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This paper uses the exogenous shock of China's formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development. [ABSTRACT FROM AUTHOR]

Green consumption has become an important part of an environmentally friendly society. Policy guidance can effectively promote green consumption. Green consumption policies are extensive because of the diversity of green consumption objects. However, the various objects are not completely isolated, and policy must have a certain level of consistency. Therefore, from the perspective of green consumption objects, this paper first uses the text mining method to mine 308 green consumption policies in China, analyzing the objects covered in China's green consumption policies. Then, the evaluation system of green consumption policy consistency was built, seven key policies were selected, and the PMC-index method was used to measure policy consistency. The results show that (i) the main objectives of China's green consumption policy are the promotion of green products and the efficient use of energy. Green services and green recycling have been subject to fewer policy efforts. (ii) In terms of the links involved in green consumption, China pays more attention to the supply and the consumption chain and less attention to the recycling chain. (iii) The quantitative evaluation results of the seven typical policies show that the policy consistency is good but not perfect. The policy shortcomings are mainly reflected in the incentive methods. Therefore, it is recommended to further improve the green consumption objectives and enrich the means and methods of green consumption guidance. [ABSTRACT FROM AUTHOR]

China's green financial pilot policies (GFPPs) are an important start to build a green financial system in China. It is also an important part of achieving the "dual-carbon" goal. This paper calculates China's total factor energy efficiency (TFEE), analyzes the impact of GFPPs on TFEE, and explores the conduction mechanism. The results show that the relationship between GFPPs and TFEE in the pilot areas is statistically significant. The GFPP mainly improves the TFEE through the green innovation effect and the optimization of the energy structure. The GFPP has a significant effect on TFEE enhancement in eastern coastal cities, non-old traditional industrial cities, and non-resource-based cities with high initial energy. China should continue to deepen the pilot work of green finance, formulate differentiated green finance policies, and ensure green technology innovation. This study provides empirical evidence for further optimization of green finance and energy efficiency. [ABSTRACT FROM AUTHOR]

With population and income growth, the need for energy has increased in developing and emerging economies, which has inevitably led to an increase in carbon dioxide emissions (CO2e). This paper investigates the impact of energy consumption on CO2e influenced by population growth, energy consumption per capita, and income. In particular, this paper investigates whether or not an increase in energy consumption, energy intensity, energy consumption per capita, population growth, and income impacts CO2e in China, India, and the USA. The study applied the non-linear Autoregressive distributed lag (NARDL) and machine learning techniques. We found a significant impact of energy consumption per capita on the CO2 emissions in China, India, and USA. Furthermore, the results revealed that, when income increased, CO2 emissions increased in India, but decreased in the USA. The results confirmed that population growth increases CO2 emissions only in India. The results revealed that a decrease in energy intensity significantly improves the environmental quality in China and India. Finally, we forecasted the CO2e trend from 2017 to 2025. The results revealed an upcoming increase in CO2e levels in China and India. Conversely, the forecasted results demonstrated a downward trend of CO2e emissions in the USA. [ABSTRACT FROM AUTHOR]

The platform economy is growing fast in the world. Its expansion, exemplified by industry giants like Amazon, Airbnb, and Uber, is reshaping societal lifestyles and commercial businesses. However, its environmental impacts are uncertain. Currently, the relationship between electronic commerce (e‐commerce) activities and environmental pollution has attracted a lot of attention. This paper attempts to construct a comprehensive theoretical model to investigate the nexus between e‐commerce and regional environmental pollution, with a particular focus on the effects of air pollution and waste pollution. Fixed effect models and China's provincial data during the period from 2008 to 2018 are employed to examine the roles of energy efficiency and resource consumption in such a nexus. The findings suggest that the development of e‐commerce is linked to a reduction in regional air pollution through the channel of improving transportation energy efficiency. The results also show that e‐commerce is associated with an increase in regional waste pollution due to the mechanism of resource consumption in the express packaging industry. As a result, the relationship between e‐commerce and environmental pollution is quite complex at the regional level, transcending a binary distinction of solely positive or negative impacts. This paper contributes to a better understanding of the complex environmental impacts of e‐commerce and helps governments design more efficient and targeted policies to reduce regional pollution. [ABSTRACT FROM AUTHOR]

