact of emission reduction. [ABSTRACT FROM AUTHOR]

As an important means of reducing carbon emissions, environmental regulation and green technology innovation have become a top research topic in academia in recent years. Existing studies have investigated the phenomenon of green technology innovation responses to carbon emissions, but there is less existing literature explaining this phenomenon from a spatial perspective and exploring the effect of the joint mechanism of carbon emissions and environmental regulation on green technology innovation. Based on the spatial econometric model, this study used the panel data of 41 cities in the Yangtze River Delta urban agglomeration from 2010 to 2019, to measure the impact of environmental regulation and carbon emissions on green technology innovation from the perspective of spatial interaction. The findings are as follows: green technology innovation in the Yangtze River Delta urban agglomeration shows a trend of "high in the east and low in the west" and has spatial autocorrelation; green technology innovation responds positively to changes in environmental regulations and carbon emissions and, by decomposing the spatial effects, it can be observed that there is a spatial spillover effect of environmental regulations and carbon emissions on green technology innovation in the surrounding areas; there is a substitution effect between environmental regulations and carbon emissions. This paper combines the above results and proposes the corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

Due to the problems of economic structure and the ways of industrial development, many countries have accumulated many ecological problems in the process of economic development, especially the increase in carbon emissions, the greenhouse effect, and the emergence of a series of problems, which makes the global ecosystem suffer severe challenges. Achieving green sustainable development has become a strategic development arrangement for all countries, and as the sports economy is closely linked to regional green development, it has become an important channel to influence regional green development by promoting sports development. Based on theoretical analysis, this paper empirically analyzes and tests the impact of government fiscal support for sports on regional carbon emissions and its mechanisms based on provincial panel data using baseline regressions, a mediated effects model, and a threshold model. We argue that the development of green and healthy sports can have a significant impact on regional green development and that government fiscal support for sports plays an important role in this process. We found that government fiscal support for sports can significantly reduce regional carbon emissions and thus promote regional green development. From the perspective of mechanism analysis, the government's fiscal support for sports leads to the orderly development of the sports industry and its associated industries, which improves the rationalization of the regional industrial structure and thus reduces regional carbon emissions. Moreover, as the carbon productivity of the region continues to rise, the impact of fiscal support for sports on regional low-carbon development gradually deepens. This paper confirms the important role of the government's emphasis on sports development in the process of achieving carbon peaking and carbon neutrality, verifies the mediating effect of industrial structure rationalization, and further quantifies the correlation through the threshold effect, extending the study of the influencing factors affecting regional carbon emissions. [ABSTRACT FROM AUTHOR]

As the largest carbon emitter in the world, China aims to reach its peak carbon emissions goal by the year 2030, while the construction industry makes a significant contribution to carbon emissions, directly affecting the country's commitment to meet its target. The present paper investigates the dynamic characteristics of carbon emissions released by China's construction industry under single- and multiple-scenario settings with altering economic growth rates, optimizing energy structures, adjusting industrial structures, and modifying carbon emission policy factors. The research results show that the total carbon emissions generally present a steady increase from the year 2000 and will reach 12,880.40 million tons (MT) by 2030 under a scenario without any intervention. Indirect carbon emissions released from associated industries account for over 96% of the total carbon emissions, while direct carbon emissions make a minor contribution to the total. Single and comprehensive scenarios have positive effects on reducing emissions; it was also observed that only under energy structure scenario III and comprehensive scenario III could carbon emissions released from the construction sector reach a peak value by 2030. The effects of emissions reductions as a result of single policies can be presented in the following order: energy structure, economic growth, carbon emissions policy factor, and industrial structure. All of the emissions reduction effects of multiple scenarios are superior to the single scenarios. The research results provide a basis and guidance for policymakers to adopt the correct steps to fulfill China's aim of achieving peak carbon emissions by the projected date. [ABSTRACT FROM AUTHOR]

