Spatiotemporal pattern and influencing factors of regional carbon emission efficiency: an empirical analysis of Jiangsu Province in China

The new quality productive forces is an advanced force committed to high-quality development and a significant driving force in achieving the "double carbon goal." To explore the impact of the new quality productive forces on carbon emissions in the Beijing-Tianjin-Hebei Region and enhance the positive effects of region's new quality productive forces on carbon emission levels and efficiency， this study aims to promote high-quality development in the Beijing-Tianjin-Hebei Region. Initially， based on urban panel data from the Beijing-Tianjin-Hebei Region from 2007-2022， in accordance with the industrial structure and current policies of the Beijing-Tianjin-Hebei region， a comprehensive evaluation method is utilized to calculate the index of new quality productive forces in the region， and the data envelopment analysis method is employed to measure the carbon emission efficiency. The development patterns and correlations between these two indices are analyzed across both temporal and spatial dimensions. Subsequently， an econometrics model is applied to examine the impact of new quality productive forces on carbon emissions and emission efficiency， and a mediation effect model is established to assess the mechanisms by which new quality productive forces affect carbon emissions through industrial structure and enhance carbon emission efficiency through technological innovation. The study produced several interesting results： ① The new quality productive forces can significantly reduce carbon emissions and improve carbon emission efficiency in the Beijing-Tianjin-Hebei Region， with results remaining robust after a series of tests， including winsorization test， alteration of control variables， and changes in estimation methods. ② After passing the mediation effect test， it is realized that new quality productive forces can influence carbon emissions through the regional industrial structure and enhance carbon emission efficiency by elevating the level of scientific and technological innovation. ③ Classifying cities in the Beijing-Tianjin-Hebei Region into resource-based cities and low-carbon pilot cities and conducting stratified regression analyses shows that the impact of new quality productive forces on carbon emissions and emission efficiency exhibits significant heterogeneity among different types of cities， providing crucial references for differentiated governance in the region. Based on the conclusions above， the study proposes recommendations from three aspects： constructing a coordinated development pattern of carbon emission reduction and efficiency， promoting the governance process of carbon emission reduction with the new quality productive forces， and developing and governing methods tailored to local conditions. These suggestions can provide new research perspectives and practical guidance for China to achieve the "double carbon goal."

Spatiotemporal analysis of carbon emission efficiency across economic development stages and synergistic emission reduction in the Beijing-Tianjin-Hebei region.

Climate change is a challenge facing all countries around the world. In response to the global climate change, China has pledged a two-stage carbon reduction goal of “dual carbon” to realize sustainable development. Industrial structure upgrading driven by green finance is an important way to reduce carbon emissions and achieve sustainable development. In this work, we investigate the impact of green finance on promoting industrial structure upgrading in Jiangsu province. We construct the grey correlation degree and coupling coordination degree model to analyze the relationship between green finance development and industrial structure upgrading with data from 13 prefecture-level cities in Jiangsu province from 2010 to 2021. The results demonstrate that green finance policies inhibit the financing tendencies of high-energy consumption industries and improve the financing difficulties of high-energy enterprises, forcing high-energy industries to transform and realize industrial upgrading. In addition, the improvement in green energy consumption structure and energy production efficiency will promote an improvement in carbon emission efficiency. Moreover, the development of green finance contributes to promoting industrial structure upgrading, putting forward new requirements for the development of green finance as well. Furthermore, the promotion of green finance and low-carbon industries provides a strong driving force for industrial structure upgrading as well as high-quality economic development in Jiangsu province. Therefore, the green finance policy system, as well as innovation in green financial products, needs to be further improved to accelerate industrial structure upgrading.

There is a dual impact of the digital economy (DE) on carbon emission efficiency (CE). An in‐depth study of the impact of the DE on “local‐adjacent” CE at different spatial scales can support the debate on whether the digital economy is empowerment or impairment. This study reassesses the DE at the urban level from a micro‐perspective by cross‐referencing the data of listed companies with the information obtained from city yearbooks. Considering the potential spatial correlation of the digital economy, we use the spatial Durbin model to examine 281 cities from 2011 to 2019 and study the influence of the DE on “local‐adjacent” CE. We further explain the pathways of this influence from the perspectives of green technological progress and industrial structural distortion. The study findings indicate that the impact of DE on local CE exhibits a “U”‐shaped pattern overall while its impact on neighboring CE shows an inverted “U”‐shaped pattern. These conclusions are still valid even after conducting a series of robustness tests, including spatial difference‐in‐differences and spatial replacement matrix. Further research reveals that the impact of DE on neighboring CE weakens with increasing spatial distance. The promoting effect of DE on neighboring CE peaks at 250 km, and the inverted “U”‐shaped pattern holds within a range of 300 km. In the initial stages, green technological progress can mask the negative impact of DE on local CE while simultaneously improving CE within a 250‐km range of neighboring areas. However, in the later stages, DE exacerbates the distortion of neighboring industrial structures, thereby reducing neighboring CE. The lack of synergy in the scale and growth rate of DE development in different regions significantly reduces the enhancement effect of the digital economy on CE.