(1) Background: The decoupling of energy consumption from economic growth in the construction industry is crucial to the sustainable development of the global construction industry. The existing studies focus on the advancements in energy efficiency by designers for building monoliths or construction techniques, involving no exploration of energy efficiency management from a regional perspective, which is unfavorable for the formulation of energy efficiency policies and systematic control of the construction industry by local governments. (2) Methods: From the perspective of regional management, this paper constructs an integrated analysis and application framework of "spatio-temporal characteristics + matching evaluation + policy design" based on the decoupling model and GIS tools. It studies the spatio-temporal characteristics of energy consumption in the construction industry in 30 provinces of China from 2010 to 2019, and its decoupling relationship with the economic development of the construction industry, and proposes an optimal zoning and recommendations for energy consumption in the construction industry, providing a reference for energy conservation management in the construction industry in China. (3) Results: First, the change of energy consumption amount (ECA) in the construction industry in the provinces was dominated by ascent, while the energy consumption intensity (ECI) predominantly decreased, and most provinces are still in a period of growth or plateau in energy consumption. Second, ECA and ECI had prominent spatial heterogeneity and aggregation. High-energy-consuming regions are concentrated along the coast and along the Yangtze River, while low-energy-consuming regions are mainly clustered in remote areas, such as the northeast, northwest, and southwest of China. Energy consumption shows a clear north-south difference in intensity, with high-intensity regions clustered in the north compared to low-intensity regions in the south. Third, most of the provinces were in strong negative decoupling, expansive coupling, and weak decoupling, and better decoupling regions were mainly gathered in south and central China. Nearly one half of these provinces showed decoupling degradation and only a few achieved evolution, with evolutionary regions clustered mainly in central and southern China. The northeast and northwest were the key problem areas of energy-saving transformation in China's construction industry. (4) Conclusion: The 30 provinces were divided into three types: leader, intermediate, and laggard, and the development goals and suggestions on low energy consumption in the construction industry for three zones were put forward, significantly improving the precision of policy design and implementation. The study in this paper expands the research perspective on energy saving management in the construction industry and provides a methodology and basis for developing energy efficiency policies and plans for the construction industry in China and similar developing countries. [ABSTRACT FROM AUTHOR]

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context. [ABSTRACT FROM AUTHOR]

As the world's largest carbon dioxide emitter, China is curbing coal consumption and increasing natural gas and renewable energy consumption to curb the increase in carbon emissions. At the same time, China is still the largest developing country in the world. Therefore, it is very important to investigate the links between different types of energy consumption, economic growth, and carbon emissions. In this paper, impulse response functions and Granger causality test based on time-varying parameters vector autoregressive model are used to explore the time-varying relationship among CO2 emissions, economic growth, and heterogeneous energy consumption (coal, gas, oil, and renewable energy) in China over the period 1980–2019. The results show that reducing coal and oil consumption and increasing natural gas and renewable energy consumption will not significantly hinder economic growth. In addition, in the long run, reducing coal and oil consumption and increasing renewable energy consumption can significantly reduce carbon emissions. However, after 1998, increasing natural gas consumption will significantly increase carbon emissions. This study reveals the time-varying relationship between heterogeneous energy consumption, economic growth, and carbon emissions, and provides suggestions for China to achieve low-carbon economic growth. [ABSTRACT FROM AUTHOR]

There are differences in economic level, population size, and technical level in different stages. At the same time, as a coal-dependent country, other regions have foreign dependence on coal. Then the primary factors and influence degrees of CO2 emissions in different stages and areas with different coal dependents will be various. This paper adds urbanization rate, industrial structure, and energy consumption structure to the traditional STIRPAT model. Based on the expanded STIRPAT model, the panel data of 30 regions in China from 2005 to 2019 are used to analyze the different stages and areas with foreign coal dependence. The main influencing factors and degree of influence of CO2 emissions. Regression finds that population size, energy consumption, and industrial structure will increase regional carbon dioxide emissions. There is an "inverted U-shaped" curve relationship between economic development, technological level, and carbon dioxide emissions. The improvement of the urbanization rate can reduce carbon dioxide emissions. Carbon dioxide emissions. An "inverted U-shaped" curve relationship between per capita GDP and CO2 emissions in three research periods from 2005 to 2010, 2011 to 2015, and 2016 to 2019. At the same time, the inhibition effect of the urbanization rate is constantly strengthened, and the promotion effect of the coal consumption ratio is decreasing year by year. The influence of population size, technical level, and industrial structure on carbon dioxide emissions in the whole country and areas with different coal dependents varies with the study period. To provide a theoretical basis for local conditions and regional management in the future direction of energy-saving and carbon reduction. [ABSTRACT FROM AUTHOR]

The presence of mycotoxins in cereals can pose a significant health risk to animals and humans. China is one of the countries that is facing cereal contamination by mycotoxins. Treating mycotoxin-contaminated cereals with established physical and chemical methods can lead to negative effects, such as the loss of nutrients, chemical residues, and high energy consumption. Therefore, microbial detoxification techniques are being considered for reducing and treating mycotoxins in cereals. This paper reviews the contamination of aflatoxins, zearalenone, deoxynivalenol, fumonisins, and ochratoxin A in major cereals (rice, wheat, and maize). Our discussion is based on 8700 samples from 30 provincial areas in China between 2005 and 2021. Previous research suggests that the temperature and humidity in the highly contaminated Chinese cereal-growing regions match the growth conditions of potential antagonists. Therefore, this review takes biological detoxification as the starting point and summarizes the methods of microbial detoxification, microbial active substance detoxification, and other microbial inhibition methods for treating contaminated cereals. Furthermore, their respective mechanisms are systematically analyzed, and a series of strategies for combining the above methods with the treatment of contaminated cereals in China are proposed. It is hoped that this review will provide a reference for subsequent solutions to cereal contamination problems and for the development of safer and more efficient methods of biological detoxification. [ABSTRACT FROM AUTHOR]

