Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China.

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant ß convergence characteristic, the speed of conditional ß convergence is significantly higher than that of absolute ß convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

What drives the green transformation of enterprises? A case of carbon emissions trading pilot policy in China.

As an important policy instrument to achieve greenhouse gas emission reduction, carbon emissions trading has also promoted the green transformation of enterprises while achieving carbon reduction targets. This study uses the implementation of the Chinese carbon emissions trading pilot policy (CETPP) as a quasi-natural experiment and analyzes the impacts of the CETPP on the green transformation of enterprises with the difference-in-differences (DID) method based on a sample of 297 listed Chinese A-share high-energy-consuming enterprises. The result findings show that CETPP can significantly promote the green transformation of enterprises. The heterogeneity analysis also reveals that CETPP has differential effects on enterprises belonging to different industries, which is caused by the fact that enterprises in different industries differ significantly in their green transformation paths and modes. Moreover, CETPP has a significant facilitating effect on the green transformation of non-state-owned enterprises compared to state-owned enterprises. Finally, marketization and enterprise social responsibility are two major mechanisms for the CETPP to promote the green transformation of enterprises. Our findings reveal that policymakers should further deepen the dynamic management of carbon emission allowances and guide enterprises to actively undertake social responsibility, thus leveraging the market regulation mechanism to promote the green transformation of enterprises.

Can carbon emission trading pilot policy drive industrial structure low-carbon restructuring: new evidence from China.

Industrial structure low-carbon restructuring is an essential channel to accelerate China's economic growth and fulfilling carbon emission reduction goals. Whether carbon emission trading pilot policy, as an influential carbon reduction instrument, fosters industrial structure low-carbon restructuring is of major significance to green economic development. This paper empirically investigates the shock of the carbon emission trading pilot policy on industrial structure low-carbon restructuring using the differences-in-differences (DID) and synthetic control method (SCM). Statistics reveal that sectors with low carbon productivity, such as electricity, steam, and hot water production and supply, ferrous metal smelting and pressing, etc., and sectors with high carbon productivity, such as electrical equipment and machinery, electronics and telecommunication equipment, etc. The industrial structure did not develop a stable trend of change before the 12th Five-Year Plan, but a stable trend of low-carbon restructuring emerged after such a period. Carbon emission trading pilot policy significantly facilitates industrial structural low-carbon restructuring. Carbon emission trading pilot policy inhibits energy-intensive industries in the industrial sector significantly, which promotes industrial structure low-carbon restructuring. Therefore, policymakers need to develop a nationwide carbon emission trading market that includes more industries to guide production factors to industrial sectors with high carbon productivity for industrial restructuring and dual carbon goals.

The impact of local government competition and green technology innovation on economic low-carbon transition: new insights from China.

The government-led Chinese economic development system determines that local government competition is a significant factor affecting the economic low-carbon transition. Driving an economic development mode with green technology innovation as the core is the critical path to realizing an economic low-carbon transition. Consequently, it is of significant practical relevance to investigate the impact of local government competition and green technology innovation on the economic low-carbon transition under the government-led Chinese economic development system. This paper systematically explores the nexus between green technology innovation and economic low-carbon transition in terms of local government competition perspective using the system generalized method of moments, panel threshold model, and geographically weighted regression on the basis of a dataset of 30 provincial administrative areas in China from a period of 2006-2019. The results indicate that green technology innovation significantly promotes the economic low-carbon transition. Local government competition not only significantly dampens the economic low-carbon transition but also considerably inhibits the positive effect of green technology innovation on the economic low-carbon transition. A significant N-shaped association is evident between green technology innovation and the economic low-carbon transition when green technology innovation is applied as a threshold, while such association is insignificant when local government competition is used as a threshold. Compared with high-competition intensity areas, green technology innovation promotes economic low-carbon transition weaker in low- competition intensity areas, while local government competition inhibits economic low-carbon transition stronger. However, local government competition significantly inhibits the positive effect of green technology innovation on the economic low-carbon transition in low-competition intensity areas, while insignificant in high-competition intensity areas. The geographically weighted regression technique as a whole also verified the above results. Therefore, policymakers should not only increase research and development investment in green technologies, but also develop a regionally linked low-carbon emission reduction system to avoid ineffective competition among governments to facilitate the earlier fulfillment of the "dual carbon" goal.

Green technology innovation and carbon emissions nexus in China: Does industrial structure upgrading matter?