Analysis of disequilibrium and driving factors of carbon emission efficiency: Evidence from five major urban agglomerations in China

Carbon emission efficiency of China’s industry sectors: From the perspective of embodied carbon emissions

A large number of foreign direct investment inflows not only promote China’s economic development but also bring environmental pollution problems., Improving carbon emission efficiency and cutting carbon emissions while maintaining China’s attractiveness to foreign investment has become a topic of concern in China. Firstly, this paper measures the carbon emission efficiency of different provinces in China with the super efficiency DEA model and studies the temporal and spatial characteristics of carbon emission efficiency. Secondly, the impact of FDI on carbon emission efficiency is investigated. FDI negatively affects carbon emissions but positively affects carbon emission efficiency. In addition, the interaction term of FDI and each channel negatively affects carbon emission efficiency, indicating that each channel has a negative impact on the relationship between FDI and carbon emission efficiency. Thirdly, the results of the sub-sample analysis show that the impact of FDI on carbon emission efficiency has the feature of regional heterogeneity. Based on the results, policy implications regarding the improvement of carbon emission efficiency are proposed.

To delve into the interrelationship between the green transformation of industry and the economy’s high-quality development, to promote the coordinated development of industrial carbon emission efficiency and digital economy, to expand the scope and research ideas related to economic and social sustainable development, and to provide scientific reference for the low-carbon sustainable development of regional economy, this article introduced a data-centric methodology for evaluating the collaborative advancement of both industrial enterprises’ carbon emission efficiency and the digital economy. To accurately gauge the carbon footprint of industrial enterprises, models focusing on carbon emissions as well as carbon emission intensity were employed. To enhance the precision of evaluation outcomes and mitigate biases stemming from subjective weighting factors, we employed the entropy weight method to objectively assign weights to each indicator. Furthermore, the super-efficient slack-based model (SBM) can solve the problem that the conclusions are biased, due to the different radial. Subsequently, a carbon-emission efficiency slack-based measure model, and models for coupling degree and coupling-coordination degree were formulated. Anhui, as a central province in China, is also an important province in the Yangtze River Delta integration development. Coordinated development of its carbon emission efficiency and digital economy has important implications for the sustainable economy advancements of other regions in China, and even other countries or regions in the world. Therefore, Anhui was selected to be the empirical research sample. The results showed that the comprehensive levels of these two systems followed an increasing trend, while the digital economy lagged. Their coupling degree fluctuated and reached its highest point in 2021, whereas their coupling-coordination degree increased, showing high coupling and low coordination overall. This study proposes specific countermeasures and suggestions for the relevant decision-makers.

Carbon emissions have become a global challenge that threatens human development. Governments have taken various measures to reduce carbon emissions, and green finance is an important and innovative way to realize carbon emission reductions. This paper uses data on a prefecture-level city in China to explore the impact of green finance on carbon emission intensity from both theoretical and empirical perspectives, and analyzes the mechanisms by which green finance affects carbon emission intensity. On this basis, this paper further analyzes the impact of green finance on carbon emission efficiency. In addition, this paper introduces variables related to the digital economy to perform a comprehensive examination of the moderating effect of digital economy development on the relationship between green finance and both carbon emission intensity and efficiency. The results indicate that green finance reduces carbon emission intensity and that green innovation, green total factor productivity and the transformation and upgrading of industry are important mediating mechanisms. Meanwhile, analysis shows that green finance improves carbon emission efficiency. This paper also finds that the digital economy significantly enhances the role of green finance in reducing carbon emission intensity and promoting carbon emission efficiency, and makes a positive contribution to promoting carbon emission reduction. The findings will contribute to strengthening the government’s capacity for environmental protection, developing green finance, and reducing carbon emissions.

What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data

The implementation of digital technology has become paramount to facilitating green and low-carbon development in dairy farms amidst the advent of digital agriculture and low-carbon agriculture. This study examined the impact of digital technology implementation on the carbon emission efficiency of Chinese dairy farms via an assessment of micro-survey data, incorporating an Undesirable Outputs-SBM model, a Tobit model, the propensity score matching technique, a quantile regression model, and an instrumental variable approach. This study examined the potential moderating influence of environmental regulations on digital technology applications and the carbon emission efficiency of dairy farms. The findings of the research indicate that the implementation of digital technology had a considerable beneficial consequence on the carbon emission proficiency of dairy farms. The statistical significance level of the mean treatment effect was 0.1161, with the most profound influence of precision feeding digital technology on the carbon emission efficiency in dairy farms. The application of digital technology has a more pronounced effect on dairy farms with lower levels of carbon emission efficiency compared to those with medium and high levels of carbon emission efficiency. The application of digital technology toward the carbon emission efficiency of dairy farms is positively moderated by environmental regulations. Finally, this paper puts forward some specific policy recommendations to achieve the strategic goal of low carbon and efficient development in dairy farms through the application of digital technology, which enriches the existing research on carbon emission reduction in dairy farms from theoretical and practical aspects.