Taking smart city pilots (SCP) in China as a quasi-experiment, this paper uses the staggered difference-in-differences (staggered DID) to examine the impact of the SCP policy on energy consumption by using panel data of 224 prefecture-level cities from 2006 to 2019. The results showed that the SCP policy reduces energy consumption and energy intensity by 3.3% and 5.3%, respectively. Heterogeneity analysis found that the energy-saving effect of the SCP policy is stronger in western cities, resource-based cities, and in cities that were the pioneering pilots. Mechanism analysis showed that smart industry transformation is the main transmission mechanism. Our findings have important practical implications for reforming urban governance models and achieving a low-carbon transition. [ABSTRACT FROM AUTHOR]

The building sector accounts for over 40% of global energy consumption. The utilization of renewable energy systems such as the solar-assisted heat pump (SAHP) in buildings has been shown to improve building energy efficiency and achieve carbon neutrality. This paper presents a review of the research and development of solar-assisted heat pumps for buildings in China. It firstly introduces the different stages of solar-assisted heat pump research. Secondly, the research on different types of heat pumps, the core components of heat pumps, the computer software used, and the economic feasibility evaluation of solar-assisted heat pumps are presented. Thirdly, the application of SAHPs in practical projects is examined and relevant regulations, standards, and policies for solar-assisted heat pump development in China are highlighted. Finally, recommendations for the future development of solar-assisted heat pumps in China are suggested. [ABSTRACT FROM AUTHOR]

China is taking initiative in energy transition to cope with the long-term controversy of its enormous energy consumption, aiming to use less carbon. Wind power, especially offshore wind energy, has become a prevailing alternative due to its low carbon emissions, renewability, competitiveness, and operation security. The layout of a transmission channel is a key consideration in marine project implementation. This paper investigates the technical characteristics, application status, and viable advantages of a conventional AC transmission, voltage source converter-based high-voltage direct current (VSC-HVDC) transmission, gas-insulated line (GIL) transmission, and hybrid HVDC transmission. A component-resolved evaluation model was proposed to estimate the costs to be incurred of four electrical transmission options for offshore wind power along the coast of Eastern China, with technical feasibility and economical considerations. Cost comparisons and component sensitivity analyses were developed with different transmission distances and capacities. Results suggest HVAC transmission and VSC-HVDC are the preferable solutions for present offshore wind farm development in Eastern China, and the economic potential of the hybrid HVDC makes it feasible for future deployment. Some conclusions can be applied in disparate regions across the globe. [ABSTRACT FROM AUTHOR]

Carbon neutrality is an important policy in the current global response to climate change and has been widely recognized by various industries. In the process of promoting carbon neutrality, new energy plays a pivotal role. In this study, the definition and connotation of new energy and its role and specific operation in the energy transition of carbon neutrality are firstly explained. Promoting new energy development requires significant green and low-carbon investments. Taking China as an example, this paper analyzes the opportunities brought by the carbon neutral process to the field of green finance and analyzes the main features and development trends of green finance in China at present. Then this paper proposes policy recommendations to strengthen the development of green finance in China in terms of improving the green financial policy system, enhancing the supply capacity of green financial services, and optimizing the supporting environment for green financial development. Finally, this paper analyzes the measures and experiences of the United States in promoting low-carbon development and proposes countermeasures for China's low-carbon development on the basis of the five major relationships that need attention in China's carbon-neutral process. That is, strengthen the top-level design and improve the regulatory policy system; optimize the energy structure and increase the proportion of clean energy; optimize the industrial structure and reduce energy consumption in key industries; build a complete low-carbon technology system and promote low-carbon technology research and development and demonstration applications, and encourage local conditions to explore low-carbon development paths. The development of green finance can contribute to the advancement of new energy technologies, thus contributing to the achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces' URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China's policymakers. [ABSTRACT FROM AUTHOR]

Different from the previous studies that mainly focus on the environmental and economic benefits of the total R&D, we specifically focus on the R&D activities in the energy field, namely, energy-saving R&D in this study, and explored the spatial impacts of energy-saving R&D on urban carbon emission performance and its internal transmission mechanisms for the first time. The heterogeneity analysis from the perspectives of different-types of R&D and different urban groups were also conducted. Results indicate that: Energy-saving R&D has obvious promoting effect on the improvement of urban carbon emission performance, among which the utility-type energy-saving R&D plays a more important role than the invention-type R&D. Energy-saving R&D promotes urban carbon performance mainly through the transmission mechanism of technical effects, while structural effect and urbanization effect negatively affects carbon emission performance due to the existence of energy rebound. Furthermore, there exists significant spatial spillover effects of energy-saving R&D on urban carbon emission performance, and economic factors play a more important role in this spatial effect. In addition, the impact of energy-saving R&D on carbon emission performance has obvious urban group heterogeneity. Finally, we proposed several policy suggestions based on the main conclusions of this study. [ABSTRACT FROM AUTHOR]

