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The interactive behaviors, such as competition and learning between local and neighboring cities, can lead to spatial spillover effects in policy implementation. Estimating the direct, indirect, and total effects of the carbon emission trading scheme (CETS) on green innovation is crucial to clarify its role and mechanisms. This paper applies the spatial difference-in-differences model to estimate these effects using data from 278 cities in China from 2009 to 2019. The results demonstrate that the CETS implementation catalyzes green innovation in pilot cities, which supports the Porter hypothesis. However, it has a negative impact on green innovation in neighboring cities of pilot cities, with this negative impact diminishing as distance increases. Notably, the total effect of the CETS is significantly negative, indicating that the CETS's average impact on green innovation in all cities is negative. Mechanism analysis reveals that the direct and indirect effects of the CETS on green innovation are strengthened in cities with higher levels of financial development, lower financial pressure, and easier economic growth targets. Notably, in cities with high-level industrial structures, the CETS not only enhances its contribution to green innovation but also mitigates its negative impact on neighboring cities. Furthermore, this paper explores the heterogeneous effects of the CETS from the perspective of its implementation measures. [ABSTRACT FROM AUTHOR]

The transportation industry (TRAI) is an important source of carbon emissions for China to achieve its peak carbon and carbon neutrality targets. This study analyzes the coupled integration of TRAI economic growth and carbon emissions in 30 Chinese provinces from 2005 to 2020 using a Tapio decoupling elasticity model and an environmental Kuznets curve (EKC) model. Then, a long-term energy alternative planning (LEAP) system model was constructed to predict and simulate the development of TRAI in China. The results of the study are as follows. (1) TRAI shows an inverted U-shaped decoupling in most provinces across the country, and most of the decoupling is usually more volatile in provinces with higher levels of economic development. In addition, COVID-19 positively contributes to the development of TRAI nationwide and in three regions. (2) The Tapio-EKC coupling curves of TRAI and eastern TRAI (ETRAI) nationwide have an inverted N-shape, and central TRAI (CTRAI) and western TRAI (WTRAI) have an inverted U-shape. Twenty-seven provinces crossed the decoupling elasticity inflection point nationwide, all provinces of ETRAI crossed the EKC inflection point, while all provinces of CTRAI and WTRAI did not cross the EKC inflection point; (3) The carbon reduction rates after scenario simulation ranged from 5.07 to 54.94%, with aircraft, ships, private vehicles, and their fuels (aviation kerosene, diesel, and gasoline) being the focus of energy saving and emission reduction. The coupled Tapio-EKC analysis model of this study is universal and can be extended to other provinces, cities, or industries. [ABSTRACT FROM AUTHOR]

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy. [ABSTRACT FROM AUTHOR]

Economic agglomeration has played a key function in China's economic transformation. Meanwhile, structural adjustment and external pressure to reduce carbon emission necessitate a deeper investigation into its relationship with carbon productivity. This paper measures economic agglomeration and carbon productivity of 281 cities in China in 2003–2017. With the purpose of exploring the spatial effect of economic agglomeration on carbon productivity, a spatial Durbin model is constructed. In addition, the mediation effect model is also adopted for examining the transmission mechanism of economic scale and technological progress on economic agglomeration as well as carbon productivity. A heterogeneity test is also carried out to uncover the varying effects of economic agglomeration on carbon productivity. The empirical results suggest that economic agglomeration raises carbon productivity not only in the local cities but also in the neighbouring ones. The impact of economic agglomeration on carbon productivity becomes more potent as the level of economic agglomeration increases. For cities with low level of economic agglomeration, the effect is mainly achieved through the expansion of economic scale, but for cities with high level of economic agglomeration, the effect is mainly achieved through technological progress. Therefore, we suggest encouraging carbon productivity improvement through technology effect in cities with high economic agglomeration level, while enhancing carbon productivity through scale effect in cities with low economic agglomeration level. [ABSTRACT FROM AUTHOR]

R&D spending upsurges technological advancement and innovation which results in lowering energy consumption and environmental degradation. The current study investigates the asymmetrical impact of R&D spending on CO2 emissions in China via employing annual data from 1980 to 2021 and the NARDL model for empirical analysis. The estimated results of the NARDL model confirmed that there are asymmetries in positive and negative coefficients of R&D spending in China. The results depict that the positive shock in R&D spending exerts a negative and statistically significant impact on CO2 emissions in both runs implying that an increase in R&D spending lowers CO2 emissions. However, the negative coefficient of R&D spending yields a positive and statistically significant impact on CO2 emissions revealing the fact that a negative shock in R&D spending results in the upsurge of CO2 emissions in China. According to these findings, the impact of positive and negative shocks in R&D spending on CO2 emissions is asymmetric. The findings also show that the impact of a negative shock in R&D spending is greater than the impact of a positive shock on CO2 emissions. In addition to the negative shock in R&D spending, increases in energy consumption, economic growth, and FDI inflows also contribute to an upsurge in CO2 emissions in China. The robustness of the estimated results is assessed using standard fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models. The FMOLS and DOLS results have been confirmed to be sound and consistent with the results of the NARDL model. The study suggests that the economic strategies should aim at investing in R&D spending to foster environment-friendly technological innovations and to lower environmental degradation in China. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei region is not only an important economic center in China, but also one of the major regions contributing to China's carbon emissions. Revealing the spatial distribution between carbon emissions and economic growth is essential for the formulation of low-carbon development policies. Following the principle from macro to micro, this paper investigates the spatial evolution trend and distribution characteristics between carbon emissions and economic growth in the Beijing-Tianjin-Hebei region from 2005 to 2020 by applying imbalance index model, the rank-scale rule, and decoupling index model. The results show that the imbalance index of carbon emissions decreased between 0.0601 and 0.0533 in a fluctuating way, indicating that the imbalance of spatial distribution of carbon emissions decreases. The imbalance index of economic growth increased between 0.0738 and 0.0851, indicating that economic growth has become more disequilibrated, and the spatial evolution of carbon emissions is not coordinated with economic growth. The Zipf dimension of carbon emissions declined from 1.1806 in 2005 to 0.9594 in 2020, and carbon emissions declined in big cities and increased in cities of the middle order. The Zipf dimension of economic growth increased from 1.1384 in 2005 to 1.2388 in 2020, and the economic growth monopoly in big cities increased. The decoupling coefficient of carbon emissions to economic growth declined, and the driving effect of economic growth on carbon emissions diminished. It is recommended that the Beijing-Tianjin-Hebei region should coordinate the allocation of factors and coordinate industrial adjustment. Hebei should accelerate industrial upgrading and establish a diversified industrial system. [ABSTRACT FROM AUTHOR]

