With global climate action lagging behind and record-breaking heat waves occurring with increasing frequency, China’s role in achieving the temperature control targets of the Paris Agreement has become increasingly critical. As the world’s largest carbon emitter, the pace, trajectory, and regional variations of China’s structural transformation will profoundly influence the global process of deep decarbonization. Based on provincial panel data from 2000 to 2021, this study integrates industrial upgrading (ISU), energy decarbonization (EDI), and population aging (PAG) into a unified analytical framework to systematically examine the joint mechanisms through which these three structural transformations affect environmental sustainability (CI). Different from existing studies that mainly explain carbon emission reduction performance from a single transformation dimension, this paper combines spatial convergence models and panel threshold regression models to further identify the spatial convergence characteristics, dynamic impact mechanisms, and nonlinear interaction effects of carbon intensity evolution. This provides empirical evidence for understanding the regional differences and phased patterns of China’s low-carbon transformation. The results reveal that CI exhibits a pronounced spatial gradient, increasing progressively from northwest to southeast, reflecting significant regional heterogeneity in decarbonization pathways. Overall, the triple structural transformation significantly promotes the decline in carbon intensity, with EDI playing the dominant role, followed by PAG and ISU. Moreover, economic growth strengthens the mitigating effect of PAG on CI. Both EDI and ISU exhibit stage-dependent effects, with their emission-reduction impacts intensifying as economic development surpasses critical threshold levels. Notably, ISU demonstrates a self-reinforcing decarbonization effect once the threshold is crossed. The impact of structural transformation on environmental sustainability is highly dependent on thresholds and context. PAG significantly cuts CI in all regions (strongest in the central, weakest in the eastern); EDI achieves significant emission reduction in the eastern and central regions. ISU lowers CI markedly in the eastern and western regions, yet this effect has not been observed in the central region. This study provides a mechanism-based explanation for regional heterogeneity in China’s low-carbon transition, and offers policy implications for designing differentiated climate strategies and optimizing the implementation of nationally determined contributions (NDCs).

Given that war can have a serious impact on the climate, this article is aimed to discuss the impact of warfare on carbon emissions by examining changes in CO2 before and during the war in Syria based on the kaya constant equation and the LMDI decomposition method. In the decade before the war, population was the largest contributor, making up 32.64% of the total 51.02% increase in carbon emissions. The only factor that offsetting carbon emissions was energy intensity, making a 22.30% curbing effect. In the early stage of the war, carbon emissions decreased by 56.38%, in which per capita GDP contributed 37.55% of the total CO2 decline. Carbon intensive of energy was the only factor promoting the carbon increase with a 4.67% contribution. In the late war, carbon emissions start to resume slow increase with energy intensity and economy turning negative to positive. It can be speculated that the impact of the war on CO2 emissions: (i) in the first years of the war, CO2 would drop significantly at the cost of significant population decline and economic recession, the least desirable and the worst way to reduce carbon emissions. (ii) if evolves into a prolonged war, it would reverse carbon emissions from decline to increase, although the population and the economy are both falling. This research, therefore contends that once war is triggered, there is no other solution to prevent this worst-case scenario of Population Decline - Economic Recession - Increased Carbon Emissions from happening, unless the war is stopped immediately.

The lockdown policies related with the COVID-19 pandemic brings carbon emissions slump, but emissions potentially restore to increase as lockdown policies relaxed and the economy recovers. In this context, this study aims to explore the changes in carbon emissions and their underlying factors in the post-COVID-19 era from a national and sectoral perspective by drawing on the experience of carbon emissions before and after the 2008 global crisis. The latest extreme event and carbon emission trends might provide some implications for curbing potential emission rebound after the pandemic. The results indicate that, (i) developing countries like China and India still struggle with carbon reduction, which need more efforts made to control continuously increased carbon emission; (ii) energy intensity and economic level are respectively major contributor and inhibitor to national and industrial emission reduction whether in developing or developed countries, while in developed countries, energy intensity has a slightly stronger impact on carbon emissions than economic level. Carbon intensity had both positive and negative impact on carbon emission, and population scale usually drove carbon emission increase, particularly in developing countries like India; (iii) Industrial carbon emissions vary widely across economies, but most industrial carbon emissions continue to decrease in developed countries while increase in developing countries. Therefore, we contend that energy intensity is the key point to prevent a potential rebound of emission in post-COVID-19 era.

