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This paper aims to study the relationship between regional logistics efficiency and economic development in 31 provinces of China and analyze their coupling coordination. To comprehensively evaluate the coordination between logistics and the economy, we introduced external indicators, such as carbon emissions, based on traditional evaluation indicators. We constructed an evaluation index system to coordinate regional logistics efficiency and economic development. The research approach used in this paper is the cross-DEA method, and data from 2010 to 2019 were selected for empirical calculation. The research findings indicate that Eastern and Northern regions of China show higher logistics efficiency, while Northwestern and Southwestern regions exhibit lower logistics efficiency. Coastal areas have relatively higher economic development levels compared to inland areas. Regarding the coupling coordination between logistics efficiency and economic development, different regions show temporal fluctuations and spatial disparities. Some regions demonstrate higher coordination between logistics efficiency and economic development, while others show lower coordination. Additionally, as the economy experiences rapid growth, logistics efficiency also improves, but the level of coordination varies among different provinces. [ABSTRACT FROM AUTHOR]

Green and low carbon reflect the high-quality development, while income distribution is an indicator of the balance of development. Is there a lack of fairness in the process of green and low carbon transition of enterprises? Using data from A-share listed companies from 2009 to 2016, this paper constructs a DID identification framework for controlling the endogeneity problem using the 2013 carbon trading policy pilot as a quasi-natural experiment to empirically test the impact of corporate low-carbon transformation on corporate labor income share in the context of carbon trading policy. The findings indicate that carbon trading policy decreases the labor income share of firms. In addition, we demonstrate that the low-carbon transition promotes labor productivity, suggesting that the Porter's hypothesis is confirmed in China, but the increase in labor wages is not in tandem with productivity growth, resulting in reduced labor income share. Heterogeneity analysis shows that the impact of carbon trading policy on labor income share is mainly pronounced in larger firms, high technology firms and persistent incumbent firms. Collectively, these results are expected to accurately improve our understanding on the impact of low-carbon transformation of enterprises on income distribution and provide reference for the government to formulate industrial policies and distribution mechanisms under low-carbon economy. [ABSTRACT FROM AUTHOR]

The scale of multi-microgrid (MMG) and hydrogen fuel cell vehicles (HFCVs) is increasing dramatically with the increase in the new energy penetration ratio, and developing an integrated energy system containing a multi-microgrid for hydrogen fuel vehicles brings great challenges to power grid operation. Focusing on the difficulties of the access of multiple microgrids for the low-carbon and economic operation of the system, this paper proposes an optimal interconnected heterogeneous multi-microgrid power–heat–carbon scheduling strategy for hydrogen-fueled vehicles. Firstly, an HFCV model is established, and then an optimal scheduling model is constructed for the cooperative trading of power–heat–carbon in a multi-microgrid, on the basis of which the low-carbon economic operation of the multi-microgrid is realized. The results of the case study show that the scheduling strategy in this paper reduces carbon emissions by about 7.12% and costs by about 3.41% compared with the independent operation of the multi-microgrid. The degrees of interaction of each multi-microgrid are also analyzed under different HFCV penetration rates. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach by combining a Backpropagation (BP) neural network with a non-angular and non-radial directional distance function to construct a BPNN-DDF model. This innovative model evaluates, decomposes, and analyzes China's agricultural sector's carbon emission rate across nine key subregions between 2010 and 2021. The key findings of this study are that China's agricultural carbon emission rate is decreasing, primarily due to technological advancements rather than technological efficiency. Subregions with robust economies and stable climates exhibit higher carbon emission efficiency, whereas those with underdeveloped economies, low agricultural technology, and volatile climates show relatively lower efficiency. The Dagum Gini coefficient analysis reveals a widening disparity in carbon emission rates among agricultural subregions, escalating from 0.174 in 2010 to 0.425 in 2021, indicating a growing gap between subregions that demands immediate attention. The kernel density distribution demonstrates an overall upward trend in China's carbon emission efficiency but also highlights an increasing divergence among subregions, particularly between the South China Area, the Huang-Huai-Hai Plain, and other regions. Therefore, this paper posits that strategies focusing on technological progress, sustainable agricultural development, regional development initiatives, and addressing inter-subregional imbalances will be crucial pathways for China's future low-carbon agricultural development. [ABSTRACT FROM AUTHOR]

The low-carbon economy represents a global transformation that encompasses production methods, lifestyles, values, national interests, and the destiny of humanity. As a significant contributor to carbon emissions, China has made a momentous strategic decision on carbon peaking and neutralization, infusing momentum into the global effort to address climate change. The rapid growth of the digital economy offers a fresh approach to achieving the "double carbon" objective and advancing the development of low-carbon transformation. Based on the panel data of 30 provinces in China, this paper uses the least square method to investigate the impact of digital economy development on regional low-carbon inclusive development. It is found that there is a significant inverted U shape in the impact of the digital economy on low-carbon inclusive development and the mechanism is resource allocation and ecological inequality. The threshold test found that the role of the digital economy in promoting low-carbon inclusive development shows a marginal decreasing trend. The inverted U-shaped impact of the digital economy on low-carbon inclusive development in the eastern and coastal areas and areas with a low level of factor productivity is more significant. Based on the knowledge factor spillover perspective, we found that the impact of the digital economy on low-carbon inclusive development has a spatial spillover effect, and this effect is more obvious under the role of R&D personnel mobility. [ABSTRACT FROM AUTHOR]

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods. [ABSTRACT FROM AUTHOR]

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000–2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society. [ABSTRACT FROM AUTHOR]