Large-scale internal migration and unprecedented urbanization have dramatically promoted economic growth in China, resulting in a rapid surge in carbon emissions in urban areas. However, few studies have investigated the causal effect of mass internal migration on carbon emissions or examined the effects of autonomous mitigation mechanisms, such as population agglomeration and technological innovation. This study identifies the causal effect of internal migration on prefectural-level cities' carbon emissions in China by employing an instrumental variable and further investigates the buffering effect of population agglomeration and technological innovation using mediating effect models. The results show that mass internal migration has a substantial impact on increasing carbon emissions in prefectural-level cities. If the proportion of inflowed migrants rises by 1% point, prefectural-level cities' carbon emissions per capita will increase by 1.9%. A series of robustness tests confirms the result. Population migration also promotes population agglomeration and technological innovation in urban areas. Two autonomous mechanisms buffer 11.9% and 5.4% of prefectural-level cities' incremental carbon emissions per capita caused by population migration, respectively. This study highlights the crucial role of population agglomeration and technological innovation in mitigating carbon emissions in cities experiencing significant migrant inflows and provides several implications for formulating relevant policies. [ABSTRACT FROM AUTHOR]

This study examines the environmental Kuznets curve (EKC) theory and its possible relevance in the context of China and the complex and one-of-a-kind interaction between financial innovation, environmental deterioration, and China. China's fast industrialization and consequent economic expansion have been accompanied by significant environmental difficulties, despite the country's status as the world's second largest economy. The research takes a secondary data approach to its investigation of this topic, drawing on a large dataset that spans many decades to ensure a thorough examination of the dynamic interaction between financial innovation and numerous environmental variables. The primary objective of this research is to determine whether or not recent developments in financial technology have contributed to the current trends of environmental degradation in China. In keeping with the central tenet of the EKC hypothesis, this study uses cutting-edge econometric techniques to investigate the existence of nonlinear correlations and inflection points. In addition, the research analyzes how policy interventions and regulatory measures have shaped the tendencies spotted. In elucidating the usefulness of financial innovation as a tool for promoting environmental betterment, this study's findings substantially contribute to the current discussion around sustainable development and economic policy. This research also carefully assesses the applicability of the EKC theory in China, providing insights that may be used to inform future environmental policy decisions. Ultimately, the success of measures to promote sustained economic growth and the preservation of the environment in China depends on an in-depth understanding of these delicate relationships. In addition, the lessons learned from this study have the potential to serve as a guide for economies facing similar difficulties. [ABSTRACT FROM AUTHOR]

The carbon emissions trading system (CETS) is a helpful policy instrument for separating carbon emissions from economic expansion, and it significantly impacts energy efficiency (EE). This study uses 30 Chinese provinces from 2007 to 2020 as its research samples, and classifies energy efficiency into single-factor energy efficiency (SFE) and total-factor energy efficiency (TFE), using the difference-in-differences model to examine the effect and mechanism of the CETS on EE. As an additional tool to assess the efficacy of the CETS, the corresponding evolution of the rebound effect of energy-related carbon emissions (RECE) is also calculated. This study shows that the CETS can significantly improve EE in China's pilot provinces. The influence mechanism indicates that the effect of the CETS on EE is influenced by the level of government governance, green innovation, and industrial structure optimization. Further study finds that after the CETS was carried out, the RECE in pilot provinces was higher than that in non-pilot provinces, and 31.4% of carbon emissions reduced by EE improvement rebounded. Therefore, the CETS has yet to realize its full carbon reduction potential. The study offers specific policy proposals for the enhancement of China's CETS in light of the aforementioned findings. [ABSTRACT FROM AUTHOR]

Exploring the relationship between economic development and carbon emissions is a hot issue of concern to academia. Taking Chinese cities as the research object, this study constructed an allometric growth model of economic scale and carbon emissions to analyze the spatial–temporal evolution of their allometric growth from 2000 to 2017. Additionally, the geographical detector model was used to reveal the driving mechanism of allometric growth. The findings were as follows. (1) The spatial–temporal patterns of economic scale and carbon emissions were dominated by hot spots. Additionally, their size distribution was in an equilibrium stage. (2) The gap between the economic growth rate of Chinese cities and the growth rate of carbon emissions grew, reflecting the remarkable achievements in carbon emission reduction in Chinese cities as a whole. The scaling exponent declined from east to the west during 2000–2008, but showed the opposite trend during 2009–2017. The proportion of cities with negative allometric growth in the sample cities increased from 76.49 to 97.86%. (3) The influence of city investment intensity, energy utilization efficiency, technological development level, social consumption level, fiscal investment level and economic development level on allometric growth gradually decreases. These factors were also the main factors affecting the spatial–temporal heterogeneity of allometric growth. (4) The impact of various factors had a synergetic enhancement effect. These factors can be classified as economic factors, environmental factors and a combination of them. Under the nonlinear coupling of multiple factors, a spatially differentiated pattern of allometric growth was formed. [ABSTRACT FROM AUTHOR]

The study examines the relationship between green finance development, carbon emission intensity, and economic development in China's 30 provinces. The entropy approach is used to calculate the green finance development index, and a panel vector autoregressive model is established using this index along with economic development and carbon emission intensity. The study finds that green finance can promote economic growth and help achieve emission reduction objectives, while increasing carbon emissions can also promote economic growth. The study also highlights regional differences, with the economic growth of the eastern and central regions negatively correlated with carbon emissions after surpassing the inflection point of the environmental Kuznets curve. Additionally, the study suggests that there is still room for growth in green finance in the western region. The findings have important policy implications for China in developing targeted development strategies. [ABSTRACT FROM AUTHOR]

Growth target management drives China's economic expansion, leading to potential carbon emission externalities. This study examines the impact of economic growth target setting on carbon emission performance in 257 Chinese localities from 2004 to 2020. Using cluster standard error regression, 2SLS instrumental variable regression, and the spatial Durbin model, the results show that moderate growth targets improve urban carbon emission performance. Conversely, aggressive targets do not enhance regional carbon emission performance. Moderate growth targets foster innovation in green technology, while aggressive ones encourage investment in fixed assets and new city growth, impeding performance. Cities in the eastern and western regions, with larger populations and less reliance on natural resources, benefit more from effective economic development targets. The establishment of local targets negatively affects neighboring regions' carbon emissions performance, particularly at a spatial scale of 100 to 200 km. This paper contributes to understanding the environmental consequences of growth target setting and offers insights for balancing economic growth with carbon emission governance in emerging market countries. [ABSTRACT FROM AUTHOR]