The evaluation of regional water and energy consumption is of great significance to improving regional resource utilization. This paper analyzed the water and energy footprints in different provinces of China, considering regional economic levels. The results indicate: (1) both the largest water footprint and water footprint per capita were in Xinjiang and agriculture had the largest value; (2) Shandong was the largest energy consumer, Ningxia had the largest energy footprint per capita, and coal occupied the largest proportion for the top five energy footprint provinces; and (3) the resource input–output efficiencies in Beijing and Fujian were high, while water and energy consumption were low and gross regional product was high, compared with the average value of China. The situations in Xinjiang and Inner Mongolia were opposite. The change of consumption pattern for each inhabitant, the adoption of water-saving technology, and an increase to water-saving awareness would be helpful to decrease regional water consumption. An increase of regional energy use efficiency and a change to reduced energy consumption would contribute to the decreasing of regional energy consumption. More attention should be paid to renewable and clean Energies. In addition to solution from the local perspective, the virtual water trade and the energy product trade may relieve regional resource pressure in some extent, and the possible influencing should be considered at the same time. This paper could provide suggestions for regional resource utilization and sustainable development. [ABSTRACT FROM AUTHOR]

This paper constructs a carbon quota allocation index that takes into account equity, efficiency and ecological construction, and calculates carbon emissions and energy consumption data in important periods based on the expected carbon emission targets and economic and social development indicators of the Chinese government. Based on the calculated carbon emissions, the zero-sum game data Envelopment model (ZSG-DEA) is used to discuss the initial allocation of regional quotas and the optimal carbon quota scheme. The results show that:(1) there is a large gap between the optimal carbon quota and the initial carbon quota allocation in Shandong, Guangdong, Jiangsu and other provinces in 2025, and the implementation of emission reduction measures should be accelerated. (2) By 2030, the final allocation of Beijing, Tianjin, Inner Mongolia, Qinghai, Shanghai, Ningxia, Liaoning and Xinjiang will be positive. The provinces with negative final allocation should carry out the work of carbon peak as soon as possible to avoid increasing the pressure of emission reduction in the future. (3) The central region faces greater pressure of emission reduction, while the western region can accept the transfer of carbon emissions from other regions over time. The research conclusions have important policy implications for establishing a fair and effective carbon quota allocation mechanism, achieving the national total carbon emission control target, stimulating the vitality of the unified carbon market, and promoting regional coordinated emission reduction. • There exist differences in carbon quota allocation among provinces in the future. • There are changes in the initial and optimal carbon quotas obtained by each province. • The role of carbon emissions between regions is changing over time. [ABSTRACT FROM AUTHOR]

The development of a city's green infrastructure is increasingly being driven by low-carbon innovation (LCI). However, knowledge of how LCI affects green economic efficiency (GEE) in China is largely unknown. This study investigates the spatial effects of LCI on GEE in 285 Chinese prefecture-level cities. The empirical findings show that LCI exerts significant beneficial effects on its GEE but negative effects on its neighbors. The positive direct effects on GEE are offset by negative indirect effects due to the lack of spatial linkage effect and the existence of the siphon and crowding-out effects among cities. Using a mediating effect model, we have identified three channels of LCI that affect GEE: industrial structure, energy consumption structure, and human capital. A further heterogeneity analysis indicates that the impacts of LCI on GEE differ by region and city development level. Furthermore, LCI in the building, greenhouse gas treatment, transportation, and sewage and pollutant treatment categories could greatly enhance local GEE. The findings herein provide an empirical experience for accurately accessing the spatial effect of LCI on GEE, and offer a critical decision-making reference for implementing a cross-city green linkage development mechanism. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable increase in the consumption of fossil energy, leading to a significant rise in environmental pollution, particularly in China due to its rapid development. This has resulted in the frequent occurrence of large‐scale fog and haze weather, highlighting the urgent need for environmental protection measures. To gain insights into the atmospheric conditions in China, an analysis was conducted on the wet deposition of polycyclic aromatic hydrocarbons (PAHs) in a remote region of Central South China from 2014 to 2017. The study revealed that the average concentrations and peak values of Ʃ16PAHs in 2014 and 2015 were considerably higher than those observed in 2016 and 2017. Furthermore, it was found that five‐ring PAH species were the predominant components during 2014 and 2015, indicating a shift in the main sources of PAHs. The peaks of Ʃ16PAHs were predominantly detected in samples collected during light rain in the winter, specifically on days without heavy rainfall. This can be attributed to the absence of heavy rain, which would otherwise reduce the concentration of air pollutants. Consequently, contaminants accumulated in the air are easily enriched in rainwater. The concentrations of Ʃ15Alkyl‐PAHs also exhibited a significant correlation with the number of rainfall days. Notably, a much higher annual average concentration of Ʃ15Alkyl‐PAHs was observed in 2017, which experienced fewer rainfall days. Coal combustion, petroleum sources, and vehicular emissions accounted for 58%, 12%, and 30% of the PAHs in the air, respectively. Despite improvements in air quality in China since 2016, it is crucial to address the elevated concentrations of PAHs in the atmosphere, particularly under adverse meteorological conditions characterized by reduced rainfall. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China's economic transition period. This study explored the relationship between FD, IND, and China's green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China's GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role. [ABSTRACT FROM AUTHOR]