Reducing carbon emission intensity is crucial for achieving sustainable development. Carbon emission intensity is expressively affected by the issuance of green bonds. Thus, it is imperative to assess the influence of green bond issuance on carbon emissions and examine their correlation. Such research holds great potential to expedite the overhaul and modernization of businesses and to construct a circular economy system. This paper uses the spatial Durbin model to draw empirical conclusions by using data from 26 provinces in China between 2016 and 2021. Firstly, under different spatial matrices, it has been analyzed that an increase of 1% in the issuance of green bonds leads to a reduction of 0.306% or 0.331% in carbon emission intensity. It shows that green bonds have the potential to substantially reduce carbon intensity. Additionally, the intensity of emissions in the current period is driven by the intensity of emissions in the previous period. Secondly, the analysis of mediated transmission suggests that green bonds can ultimately reduce carbon emission intensity by changing the energy consumption structure or improving the efficiency of green technology innovation. Thirdly, the analysis of heterogeneity shows that the inhibitory effect of green bond issuance on carbon emissions is stronger in less economically developed regions than in economically developed regions. There is a significant inhibitory effect of green bond issuance in neighboring provinces on local carbon emission intensity. This effect is present only in provinces in less economically developed regions and not in economically developed regions. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

Under the goal of " carbon peaking and carbon neutrality," how to create a low-carbon and green development path for cities is an urgent problem in China. One of the effective ways to solve this problem is through industrial collaborative agglomeration. In this paper, panel data of 276 prefecture-level cities in China from 2010 to 2020 was selected to investigate the relationship between the collaborative agglomeration of manufacturing and producer service and carbon emission intensity by using a fixed-effects model, intermediary model, and SDM in multiple dimensions. The study found that the collaborative agglomeration of manufacturing and producer service industries significantly influences carbon emission intensity with an inverted U-shaped curve, and the agglomeration of manufacturing and producer service industries can influence carbon emission intensity through technological progress. In addition, the inverted U-shaped impact of collaborative agglomeration of manufacturing and producer services on carbon emission intensity has a spatial spillover effect, and the spatial spillover effect generated is stronger than the impact on the local area. Moreover, from the perspective of industry heterogeneity, compared with low-end producer services, the agglomeration of manufacturing and high-end producer services can play a better role in carbon emission reduction. Regarding regional heterogeneity, compared with the central and western regions, the impact of manufacturing and production services agglomeration on carbon intensity is far more obvious in the more economically developed eastern regions. [ABSTRACT FROM AUTHOR]

Coordinating policies is an essential guarantee for carbon emission reduction and sustainable development. Based on the theoretical framework of the policy paradigm, we quantitatively analyze 266 policy documents on promoting carbon emission trading and green financial policies from 2011 to 2022 using the content analysis research method. Based on the matching network of "policy objectives-policy tools," we analyze the synergistic characteristics of carbon emission trading policies and green financial policies in promoting carbon emission reduction targets and reveal the matching mode of "objectives-tools" of green financial policies by using social network analysis. It is found that, first, from the perspective of policy objectives, the main policy objectives of carbon emissions trading are to promote green innovation of enterprises, and the main policy objectives of green finance are to promote green development, which reflects the consistency and endogenous motivation of policy objectives. Secondly, command-control and market incentive policy tools are the main policy tools in the structure of policy tools. The proportion of public participation policy tools is small, and there is a structural asymmetry. Third, carbon emissions trading tools focus on supervision, adjustment, and platform construction. The green financial policy tools have the characteristics of guidance, public welfare, and externality. The two constitute a complementary, embedded, and integrated ' double synergy ' carbon emission reduction policy. Based on this, this paper puts forward some suggestions to promote policy coordination and provides a reference for China to achieve the dual carbon goal. [ABSTRACT FROM AUTHOR]

Enhancing the level of coupling coordination between the digital economy (DIE) and carbon emission efficiency (CEE) is not only an inevitable choice for achieving the goals of energy conservation and emission reduction and promoting green development in China, but also a key path to implementing China's "Double Carbon" strategy. Based on the relevant statistical data of 30 provincial-level regions in China from the period covering 2011 to 2019, this paper empirically analyzed the coupling coordination between the DIE and CEE and its influencing factors. In this study, an improved coupling coordination degree (CCD) model was used to evaluate the degree of the coupling and coordinated development of the DIE and CEE in provincial regions of China. Finally, based on the Technology-Organization-Environment (TOE) framework, a fuzzy-set qualitative comparative analysis (fsQCA) method was employed to identify the realization path of the coupling and coordinated development of the DIE and CEE from the perspective of configuration. The results demonstrated that the coupling coordination between the DIE and CCE in China demonstrated a gradual upward trend, and exhibited regional differences, showing a decreasing trend of east > middle > west. Regarding the influencing factors, no single influencing factor could act as a necessary condition for the high CCD, the coupling and coordinated development of the DIE and CEE is a multifactorial synergy. There were five paths for the high degree of coupling coordination between the DIE and CEE, which were divided into three types: organization-environment-led type, environment-led type, and technology-organization-led type. Furthermore, technological innovation level and industrial structure could substitute for one another in some conditions, and environmental regulation and economic development level were synchronized. These conclusions provide a theoretical basis for countries to formulate policies to promote the coupling and coordinated development of their DIE and CEE. [ABSTRACT FROM AUTHOR]

Low-carbon pilot policies are an important way to achieve the goal of "peak carbon neutrality" and are of great significance to China's international commitments. Based on a sample of 282 prefecture-level cities from 2006 to 2020, this paper investigates the impact of low-carbon pilot policies on urban carbon efficiency using a quasi-natural experiment with three batches of low-carbon pilot cities in 2010, 2012, and 2017, respectively. It is found that: (1) low-carbon pilot cities can improve urban carbon emission efficiency, which is still valid after a series of robustness tests such as the parallel trend test, placebo test, PSM-DID, and counterfactual test; (2) low-carbon pilot cities can enhance urban carbon emission efficiency by promoting the level of urban innovation and advanced urban industrial structure; and (3) the impact of low-carbon pilot policies on urban carbon emission efficiency is heterogeneous across cities with different geographical locations, population sizes, and resource endowment types. The findings provide policy insights for the promotion of low-carbon pilot policies and strengthening the construction of low-carbon pilot cities. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals. [ABSTRACT FROM AUTHOR]