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations. [ABSTRACT FROM AUTHOR]

Excessive carbon emissions are the major challenge to global sustainable development. In the context of the coronavirus pandemic, pressure on global economic growth is gradually rising, threatening established carbon reduction targets. However, the relationship between economic growth pressures and carbon emission intensity has yet to be clearly discussed. Thus, this study quantitatively discusses the impacts of economic growth pressures from central (EGPN) and provincial (EGPP) governments on city carbon intensity. The study is based on data from China's city panels from 2005 to 2019. This study finds that (1) there is a U-shaped correlation between economic growth pressure and a city's carbon emission intensity, whether the economic growth pressure comes from the central government or the provincial government; (2) carbon emission intensity is more sensitive to economic growth pressure from the provincial government than it is to economic growth pressure from the central government. The findings of this study will help enhance the understanding of the relationship between economic growth pressure and carbon emission intensity, and can also provide a reference for global sustainable development that balances economic growth and environmental protection. [ABSTRACT FROM AUTHOR]

China is the world's largest energy consumer and CO2 emitter, facing a great challenge of CO2 emission mitigation. In order to identify driving factors for prefecture-level carbon emissions in western China, a case study of Haixi was performed. Time series analysis was undertaken in this study to provide a holistic picture of the historical trend of energy-related CO2 emissions. The relationship between CO2 emissions and economic growth was also explored based on decoupling analysis. Subsequently, logarithmic mean Divisia index was adopted to identify key factors driving CO2 emissions. Results showed that the energy-related CO2 emissions generated by Haixi increased to 18.18 million tons in 2016 from 7.26 million tons in 2007. Results also indicated that Haixi had not achieved decoupling of economic growth from CO2 emissions. Economic activity was the dominant positive driver in changes of CO2 emissions, while industrial structure was the dominant negative driver over the entire period. Suggestions based on empirical findings and local realities were proposed, which may provide reference for stakeholders regarding the reduction of CO2 emissions. [ABSTRACT FROM AUTHOR]

The Chinese government has established carbon emissions trading schemes (CETS) to cope with climate change and reduce carbon emissions. This paper uses the multi-period difference in difference (DID) model to explore the impact of CETS on cities' GDP and GDP growth rates in China based on data from 285 prefecture-level cities from 2008–2019. We find that CETS increase the level of GDP by 3.4% and the GDP growth rate by 0.004. The mechanism of increasing GDP includes promoting investment and innovation, and the mechanism of increasing the GDP growth rate includes only promoting the investment growth rate. Thus, CETS satisfy both the weak and strong versions of the Porter Hypothesis from the GDP and growth rate perspectives. In the heterogeneity analysis, we find that the impacts of CETS on cities' GDP and GDP growth rates decrease with the increase of the corresponding dependent variable. In the spatial spillover analysis, we find that the establishment of CETS has a positive spillover effect on the GDP of cities around 50 km and 150 km, and a negative spillover effect on the GDP of cities around 200 km, but there is no spatial spillover effect on the GDP growth rate. [ABSTRACT FROM AUTHOR]

This research investigates the resource curse hypothesis and environmental sustainability by integrating China's natural resources, renewable energy, and urbanization. However, the EKC N shape describes the complete picture of the EKC hypothesis for the growth-pollution relationship. The findings of FMOLS and DOLS show that economic expansion positively drives carbon dioxide emissions in the beginning, then negatively so after the target level of growth is reached. Continuing economic expansion in China does not maintain the intended level and again has a beneficial impact on the country's carbon dioxide emissions. However, the EKC U, inverted U, and N shapes persist in the growth-pollution connection over the long term. Although adopting renewable energy and urbanization help reduce carbon dioxide emissions, the formation of fixed capital worsens environmental conditions. Natural resource rents are a major cause of environmental degradation and the resource curse that has plagued China. Economic growth, as well as its square and cube, has a causal effect on CO2 emissions, as shown by the frequency domain causation. Carbon dioxide emissions at frequencies of 0.05, 1.50, and 2.50 are momentarily predicted by the use of renewable energy and urbanization. The investigation recommends switching to renewable energy sources owing to low cost and the potential to limit the overuse of non-renewables. To balance the overdo of natural resources and ensure continued long-term growth-environment sustainability, technological advancement is recommended as a countermeasure as a mitigating mechanism. [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]

Based on the environmental Kuznets curve (EKC) hypothesis and using Chinese provincial panel data from 2002 to 2019, this study examines how different types of healthcare expenditure and levels of economic development and energy consumption contribute to carbon emissions regionally. Considering the wide regional differences in the development levels of China, this paper uses quantile regressions and draws the following robust conclusions: (1) The EKC hypothesis was validated by all methods in eastern China. (2) The carbon emission reduction of government, private, and social health expenditure is confirmed. Furthermore, the impact of health expenditure on carbon reduction decreases from East to West. (3) Government, private, and social health expenditure all cause reductions in CO2 emissions, with private health expenditure having the largest negative effect on CO2 emissions, followed by government health expenditure and finally social health expenditure. Overall, the limited empirical work available on the impact of different kinds of health expenditure on carbon emission in the existing literature, this study greatly assists policy makers and researchers to understand the importance of health expenditure in improving environmental performance. [ABSTRACT FROM AUTHOR]

The significance of the sports economy to the national economy is growing as the country develops rapidly. The term "sports economy" is used to describe the economic activities that are directly or indirectly tied to sports. This work introduces a multi-objective optimization model for a green supply chain management system, intending to reduce the economic and environmental impact of storing and transporting potentially dangerous products. This research intends to examine the impact of the sports sector on green economic growth and competitiveness in the China region. An empirical analysis is conducted to determine the relationship between sports economics and green supply chain management by employing data on 25 provinces of the China region for 2000 and 2019. To fulfill this study's objective and determine the effect of carbon emission, this study will use renewable energy, sports economics, green supply chain management, information and communications technology, and waste recycling as explanatory variables. In order to obtain the desired objectives, the current study will use the short run and long run of the cross-sectionally augmented autoregressive distributed lag and pooled mean group tests. Besides, this study uses an augmented mean group, fully modified ordinary least square, and dynamic ordinary least square estimations for the robust check. In contrast, renewable energy, green supply chain management, sports economics, information and communications technology, and waste recycling decrease CO2 emissions and, therefore, facilitate the carbon-abatement agenda of the China region. [ABSTRACT FROM AUTHOR]

