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[Display omitted] • Factors influencing carbon emissions in 119 underdeveloped nations are explored. • The renewable energy transition-carbon emission nexus is inverted U-shaped. • Technological innovation drives higher emissions of carbon dioxide. • Good governance is essential in abating carbon dioxide emissions. • Financial globalization does not influence carbon dioxide emission levels. Underdeveloped nations are often confronted with unsuccessful attempts made at containing their atmospheric discharges of carbon dioxide. In this regard, high fossil fuel dependency within these nations is assumed to be the major factor that has compromized their emission-abatement objectives. Notably, the renewable energy deployment rate in the majority of the underdeveloped nations worldwide has not only been below par but these nations have rather evolved to become more reliant on fossil fuels over time. Against this backdrop, this study considers a case study of 119 underdeveloped nations and explores the non-linear environmental effects of undergoing renewable energy transition, controlling for their levels of technological innovation, governance, and financial globalization. Overall, using data from 2001 to 2021 and employing advanced panel estimators, the findings in a nutshell endorse the inverted U-shaped relationship between more renewable energy use and per capita carbon dioxide emissions in the long run. Besides, improving governance quality is observed to impose emission-reducing effects while technological progress boosts emissions in the long run. By contrast, financial globalization is not witnessed to influence the long-run emission figures of the concerned underdeveloped nations. However, the findings are heterogeneous across underdeveloped nations belonging to different development stages and regional locations. Nevertheless, it is recommended that underdeveloped nations should design policies relevant to amplifying renewable energy deployment rates, develop green technologies, establish a sound democratic environment, and green their financial globalization policies in order to realize their carbon emission-abatement objectives. [ABSTRACT FROM AUTHOR]

Around the world, the green economy is seen as a practical way to achieve sustainability. New research using data from the African nations' nexus of institutional quality and technological innovation on renewable energy. This research aims to shed light on how institutional quality spending promotes green technological innovation expansion and institutional quality in the African nations. Therefore, this study examined the effects of technological innovation, established value, renewable energy, vestige oil energy, and financial development on CO2 releases in 24 African nations between 2001 and 2019. A second-generation panel approach was utilized to examine the interdependencies between the variables across time. These methods accounted for heterogeneity and the possible of remaining cross-sectional dependence. Empirical data from the Commonly Correlated Effects Mean Group (CCEMG) estimate and the Augmented Mean Group (AMG) estimator suggests that using eco-friendly technical advancement and renewable energy sources significantly reduces carbon dioxide emissions. But there are several variables linked to rising CO2 emissions, such as improved institutional frameworks, thriving economies, and a preference for fossil fuels over renewable energy. Based on these findings, we concluded that for African countries to achieve their sustainable development objectives, they need to increase their investment in environmentally friendly technological advances and renewable energy sources. • Green economy key to sustainability worldwide. • Research examines African nations' innovation on renewable energy. • Eco-friendly tech and renewables reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

• Most papers show that the Paris Agreement (PA) has led to decline in global emissions. • However, it is not clear whether this is due to efficiency gains or economic decline. • We adopt a treatment method to study impact of the PA on global emissions intensity. • Our results show a 4.1% decline in global emissions intensity driven by the PA. • We also find that some countries might be too poor to achieve reductions under the PA. Dozens of studies point to evidence of significant reductions in carbon emissions driven by climate agreements, such as the Paris climate agreement. However, these studies fail to answer a pertinent question, that is, are global carbon emission reductions due to reduced production activities or are production processes becoming more efficient as less carbon is emitted per unit output due to technical progress? Such an understanding is important to evaluate the tension between environmental quality and economic growth. Therefore, drawing on difference-in-differences approaches, our results suggest a significant decline in global carbon emission intensity due to the 2015 Paris climate agreement, which is estimated at a 4.1% reduction on average over the sample period. Our evidence also shows that while some countries may be too poor to generate meaningful outcomes from climate agreements, the associated rate of reduction in carbon intensity in others tends to decline above a certain income threshold. These results imply that although there has been a worldwide increase in production efficiency and technical progress, at least owing to the Paris climate agreement, reaching net zero carbon emissions globally requires new approaches, especially for the very poor countries relative the ones at the top of the income ladder. [ABSTRACT FROM AUTHOR]