Introduction: According to the seventh national population census in China, the proportion of people aged 65 and above in the population reached 13.5%. The aging trend is more pronounced in rural areas, indicating that China has entered an aging society. This article focuses on agricultural carbon emissions in the context of aging, studying the impact of rural population aging on agricultural carbon emissions. Study objectives: Under the background of deepening population aging, let us discuss how to maintain the green and sustainable development of agriculture in China. Methodology: Fixed effects and mediating effects models are used. Technical efficiency is used as a mediating variable to discuss the relationship between rural population ageing, technical efficiency and agricultural carbon emissions. Results: This paper adopts the classical carbon emission calculation theory of IPCC to measure agricultural carbon emissions from 2010 to 2019, and China's plantation carbon emissions show an "inverted U-shaped" trend, reaching a high level in 2015 and then starting to decline. In addition, the fixed-effects benchmark regression found that the aging of the rural population promotes agricultural carbon emissions, and the technical efficiency of agriculture suppresses agricultural carbon emissions. Finally, the mediating effect model is applied to explore the relationship between the three. Using technical efficiency as the mediating variable, it is found that under the masking effect, rural population aging will weaken agricultural carbon emissions through technical efficiency, thus achieving the suppression of agricultural carbon emissions. Policy recommendation: The formulation and modification of agricultural carbon-reducing policy row policies should take full account of the broader context of rural population ageing; increase the interconnectedness and interaction between rural population ageing and agricultural production technology, and actively play a positive role in promoting the efficiency of agricultural technology as a result of rural population ageing; and, in accordance with the actual situation of agricultural research, appropriately increase the strength of financial support for agriculture to improve agricultural technology and promote low-carbon development in agriculture. [ABSTRACT FROM AUTHOR]

Transitioning to low-carbon energy is key for reaching carbon neutrality and modernizing our energy systems, but it presents significant cost-related challenges for energy businesses. To foster optimal outcomes, this paper develops a game model including power generators, high-energy businesses, and consumers in the carbon trading framework. The model explores how different entities evolve their low-carbon strategies under social learning influence to optimize utility. Stability analysis of strategy and simulation experiments reveal the following findings: (1) Greater carbon quotas reduce power generators' low-carbon transition willingness while high-energy-consuming enterprises and consumers remain unchanged. (2) Higher prices for low-carbon products offered by high-energy-consuming enterprises boost low-carbon transition motivation across all parties. (3) Increased green premiums enhance revenue for all parties but are constrained by policy and carbon pricing. (4) Both direct and indirect increases in carbon emissions negatively impact the revenue and utility for all stakeholders. (5) Increasing social learning effect fosters a shift towards low-carbon strategies, accelerating the attainment of game equilibrium, and enhancing market stability and sustainability. This research provides decision support for carbon trading policy design and low-carbon transition of energy enterprises. [ABSTRACT FROM AUTHOR]

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [ABSTRACT FROM AUTHOR]

Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an "inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications. [ABSTRACT FROM AUTHOR]

The development of globalization has separated the production and consumption of products spatially, and the international trade of products has become a carrier of embodied carbon trade. This paper adopted the perspective of value-added trade to calculate the amount of embodied carbon trade of China from 2006 to 2015 and perform a structural decomposition analysis of the changes in China's embodied carbon trade. This study found that: (1) China's embodied carbon exports are much larger than its embodied carbon imports, and there are differences between countries. China imported the largest amount of embodied carbon from South Korea, and it exported the largest amount of embodied carbon to the United States. (2) The structural decomposition analysis shows that changes in the value-added carbon emission coefficient during the study period would have caused China's embodied carbon trade to decrease, and changes in value-added trade would have caused China's embodied carbon trade to increase. Therefore, countries trading with China need to strengthen their cooperation with China in energy conservation, emission reduction, and product trade. In order to accurately reflect China's embodied carbon trade, it is necessary to calculate embodied carbon trade from the perspective of value-added trade. [ABSTRACT FROM AUTHOR]

In the context of China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, as well as its strategy to build a strong transportation country, it is of foremost importance to study the carbon emission reduction effect of the opening of high-speed rail (HSR). This paper innovatively introduces the frequency of HSR stops as an indicator of HSR operation, and uses a time-varying difference-in-difference (DID) model, a mediating effect model and a spatial DID model to assess the direct and indirect impact, transmission mechanism, and spatial spillover effects of the opening and operation of HSR on carbon emission reduction based on a panel of 279 prefecture-level cities from 2003 to 2017. We found that the opening and operation of HSR significantly reduced urban carbon emissions. The direct transmission mechanism analysis shows that the opening of HSR can reduce carbon emissions by replacing highway passenger traffic. Indirect mechanism analysis shows that the opening of HSR can reduce carbon emissions through technological effect, structural effect and opening effect. The test of spatial spillover effect shows that the opening of HSR can promote carbon emission reduction not only in node cities, but also in neighboring cities. [ABSTRACT FROM AUTHOR]