Decoupling CO2 emissions from economic growth is an important cornerstone of a country's decarbonization policy. Existing research in this avenue has mainly focused on decoupling the production-based (PB) CO2 emissions from economic growth at the national level, while decoupling the consumption-based (CB) and income-based (IB) CO2 emissions from economic growth, especially at the sectoral level, has received less attention. Using China's Zhejiang province as a case study, we conduct one of the first studies focused on decoupling the PB, CB, and IB CO2 emissions from economic growth at the sectoral level. Our results reveal that (1) during 2002–2017, the sectoral decoupling level varies greatly among the three different perspectives; (2) most of the examined sectors were in unstable decoupling states while some sectors even reverted to coupling states; and (3) the drivers of CO2 emissions at the sectoral level reveal important factors, such as emissions intensity, production structure, and final demand structure, that contribute toward decarbonization. Our study demonstrates to policymakers how utilizing a multi-perspective evaluation of the decoupling of emissions from sectoral economic growth can improve the accuracy of decarbonization policies and identify critical sectors toward CO2 reduction objectives. [ABSTRACT FROM AUTHOR]

The logistics business is a crucial contributor to economic development, yet it is also the leading source of carbon emissions. Economic growth at the expense of environmental deterioration is a challenging issue; this phenomenon offered a new avenue for scholars and policymakers to investigate and address these issues. The recent study is one of the attempts to explore this intricate subject. The goal of this research is to determine whether or not the Chinese logistics sector has an impact on Pakistan's GDP and carbon emissions as a result of CPEC. The research utilized data from 2007Q1 to 2021Q4 using the ARDL approach for an empirical estimate. Due to the mixed order of variable integration and finite data set, the ARDL technique is well deserved, which helps reach sound policy inferences. The study's key results indicated that China's logistic business enhances Pakistan's economic development and carbon emissions in the short and long term. Similarly, China's energy usage, technology, and transportation contribute to Pakistan's economic progress at the price of environmental damage. The empirical study may be a model for other developing nations, given Pakistan's viewpoint. With the support of the empirical results, policymakers in Pakistan and other associated countries would be able to plan for sustainable growth in conjunction with CPEC. [ABSTRACT FROM AUTHOR]

This paper investigates the role of export quality in climate action goal of the sustainable development goals in emerging Asian countries. For this purpose, the empirical model that observes the impact of real GDP, energy use and export quality index on carbon emissions is constructed and is analyzed by ARDL bounds testing approach for the period from 1970 to 2014. We also include the square of real GDP as independent variable to observe the existence of environmental Kuznets curve (EKC) hypothesis which implies the parabolic relationship between economic growth and environmental degradation. The findings show that increase in export quality leads to a fall in CO2 emissions for China and India. In contrast, the effect of increasing export quality increases CO2 emissions in Thailand and the Philippines. Lastly, our asymmetric causality results show that the positive shocks of export quality causes positive shocks of CO2 emissions in Thailand and Indonesia. Furthermore, we found that positive export quality shocks cause negative CO2 emissions shocks in India while negative export quality shocks cause positive CO2 emissions shocks in China. We also confirm the inverted U-shaped EKC hypothesis in China and Thailand. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of foreign direct investment (FDI) inflow, financial development, economic growth, globalization, innovation, and urbanization on the carbon dioxide emissions in China by using long-ran time series dataset from 1970 to 2021 which for the first time used the newly developed dynamic autoregressive distributed lags (ARDL) simulation model for results analysis. Dynamic ARDL simulation model removes the shortcomings of the traditional ARDL models by predicting the actual change (positive and negative shocks) in the independent variables and its impact on the main dependent variables by 5000 simulations through graphical representation. The findings of the long-run dynamic ARDL simulations indicate that FDI inflow, globalization, and innovation negatively and significantly impact the environmental degradation while financial development, economic growth, and urbanization cause to increase the environmental degradation in China. Recommendations are suggested based on the findings of this study. [ABSTRACT FROM AUTHOR]

As a noteworthy initiative of financial supply-side reform to precisely support the green development system, can green finance (GF) help achieve the dual goals of "carbon peaking" and "carbon neutrality"? Using data from China's provincial panel between 2007 and 2019, this paper measured the green finance index by the entropy method and the carbon emission efficiency (CEE) with carbon emission as the non-desired output by the Super-SBM model. Then, the influence of GF on CEE was empirically investigated by the dynamic panel model and the spatial Durbin model. The findings show that GF can significantly improve CEE and has a positive spillover impact on CEE in provinces with close economic ties; the upgrading of the industrial structure is a key mediator in the transmission of GF to CEE; and regional heterogeneity analysis finds that GF notably improves CEE in eastern, high development levels of economic and GF regions. The research can offer some theoretical and empirical references for green finance to contribute to low-carbon economic growth. [ABSTRACT FROM AUTHOR]

China seems ambitious to achieve a "carbon emissions peak" before 2030 and "carbon neutrality" before 2060. To deal with this emissions mitigation plan, technology innovations are regarded as a crucial factor. However, considering its rebound effect (CO2 emissions driving effect) through economic growth, technology innovations might not prove a promising contributor to CO2 reduction. Therefore, there is a need to investigate further the nexus between technology innovations and CO2 emissions for conclusive debate. Based on the data of 215 cities in China, this paper uses mediating effects model to investigate the direct and indirect impacts (through economic growth and industrial structure transformation) of technology innovations on CO2 emissions from a microeconomic perspective. The main results suggest that technology innovations generally increase CO2 emissions in China both directly and indirectly. The impact of technology innovations and economic growth on CO2 emissions indicated the EKC characteristics. Furthermore, the contributions of technology innovations to CO2 emissions are distinguished in different regions. Thus, there is an urgent need for China to promote innovations in "clean technology" and to transform industrial structure to the tertiary industry to achieve the targets of carbon neutrality and emissions peaking. [ABSTRACT FROM AUTHOR]

THe central challenge facing China's sustainable development is how to strike a balance between economic growth and environmental conservation. In China's ongoing economic revolution, green finance is more important than ever. The study empirically examined how green finance and innovation affect carbon emissions using panel data from 30 Chinese provinces gathered between 2010 and 2020. The empirical analysis is undertaken to utilize a series of methods to investigate the impact of green finance on carbon emissions. The findings show that increased green finance, innovation, and industrial structure reduce carbon output. Moreover, carbon emissions increase with increasing trade openness and economic growth. In order to achieve sustainable development goals through economic and environmental sustainability, it has been discovered that green finance can foster green technology innovation and green business. [ABSTRACT FROM AUTHOR]