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

The rapid increase in novel coronavirus (COVID-19) patients also means a rapid increase in medical waste that could carry the novel coronavirus (SARS-CoV-2). How to safely dispose of medical waste caused by COVID-19 is a huge challenge that needs to be solved urgently. The outbreak of the COVID-19 has led to a significant increase in the daily generation of medical waste in China and has placed a severe test on the Chinese medical waste disposal system. Unlike ordinary wastes and garbage, medical waste that is untreated or incompletely treated will not only cause environmental pollution, but also directly or indirectly cause infections and endanger people's health. Faced with difficulties, the Chinese government formulated a policy for medical waste management and a response plan for the epidemic, which provides policy guarantee for the standardized disposal of epidemic medical waste. In addition, the government and medical institutions at all levels formed a comprehensive, refined, and standardized medical treatment process system during research and practice. China has increased the capacity of medical waste disposal in various places by constructing new centralized disposal centers and adding mobile disposal facilities. China has achieved good results in the fight against COVID-19, and the pressure on medical waste disposal has been relieved to a certain extent. However, the global epidemic situation is severe. How to ensure the proper and safe disposal of medical waste is related to the prevention and control of the epidemic situation. This study summarizes China's experience in the disposal of medical waste in the special case of COVID-19 and hopes to provide some reference for other countries in the disposal of medical waste.

Cities play a major role in decoupling economic growth from carbon emission for their significant role in climate change mitigation from national level. This paper selects Beijing (economic center and leader of emission reduction in China) as a case to examine the decoupling process during the period 2000–2015 through a sectoral decomposition analysis. This paper proposes the decoupling of carbon emission from economic growth or sectoral output by defining the Tapio decoupling elasticity, and combined the decoupling elasticity with decomposition technique such as Logarithmic Mean Divisia Index approach. The results indicate that agriculture and industrial sectors presented strong decoupling state, and weak decoupling is detected in construction and other industrial sectors. Meanwhile, transport sector is in expansive negative decoupling while trade industry shows expansive coupling during the study period. Per-capita gross domestic product, industrial structure, and energy intensity are the most significant effects influencing the decoupling process. Agriculture and industry are conducive to decoupling of carbon emissions from economic output, while transport and trade are detrimental to the realization of strong decoupling target between 2000 and 2015. However, construction and other industrial sectors exerted relatively little minor impact on the whole decoupling process. Improving and promoting energy-saving technologies in transport sector and trade sector should be the key strategy adjustments for Beijing to reduce carbon emissions in the future. The study aims to provide effective policy adjustments for policy makers to accelerate the decoupling process in Beijing, which, furthermore, can lay a theoretical foundation for other cities to develop carbon emission mitigation polices more efficiently.

China wants to embrace blockchain, the technology has triggered a new round of technological innovation and industrial change and has huge potential to enhance sustainable development capabilities in many areas. The purpose of this paper is to explore the global status of China’s blockchain research. This study applies bibliometric analysis to perform statistical and correlation analysis on the blockchain literature from 2013 to 2019 included in the Web of Science (WOS) database and draws the social network with visual analysis technology. The statistical results show that China is the country that publishes the most blockchain papers in the world, leading the global blockchain research. Research institutions and authors from China also dominate global blockchain research. Further, this paper investigates the development process of China’s blockchain research and determines the development stage through a comparative analysis with the United States. More specifically, the paper comprehensively analyzes the current status of China’s blockchain research from three perspectives: the subject area, high-yield institutions and high-yield authors. The results indicate that China’s blockchain research is experiencing rapid growth, and the research scope is constantly expanding, with the research focus gradually shifting to applied research. Finally, this paper summarizes the challenges faced by China’s blockchain research and puts forward corresponding policy recommendations for reference by policy makers.