Exploring the complex relationship between population change and surface urban heat island (SUHI) effect has important practical significance for the ecological transformation development of shrinking cities in the context of the prevalence of urban shrinkage and the global climate change. This paper compares the population change and SUHI effect between population shrinking region (Northeast Region, NR) and population growing region (Yangtze River Delta, YRD) in China, and explores their differences in driving mechanisms, using GIS spatial analysis and Geodetector model. Our results indicated that there are significant differences in population changes and SUHI intensity between these two regions. About 72.22% of the cities in the NR were shrinking, while their SUHI intensities increased by an average of 1.69°C. On the contrary, the urban population in the YRD shows a linear growth trend, while their SUHI intensities decreased by 0.11°C on average. The results of bivariate Moran's I index also indicated that the spatial correlation between the urban population changes and the SUHI intensity changes are not significant in the above regions. Furthermore, there are significant differences in the primary drivers of SUHI variations between these two regions. In the NR, underlying surface changes, including the changes of green coverage and built-up areas, are the most important driving factors. However, atmospheric environment changes, such as carbon dioxide emission and sulfur dioxide emission, are the key drivers in the YRD. Northam's theory of three-stage urbanization and environmental Kuznets curve hypothesis are powerful to explain these differences. [ABSTRACT FROM AUTHOR]

This paper examines the linear effects of economic growth on carbon emissions and their impact on mortality and morbidity rates in specific regions sub-Sahara Africa, Middle-East and North Africa, Europe and Central Asia (SSA, MENA, ECA). By analyzing longitudinal data for 82 panels over 30 years, we investigate the relationships between energy usage, per capita GDP, life expectancy, and carbon emissions. Our estimation results show positive correlations between energy use, carbon production, and life expectancy in both the combined sample and individual regions. However, death rate has a negative relationship with carbon production in the combined sample, MENA, and SSA regions. Per capita GDP positively influences carbon emissions and life expectancy in the combined sample and ECA, MENA, and SSA regions. We also identify asymmetric relationships between per capita GDP and carbon production, with evidence supporting the Environmental Kuznets Curve hypothesis for the combined and ECA samples, and an N-trajectory for SSA. These findings emphasize the importance of region-specific approaches to sustainable development, considering the unique environmental and economic challenges each region faces. Policymakers should consider our research insights when designing policies to mitigate the negative impacts of economic progress on the environment. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, internet enterprises have sprung up, bringing about huge power consumption due to constantly expanding enterprise scale, which in turn leads to significant carbon emissions. Additionally, the large influence of internet enterprises on the public and other businesses makes it particularly necessary to pay attention to their carbon emission reduction efforts. To explore the evolution path and patterns of carbon emission reduction among internet enterprises under the carbon neutrality goal, this paper constructs an evolutionary game model for internet enterprises to enter the carbon emissions trading market based on Externality and Sustainable Development Theories, while considering constraints from the carbon market, financial institutions and the public. The model utilizes Python 3.8.2 software for numerical simulations, aiming to push internet enterprises towards low-carbon development. The research findings indicate that: (1) Carbon emission reduction behavior of internet enterprises exhibits significant externality, and when constraints are weak or incentives are not evident, the motivation for enterprises to reduce carbon emissions is insufficient. (2) The carbon market can effectively promote carbon emission reduction among internet enterprises, and the strategy of entering the carbon market becomes the preferred option for these enterprises gradually. (3) Multiple constraints, including emission reduction costs, penalty for non-compliance, government subsidies, financing costs, opportunity losses, and reputation losses, can force internet enterprises towards low-carbon development. [ABSTRACT FROM AUTHOR]

This paper explores a two-level prefabricated building closed-loop supply chain (CLSC) comprising a retailer and a prefabricated building manufacturer (PBM) under carbon cap-and-trade legislation and the government subsidies of carbon emission reduction (CER). In this CLSC, the PBM and the retailer recycle used products through their independent recycling channels. The optimum pricing and CER strategies within both decentralized and centralized systems, respectively, are analyzed. The Stackelberg game is used in the decentralized system to determine the optimum PBM's CER level and the retailer's pricing. By analysis, it concludes that increasing the carbon trading price can stimulate prefabricated construction corporations to improve their CER level, and that the government subsidy rate has a great effect on the profits of the PBM. Numerical examples with sensitivity analysis are used to further evaluate the roles of important factors in the optimum CER and pricing solutions of the prefabricated building CLSC in two dissimilar systems. [ABSTRACT FROM AUTHOR]

Sustainable development is the theme of world economic development in the 21st century. As a key part of sustainable development, sustainable land use (SLU) encompasses economic development and environmentally friendly and social progress. In recent decades, China has formulated many environmental regulatory policies to achieve sustainable development and "carbon peaking and carbon neutrality (double-carbon)" goals, among which the carbon emission trading scheme (CETS) is the most representative and provides valuable research. In this paper, we aimed to reflect the spatio-temporal evolution of SLU in China under the influence of environmental regulatory policies through an indicator measurement strategy based on the DID estimation method. The study conclusions are as follows: (1) The CETS can effectively improve SLU from the perspectives of economic development and environmentally friendly progress, and the impact has primarily been in the pilot areas. And, its effectiveness is closely linked to local locational factors. (2) With respect to the dimension of economic development, the CETS has not changed the provincial distribution patterns of SLU; rather, it continues to remain "high to low, east to west". However, regarding the environmentally friendly progress dimension, the CETS has significantly changed the provincial distribution patterns of SLU, which are characterized by spatial agglomeration with urban agglomerations such as the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) as the core. (3) The screening results of the SLU indicators based on economic development showed that the CETS primarily improved the innovation capacities of pilot regions, and the impacts on economic levels were relatively small. Similarly, the screening results of the SLU indicators based on environmentally friendly progress showed that the CETS had primarily acted on reducing pollution emission intensity and strengthening greening construction, revealing only short-term effects on improving energy use efficiency. Based on the above, this paper explored the meaning and role of the CETS in more detail, with a view to providing insight into the implementation and formulation of environmental regulation policies. [ABSTRACT FROM AUTHOR]