China is the largest CO2 emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO2 emissions and their drivers is important for the actions of peaking CO2 emissions before 2030 in China. CO2 emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China's CO2 emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO2 emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China's carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels. [ABSTRACT FROM AUTHOR]

Background: Conducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China. Results: Based on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons. Conclusions: China's provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity. [ABSTRACT FROM AUTHOR]

Since its reform and opening, the Chinese economy has witnessed unprecedented development. This long-term high-speed development has significantly impacted the environment. In the process of energy development and consumption, the environment has been severely polluted, and greenhouse emissions have increased. This has resulted in environmental imbalances such as global climate change, rising sea levels, and extreme weather. Using annual energy consumption (EC), economic growth (GDP), and carbon dioxide (CO2) emissions in China from 1981 to 2021, this study employed the maximum likelihood estimation method to estimate the parameters of the nonlinear MS-VAR model. Cointegration tests, regimes analysis methods, and impulse response function analysis methods were adopted to explore the differences or similarities in the dynamics of the three under various regimes. The research results are as following. (i) The cointegration test findings demonstrate a long-term equilibrium relationship among EC, economic growth, and CO2 emissions. (ii) Regime analyses exhibit that there are three regimes: "low-level regime," "medium-level regime," and "high-level regime." The three regimes have a mutual transfer transmission mechanism that exhibits nonlinear properties. (iii) Impulse response function analyses show that external EC and GDP shocks favorably impact the other two variables in all three regimes. EC in all three regimes is negatively impacted by external CO2 shocks. Moreover, in the "low-level regime," the relationship between CO2 and GDP has an inverted U-curve, whereas in the other two regimes, the relationship has a negative association. This study can help China formulate reasonable and effective CO2 emissions reduction and energy policies and successfully achieve the emissions reduction goal of the 14th Five Year Plan as well as dual carbon goals. [ABSTRACT FROM AUTHOR]

Yunnan is rich in renewable energy resources. An understanding of its energy structure and developmental trajectories would assist in enabling the design of suitable decarbonizing pathways and how to fit into the national 30–60 agenda. Drawing from endogenous growth theory and time series analysis, our study employs comparative functions and scenario assessments to predict the changes in the key economic indicators, such as GDP, industrial structure shifts, population, and urbanization rates, during the low-carbon transition. We further show energy structure patterns and intensity trends using regression-modeling and data-fitting methods. Based on our analyses, we project that by 2035, Yunnan's GDP will grow to CNY 5.4761 trillion, with secondary and tertiary industries contributing 88.8%. The population is estimated to grow to 52.08 million with an urbanization rate of 70%. Moreover, fossil fuel energy consumption is forecasted to diminish to 38.7%, and energy consumption intensity is projected to be reduced to 0.38 tons of standard coal per CNY 10,000. If these metrics follow the inherent endogenous growth trend, Yunnan's emissions are forecasted to peak at around 220 million tons of CO2 by 2030. These findings not only provide a data foundation for Yunnan's low-carbon development goals but also illuminate pathways for regions rich in renewable resources to transition towards sustainable growth, emphasizing the harmony between advancement and environmental stewardship. [ABSTRACT FROM AUTHOR]

Alleviating energy poverty is a significant issue in many countries, for which effective energy poverty measurement is the prerequisite. This paper seeks to make a comprehensive and systematic assessment of the energy poverty performance of 30 provinces in China for 11-year period (2007–2017) by emphasizing their spatial-temporal changes. The paper firstly constructed energy poverty measurement conceptual framework based on overviewing previous studies. Secondly, a technique based on the integrated fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and Data Envelopment Analysis - Assurance Region (DEA-AR) for performance assessment was proposed. Then, the energy poverty performance was assessed, and clustering analysis was conducted to investigate the spatial-temporal changes of energy poverty in China. Finally, sensitivity analysis was applied for verifying the robustness of the proposed method. It is found that the energy poverty condition in China tends to be similar among most regions, but the gap between the best and worst performers gets wider. In addition, provinces with better economic conditions, have higher performance and are classified into the progressive group for alleviating energy poverty. On the contrary, provinces, whose economy lags, have regressed the most and are also the worst performers in reducing energy poverty. Thus, policies to improve household energy cleanability in high-income provinces and increase energy affordability in low-income regions would help reduce energy poverty in China. • A comprehensive energy poverty assessment framework was proposed. • An integrated Fuzzy DEMATEL/DEA-AR was proposed to evaluate the performance. • The comparison between the proposed Fuzzy DEMATEL/DEA-AR model and conventional DEA was conducted. • Spatial and temporal analysis of energy poverty performance in China was conducted. [ABSTRACT FROM AUTHOR]