Blockchain technology has been ushering in nothing short of a decentralized revolution. Distributed/decentralized energy is recognized the best way to ensure energy sustainability in the future. An open question is what promise the integration of blockchain and energy hold for energy future. This paper systematically reviews the theory of blockchain and explores the current status of energy blockchain research and applications using a visual bibliometric analysis method and the Scopus database from 2014 to 2020. The results show that the number of publications about blockchain technology in the energy sector have been skyrocketing, especially since 2018, which indicates the combining blockchain technology with energy sector is a new cross-cutting research area with increasing attention. At the national level, developing countries begin to move to the world stage, catching up or even surpassing several traditional developed countries in the field of energy blockchain. Cluster analysis results show that the existing energy blockchain research focuses on renewable energy, trying to solve the bottlenecks in its development process, and providing better solutions for the replacement of fossil energy by renewable energy. We therefore contend that blockchain may be fueling the renewable energy and powering our energy sustainability. Finally, the possible future development trend of energy blockchain is offered.

The coronavirus disease (COVID-19) is seriously threatening world public health security. Currently, >200 countries and regions have been affected by the epidemic, with the number of infections and deaths still increasing. As an extreme event, the outbreak of COVID-19 has greatly damaged the global economic growth and caused a certain impact on the environment. This paper takes China as a case study, comprehensively evaluating the dynamic impact of COVID-19 on the environment. The analysis results indicate that the outbreak of COVID-19 improves China's air quality in the short term and significantly contributes to global carbon emission reduction. However, in the long run, there is no evidence that this improvement will continue. When China completely lifts the lockdown and resumes large-scale industrial production, its energy use and greenhouse gas (GHG) emissions are likely to exceed the level before the event. Moreover, COVID-19 significantly reduces the concentration of nitrogen dioxide (NO2) in the atmosphere. The decline initially occurred near Wuhan and eventually spread to the whole country. The above phenomenon shows that the decreasing economic activities and traffic restrictions directly lead to the changes of China's energy consumption and further prevent the environment from pollution. The results in this study support the fact that strict quarantine measures can not only protect the public from COVID-19, but also exert a positive impact on the environment. These findings can provide a reference for other countries to assess the influence of COVID-19 on the environment.

With the boom of vehicles, especially the dramatic rise of private car ownership, in China, transport CO2 emission in China has surged. However, China has been taking the responsibility to cut down carbon emissions and to make positive efforts towards technology innovations in the transport sector. Breaking the link between transport carbon emissions and transport turnover capacity for the past decades should be analyzed. The paper tested the decoupling degree and ranked its potential determinants for every transport mode in consideration of specific transport mode characteristics. We extended the original Kaya identity to make the factor analysis more pertinent to the analysis of transport-related CO2 emissions. Besides, we combined the decomposition technique with decoupling analysis, decomposing the transport decoupling index into five distinct aspects to detect the key drivers of the decoupling of transport-related CO2 emissions from transport turnover volume. Moreover, we analyzed the relationship between transport-related CO2 emission and transport output, which also offers a novel perspective on transport and corresponding environmental research. The results uncovered that a weak decoupling state appeared between 1990–1995 and 2000–2010 in China’s transport sector. Transport energy efficiency exerted the most significant impact in accelerating the decoupling of transport-related CO2 emissions from turnover volume for all transport modes while the energy mix effect impeded the decoupling evolution in most observed periods. Railway transport turnover and rail locomotives shared rises boosted by decoupling evolution, while vehicular transport showed adverse effects. The rise of the transport facilities’ shares of railways, waterways, and airways also advanced the decoupling evolution. Hence, policies of switching travel modes and establishing a “smart growth” pattern for private vehicles should be considered.