As a major energy sources province in China, Shaanxi Province ranks top three in terms of raw coal production in China and undertakes the important task of ensuring national energy supply and security. Affected by the endowment of energy resources, fossil energy accounts for a large proportion of the energy consumption structure in Shaanxi Province, and it will face huge challenges under the severe carbon emission situation in future. In order to analyze the relationship between energy consumption structure, energy efficiency and carbon emissions, the paper introduces the concept of biodiversity into the energy industry. Taking Shaanxi Province as an example, the paper calculates the energy consumption structure diversity index and analyzes the impact of energy consumption structure diversity on energy efficiency and carbon emissions in Shaanxi Province. The results shows that the diversity index and equilibrium index of energy consumption structure in Shaanxi exhibits a slow upward trend in general. In most years, energy consumption structure diversity index in Shaanxi is higher than 0.8, and the equilibrium index is higher than 0.6. The carbon emissions of energy consumption in Shaanxi generally show increasing trend, and the carbon emissions have increased from 5,064.6 tons to 21899.67 tons from 2000 to 2020. The paper also shows that Shaanxi H index is negatively correlated with total factor energy utilization efficiency in Shaanxi, and positively correlated with carbon emissions in Shaanxi. The main reason is the internal substitution of fossil energy, and the proportion of primary electricity and other energy sources is still relatively low, which leads to a higher level of carbon emissions. [ABSTRACT FROM AUTHOR]

Over the past four decades, China have experienced rapid economic growth but also a widening urban-rural income gap and deteriorating air quality. Based on the panel data of 30 provinces in China from 2006 to 2017, this paper investigates the effect of narrowing the urban-rural income gap on carbon emission reduction and pollution control by using OLS method. The empirical results indicate that: the narrowing of the urban-rural income gap has a positive impact on pollution control, while there are regional differences in the impact on carbon emission reduction. In the perspective of the whole country and central and western regions, the narrowing of the urban-rural income gap is conducive to carbon emission reduction. However, the narrowing of the urban-rural income gap increases carbon emissions in the eastern regions where economic development is at high level. This paper provides a theoretical basis and policy reference for promoting urban-rural integration and construction of ecological civilization. [ABSTRACT FROM AUTHOR]

In order to explore whether China-Africa exchange will influence on the African environment. This paper selects four paths of China-Africa exchanges and explores the impact of each path on the African environment under the influence of different factors. We found that construction income and Africa's exports to China will increase Africa's carbon emissions. Foreign direct investment and China's exports to Africa will lead to a reduction in carbon emissions in Africa. The resource moderation will reduce the significance of the environmental impact of each path on Africa. Based on the above conclusions, several suggestions are made on the policies and actual operations in the path of China-Africa exchanges. [ABSTRACT FROM AUTHOR]

Facing increasingly severe environmental problems, as the largest developing country, achieving regional carbon emission reduction is the performance of China's fulfillment of the responsibility of a big government and the key to the smooth realization of the global carbon emission reduction goal. Since China's carbon emission data is updated slowly, in order to better formulate the corresponding emission reduction strategy, it is necessary to propose a more accurate carbon emission prediction model on the basis of fully analyzing the characteristics of carbon emissions at the provincial and regional levels. Given this, this paper first calculated the carbon emissions of eight economic regions in China from 2005 to 2019 according to relevant statistical data. Secondly, with the help of kernel density function, Theil index and decoupling index, the dynamic evolution characteristics of regional carbon emissions are discussed. Finally, an improved particle swarm optimization radial basis function (IPSO-RBF) neural network model is established to compare the simulation and prediction models of China's carbon emissions. The results show significant differences in carbon emissions in different regions, and the differences between high-value and low-value areas show an apparent expansion trend. The inter-regional carbon emission difference is the main factor in the overall carbon emission difference. The economic region in the middle Yellow River (ERMRYR) has the most considerable contribution to the national carbon emission difference, and the main contributors affecting the overall carbon emission difference in different regions are different. The number of regions with strong decoupling between carbon emissions and economic development is increasing in time series. The results of the carbon emission prediction model can be seen that IPSO-RBF neural network model optimizes the radial basis function (RBF) neural network, making the prediction result in a minor error and higher accuracy. Therefore, when exploring the path of carbon emission reduction in different regions in the future, the IPSO-RBF neural network model is more suitable for predicting carbon emissions and other relevant indicators, laying a foundation for putting forward more scientific and practical emission reduction plans. [ABSTRACT FROM AUTHOR]

The funding statement for this paper is incorrect. The correct funding statement is: This research is funded by the University of Economics and Law, Ho Chi Minh City, Vietnam, and Vietnam National University, Ho Chi Minh City, Vietnam.By Nguyen Thi Quy; Nguyen Chi Hai and Ha Thi Thieu DaoReported by Author; Author; Author [Extracted from the article]

Background and aim: Colonoscopy is the gold-standard screening test for colorectal cancer. However, it has come under scrutiny for its carbon footprint and contribution to greenhouse gas (GHG) emissions compared to other medical procedures. Notwithstanding, screening colonoscopies may have a positive effect on GHG emissions that is unknown. This study estimated the carbon emissions prevented by screening colonoscopies in the U.S. Methods: Using the reported number of screening colonoscopies performed annually in the U.S. and the absolute risk reduction (ARR) reported in the NorDICC trial, we calculated the expected minimum number of cancer treatment and surveillance visits prevented through screening based on the cancer stage. The average carbon emission averted per mile traveled was computed using the Environmental Protection Agency's (EPA) GHG equivalencies calculator. The final estimate of carbon emissions averted over a decade by screening colonoscopies performed in one year was determined. Result: 6.3 million screening colonoscopies performed in one year prevent 1,134,000 colorectal cancers over a ten-year period. Of these, 38∙3% (434,254) are localized, 38∙8% (440,281) are regional, and 22∙9% (259,465) are metastatic disease. The minimum number of post-diagnosis visits prevented is 11 for stage I, ≥ 21 for stage II, ≥25 for stage III, and ≥ 20 for stage IV disease, comprised of diagnostic, surgical evaluation, chemotherapy, and surveillance visits. The total number of visits prevented by screening is 2,388,397 for stage I, 5,254,421 for stage II, 13,120,369 for stage III, and 9,210,972 for stage IV disease. Approximately 395 million miles of travel and 158,263 metric tons of CO2, equivalent to 177 million pounds of coal burned, 19 billion smartphones charged, or 18 million gallons of gasoline consumed, were saved over ten years through screening. Conclusion: Colorectal cancer screening decreases cancer-related GHG emissions and minimizes the environmental impact of cancer treatment. [ABSTRACT FROM AUTHOR]