The evaluation of inter-provincial carbon emission efficiency and the analysis of its influencing factors hold great practical significance for reducing carbon emissions and promoting sustainable development in ecological management. To address the shortcomings of existing research in the classification evaluation of carbon emission efficiency and account for the impacts of different environmental regulatory policies on carbon emissions, this paper aims to examine the impact of formal and informal environmental regulations on carbon emission efficiency. This is accomplished by utilizing a combination of the data envelopment analysis (DEA) model, entropy weighting, and k-means cluster analysis methods. The fixed-effects model is also applied to examine the influences of different factors on carbon emission efficiency under different categories. To conduct the case studies, carbon emission management data from 30 provinces in China are collected, and the results show the following: (1) Formal environmental regulations exhibit a “U-shaped” relationship with carbon emission efficiency, whereas informal environmental regulations have an “inverted U-shaped” relationship with carbon emission efficiency. (2) Under the cluster analysis of carbon emission efficiency, formal environmental regulations are found to have a stronger incentive effect on inter-provincial carbon efficiency compared to informal environmental regulations. This study carries significant theoretical and practical implications for China’s timely attainment of its double-carbon target.

China faces the key issue of improving the efficiency of carbon emissions, in its endeavor of building a low-carbon economy and reducing carbon emissions. This paper adopts the super-slack-based measure (SBM) model with a bad output to measure the carbon emission efficiency of each Chinese province from 2000 to 2019, and further uses the Tobit model to analyze the impact of environmental regulation, technological progress, and the interaction between the two on carbon emission efficiency. The results show that: China's carbon emission efficiency presents a large inter-provincial difference. Only a few provinces like Shanghai and Beijing reached the efficient frontier, while all the other provinces failed to do so. Overall, most Chinese provinces have a huge potential for improving carbon emission efficiency. By dividing China into three regions, it could be seen that the eastern region had the highest carbon emission efficiency, followed in turn by the central region and the western region. According to the spatiotemporal variation of carbon emission efficiency, most provinces with a high carbon emission efficiency belong to the economically developed eastern region, while most central and western provinces did not realize satisfactory carbon emission efficiency. With the elapse of time, the carbon emission efficiency in most provinces declined to varied degrees, while that of a few provinces was on the rise. The results of the Tobit model show that both environmental regulation and technological progress both significantly promoted carbon emission efficiency, but their cross term clearly suppressed carbon emission efficiency. When it comes to the control variables, carbon emission efficiency has a significantly positive relationship with opening-up, and a significantly negative relationship with industrial structure, financial development, energy structure, and urbanization level.

Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries

The freight transport industry is an important field in which to achieve the goal of carbon emission reduction within the transportation industry. Analyzing the spatial–temporal characteristics and regional differences in the freight transport industry’s carbon emissions efficiency (CEE) is an essential prerequisite for developing a reasonable regional carbon abatement policy. However, few studies have conducted an in-depth analysis of the freight transport industry’s CEE from the perspective of geographic space. This study combines the super-efficiency slack-based measure (SBM) model and the window analysis model to measure the freight transport industry’s CEE in 31 Chinese provinces from 2008 to 2019. We then introduced a spatial autocorrelation analysis and the Theil index to analyze the spatial–temporal evolution characteristics and regional differences in the freight transport industry’s CEE in China. The results show that (1) the overall level of the freight transport industry’s CEE is low, with an average of 0.534, which showed a weak downward trend during the study period. This indicates that the freight industry’s CEE has not improved, and there is a massive requirement for energy conservation and emission reduction. (2) From 2008 to 2019, CEE gradually shows a spatial distribution pattern of being “low in the west and high in the east,” with a significant, positive spatial correlation (all passed the significance level test at P < 0.01). This indicates that the spatial diffusion and inhibition of the freight transport industry’s CEE in adjacent areas cannot be ignored. (3) The overall differences in the freight transport industry’s CEE show a fluctuating upward trend from 2008 to 2019. The inter-regional differences of the three regions (east, central, and west) are the main contributors of the total differences. Therefore, narrowing inter-regional gaps in CEE is one of the main ways to improve the freight transport industry’s CEE.