Coordinated effects of energy transition on air pollution mitigation and CO2 emission control in China

Unlocking a greener future: The role of digital finance in enhancing green total factor energy efficiency

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

Clean energy production index and CO2 emissions in OECD countries

This study aimed to analyze the relationship between the energy transition index recently developed by Lau et al. (2023) and CO2 emissions in OECD countries. The topic is relevant, as the energy transition is a sustainable path to reduce CO2 emissions in countries. The CS-ARDL approach is used to estimate the short-run and long-run coefficients. Additionally, the Dumitrescu and Hurlin (2012) test is employed to determine the causal relationship between the variables. It was found that a 1 % increase in clean energy production reduces CO2 emissions by 0.33 % and 0.23 % in the short and long run, respectively. Furthermore, a bidirectional causal relationship exists between CO2 emissions and the energy transition indicator. This suggests that policies implemented by OECD countries to reduce carbon emissions will inevitably entail the adoption of cleaner energy sources.

Synergistic relationship and interact driving factors of pollution and carbon reduction in the Yangtze River Delta urban agglomeration, China

The urban agglomeration is the most concentrated region of economy, population, and industry. It is also the key area of carbon emissions (CE) and air pollution management. CE and air pollution have the possibility of collaborative governance due to the same root and the same source of them. To achieve the goal of sustainable development, it is important to study the coordinated relationship of CE and atmosphere pollutants in urban agglomerations. However, most researches have ignored the synergistic relationship between CE and air pollutants. Furthermore, there is limited current study on the driving factors of the synergistic relationship between air pollutants and CE. To fill these research gaps, we first explore the spatial-temporal evolvement law of CE and PM2.5 utilizing satellite remote sensing data sets. Secondly, we analyze the synergistic relationship of CE and PM2.5 in the Yangtze River Delta (YRD) urban agglomeration using the coupling coordination degree (CCD) model from 2000 to 2020. At last, we further study the influencing factors of the synergistic relationship of CE and PM2.5 based on the geo-detector model. The findings display that (1) in 2020, the total CE in the YRD urban agglomeration is 2.24 billion tons, accounting for 22.5% of China, but its growth rate has gradually dropped to 7.25%. Besides, the PM2.5 concentration shows a waving upward-downward tendency. In 2020, the range of higher PM2.5 regions significantly decreased, and air quality gradually improved. (2) The CCD of PM2.5 and CE is at the coordination level in general (CCD > 0.6) between 2000 and 2020, which can realize the coordinated governance of pollution and carbon reduction. (3) Digital elevation model (DEM), topographic relief (RDLS), and population density have a higher degree of influence on the synergistic relationship between CE and PM2.5. Besides, the interaction of topographic and socio-economic factors is the main driving factor between the two. This paper can provide a referral for decision-makers to synergistically make plans for pollution and carbon reduction and facilitate the sustainable development of urban agglomerations.

Exploring the impact of natural resources and energy transition on CO2 intensity in China

As reported at the 26th UN Climate Change Conference (COP26), worsening climate situation has led to frequent extreme weather events around the world. The main cause of climate change is carbon emissions from human activities. While realizing rapid economic development, China has become the world's largest energy consumer and carbon emitter. To achieve the goal of carbon neutrality by 2060, it should reasonably use natural resources (NR) and promote energy transition (ET). In this study based on panel data on 30 Chinese provinces from 2004 to 2020, second generation panel unit root tests were performed after validating slope heterogeneity and cross-sectional dependency. Mean group (MG) estimation and error correction model were used to empirically test the impact of natural resources and energy transition on CO2 intensity (CI). The results show that natural resources exerted adverse effects on CI, whereas ET, economic growth and technological innovation were beneficial to CI. Analysis of heterogeneity indicates that natural resources exerted the greatest impact on CI in central China, followed by west China. Its impact in east China was positive but did not pass significance test. West China achieved the best result in carbon reduction through ET, followed by central China and east China. The robustness of the results was checked with augmented mean group (AMG) estimation. Our policy suggestions are to urge reasonable development and utilization of natural resources, accelerate ET to replace fossil fuels with renewable energy, and implement differentiated policies on natural resources and ET based on regional characteristics.

Toward China's green growth through boosting energy transition: the role of energy efficiency

The primary purpose of this study is to quantitatively evaluate whether low-carbon energy transition has achieved preliminary progress in facilitating China's green evolution of economy following the provincial dataset. Besides, how improved energy efficiency moderates the influence of energy transition on green growth and the mediation effects are also quantitatively explored. The primary findings insist that low carbonization energy transition is positively associated with green growth, a finding detected by a series of sensitivity checks. Besides, the reciprocal actions between adjusting energy structure and raising energy productivity can effectively strengthen their roles in promoting green growth. In addition, boosting clean energy transition plays an indirect role in green growth by enhancing energy productivity while directly facilitating green growth. Following the three outcomes, this study puts forward some policy implications on enhancing governmental supervision, promoting clean energy evolution, and upgrading ecological protection technologies.