China's carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China's eight economic regions accurately is the premise for realizing China's carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance's static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions' carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions' carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents' living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors. [ABSTRACT FROM AUTHOR]

In the past 40 years, the continuous strengthening of the greenhouse effect has led to a significant increase in the global average temperature. Although people's understanding of climate change has been strengthened, the world has not yet witnessed a significant decline in pollutant emissions; hence it is imperative to get to the root cause. This paper is based on the STIRPAT model framework and uses the panel data of G20 countries over the period 1999–2019 to examine the role of financial development on carbon emissions under good governance. The results show that financial development significantly promotes carbon dioxide emissions, and the impact presents an inverted "U"-shaped trend when the quadratic term of financial development is introduced. Surprisingly, governance quality indicators increase carbon emissions. However, financial development accompanied by good governance suppresses carbon emissions. Moreover, according to the grouped results of developed and developing countries, different nations should adopt differentiated strategies in development finance to implement the carbon emission targets proposed by the G20. In addition, this paper also confirms the existence of the Environmental Kuznets Curve hypothesis. In light of this, policymakers should optimize the quality of governance while shifting their agendas toward environmentally responsible financial practices to promote financial development to improve environmental quality effectively. Furthermore, strengthen international cooperation, enhance public environmental protection concepts, and take joint actions to achieve low-carbon and win-win results. [ABSTRACT FROM AUTHOR]

The world's major economies are striving to control carbon emissions and avoid irreversible impacts on the natural environment. Therefore, innovative green technologies are crucial for both government departments and the private sector as an important way to address carbon emissions. This study aims to investigate the link between the government's smart city construction and corporate green innovation and optimize the policy guidelines that drive green innovation in enterprises. This study analyzes 6,104 panels of Chinese listed companies from 2007–2019. An approach called the Differences-in-Differences model was applied to evaluate hypotheses. The empirical results suggest that smart city pilots drove substantial green innovation in businesses. The marketization process has a moderating effect on the impact of smart city pilots on substantive green innovation in enterprises. Moreover, marketization process has a threshold effect in smart city pilots influencing the substantive green innovation of enterprises, and the effect of smart city drivers influencing the substantive green innovation of enterprises increases significantly when regional marketization process reaches a certain level. The findings of this study provide valuable guidance for policy designers to promote corporate green innovation at both the hardware facility level and the market system level of cities when developing policies related to green innovation. [ABSTRACT FROM AUTHOR]

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is China's substantial energy consumption base. The issue of carbon emission efficiency directly affects the ecological protection and high-quality development of the YRB. It is the key to achieving carbon peak in 2030 and carbon neutralization in 2060 ("30.60") double carbon emission reduction targets. Therefore, taking YRB as the research object, this paper first calculates the carbon emission and the decoupling state in the YRB. Secondly, the super-efficiency slacks-based measurement (SE-SBM) model is combined with the Malmquist index to analyze the temporal and spatial evolution characteristics of YRB's carbon emission efficiency from static and dynamic perspectives. Thirdly, the dynamic evolution characteristics of carbon emission efficiency are analyzed with the help of the Kernel density function. Finally, the Tobit model analyzes the influencing factors of YRB's and China's carbon emission efficiency. The results show that: (1) Among the nine provinces of YRB, the decoupling state between carbon emissions and economic growth in most provinces changes from weak decoupling to strong decoupling, and the decoupling elasticity index shows a fluctuating downward trend. (2) There are significant differences in carbon emission efficiency among provinces, but on the whole, it shows a stable growth trend. The high-value area of carbon emission efficiency is increasing, and the phenomenon of two-level differentiation is improving. The decline of the technological progress index causes the Malmquist index in Qinghai and Ningxia. On the contrary, the rise of the Malmquist index in the other seven provinces is caused by improving the technical efficiency index. (3) Industrial structure, economic development, and industrialization are the main positive factors affecting YRB's carbon emission efficiency. Urbanization level, green development level, and energy consumption level are the leading negative indicators hindering YRB's improvement of carbon emission efficiency. Therefore, targeted emission reduction suggestions should be formulated according to YRB's resource endowment and development stage characteristics. [ABSTRACT FROM AUTHOR]

The excessive carbon emissions not only intensify the global climate change, but also seriously restrict the sustainable development of social economy. However, improving industrial carbon emissions efficiency is the most directly effective way to reduce emissions. Therefore, accurate measurement and analysis of carbon emissions efficiency and evolution characteristics of China's industrial system is the basis for China to improve carbon emissions efficiency. Based on this, we adopted energy consumption method and input-output method to calculate and analyze the industrial carbon emissions efficiency and evolution characteristics of China from 2002 to 2015. The results show that (1) If carbon emissions from cement production are ignored and only energy-related carbon emissions are considered, the calculation results of carbon emissions efficiency of heavy industry will be overestimated about 30%. (2) Compared with 2002, China's industrial carbon emissions efficiency increased by about twice in 2015. Specifically, the optimization of carbon emissions efficiency in agriculture, construction, light industry and service industry is very obvious, which has increased by 5.65, 5.00, 4.69 and 4.68 times respectively; The optimization of carbon emissions efficiency in power sector, heavy industry, transportation and chemical industry was obvious, which increased by 2.55, 2.01, 1.86 and 1.47 times respectively; The carbon emissions efficiency of the fossil energy sector has decreased by 0.36 times. (3) The carbon emissions efficiency of various industries has significant differences. Among them, agriculture, service industry, light industry and construction industry are generally industries with higher carbon emissions efficiency, while power sector, fossil energy sector, transportation industry, chemical industry and heavy industry are generally industries with lower carbon emissions efficiency, especially power and fossil energy sectors. Based on these, this paper provides policy implications and scientific evidence for accurately improving carbon emissions efficiency from the perspective of carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