CO2 emissions have become a topical issue worldwide, but few studies have considered the spatial effect of income on carbon emissions and explored the relationship between CO2 emissions and income by establishing direct, indirect, and total environmental Kuznets curves (EKCs). Using an annual panel dataset collected over the 1997–2017 period in China, this study first analyzed the spatiotemporal evolutionary process of CO2 emissions and subsequently developed direct, indirect, and total EKC-based spatial Durbin model (SDM) and partial derivative approach. These results indicate that, first, CO2 emissions have characteristic positive spatial autocorrelation, with gravity centers that have shifted westward. Second, the direct EKC forms a line, while the total EKC resembles a lying-S shape as well as the total EKC, which indicates that compared to local economic growth, neighboring growth plays a very different role in impacting local CO2 emissions. Furthermore, neighboring economic growth seems to have stronger impacts on local emissions, and the turning point of the total EKC comes much earlier than that of the conventional EKC due to the spillover effects of economic growth. Finally, the growth of the population, as well as the rise of energy intensity, can stimulate CO2 emissions in both local and neighboring regions. Industrialization seems to have a nonsignificant impact on emission changes due to the offsetting effects of the positive direct and negative indirect impacts of the share of secondary industry. Improvements in local urbanization may lead to an increase in emissions, while neighboring improvements may have stronger restricting effects; thus, urbanization improvement is beneficial to emissions reduction. This study provides more scientific information from both local and neighboring perspectives, which may differ from conventional results but still be beneficial for emissions reduction policy-makers to introduce corresponding measures. [ABSTRACT FROM AUTHOR]

As the world's most populous developing country and the world's largest carbon emitter, China has already completed its 2020 Intended National Determined Contribution set out in the Paris Agreement. It achieved this result by adopting a dual-goal strategy of economic growth and reducing carbon emission, that is, low-carbon economic growth. Based on both parametric and nonparametric methods, quantitative and qualitative conclusions about China's low-carbon economic growth are presented. It is found that since the beginning of this century, China has maintained an annual growth efficiency of 1% in low-carbon total factor productivity (TFP) and low-carbon technological progress rate. The Eastern region has leading advantages in low-carbon TFP, low-carbon technology advancement, low-carbon efficiency, and low-carbon technology revolution. It has the potential to be the first to reach its CO2 turning point. The inefficiency ratio of labor and CO2 is higher at the national level and in the Eastern region, with the two accounting for about seven tenths and eight to nine tenths, respectively. The difference is that at the national level, the input–output inefficiency is similar, and the inefficiency in the Eastern region is more from the input side, about eight tenths. At the present stage, China is still lenient in the implementation of low-carbon environmental regulations. In the future, the adjustment of low-carbon policy should pay attention to regional heterogeneity, focus on reducing labor and CO2 inefficiency, and be more stringent in policy implementation. [ABSTRACT FROM AUTHOR]

Low-carbon innovation plays an essential role in carbon reduction worldwide. This study investigates the nexus between low-carbon innovation, economic growth, and carbon emissions by the dynamic spatial Durbin model from 2007 to 2020. First, the Moran index results verify the provincial spatial agglomeration of carbon emissions. High-emission provinces concentrate in major economic zones and energy extraction areas. Second, the effect decomposition results show that long-term and short-term effects are consistent. Low-carbon innovation has a significant mitigation effect on carbon emissions in local regions, which effect, however, is not significant in the adjacent areas. The environmental Kuznets curve hypothesis is validated locally, but all provinces and cities have not reached the inflection point of the environmental Kuznets curve, and the linkage effect in adjacent regions remains insignificant. The above results have been tested to be robust. Third, the results of the mechanism analysis show that environmental policies, absorptive capacity, and financial development play a moderating role in the relationship between low-carbon innovation and carbon emissions. Finally, the heterogeneity test showed significant differences between Eastern, Central, and Western. The direct effect of low-carbon innovation exists in Eastern and central regions; the spillover effect of low-carbon innovation is only in the eastern region. In addition, corresponding measures are proposed based on the conclusions. [ABSTRACT FROM AUTHOR]

Under the background of the deep convergence of China's digital services and manufacturing, it is of great significance to investigate the effects of the convergence of digital services and manufacturing on economic growth and carbon emissions reduction to the application of digital technology in the whole world. This paper constructs a simultaneous equation model and uses three-stage least squares to estimate the effect and mechanism of industrial convergence on economic growth and carbon emissions. The results show that (i) industrial convergence improves the change of total factor productivity (TFP) and the change of technical efficiency, and the reduction of carbon emissions is the main factor driving the growth of TFP and technical efficiency; (ii) industrial convergence and carbon emissions show a significant U-shaped relationship; (iii) the heterogeneity analysis shows that the convergence of capital-intensive, technology-intensive and labor-intensive manufacturing with digital services will help to improve the growth of TFP, it can inhibit carbon emissions first and then promote it. Therefore, the government should take targeted measures to promote industrial convergence of digital services and manufacturing according to the economic development and industry characteristics, so as to give full play to its positive role in economic growth and emissions reduction. [ABSTRACT FROM AUTHOR]

To cope with climate change, China commits that it will strive to achieve carbon peaking by 2030. Using the Cointegration model and the Markov Chain model, this paper forecasts China's carbon emissions during 2019–2030 in six scenarios, and assesses the contribution of optimizing the energy mix to China's carbon peaking. The research obtains three main conclusions. Firstly, optimizing the energy mix will contribute to achieving China's carbon peaking. In the economic slow-growth scenario, taking China's planned target of energy mix (PTEM) into account, the carbon peaking year will be brought forward from 2028 to 2023. In the economic medium-speed-growth scenario, optimizing the energy mix will make China achieve carbon peaking in 2028. Without considering the PTEM, however, the carbon emissions will not peak before 2030. In the economic fast-growth scenario, the peaking year will not occur whether considering the PTEM or not, but the growth rate of carbon emissions with the PTEM will be far lower than that without considering the PTEM. Secondly, in all three economic growth scenarios, optimizing the energy mix will largely reduce the growth rate of carbon emissions, and thus significantly reduce the peak value of carbon emissions. Thirdly, optimizing the energy mix has a negative adjusting effect on the impact of economic growth on the growth rate of carbon emissions, and the negative effect rise as the economic growth rate increases. [ABSTRACT FROM AUTHOR]