The total amount of carbon emissions is limited by a fixed-sum constraint in the context of China's dual carbon target, a factor ignored in previous studies on carbon productivity. To address this gap, this study proposes a bootstrapped Malmquist-Luenberger productivity index approach with fixed-sum undesirable outputs. The proposed method not only considers the fixed-sum feature of undesirable outputs; it also reduces the calculation error caused by small samples and improves the accuracy of the measurement. We then apply the proposed approach to measure the carbon productivity growth of the thermal power industry in 30 provincial regions in China from 2011 to 2020 and analyze the driving forces of carbon productivity growth. The empirical results show that during the study period, China's overall carbon productivity increased with an annual average growth rate of 0.61 % due to CO 2 emission efficiency improvement and technological progress. Additionally, 18 provincial thermal power industries had average carbon productivity growth rates between 0.06 % and 4.86 %, while the remaining 12 provinces showed carbon productivity declines at rates ranging from 0.03 % to 2.90 %. The driving forces of such growth differ among the 30 provinces. Policy suggestions are given for improving the carbon productivity of China's thermal power industry. • This study proposes a novel Malmquist-Luenberger productivity index approach. • The proposed approach considers the fixed-sum CO 2 emission constraint. • The proposed approach integrates the bootstrap technique. • We measure the carbon productivity growth of China's thermal power industry. • Targeted countermeasures are provided to improve the carbon productivity. [ABSTRACT FROM AUTHOR]

In 2020, China pledged at the United Nations General Assembly to achieve carbon peaking by 2030 and carbon neutrality by 2060, and so far, China has made remarkable achievements in actively realizing carbon emission reduction and maintaining the international ecological environment. Among them, the role played by the technological progress bias of different elements is worth paying attention to. In addition, according to the current situation of energy consumption in China, based on the capital-labor technological progress bias, considering the introduction of the direction of technological progress of the energy factor plays a key role in analyzing the carbon productivity of China. This study utilizes panel regression to explore the paths and effects of capital-labor and factor-augmenting technological progress biases on carbon productivity. We also closely examine the regional heterogeneity and spatial spillover effects of technological progress biases. This study is based on inter-provincial panel information from China from 2006 to 2020. The study found that: (1) When the technological progress bias favors the capital factor among capital and labor, it will significantly increase the level of carbon productivity; The introduction of energy factor innovations,energy-enhanced technological progress, base on the technological progress bias of capital and labor will significantly reduce the level of carbon productivity. (2) Through the threshold model analysis, it is found that there is a nonlinear effect of technological progress bias on carbon productivity. Among them, in capital-biased and energy-enhanced provinces, the impact of capital-technology progress bias on carbon productivity shows an inverted "U" trend of first increasing and then decreasing. (3) In capital-labor technological progress, when technological progress is biased toward capital, it will significantly increase carbon productivity in the Northeast, East and West regions, while the opposite is true in the Central region, and the spillover effect of capital-labor technological progress is not significant; energy-enhanced technological progress bias has a negative impact on carbon productivity in all four major regions, and there is a significant spillover effect in all four major regions. [ABSTRACT FROM AUTHOR]