Global warming, caused by greenhouse gas emissions, is a major challenge for all human societies. To ensure that ambitious carbon neutrality and sustainable economic development goals are met, regional human activities and their impacts on carbon emissions must be studied. Guizhou Province is a typical karst area in China that predominantly uses fossil fuels. In this study, a backpropagation (BP) neural network and extreme learning machine (ELM) model, which is advantageous due to its nonlinear processing, were used to predict carbon emissions from 2020 to 2040 in Guizhou Province. The carbon emissions were calculated using conversion and inventory compilation methods with energy consumption data and the results showed an "S" growth trend. Twelve influencing factors were selected, however, five with larger correlations were screened out using a grey correlation analysis method. A prediction model for carbon emissions from Guizhou Province was established. The prediction performance of a whale optimization algorithm (WOA)-ELM model was found to be higher than the BP neural network and ELM models. Baseline, high-speed, and low-carbon scenarios were analyzed and the size and time of peak carbon emissions in Liaoning Province from 2020 to 2040 were predicted using the WOA-ELM model. [ABSTRACT FROM AUTHOR]

Without fundamentally altering how humans generate and utilise energy, there is no effective strategy to safeguard the environment. The motivation behind this study was to analyse the effectiveness of renewable energy in addressing climate change, as it is one of the most pressing global issues. This study involved the analysis of panel data covering 138 nations over a 27 year period, from 1995 to 2021, making it the latest addition to the existing literature. We examined the extent of the impact of renewable energy on carbon dioxide over time using panel, linear, and non-linear regression approaches. The results of our analysis, revealed that the majority of countries with the exception of Canada, exhibited a downward trend, underscoring the potential of increasing renewable energy consumption as an effective method to reduce carbon dioxide emissions and combat climate change. Furthermore, to reduce emissions and combat climate change, it is advisable for nations with the highest carbon dioxide emissions to adopt and successfully transition to renewable energy sources. [ABSTRACT FROM AUTHOR]

The unavoidable option for socially sustainable development is a low-carbon economy. One of the essential steps for China to attain high-quality development is reducing carbon emissions. It is necessary to realize low-carbon development in Sichuan, as it is not only an important economic zone but also an ecological protected area. The concurrent relationship among energy consumption, carbon emissions, and economic growth was examined in this study using the Tapio decoupling indicator, and the factors affecting energy consumption and carbon emissions in Sichuan were broken down using the logarithmic mean Divisia indicator (LMDI). The findings demonstrate a fundamental relative decoupling relationship between Sichuan's energy use and carbon emissions. Analysis of energy consumption and carbon emissions in Sichuan Province from 2005 to 2020 shows distinct patterns. From 2005 to 2012, in 2014, and from 2016 to 2020, the relationship between energy use and carbon emissions was relatively decoupled, with decoupling values ranging between 0 and 1. Absolute decoupling occurred in specific years: 2010, from 2013 to 2018, and in 2020. These periods are characterized by economic growth alongside reductions in carbon emissions. Factors affecting energy consumption and carbon emissions were consistently analyzed, showing similar impacts throughout the study periods. We find that population and economic growth are the main driving forces of these effects. The effects of energy intensity and industrial structure mainly play restraining roles, and the latter has a slightly weaker effect than the former. [ABSTRACT FROM AUTHOR]

Many pastoral farmers are searching for ways to lower the carbon emission footprint that is generated by livestock. Planting trees on the farm is currently a popular option for farmers to offset their emissions yet requires knowledge of suitable tree species and locations to plant them. This paper describes a decision-support tool aimed at helping farmers to create and visualise different planting designs while balancing the objectives of sequestering carbon and maintaining farm profitability. We take an innovative approach by combining virtual reality technology with biophysical models to create an environment where the user can actively create virtual future farm scenarios. Through the creation process, the user can simultaneously balance multiple objectives including farm aesthetics, economic returns, business and environmental ambitions, and carbon emissions (net) balance. For this proof-of-concept study, we incorporate virtual reality technology in Unreal Engine, environmental and financial data, and high-resolution spatial layers from an operational 400-hectare livestock farm in New Zealand. [ABSTRACT FROM AUTHOR]

The low-carbon city pilot policy is an environmental regulation aimed at reducing carbon emissions at the municipal level. Previous research mostly focused on evaluating its environmental performance and discovered it could enhance pilot cities' low-carbon innovation. However, the effects of the low-carbon city pilot policy on firm-level low-carbon innovation and their economic impact have yet to be investigated. This research uses a sample of Chinese A-share listed firms and the difference-in-difference method to examine the effect of the low-carbon city pilot policy on firms' low-carbon innovation. The baseline regression showed that the low-carbon city pilot policy could greatly encourage low-carbon innovation among firms in pilot cities. The mechanism analysis demonstrated that this improvement effect is attained by easing these firms' financing constraints. According to the heterogeneity analysis, we discovered that state-owned firms and firms situated in pilot zones with municipal secretaries who have larger promotion incentives are more susceptible to this policy. Additionally, the research on this policy's economic impact revealed that, following its adoption, the market value and comparative advantages of the firms in the pilot areas also increased. The findings of this study have implications for the enhancement and national expansion of low-carbon policies adopted at the city level. [ABSTRACT FROM AUTHOR]