After reform and opening-up, rapid industrialization and urbanization led to environmental degradation in China, including excessive energy consumption, soil contamination, and water pollution. Toward sustainable development, the Chinese government has promoted the introduction of clean energy sources such as geothermal and hydroelectric power generation, which have reduced the environmental burden. However, the impact of this energy shift on environmental improvement and economic growth is unclear. This study empirically analyzes the impact of green energy deployment and economic growth on CO2 emissions in China. The analysis of time series data from 1980 to 2020 shows that in the long run, a 1% increase in renewable energy significantly reduces CO2 emissions by 0.87%, and a 1% increase in GDP significantly increases CO2 emissions by 0.26%. In contrast, in the short run, the negative effect of renewable energy on CO2 emissions and the positive effect of GDP on it are not significant. This result was confirmed after the robustness checks. Based on the results obtained, several policy recommendations are made. [ABSTRACT FROM AUTHOR]

Using panel data from 30 provinces and cities in China over the period 2013–2019, we intend to explore the mechanism and regional heterogeneity of the influence of digital economy development on carbon emissions. Specifically, this relationship is analyzed by including the geographical variable coefficient model into the chain mediation effect model, taking spatial correlation and heterogeneity into account. The results indicate that the digital economy decreases carbon emissions by enhancing energy intensity, but raises carbon emissions by fostering economic expansion, making digital economy a net contribution to carbon emissions. Moreover, the effect of the digital economy on carbon emissions varies by geographic location. For instance, the total impact is the greatest in northern China, followed by the southwest and southeast, and relatively minor in the northwest and south. Our findings contribute to the existing research and offer policymakers with a theoretical reference, allowing them to customize carbon reduction plans to local conditions. [ABSTRACT FROM AUTHOR]

Reducing exhaust emissions, promoting economic development, and realizing clean energy utilization have always been concerns in China. To measure the relationship between them, this study selects the data related to energy consumption, economic growth, and exhaust emissions from 2000 to 2019 in 30 Chinese provinces. By constructing a panel vector autoregressive (PVAR) model, the dynamic relationship between them in China is quantitatively analyzed. The results show that there is a long-term interaction between energy consumption, economic growth, and exhaust emissions. Among them, economic growth is highly dependent on energy consumption, but it can promote the reduction of exhaust emissions. However, energy consumption will produce a large amount of industrial waste, such as sulfur dioxide and carbon dioxide emissions. Specifically, the industrial structure and energy structure have the most obvious impact on reducing industrial sulfur dioxide emissions and carbon emissions, with the proportion between 0.071–0.090 and 0.031–0.032, respectively. Therefore, the adjustment of industrial structure and energy structure is the key to exhaust emission reduction. [ABSTRACT FROM AUTHOR]

This study dynamically examines the impact of foreign direct investment and other explanatory variables on economic growth and carbon emissions, and tests the validity of pollution haven hypothesis (PHH) and environmental Kuznets curve (EKC) hypothesis in China, India, and Singapore from 1980 to 2020. The results of Westerlund's (Oxford Bulletin of Economics and Statistics, 69(6):709–748, 2007) panel cointegration test illustrate long-run equilibrium relationships among the proposed set of panel variables in the model. The estimated parameters of the AMG, CCEMG, and MG estimators in each of the specified models show that renewable and non-renewable energy consumption, foreign direct investment, and capital accumulation all have significant and progressive effects on economic growth. However, the labor force is insignificant and carbon emissions have a significant negative impact on economic growth. Non-renewable energy consumption significantly stimulates and renewable energy consumption significantly reduces carbon emissions. Moreover, the moderating role of non-renewable energy in the impact of foreign direct investment on carbon emissions is significantly positive, thus validating the PHH. The moderating role of renewable energy consumption in the impact of foreign direct investment on carbon emissions is significantly negative. The study's analysis also clearly validated the inverted U-shaped EKC hypothesis in China, India, and Singapore. Policymakers in emerging economies must prioritize the maturity of renewable energy, which not only increases productivity but also protects the environment from damage by reducing carbon dioxide emissions. The governments of China, India, and Singapore should initiate direct foreign inflows based on advanced and clean technologies to avoid environmental degradation and drive higher growth in these economies. [ABSTRACT FROM AUTHOR]

We researched China's climate and sustainable development goal with relevant and susceptible instruments capable of inducing and mitigating carbon emissions. Amidst the contributor to the global carbon emissions, China is caught in between mitigating its carbon emission and aiming towards placing its national contribution of emissions to the acceptable levels of 1.5 °C and below 2 °C. Following the intricacies surrounding China's sustainable development as it contains its economic and environmental performance, we adopt China's data of 1980 and 2018 with different scientific approaches (nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least square test, and bootstrap Granger causality) with different instruments (such as economic growth, financial development, renewable energy, and innovation policies) to research China's sustainable development. For clear exposition and insight into our findings with policies attached, we draw a conclusion from the outcomes of the mentioned approaches. From NARDL and dynamic ordinary least squares (DOLS), we find that economic growth through economic activities is statistically significant in determining the trend (increase) of carbon emissions in China in both periods (short run and long run). However, other selected instruments (financial, renewable, and innovation policies) tend towards controlling and moderating the carbon emissions in China. Thus, China has good prospects to mitigate its carbon emissions if considered tailoring its policies towards favorable instruments. From bootstrap Granger causality, we find similar inferential results that support previous findings thereby confirming the positive implication of the selected instruments to China's sustainable development. Hence, the nexus that is established among the selected instruments clearly show the importance of technological innovation and renewable energy in mitigating carbon emissions. [ABSTRACT FROM AUTHOR]

This study aims to analyze the asymmetric long-run relationship between economic growth (EG), foreign direct investment (FDI), and carbon emissions (CO2) within the context of the environmental Kuznets curve (EKC) and the pollution haven hypothesis (PHH) in China. Employing the quarterly data from 1982Q1 to 2018Q4, we have used novel techniques to meet the stated objectives of our study, named quantile ARDL and quantile Granger causality. The study provides novel outcomes using the advanced quantile ARDL and quantile Granger causality tests. The significant implication of this method is that it provides locational asymmetry. We find strong evidence of the EKC and PHH for China based on the empirical results of linear and nonlinear ARDL models. Similarly, findings of quantile Granger causality validate the bidirectional relationship among all variables in upper and lower quantiles. Moreover, the results of the Wald test confirm the asymmetric long-run relationship between FDI and carbon emissions (CO2). Thus, legal measures must be enhanced, accepted, rigorously imposed, and monitored in all provinces to assure a further reduction in carbon emissions. This study will be conducive for the policymakers to combat environmental contamination concerning economic growth and FDI inflow in China. [ABSTRACT FROM AUTHOR]