Agricultural carbon mitigation is one of the most important components of China's carbon mitigation goals. This paper calculates China's agricultural carbon emissions (ACEs) from 2000 to 2020, studies the spatio-temporal evolution characteristics of China's ACEs, and aims to provide references for the development of China's ACEs reduction policies. The results show that the total ACEs in China presented an inverted "W" trend, with a slight increase of 2.81% in total. China's ACEs mainly came from livestock and poultry breeding and agricultural material inputs. Although the carbon emissions from rice cultivation and soil accounted for a relatively low proportion, they showed an uptrend of fluctuation during the study period. From a geographical point of view, ACEs in the regions of central China and western China were relatively high compared to those in eastern China. Carbon emissions decreased for crop farming and livestock and poultry breeding in most eastern provinces. Crop farming carbon emissions grew, while the emissions from livestock and poultry breeding declined in most of the central provinces. Carbon emissions increased for crop farming and livestock and poultry breeding in most western provinces. Therefore, the existing low-carbon agricultural policies should be optimized, crop farming technologies should be improved, and specific policies should be applied in the corresponding regions to support China's ACEs reduction. [ABSTRACT FROM AUTHOR]

China has entered a new stage of integrated development of urban and rural areas under the constraints of scarce land resources and the need for high-quality economic and social development. While there is concern about the state and speed of urban–rural integrated development (URID), increasing attention is being paid to efficiency improvement. This paper comprehensively measures the efficiency of URID from the input–output perspective, taking into account the impact of carbon emissions; it also studies the efficiency of URID and its developmental spatiotemporal characteristics in 73 cities within three major city clusters in the Yangtze River Economic Belt (YREB) from 2010 to 2019, and analyzes the input–output optimization strategies for URID within each of these major urban systems. The results show that (1) the comprehensive efficiency evaluation system constructed by the study can more objectively reflect the state and trends of URID. From 2010 to 2019, the efficiency of URID in the three major city clusters in the YREB showed a downward trend; in cities with better economic development, the efficiency of URID was lower than in cities with average economic development, where carbon emission indicators showed a significant impact. (2) The spatial distribution of URID efficiency in the three major city clusters in the YREB follows an inverted "U" shape; URID efficiency in the urban agglomeration in the middle reaches of the Yangtze River (MRYRUA) is higher than in the Chengyu urban agglomeration (CYUA), where it is higher than in the Yangtze River Delta urban agglomeration (YRDUA). (3) The input redundancy rates are high in the indicators for culture, sports and media, energy conservation and environmental protection, urban and rural communities, and housing security expenditures. Carbon emission redundancy has a negative impact on efficiency in URID. Based on the high redundancy rates of each input–output indicator, this paper proposes methods to optimize the efficiency of URID in each of the three major city clusters and provides directional guidance for promoting the high-quality development of regional urban–rural integration. [ABSTRACT FROM AUTHOR]

This paper used the Gini coefficient, standard deviation ellipse, and spatial autocorrelation model to analyze the overall changes, regional differences, spatio-temporal evolution pattern, and clustering characteristics of carbon emissions in 87 counties in Gansu Province from 1997 to 2017, based on which driving factors of carbon emissions were detected using the geographic detector model, so as to provide a reference for promoting low-carbon green development and ecological civilization construction in Gansu Province. The empirical research results found that county carbon emissions in Gansu Province showed a "first urgent and then slow" upward trend, and the difference in carbon emissions level has a slightly decreasing trend, and there are significant regional differences. Compared with other regions, the difference in county carbon emissions level in the Longzhong region has a smaller decline. Meanwhile, the county carbon emissions show spatial differentiation characteristics "medium-high and low-outside," among which the carbon emissions in areas with better economic foundations are much higher than those in other areas, and the spatial polarization effect is obvious. In addition, there is a significant spatial positive correlation between county carbon emissions. The counties with high-high clusters are relatively stable, mainly concentrated in the Longzhong region, while counties with low-low clusters are slightly reduced, mainly concentrated in the southern ethnic region and the Longdongnan region, and the county carbon emission clusters type has a spatial locking effect. This is mainly due to the large differences in economic scale, industrial structure, and population size in Gansu Province, and the interaction between economic scale and other factors has a more significant impact on the spatial differentiation of carbon emissions. Moreover, the leading influencing factors of county carbon emission differences also have regional differences. Therefore, differentiated and targeted carbon emission reduction strategies need to be implemented urgently. Due to the lack of real county energy consumption statistics, the research results need to be further tested for robustness. [ABSTRACT FROM AUTHOR]