Human activities are closely related to energy consumption and carbon emissions. Nevertheless, in the context of energy transition and carbon emission reduction policies, the impact of population migration on energy consumption and carbon emissions is still unclear. Based on the panel data of 30 provincial regions in China from 2000 to 2019, this paper uses the spatial Durbin model to perform a spatial econometric investigation on this issue. In particular, this study innovatively embeds two spatial weight matrices, an economic gravity weight matrix and a population migration weight matrix, into the spatial Durbin model to capture the spatial spillover effects of population migration. The results show that population migration has obvious adverse impacts on the provinces with net outward population migration. Specifically, on the one hand, population migration increases energy consumption and energy poverty, but energy poverty is more severe in provinces with net outward population migration. On the other hand, population migration increases the carbon emissions and carbon reduction barriers of the provinces with net outward population migration and has no significant impact on the carbon emissions and carbon reduction barriers of the provinces with net inward population migration. Besides, the results of heterogeneity analysis show that population migration causes gaps in the energy structure among regions and even triggers the carbon transfer to provinces with net outward population migration. On the whole, the research results of this paper all prove that population migration increases the carbon reduction barriers in the provinces with net outward population migration. These empirical insights enrich the relevant theoretical achievements of the impact of population migration on energy consumption and carbon emissions and have a high reference value for formulating more reasonable regional energy transition and carbon emission reduction policies. • The spatial spillover effects of population migration on energy consumption and carbon emissions are explored. • The spatial Durbin model is used in this paper. • The spatial Durbin model is embedded with the economic gravity weight matrix and the population migration weight matrix. • Population migration has obvious adverse impacts on the provinces with net outward population migration [ABSTRACT FROM AUTHOR]

According to the World Bank, energy efficiency is a critical facilitator of most Sustainable Development Goals. Its contribution to CO2 emission reduction is astounding. Environmentalists have recently emphasized the essential need to determine energy efficiency causes. This research broadens the debate's horizons by proposing additional possible energy efficiency factors using data from the Chinese economy. From 1990 to 2020, we examined the influence of investment in renewable energy resources, financial inclusion, industrial production, and trade openness on China's energy efficiency and climate risk. Additionally, this study is added to the literature by examining the causal relationships between variables while considering the temporal dimension. The findings indicate that industrial production, financial inclusion, public R&D on renewable energy, and trade openness contribute significantly to China's energy efficiency and climate risk. All other factors, except industrial production, are positively associated with energy efficiency. The path of causality is established from energy efficiency and climate risk to financial inclusion, industrial production, renewable energy, public research and development budgets, and trade openness. According to the findings, changes in energy performance have frequency-changing impacts on all variables. Policymakers believe that the financial system must be strengthened since this will significantly influence renewable energy. [ABSTRACT FROM AUTHOR]

Urban public buildings carbon emissions exhibit an upward trend and have a large potential in carbon emission reduction. The analysis of spatiotemporal characteristics and driving factors for urban public buildings carbon emissions is essential in formulating effective policies for carbon reduction, meeting commitments to peak carbon emissions and achieving carbon neutrality. This study takes China's urban public buildings carbon emissions as the research object, employing methods such as spatial autocorrelation analyses, kernel density estimation analyses, and the LMDI decomposition methods to explore the spatiotemporal characteristics and regional disparities in carbon emissions from 2006 to 2019. Furthermore, it quantifies the contributions of driving factors to the spatiotemporal changes in urban public buildings carbon emissions. The results show the following: (1) Urban public buildings carbon emissions among provinces are consistently increasing, indicating an overall upward trend. The spatial distribution highlights significant regional disparities. (2) The spatial characteristics of urban public buildings carbon emissions were basically stable. The eastern coastal regions demonstrate a high-high cluster, while the western regions exhibit a low-low cluster. The overall cluster evolution showed a decreasing trend from east to west. (3) Per capita urban public building area, economic density, urbanization rate, and population size serve as driving factors for carbon emissions from urban public buildings, while energy efficiency and energy consumption intensity act as inhibitory factors. The findings of this research can assist policymakers in getting a deeper comprehension of urban public buildings carbon emissions and providing a scientific basis to formulate appropriate carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

The development of the tertiary industry is of great significance for promoting industrial structure, optimizing and upgrading it, and achieving regional energy conservation and emission reduction goals. This study adopts a quantitative method to analyze the spatio-temporal pattern of carbon emissions from China's tertiary industry from 2004 to 2019. In order to analyze emissions from aspects such as energy structure, energy intensity, energy carrying capacity, industrial structure, level of industrial development, income level, consumption capacity, energy consumption intensity, and population size, this study establishes a hybrid factor decomposition model called the "energy-industry-consumption" research framework. The study shows that carbon emissions from China's tertiary industry have been increasing year by year from 2004 to 2019, with a growth rate of 353.10%. Transportation is the largest contributor to the increase in carbon emissions from China's tertiary industry. The carbon emissions from the tertiary industry in each province show four types: high-speed growth, low-speed growth, fluctuating growth, and stable growth. During the study period, carbon emissions produce a spatial heterogeneity with the highest emissions in the south and lowest in the northwestern part of China. The spatial pattern of per capita carbon emissions is not significant. Guangdong has the highest carbon emissions, and Shanghai and Beijing have higher per capita carbon emissions. Industrial factors and consumption factors have a positive effect on carbon emissions in China's tertiary industry, while energy factors have a negative effect. The leading factor of carbon emissions in China's tertiary industry has gradually shifted from energy to industry. [ABSTRACT FROM AUTHOR]