By revealing the temporal and spatial differentiation of China's regional tourism carbon emissions and its decoupling relationship with tourism economic growth and identifying the key factors affecting tourism carbon emissions, this paper is expected to provide a reference for the formulation and implementation of China's regional tourism industry emission reduction policies and measures. Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data exhibit a primary characteristic of weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions. [ABSTRACT FROM AUTHOR]

With less than ten years left to meet its pledge to peak carbon dioxide emissions (peak emissions hereafter) by 2030, China has entered a critical emissions reduction stage. How to meet this commitment in a context in which GDP per capita will double from 2020 by 2035 is a major decision-making issue for the Chinese government and people and one which warrants further study. To reveal the relationships between the GDP growth rate, the rate of decrease of carbon intensity and the time to reach peak emissions, this study translates the question as to "when China's carbon emissions peak will occur" into "how can one control the rate of carbon intensity decrease at a given GDP growth rate". In the light of the results of a random forest algorithm used to identify and project the key drivers of carbon intensity in China, a mathematical model was developed to simulate different scenarios relating to decreases in carbon intensity. The date at which Chinese carbon emissions will peak is predicted by comparing the rate of decrease of carbon intensity with the GDP growth rate. The results show that the time to peak emissions depends on the relationship between the rate of decrease of carbon intensity and the GDP growth rate, where the former depends mainly on the energy structure and policy. If China's annual GDP growth rate were 5.0% during the 15th Five-Year Plan, and if the share of non-fossil energy in total energy consumption were 23.0% or above, China's carbon emissions will peak before 2030. If the share of non-fossil fuels were 20.0% or less, China might not be able to reach its 2030 target. In this latter case an acceleration in the pace of energy restructuring would be required to reach peak emissions before 2030. The projected peak emissions scenarios suggest that the carbon peak will occur between 2025 to 2029, with average peak emissions of 11.2 billion tons and a distribution ranging from a minimum of 10.5 billion and a maximum of 11.9 billion tons. If the GDP growth rate were 4.5%, 5.5% or 6.0% during the 15th Five-year Plan, the share of non-fossil energy must reach 23.0%, 25.0% or 27.0%, respectively, to ensure that emissions peak by 2030. The results of this study provide a series of reference points for China's pursuit of feasible pathways to peak carbon emissions by 2030. [ABSTRACT FROM AUTHOR]

This paper constructs the calculation method of total factor efficiency and low-carbon technology progress indicators based on the undesirable SBM model and Malmquist-Luenberger productivity index model, and this empirically analyzes the relationship between green credit, environmental regulation, and total factor efficiency or low-carbon technology progress by using the panel data of 30 regions in China from 2009 to 2018. The study results show that the carbon emission efficiency evaluation results of each region vary greatly; the largest gap reaches 400%. China's overall carbon emission efficiency has 35–40% room for improvement, and it is increasing year by year at the rate of 0.96% mainly depends on the improvement of technological progress. The improvement of China's carbon emission efficiency should focus on the central and western areas. The relationship between the green credit level and the total factor efficiency and low-carbon technical progress is significant "U-shaped" curve, while the relationship between the environmental regulation intensity and the total factor efficiency and low-carbon technical progress is significant "inverted U-shaped" curve. The proportion of tertiary industry, ownership structure and average scale of industrial enterprises have significant positive, negative and negative effects on regional total factor efficiency and low-carbon technical progress. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to analyze the relationship between urban public infrastructure, CO2 emissions and economic growth in China for the period 1990–2019 using the Dynamic Conditional Correlation Multivariate GARCH model (DCC-MGARCH) and Autoregressive Distributed Lagged Model (ARDL). According to the estimated results of the DCC-MGARCH, there is a positive influential effect among the economic growth, urban public infrastructure and CO2 emissions, while there is a negative influential effect between the urban public infrastructure and CO2 emissions, but the duration of volatility between the urban public infrastructure and CO2 emissions is relatively weak. Among such relationships, the conditional correlation also shows there is an effect in S-shape between the urban public infrastructure and economic growth, but the trend variation between the urban public infrastructure and CO2 emissions shows an inverted "U-shape" of Environmental Kuznets Curve Theory. The results of the ARDL-Bounds test show that there are long-term co-integration relationships and short-term dynamic correlation among all models. Also, the results of error correction terms show that the speed deviating economic growth and CO2 emissions from the long-term equilibrium and CO2 emissions deviating from the long-term equilibrium is larger than that of the urban public infrastructure, which indicates that the relationship between the economic growth and CO2 emissions is relatively strong. [ABSTRACT FROM AUTHOR]

This study examines to what extent foreign private investment (FPI) affects the clean industrial environment and sustainable economic growth through developed countries investment in China. Moreover, this study investigates an association among FPI, CO2 emission, energy consumption, trade openness, and sustainable economic growth. This study uses random effects and generalized least squares (GLS) and panel VAR estimators for data analysis. The results show that China's economy has a great positive impact on the location and choice of investment in domestic markets in emerging countries and developed countries. In addition, investment in emerging and developed economies has increased the contribution of domestic enterprises and environmental sustainability to the national economy. The further results show that foreign private investment and gross domestic investment have positive impact on sustainable economic growth. [ABSTRACT FROM AUTHOR]

Reaching the peak of carbon dioxide emissions is the basis and premise of carbon neutrality. In this paper, the factor decomposition model was used to analyze the influencing factors and effects of carbon dioxide emissions. Causal chain model of elastic decoupling was established. The historical decoupling state between carbon dioxide emissions and economic growth and the decoupling effect of its influencing factors were analyzed. The prediction model of carbon dioxide emissions was used to explore the change trend of China's carbon dioxide emissions and its peak in the short and medium term in the future. The elastic decoupling trend between carbon dioxide emissions and economic growth was predicted. The results show that economic growth is the main force driving carbon dioxide emissions. Both energy intensity and energy consumption structure have a strong inhibiting effect on carbon dioxide emissions except for a few years, but the former has a more significant inhibiting effect than the latter. In general, the elastic decoupling between carbon dioxide emissions and economic growth has experienced a state from weak decoupling to growth linkage and then to weak decoupling. And this weak decoupling trend will continue to increase in the short and medium term. During the 14th Five-year and 15th Five-year period, if the average annual economic growth rate will be maintained at 4.61 to 5.85%, energy intensity will be reduced by 16.14 to 18.37%, and the proportion of non-fossil energy in the energy consumption structure at the end of the 14th, 15th, and 16th Five-Year Plan period will be around 19.9%, 23.2%, and 26.1%, respectively, and then the intensity of carbon dioxide emissions will continue to decline. It is expected to reach the peak of carbon dioxide emissions between 10,453 and 10,690 billion tons from 2025 to 2027. And the earlier the peak time is, the smaller the peak is, which would provide valuable time for carbon neutrality and room to reduce carbon dioxide emissions in the medium and long term. [ABSTRACT FROM AUTHOR]