Innovation infrastructure (INN) compatible with industrial carbon emissions (ICE) reduction is crucial for peaking carbon emissions in the industrial sector. However, INN in China requires urgent strengthening. We elucidate the inverted U-shaped correlation between INN and ICE based on an expanded endogenous growth model, then empirically verify the nonlinear correlation between the two and its effect mechanism using data from 30 provinces in China from 2012 to 2022. The findings present that the correlation between INN and ICE exhibits an inverted U-shaped characteristic. The inverted U-shaped effect of INN on ICE mainly comes from major science and technology infrastructure, as well as science and education infrastructure. Virtual agglomeration, technology progress, energy structure optimization and industrial structure adjustment are vital mechanisms by which INN reduces ICE. However, the ambiguity surrounding data rights and the prevalence of digital platform monopolies impede the potential of virtual agglomeration to diminish ICE. Furthermore, there is also an inverted U-shaped correlation between INN and ICE in geographically adjacent areas. [Display omitted] • An expanded endogenous growth model is constructed. • The index system of innovation infrastructure (INN) is established. • The effect mechanism of INN on industrial carbon emissions (ICE) is investigated. • INN has a significant spatial spillover effect on ICE in geographically adjacent areas. • Promoting INN construction to fully leverage the role of reducing ICE. [ABSTRACT FROM AUTHOR]

• Evaluated the static and dynamic impacts of dual-pilot cities on carbon productivity. • Assessed the synergistic effects of dual-pilot cities on carbon productivity. • Analyzed the heterogeneous effects of pilot city implementation sequence. • Dual-pilot cities have enhanced urban carbon productivity. • Dual-pilot cities boost carbon productivity more than single-city models. The combination integration of urban development models (CUDM) emerges as pivotal in aligning economic development with achieving the "dual-carbon" goals. However, existing studies often overlook CUDM's potential for carbon emissions reduction. Multi-period DID models exhibit excellent performance in mitigating endogeneity and eliminating omitted variables. This study examines the impact of the combination integration of innovative and smart city development models (CISDM) on urban carbon productivity, employing a multi-period DID model with data spanning 2006–2019 from Chinese cities. The results indicate that: (1) CISDM significantly enhances urban carbon productivity by 0.2064 units, primarily driven by innovation-induced technological advancements and structural effects. (2) CISDM yields a significant synergistic effect on urban carbon productivity. Compared to single pilot cities, CISDM significantly increases a city's carbon productivity by 0.1854 units. This effect is particularly noticeable when prioritizing "smart" initiatives over innovation. (3) The effect of the CISDM on carbon productivity varies according to city characteristics. While environmental regulations amplify CISDM's positive impact, resource-based cities struggle to harness its effective. This study underscores the potential of leveraging CUDM to promote "dual-carbon" objectives, providing valuable insights for policy synergies. [ABSTRACT FROM AUTHOR]

Transport infrastructure is an important source of carbon emissions, and different transport infrastructure have varying impacts on urban carbon emissions, which has been lacking in previous research. This study selects 273 cities in China from 2005 to 2020 and applied the spatial Durbin model and mediation effect model to empirically examine the direct impact effects, spatial spillover effects, scale and regional heterogeneity, and indirect pathways of highways, high-speed railways, and urban rail on urban carbon emissions. The results shows that: (1) Overall, high-speed railways have a significant reducing effect on carbon emissions, while highways and urban rail have the opposite effect. (2) Considering spatial spillover effects, highways and high-speed railways can promote carbon emissions reduction in neighboring areas, while urban rail has the opposite effect. (3) For scale heterogeneity, urban rail can reduce carbon emissions in mega-cities, while highways and high-speed railways have the opposite effect. For regional heterogeneity, all three types of transport infrastructure can reduce carbon emissions in western cities, but increase urban carbon emissions in eastern and central regions. (4) Industrial upgrading, technological progress, and population agglomeration are all pathways through which transport infrastructure indirectly affects carbon emissions, but their effects vary for different transport infrastructure. • Urban rail can reduce carbon emissions in mega-cities, while highways and high-speed railways have the opposite effect. • Transportation infrastructure has a significant spatial spillover effect on urban carbon emissions. • Transportation infrastructure has significant differences in carbon emissions across cities of different scales and regions. • The indirect impact path is industrial structure upgrading, technological progress, and population agglomeration. [ABSTRACT FROM AUTHOR]