Developing energy from renewable sources and modernizing the energy system are critical components of China's efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China's energy revolution is financial limitations, which are inextricably linked to the country's economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources. [ABSTRACT FROM AUTHOR]

• GDIM is used to investigate the influencing factors of energy-related PM 2.5 emissions in China. • A novel decoupling indicator is constructed based on GDIM decomposition results. • The increase of PM 2.5 emissions is primarily due to output scale and energy use effects. • Emission intensity and emission coefficients are the main factors decreasing PM 2.5 emissions. • Technical effect plays an important role in promoting the decoupling of PM 2.5 emissions. Haze pollution has become a severe threat to China's sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM 2.5 pollution and explore how to decouple economic growth from PM 2.5 pollution. Compared with the traditional decomposition analysis methods, the Generalized Divisia Index Method (GDIM) integrates multiple influencing factors and provides a fuller understanding of the influence mechanism of PM 2.5 pollution. In addition, the decomposition of the monitored PM 2.5 concentration in previous studies leads to ambiguous economic meanings of some relative impact indicators. In this paper, GDIM decomposition is applied to study the driving factors of energy-related PM 2.5 emissions changes in China. Then, based on the GDIM decomposition results, this paper constructs a novel decoupling indicator to study the decoupling relationship between PM 2.5 emissions and economic growth, and the contributions of the technical and non-technical factors to the decoupling indicator are quantified. The main results are summarized as follows. (1) The output scale effect and energy use effect are the main reasons for the increase of PM 2.5 emissions, while the emission intensity effect and emission coefficient effect make major contributions to decreasing PM 2.5 emissions. The effects of population, per capita emissions, per capita output, and energy intensity are relatively minor. (2) China and its three economic regions move from strong decoupling in 1998–2000 to weak decoupling during 2000–2010, and then into strong decoupling over the period of 2010–2014. (3) Technical effect plays an important role in promoting the decoupling between PM 2.5 emissions and economic growth, but its contribution shows a downward trend over time. In contrast, non-technical effect hinders the decoupling process, and its contribution is decreasing from 2000 to 2014. This paper is supportive for formulating emissions reduction policies and improving China's air quality. [ABSTRACT FROM AUTHOR]

This study's objective is to analyze ecological footprints that exist among China's economic growth, energy consumption, carbon dioxide emissions, and the revenue that is generated from tourism in other countries. The years 1995 through 2020 are the focus of this particular research endeavor. The relationship between tourism and carbon emissions has been discovered by a large number of researchers; nevertheless, the findings have been inconsistent and do not give a clear picture of the situation. We can only hope that the results of the study will improve the existing body of knowledge on tourism and the quality of the surrounding environment. Throughout the whole of this investigation, the autoregressive distributed lagged (ARDL) model was used to explore both long-run and short-run estimations. A dynamic ordinary least squares (DOLS) model was used in the study to arrive at long-term estimations that could be relied upon. Even though money from tourism does not have a substantial influence on the quality of the environment in China, growth and increasing energy usage are primary donors to carbon emissions in the nation. ARDL model's long-term projections were shown to be correct by the DOLS approach, which offered this validation. The results of the research provide fresh insights into the body of knowledge that has been accumulated on the subject of the linkage between tourism and the natural environment. Because the receipts from tourism do not have any significant negative exteriority toward the environment, energy usage is an important element of environmental degradation and policymakers should prioritize the development of the tourism sector over energy-focused manufacturing activities to maintain the growth of the nation in the upper quartiles. This is because tourism does not have any significant negative externalities on the environment. Sustainable tourism minimizes environmental and cultural damage while boosting profits. Developing the appropriate technology, physical infrastructure, and human capital requires money, time, and effort. [ABSTRACT FROM AUTHOR]

This empirical study investigates the dynamic interconnection between fossil fuel consumption, alternative energy consumption, economic growth and carbon emissions in China over the 1981 to 2020 time period within a multivariate framework. The long-term relationships between the sequences are determined through the application of the Autoregressive Distributed Lag (ARDL) bounds test and augmented by the Johansen maximum likelihood procedure. The causal relationships between the variables are tested with the Granger causality technique based on the Vector Error Correction Model (VECM). Empirical results reveal the existence of a statistically significant negative relationship between alternative energy consumption and carbon emissions in the long-term equilibrium. Furthermore, the VECM results demonstrate that both carbon emissions and fossil fuel consumption have unidirectional effects on economic growth. Additionally, the study highlights a short-term unidirectional causal relationship from economic growth to alternative energy consumption. These findings suggest that a reduction in fossil fuel consumption in the short run may indirectly impede the development of alternative energy. The study proposes that China should expedite the development of alternative energy and control the expansion of fossil fuel consumption to attain its carbon reduction target without hindering economic growth. [ABSTRACT FROM AUTHOR]