In the late stages of its industrialization, China's economy still largely relies on energy. With increasing pressures to protect the environment and reduce carbon emissions, in 2013, the Chinese government officially issued four policies in succession to control total energy consumption. In this paper, we use the single difference model to estimate the average and dynamic economic impacts of such policies. We also introduce the energy dependence degree and divide all industrial sectors into two categories to estimate heterogeneous and dynamic policy effects based on the difference-in-differences (DID) model. Our empirical study shows that the implementation of energy consumption control policies results in a decrease in economic growth rates. Meanwhile, the negative dynamic economic effects of such policies decrease levels of volatility. Furthermore, such policies have heterogeneous economic effects on levels of energy dependence across sectors and have more significantly negative economic impacts on heavily energy-dependent industries but with hysteresis. Heterogeneous and dynamic economic effects on heavily energy-dependent industries are decreasing. We conclude with recommendations on ways to mitigate the negative effects observed. [ABSTRACT FROM AUTHOR]

Determine the main factors affecting carbon emissions of the Chinese steel industry is indispensable commitments to achieve the sustainable development of China. Hereby, based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIPRAT) model, this paper combines the economic growth function, carbon emission production function, and the FDI function of the Chinese steel industry, and uses the three-stage least square equation model (3SLS) to analyze the relationship between China's economic growth, carbon emissions in the steel industry, and FDI (foreign direct investment) inflows. The results document a complete two-way causal relationship of three variables in the whole country and the Western region, while the relationship in the Eastern region and the Central region is not complete. Moreover, there are no bidirectional causal relationship between carbon emissions and FDI in the Eastern region, while only bidirectional causality between carbon emissions and FDI in the Central region. These findings are of great significance for the Chinese steel industry to formulate effective emission reduction policies. [ABSTRACT FROM AUTHOR]

Industrial restructuring is a significant measure for low-carbon transition. In principle, carbon emissions can be effectively reduced by limiting the output of high-emission sectors; however, the socio-economic effects of the sectors should also be considered. Moreover, owing to the limitations of the method or data, the interactions between households and production sectors have been neglected in the study of industrial restructuring, resulting in an incomplete and potentially biased understanding of the role of households. To fill this gap, we applied a semi-closed input–output model to identify key sectors by economic and emission linkages and measure the employment impacts (direct, indirect, and induced) of reduced carbon emissions. The empirical results for China in 2010–2018 showed that relatively small changes in key emission sectors would significantly affect the economic growth, and reduced carbon emissions reduction would generally lead to high job losses. Promoting labor-intensive sectors, particularly the service sector, is conducive to achieving a "multi-win" situation for economic development, carbon emission reductions, and stable employment. Furthermore, our results highlight the significance of households: expanding consumption and increasing household income can bring multiple benefits, such as economic growth, job creation, and low carbon emissions. These findings can provide useful information for identifying the optimized path of restructuring and helping achieve the sustainable development of the environment, economy, and society. [ABSTRACT FROM AUTHOR]

Due to the significant changes of economic growth rate, energy structure, and energy efficiency in China, whether energy policies should be adjusted is worthy of attention. This paper employs ARDL-ECM and Toda-Yamamoto causality to investigate the relationship between renewable energy, non-renewable energy, carbon emissions, economic growth, gross fixed capital formation, and urban population spanning the period 1960–2019 in China. The examined results of ARDL-ECM indicate that renewable energy both have positive impact on GDP in the short term and long term but non-renewable energy only have positive impact on GDP in the short term. Additionally, based on Toda-Yamamoto causality, this paper finds a unidirectional Granger causality running from economic growth to energy consumption and from energy consumption to carbon emissions. And there is a unidirectional Granger causality running from GDP to non-renewable energy consumption. It is worth noting that renewable energy consumption and economic growth have positive bidirectional influence on each other. The study findings suggest that stricter energy conservation and pollution emission reduction policy should be implemented and renewable energy should be applied on a larger scale in order to achieve the goal of sustainable economic development. [ABSTRACT FROM AUTHOR]

China is the largest carbon dioxide emitter in the world. Over the past decades, China has made great efforts to retard the carbon emission growth. This paper aims to quantify the driving forces of stagewise provincial carbon emission changes and expound the bottom-up provincial efforts aligned with China's carbon abatement targets. During 2007–2012, economic growth as the major contributing factor posed a great increase in provincial carbon emissions, especially in underdeveloped provinces such as Inner Mongolia, Anhui, Guangxi, Shaanxi, and Ningxia. During 2012–2017, the energy intensity effect replaced economic growth as the largest contributor to provincial carbon emission changes. In both two periods, industrial structure upgrading and renewable energy expansion are conducive to mitigate provincial carbon growth, and the power sector will continue to contribute to national decarbonization. Lastly, this paper provides insight into national and provincial carbon emission abatement. [ABSTRACT FROM AUTHOR]

China's rapid economic development is unavoidably characterized by environmental pitfalls, especially the carbon dioxide emissions of greenhouse gases from the use of energy. Since there is a relationship between economic growth, energy consumption, and carbon emission, it is important to note that the continuous improvement and application of solar energy technology will play an active role in carbon emission reduction. Therefore, this paper uses the Kuznets curve to probe the relationship between economic growth, solar energy technology, and carbon emissions. The results show that solar energy technology, economic development level, and carbon emissions are all integrated at the second order with a long-run cointegration relationship. In the long-run, the relationship between economic growth and carbon emissions is inverted "U," which suggest that there is a turning point of carbon emissions, where the carbon emissions first increase with economic growth and then gradually decrease, which is reflected in the image as an inverted "U" curve. Also, the impact of economic factors on carbon emissions and the innovation of solar technology can negatively influence carbon emissions, and enhancing the innovation of solar technology has a positive effect on reducing carbon dioxide emissions. [ABSTRACT FROM AUTHOR]