Compared with traditional technological innovation modes, green technology innovation is more targeted for low carbon development and critical support for countries worldwide to combat climate change. The impact of green technology innovation on carbon emissions is considered in terms of fixed effect and mediating effect models through industrial structure upgrading. For this purpose, the sample dataset of 30 provincial administrative areas in China from 2008 to 2020 is employed. The results demonstrate that green technology innovation exerts significantly inhibitory effects on carbon emissions, whose conclusion still holds after removing municipalities and replacing the dependent variable. Industrial structure upgrading is vital for green technology innovation to diminish carbon emissions. There is significant regional heterogeneity in the effects of green technology innovation on carbon emissions, i.e., the direct and indirect impact of green technology innovation on carbon emission reduction is significant in the eastern-central area, but its effect is insignificant in the western region. Therefore, it is essential to realize carbon emission reduction by further bolstering green technology innovation and accelerating industrial structure upgrading to fulfill the synergy of technology and structure.

Does government intervention affect CO2 emission reduction effect of producer service agglomeration? Empirical analysis based on spatial Durbin model and dynamic threshold model.

Achieving carbon peak and carbon neutrality is an inherent requirement for countries to promote green recovery and transformation of the global economy after the COVID-19 pandemic. As "a smoke-free industry," producer services agglomeration (PSA) may have significant impacts on CO2 emission reduction. Therefore, based on the nightlight data to calculate the CO2 emissions of 268 cities in China from 2005 to 2017, this study deeply explores the impact and transmission mechanism of PSA on CO2 emissions by constructing dynamic spatial Durbin model and intermediary effect model. Furthermore, the dynamic threshold model is used to analyze the nonlinear characteristics between PSA and CO2 emissions under different degrees of government intervention. The results reveal that: (1) Generally, China's CO2 emissions are path-dependent in the time dimension, showing a "snowball effect." PSA significantly inhibits CO2 emissions, but heterogeneous influences exist in different regions, time nodes, and sub-industries; (2) PSA can indirectly curb CO2 emissions through economies of scale, technological innovation, and industrial structure upgrading. (3) The impact of PSA on China's CO2 emissions has an obvious double threshold effect under different degree of government intervention. Accordingly, the Chinese government should increase the support for producer services, dynamically adjust industrial policies, take a moderate intervention, and strengthen market-oriented reform to reduce CO2 emissions. This study opens up a new path for the low-carbon economic development and environmental sustainability, and also fills in the theoretical gaps on these issues. The findings and implications will offer instructive guideline for early achieving carbon peak and carbon neutrality.

Analysis of the Impact of Livestock Structure on Carbon Emissions of Animal Husbandry: A Sustainable Way to Improving Public Health and Green Environment.

Carbon emissions of animal husbandry have been gaining increasing attention due to their high share in global carbon emissions. In this regard, it is essential to assess the regional differences, dynamic evolution patterns, convergence characteristics, and the impact of livestock structure on carbon emissions of animal husbandry. Using data from 30 provincial administrative regions from 2000 to 2018 in China, this study employs the Thiel index method, kernel density analysis, and convergence analysis to quantify the impact of livestock structure on carbon emissions of animal husbandry. The statistical results reveal that carbon emissions of animal husbandry exhibit a rising and declining trend. Specifically, the carbon emissions of animal husbandry are highest in agricultural areas (with a declining trend), followed by agro-pastoral areas (with a declining trend), and the pastoral areas (with a rising trend). It is further revealed that there are no δ convergence and ß convergence of carbon emissions of animal husbandry. Finally, essential and useful policy recommendations are put forward to inhibit carbon emissions of animal husbandry.

Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China.

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted "U-shaped" characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted "N" shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China's carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east-west direction) changes greatly, while the latitude of the gravity center (north-south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace.

Objective: This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. Methods: The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. Results: A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. Conclusions: Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

Does Low-Carbon City Pilot Policy Alleviate Urban Haze Pollution? Empirical Evidence from a Quasi-Natural Experiment in China.

As a comprehensive environmental regulation, the low-carbon city pilot policy (LCCP) may have an impact on haze pollution. The evaluation of the effectiveness of LCCP on haze pollution is greatly significant for air pollution prevention and control. Taking LCCP as the starting point, in this study we constructed DID, PSM-DID, and intermediary effect models to empirically test the impact and mechanism of LCCP on haze pollution, based on the panel data of 271 cities in China from 2005 to 2018. The findings show that (1) LCCP has significantly reduced the urban haze pollution, and the average annual concentration of PM2.5 in pilot cities decreased by 14.29%. (2) LCCP can inhibit haze pollution by promoting technological innovation, upgrading the industrial structure, and reducing energy consumption. Among these impacts, the effect of technological innovation is the strongest, followed by industrial structure, and energy consumption. (3) LCCP has significantly curbed the haze pollution of non-resource dependent cities, Eastern cities, and large cities, but exerted little impact on resource-dependent cities, Central and Western regions, and small and medium-sized cities. (4) LCCP has a spatial spillover effect. It can inhibit the haze pollution of adjacent cities through demonstration and warning effects. This study enriches the relevant research on LCCP and provides empirical support and policy enlightenment for pollution reduction.