This study aims to provide a scientific basis to address the strategies for sustainable development of urban tourism industry. By using the Life Cycle Assessment method, it decomposes tourism activities into seven different functional units (different tourism activities)-transportation, catering, accommodation, sightseeing, shopping, entertainment and waste disposal-based on the expression of services provided by tourism activities, and determine the boundary range of each different functional unit in terms of the pathways and the functional orientation of the products (resources and energy) provided by the services of each functional unit. A "bottom-up" model is then constructed to measure the carbon footprint of tourism. Based on data collected from various sources for the period 2014–2019, it compares the composition and differences of domestic and international tourists' carbon footprints in Chenzhou City, one of inland mountainous regions of central China, through several steps, including target and scope definition, inventory analysis, impact evaluation and life cycle interpretation. Results show that domestic tourists contributed more than 90% of the total annual carbon footprints to the city, ranging from 76.8809 × 106 kg to 194.6067 × 106 kg. Transportation is the dominant category, accounting for over 80% of the total carbon footprints. The study suggests that optimizing tourism resources, reducing transportation distances, and switching to low-carbon modes can effectively reduce the tourism carbon footprints in Chenzhou and similar regions. This study reveals the structural characteristics of the tourism carbon footprint and its influencing factors and provides valuable insights for policy development involving energy saving and low carbon tourism, thus enhancing the long-term sustainability of tourism development in an urban tourism destination like Chenzhou. [ABSTRACT FROM AUTHOR]

Financial development is an important factor affecting energy consumption, but the impact of financial development on energy consumption may not be linear. This paper explores the nonlinear correlation between financial development and energy consumption from the perspective of spatial spillover using a sample of 30 provinces in China from 2005 to 2018. We analyze financial development from three perspectives of indirect finance, direct finance and financial openness. The main findings are yielded as follows. (1) The stock market exerts an inhibitory effect, while bank loans demonstrate a promoting effect on energy consumption. (2) The spatial spillover effects of the stock market and bank loans differ due to the competitive and catch−up effects. (3) Foreign direct investment (FDI) decreases energy consumption in a region as well as in surrounding areas. (4) In terms of regional heterogeneity, evident differences are found across regions. The results suggest that policymakers should encourage the development of the stock market and balance the development of the stock market among different regions, vigorously develop green lending, improve market-oriented conditions to attract foreign capital and give full play to the positive role of FDI in energy conservation and emission reduction. • Energy consumption in different regions is spatially dependent. • Spatial spillovers between financial development and energy consumption are observed. • Spillovers of financial development on energy consumption has regional heterogeneity. • Foreign direct investment helps to reduce energy consumption in and around a region. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop a model based on panel data from 30 Chinese provinces to assess rebound and fixed effects, as well as policy implications, in order to provide a theoretical foundation for the rapid advancement of lowcarbon transformation, which is a necessary result of a new stage in environmental growth. The results of primary analysis are the CO2 emission intensity reached 15.72 kt/million yuan in 2019, which was an great decline of 21.86 kt/million yuan as compared with the intensity in 2001; the average value of technological progress reached 1.017, waved like a U shape. The findings of empirical study show that the amount of energy CO2 emission rebound ranged widely; the average value of the rebound impact was -0.449; a 1% rise of technological progress led a 0.442% CO2 emission decrease, notably in Central and Western China. The results lower the cost of drafting energy policies as well as the decision-making involved and increase the economic advantages and transformation rate of technical breakthroughs in practice. [ABSTRACT FROM AUTHOR]

The challenges of environmental protection are especially prevalent in South and Southeast Asian nations, which adversely affects their sustainable developmental goals. During the last two decades, increased industrialization and urbanization have caused massive air pollution, particularly in the most industrialized and densely populated countries. Due to China's fast economic expansion and development, the demand for natural resources has increased, resulting in climate change, biodiversity loss, soil degradation, and environmental risks. China's ecological footprint has been the subject of little investigation on the premises of a circular economy. This study used a literature review methodology on the critical key factors that hinder or facilitate the transition of a linear economy towards a circular economy. Further, based on the literature review, this study used industrial ecology, energy efficiency, and waste recycling technology factors to analyze the role of the circular economy on the country's environmental sustainability agenda for the period of 1975–2020. The results show that in the short run, the link between ecological footprints and per capita income is monotonically decreasing; however, in the long run, the relationship is U-shaped. In both the short and long run, waste recycling technology and cleaner manufacturing significantly decrease ecological footprints. Renewable energy consumption increases ecological footprints in the short run but decreases them in the long run. The management of natural resources reduces ecological footprints to support the 'resource blessing' hypothesis. The Granger causality corroborated the unidirectional relationship between ecological footprints, oil rents, and urbanization and ecological footprints. In addition, economic growth Granger causes industrialization and waste recycling technology while green energy Granger causes economic growth, industrialization, and recycling technology. The two-way link between economic development and urbanization exists within a nation. The variance decomposition analysis (VDA) predicts that in the future, China's natural resources, green energy demand, and technological spillover will limit its ecological footprint through material and technology efficiency. [ABSTRACT FROM AUTHOR]

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China's increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China's short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China's short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China's CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China's CO2 emissions. [ABSTRACT FROM AUTHOR]

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context.