Driving forces and relationship between air pollution and economic growth based on EKC hypothesis and STIRPAT model: evidence from Henan Province, China

The aim of this research is to analyze the main influencing factors and relationship between atmospheric environment and economic society. Using the panel data of 18 cities in Henan Province from 2006 to 2020, this paper employed some advanced econometric estimation included entropy method, extended environmental Kuznets curve (EKC) and STIRPAT model to conduct empirical estimations. The results show that most regions in Henan Province have verified the existence of the EKC hypothesis; and the peak of air pollution level in all cities of Henan Province generally occurred in around 2014. Multiple linear Ridge regression indicated that the positive driving forces of air pollution in most cities in Henan Province are industrial structure and population size; the negative driving forces are urbanization level, technical level and greening degree. Finally, we used the grey GM (1, 1) model to predict the atmospheric environment of Henan Province in 2025, 2030, 2035 and 2040. What should pay close attention to is that air pollution levels in northeastern and central Henan Province will continue to remain high.

The impact and transmission mechanism of green credit policy on energy efficiency: new evidence from China

Green credit policy is the primary means for financial institutions to fulfill their environmental responsibilities. It is an issue worthy of attention whether green credit policy can achieve the effect of energy conservation, efficiency improvement, pollution reduction, and carbon reduction. This study uses the difference-in-difference method to test the impact of green credit policy on energy efficiency. The results show that green credit policy led to a significant decrease in energy intensity of green credit-restricted sectors while impeding the advancement of green total factor energy efficiency. The heterogeneity results show that the energy efficiency of large-scale, light textile manufacturing, resource processing industries, and clean industries are more significantly affected. Green credit policy can achieve energy conservation and has a linkage effect on pollution and carbon reduction. Although the constraint effect of green credit policy has effectively suppressed energy intensity, it also leads some industries to face a vicious cycle of "enhanced financing constraints-weakened innovation impetus," which in turn makes it challenging to improve green total factor energy efficiency. The above findings confirm the effectiveness of green credit policy in energy conservation and emission reduction. Also, they indicate the necessity of further improvement of the green financial policy system.

China's ambitious low-carbon goals require fostering city-level renewable energy transitions

Cities in China, as elsewhere, are increasingly playing a crucial role in mitigating climate change. We developed a panel dataset on renewable energy transition in Chinese cities, and assessed the CO₂ emissions reduction of city-level renewable energy transition. We found that city-level renewable energy transition only reduced 446 million tonnes of CO₂ emissions from 2005 to 2019. Moreover, the 2030 carbon peak target will be missed in the business-as-usual scenario. The CO₂ emissions reduction of city-level renewable energy transition will significantly increase in the policy constraint scenario and in the technology breakthrough scenario, and the 2030 carbon peak target will likely be reached in both these scenarios, with a range of possible CO₂ emissions in 2030 equal to 8.34-10.43 and 8.00-10.07 billion tonnes, respectively. In this study, we were the first to assess the historical contribution and prospective trajectory of CO₂ emissions reduction of China's city-level renewable energy transition.

Reconfirmation of the symbiosis on carbon emissions and air pollution: A spatial spillover perspective

Many studies have confirmed the co-emission characteristics of air pollution and carbon emissions. However, studies on the evolution and synergistic factors of the symbiosis of air pollution and carbon emissions over long time scales from a spatial spillover perspective are rare. Here, we identify the spatial evolution and agglomeration characteristics of carbon emissions and air pollution symbiosis by applying local autocorrelation analysis and geographical concentration and by using the dynamic spatial autoregressive model for multiple synergistic factors at city levels during 2006-2019 in China. The results are: (1) The spatial agglomeration and symbiosis of carbon emission and air pollution are similar and show strong spatial locking, as well as path-dependent properties. (2) The spatial imbalance of carbon emission agglomeration and pollution agglomeration gradually improved over time; the concentration centers are all located in Henan province, shifting northward. (3) The symbiosis between both carbon emission agglomeration and pollution agglomeration has significant "spatial and temporal scale effects", and the economic growth is nonlinear. Additionally, innovation vitality has a negative synergistic driving effect on this relationship. In addition to the results above, rapid industrialization and urbanization are taking place in China. Hence, serious actions against greenhouse gases and air pollutants are imminently needed.