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of "carbon peak and carbon neutrality" as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China's energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China's energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China's total energy carbon emissions show a "rising-declining-rising" trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China's Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China's energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China's catch-up provinces develop their economies at the expense of the environment and energy consumption. [ABSTRACT FROM AUTHOR]

The automotive sector is a significant contributor to global carbon emissions and, as such, to climate change. However, there is currently no publicly available sector-wide insight into the extent to which Original Equipment Manufacturers (OEMs) in the automotive sector have implemented decarbonization strategies and carbon management activities. This paper addresses this issue by collecting and analyzing the implementation status of all OEMs with annual sales of more than 500,000 light-duty vehicles. For this purpose, a structured set of criteria is developed that allows conclusions to be drawn for several areas of corporate carbon management. The analysis shows a sector-wide implementation of decarbonization strategies and carbon management activities. However, significant regional differences in external communication, data transparency and methodological inconsistencies emerge. This study provides both academics and practitioners in the automotive industry with valuable insights into current reporting practices, target setting and communicated mitigation actions. On this basis, scientists can project possible greenhouse gas (GHG) emission pathways, align their research with the focus of practitioners to provide them with science-based data, and work on the methodological inconsistencies. Furthermore, the developed criteria provide a useful toolbox for the holistic analysis of other industries. [ABSTRACT FROM AUTHOR]

Against the dual background of the vigorous shape of digital economy and the severe pressure for carbon reduction, exploring the mechanism of the relationship between digitalization level and carbon reduction of highly energy-intensive enterprises is one of the current hot topics in theoretical and practical circles. This paper selects panel data of listed companies with high energy consumption from 2007 to 2019 and adopts a threshold-regression method to empirically test the impact of digitalization level on corporate carbon emission reduction It turns out that the digitalization level of enterprises has both an "inverted U" effect and a heterogeneous effect on carbon emissions. Enterprise technological innovation has both a threshold action and a regulating action on the influence of digitalization level on carbon emissions. It can play a role in accelerating the digitalization level to the inhibition of the increase in carbon emissions in advance and has a reinforcing effect in accelerating the reduction of enterprise carbon emissions. [ABSTRACT FROM AUTHOR]

This paper investigated the effects of carbon emissions and board gender diversity on firm performance using quantile regression. This approach explores the heterogeneity of the effect of carbon emissions on the performance of firms and overcomes some of the drawbacks of OLS regression. This research aimed to identify whether carbon emissions significantly impact firm performance using accounting- and market-based performance measures and how this impact varies according to a company's size. Another objective was to determine how females on a board of directors could impact such a relationship. The data used were a sample of 1382 companies in emerging markets from 2008 to 2021. The findings show that carbon emissions negatively affect small-size companies consisting of both high-performing and low-performing companies; however, as the size of the companies increases, the effect of carbon emissions becomes positive regardless of whether they are high- or low-performing companies. The presence of females on a board has a minimally significant negative effect on a firm's performance, irrespective of whether it is conditioned on size. This research contributes to the literature on the impact of carbon emissions on company performance, both conditional and unconditional on size. Furthermore, the results show that the relationship between carbon emissions and performance depends on size, as revealed using the novel econometrics model developed in this study. This study also shows the importance of the presence of females on a board of directors. [ABSTRACT FROM AUTHOR]