The contradiction between economic growth and carbon emissions in China and India is the most prominent in the world. Both countries have faced tremendous pressures to curb carbon emissions, because they are major source of new added emission sources. Meanwhile, both countries have faced greater pressures to achieve industrialization and urbanization in order to eradicate poverty. Better understanding the decoupling status and its drivers can serve to develop effective policy to achieve economic growth without an increase in emission. This paper comparatively analyses the decoupling effect of the economic growth from the carbon emissions as well as its drivers during the period 1980–2014 in China and India. The Tapio decoupling model was used to analyze the decoupling status, and the co-integration theory and the impulse response functions were applied to investigate the effects of urbanization, industrialization, per capita GDP and carbon emission intensity to decoupling. The results show that China mainly performed a weak decoupling of economic growth from carbon emissions in 1980–2014, while the decoupling status of India was no regular. In China, carbon emission intensity is the biggest contributor of decoupling, followed by urbanization, per capita GDP, and industrialization. In India, the biggest driver of decoupling is also the carbon emission intensity, followed by urbanization, industrialization, and per capita GDP. Therefore, improving energy efficiency is the best policy to toward economic growth without emission growth in China and India.

China and the United States (U.S) produce approximately one-third of global economic output, and emit more than two-fifths of global total carbon emissions. Comparing the decoupling of economic growth from carbon emissions in China and the U.S. can inform the development of effective mitigation strategies for those two countries and the world. In this study, we compared both the carbon emissions performance and the decoupling performance between China and the U.S. We quantified the decoupling status in China and U.S. using the Tapio decoupling indicator, and decomposed the decoupling index to explore the driving factors affecting the decoupling using the Logarithmic Mean Divisia Index (LMDI) technique. The results show that China experienced expansive coupling and weak decoupling in most years between 2000 and 2014; the U.S. experienced mostly weak and strong decoupling. In general, income and population effects restricted decoupling, whereas the energy intensity and energy mix effects promoted the decoupling process in China and the U.S. In addition, the carbon intensity effect exerted negative and positive effects on decoupling in China and the U.S., respectively.

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output.

This paper adopts the vector auto-regression model (VAR) to study the dynamic effect of renewable energy consumption on carbon dioxide emissions. Our model is based on a given level of primary energy consumption, economic growth and natural gas consumption in the US, from 1990 to 2015. Our results indicate that a long-running equilibrium relationship exists between carbon emissions and four other variables. According to the variance decomposition of carbon dioxide emissions, the use of primary energy has a positive and notable influence on CO2 emissions, compared to other variables. From the Impulse Response Function (IRF) results, we find that the use of renewable energy would remarkably reduce carbon emissions, despite leading to an increase in emissions in the early stages. Natural gas consumption will have a negative impact on CO2 emissions in the beginning, but will have only a modest impact on carbon emission reductions in the long run. Finally, our study indicates that the use of renewable forms of energy is an effective solution to help reduce carbon dioxide emissions. The findings of our study will help policy makers develop energy-saving and emission-reduction policies.

Developing low-carbon agriculture requires investigating the trajectory, decoupling statuses, and driving forces of agricultural carbon emissions. This study explored the evolution of agricultural carbon emissions based on 18 kinds of major carbon emission sources in Henan Province of China, which produces approximately one-tenth of China's total grain output. We then analyzed the relationship between carbon emissions and economic growth using the decoupling elasticity model, and identified the factors driving the decoupling status. This analysis was done with a decoupling elasticity model, using the Logarithmic Mean Divisia Index technique. There were three key results: (1) Agricultural carbon emissions totaled 16.61 million tons in 1999, and increased by 7.99% to 17.93 million tons in 2014, with an average growth rate of approximately 0.65%; (2) The decoupling relationship between agricultural carbon emissions and economic output was dominated by weak decoupling during the study period; (3) Agricultural labor productivity was the leading contributor to changes in agricultural carbon emissions, followed by farming-animal husbandry carbon intensity, labor, and agricultural structure.