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency. [ABSTRACT FROM AUTHOR]

In a changing climate, it is vital to focus on the co-benefits of the pollution control and carbon emission reduction. Based on calculation of emission equivalent, the synergy coefficient is further calculated to quantitatively analyze the co-benefits of air pollution control and carbon reduction in the Yangtze River Delta; Scenario analysis in co-benefits in the Yangtze River Delta from 2026–2035 is thoroughly proposed after STIRPAT model is designed based on influencing factors confirmation including population size, economic scale, industrialization level, urbanization rate and energy intensity from measuring dimensions of synergy coefficient. The results show that the Yangtze River Delta region can partially achieve synergistic emission reduction by 2026 and realize comprehensive synergistic emission reduction of air pollution and carbon emissions not late than 2030, which provides a reference for promoting the decision-making of the new stage of long-term carbon and pollution reduction, and further, realizing carbon peak regulation and carbon neutrality. [ABSTRACT FROM AUTHOR]

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China's energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future. [ABSTRACT FROM AUTHOR]

To better understand the agricultural resources and environmental problems of the provinces along The Belt and Road in China, it is critical to investigate their agricultural carbon emission efficiency and evolutionary trends. Based on the panel data of 18 key provinces and cities between 2006 and 2015, this paper evaluated the agricultural carbon emission efficiency with the data envelopment analysis–Malmquist model and further explored their dynamic evolutionary trends. There were several main findings. First, the efficiency levels of agricultural carbon emissions showed significant regional differentiation among the areas, with that along the 21st-Century Maritime Silk Road being much higher than that along the Silk Road Economic Belt. Second, technical efficiency was the key factor that restricted the improvement of the comprehensive efficiency of agricultural carbon. Third, most provinces invested in too many redundant and unreasonably allocated resources, showing a trend of diminishing returns to scale. Last, According to dynamic evolution analysis, the total productivity still demonstrated a diminishing trend. This paper provides some suggestions for effectively improve the efficiency of agricultural carbon emissions in China, such as optimize the agricultural industrial structure, increasing the investment of carbon emission reduction technology, and implementing a carbon emission quota clearing system. This paper contributes to the improvement of the environment in China. [ABSTRACT FROM AUTHOR]

Strategies to mitigate climate change often center on clean technologies, such as electric vehicles and solar panels, while the mitigation potential of a quality educational experience is rarely discussed. In this paper, we investigate the long-term impact that an intensive one-year university course had on individual carbon emissions by surveying students at least five years after having taken the course. A majority of course graduates reported pro-environmental decisions (i.e., type of car to buy, food choices) that they attributed at least in part to experiences gained in the course. Furthermore, our carbon footprint analysis suggests that for the average course graduate, these decisions reduced their individual carbon emissions by 2.86 tons of CO2 per year. Surveys and focus group interviews identify that course graduates have developed a strong personal connection to climate change solutions, and this is realized in their daily behaviors and through their professional careers. The paper discusses in more detail the specific components of the course that are believed to be most impactful, and the uncertainties associated with this type of research design. Our analysis also demonstrates that if similar education programs were applied at scale, the potential reductions in carbon emissions would be of similar magnitude to other large-scale mitigation strategies, such as rooftop solar or electric vehicles. [ABSTRACT FROM AUTHOR]

This paper explored how the government provides low-carbon subsidies for the manufacturers, retailers, and consumers in a secondary supply chain under cap-and-trade scheme. We calculated the best prices, emissions reductions, and the demands for common and low-carbon products when subsidizing each of the abovementioned market players. In particular, a comparative analysis of their equilibrium outcomes was made thereafter. The MATLAB simulation found that the optimal emissions reductions under the three subsidy modes were even and positively correlated to low-carbon subsidies, which, however, negatively correlated to the prices of both product types. Higher subsidies drove up demand for low-carbon products and dragged down that for common goods. But the prices of these products maintained the highest levels when consumers were subsidized; demand for common products was greater when subsidies went to retailers than to manufacturers or consumers, consequently generating the largest emissions and highest profits. When the subsidies were greater than 2pcBJ+4pc2K+12kε+4pc2e1J−18e1εJ48pcε , all three subsidy modes saw a drop in total carbon emissions. That being so, the government should offer proper subsidies before seeing energy-saving progress. [ABSTRACT FROM AUTHOR]

This paper re-examines the relationships between night-time light (NTL) and gross domestic product (GDP), population, road networks, and carbon emissions in China and India. Two treatments are carried out to those factors and NTL, which include simple summation in each administrative region (total data), and summation normalized by region area (density data). A series of univariate regression and multiple regression experiments are conducted in different countries and at different scales, in order to find the changes in the relationship between NTL and every parameter in different situations. Several statistical metrics, such as R2, Mean Relative Error (MRE), multiple regression weight coefficient, and Pearson's correlation coefficient are given special attention. We found that GDP, as a comprehensive indicator, is more representative of NTL when the administrative region is relatively comprehensive or highly developed. However, when these regions are unbalanced or undeveloped, the representation of GDP becomes weak and other factors can have a more important influence on the multiple regression. Differences in the relationship between NTL and GDP in China and India can also be reflected in some other factors. In many cases, regression after normalization with the administrative area has a higher R2 value than the total regression. But it is highly influenced by a few highly developed regions like Beijing in China or Chandigarh in India. After the scale of the administrative region becomes fragmented, it is necessary to adjust the model to make the regression more meaningful. The relationship between NTL and carbon emissions shows obvious difference between China and India, and among provinces and counties in China, which may be caused by the different electric power generation and transmission in China and India. From these results, we can know how the NTL is reflected by GDP and other factors in different situations, and then we can make some adjustments. [ABSTRACT FROM AUTHOR]