Urban carbon emissions contribute significantly to global warming, but various factors impact these emissions. This study focuses on the correlation between urban form and carbon emissions. Urban form is an entity that can be directly manipulated and optimized by disciplines such as architecture, urban design, and urban planning. The improvement of urban form, particularly at the meso–micro scale, is relatively rapid and affordable compared to other carbon-related factors, such as macro-industry or energy structure. Therefore, conducting a study on the correlation between urban form and carbon emissions is crucial, and the findings will provide direct scientific support for low-carbon city planning. The paper combines bibliometric analysis with a literature review. First, we explore research hotspots and trends using bibliometric analysis. Second, we organize the literature review based on the main research components, methods, and findings in this field. Finally, we propose a framework and direction for future research. It was found that (1) numerous study methodologies are currently being used to investigate the direct and indirect impacts of urban form on carbon emissions, with Chinese scholars' research progressing rapidly; (2) the primary focus of the study is on the carbon emissions related to residents' consumption, and there are still issues with inconsistent measurement approaches; (3) there is more research conducted on the macro-scale of cities but not enough on the meso–micro scale. Future research must focus more on meso–microscale analysis, quantifying the key influences and pathways of urban form on carbon emissions. Additionally, it is crucial to establish a comprehensive research framework that can serve as a guide for more effective urban development aimed at reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Exploring carbon emissions in Suzhou, a city with a significant heavy industry presence, and understanding the factors that influence these emissions are crucial in achieving China's dual-carbon goals within the framework of global climate governance. This study utilized land use data and statistical data from 2008 to 2020 in Suzhou. The carbon emission coefficient method was employed to calculate carbon emissions, while GIS technology was used to analyze their temporal and spatial distribution, as well as carbon emission risk. Additionally, the LMDI model was applied to investigate the contribution of influencing factors and TAPIO was used to analyze the decoupling relationship between the main influencing factors and carbon emissions. The study yielded the following findings: (1) From 2008 to 2020, land use changes in all regions of Suzhou are obvious, and there are mutual transformations among different land types. (2) The overall carbon emission in Suzhou showed an upward trend, with a spatial distribution characterized by higher emissions in the northern regions and lower emissions in the southern regions. (3) The risk and pressure index of carbon emission in all regions of Suzhou are too large, and the amount of carbon emission and carbon absorption is seriously out of balance, resulting in an overall carbon imbalance. (4) Among the influencing factors on land use carbon emissions in Suzhou, energy intensity exerted the strongest negative effect, and economic growth showed the strongest positive effect. (5) Decoupling analysis showed that economic growth and carbon emissions are generally shifting towards a strong decoupling and, except for Zhangjiagang, other regions have a good development model. Based on the research findings, this paper proposes specific suggestions for reducing carbon emissions, aiming to provide actionable recommendations for Suzhou and other urban areas in achieving low-carbon and environmentally sustainable cities. [ABSTRACT FROM AUTHOR]

Energy efficiency in high-density urban areas is increasingly gaining more attention as the energy crisis and environmental issues worsen. Urban morphology is an essential factor affecting the energy consumption and solar energy development potential of buildings. In response to the research gap of previous studies that only analyzed building energy consumption or solar energy potential from a single objective, this paper aims to combine the two objectives of block-scale building energy consumption and solar development potential to explore the joint influence of urban residential morphological elements on correlations between the two. By investigating and summarizing 100 sample cases of Wuhan city blocks, 30 urban residential block prototypes were constructed. The correlations between the leading morphological indicators of the blocks with the building energy consumption and solar energy potential of the residential prototypes were quantified, respectively. The study results show that at certain floor area ratios, the highest solar power generation can be achieved with a mixture of high-rise slabs and high-rise towers, but the building energy intensity level is relatively high; combining building energy consumption and solar power generation, the residential block form of high-rise towers and low-rise villas has incredible energy-saving potential. In addition, the regression analysis results show that three block form indicators, namely the roof-to-envelope area ratio, compacity, and site coverage, have the most prominent influence on building energy intensity and solar power generation, and they all show positive correlations. This study can provide suggestions for urban residential planners and managers to promote urban energy conservation at the design stage. [ABSTRACT FROM AUTHOR]