The chemical industry is one of the fundamental industries of economic development. Coordinating the relationship between economic growth and carbon emissions (EGCE) is crucial in realizing the carbon neutrality target. The Tapio model and decomposition models are used to assess the degree of decoupling within the Chinese chemical industry and identify pivotal factors impeding progress. The energy efficiency decomposition model explores the crux of failure to achieve strong decoupling. This framework further decomposed the energy intensity to uncover how technical efficiency, technological progress, and input substitution changes affect energy intensity and CO 2 emission. The paper found that the EGCE of the chemical industry were stable in a weak decoupling mode from 2009 to 2019. The reliance on technological progress to reduce energy intensity and emissions is insufficient (accounting for only 18.2%), far below the cumulative contribution from economic growth (accounting for 57.76%), which has become the crux of the strong decoupling failure. Technological advancements drive energy substitution for labour, resulting in 244.41 Mt of cumulative carbon emissions. The improper factor input allocation hinders the desired reduction in energy intensity and carbon emissions. A series of policy implications are proposed based on the insights derived from the decomposition analysis to foster sustainable practices in the chemical industry. [Display omitted] • This study focuses on the decoupling in chemical industry. • The decoupling between economic growth and carbon emissions (EGCE) is explored. • The reasons for the failure of strong decoupling from technical perspective are tested. • The decoupling between EGCE of chemical industry was in a weak decoupling mode. • Technological progress accelerated the substitution of energy for labour. [ABSTRACT FROM AUTHOR]

Renewable energy attracts attentions by removing the harmful effects of fossil fuels and protecting the environment. The focus of this study is to investigate GDP per capita, carbon dioxide emissions per capita, technological innovation and trade as the determinants of renewable energy production for the selected Latin American countries during the 1991–2014 period. Technological innovation factor is rarely investigated as one of the determinants of renewable energy. The country group includes Argentina, Brazil, Mexico, Colombia, Chile and Guatemala. Empirical results are obtained by applying panel estimation methods. Cointegration relationship among the analyzed variables is confirmed by Pedroni and Westerlund panel cointegration tests. According to the empirical analysis, GDP per capita, technological innovation and trade have positive and statistically significant impact on renewable energy production per capita. On the other hand, it is found that carbon dioxide emissions and renewable energy production are negatively associated. In addition, the elasticity estimates with respect to technological innovation and trade are found as close to each other. The policy implications of the empirical findings are argued as well. [Display omitted] • The drivers of renewable energy generation are analyzed by panel data techniques. • A 1% rise in output is found to increase renewable energy production by 0.26%. • The effects of technological innovation and trade are estimated as very close. • The elasticity estimate with respect to CO2 emission is found as −0.196. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI), as the core driving force of industrial and technological innovation, has brought technological advances that profoundly affect energy conservation and carbon emission reduction in China. This study constructs a dynamic spatial Durbin model to empirically analyze AI's mechanism path using Chinese inter-provincial panel data as a sample. The findings show the following: (1) Spatial and temporal variations exist in AI development levels and energy conservation and carbon emission reduction. (2) AI has a significant negative impact on energy consumption efficiency and a significant negative spatial spillover effect on carbon emission efficiency, and the effects of AI development level on energy conservation and carbon emission reduction are mainly dominated short term. (3) Green technological progress on both the input and output sides has not played a determinant role in the effects of AI on energy conservation and carbon emission reduction. (4) Given the moderating effect of the degree of factor market development, the improvement of green technological advances can play a positive role in the impact of AI on energy and carbon reduction. These findings suggest the need to provide policy support for energy conservation and carbon emission reduction by improving the spatial and regional linkage mechanism to narrow spatial and temporal differences in development levels, formulating AI policies addressing regional heterogeneity to promote the full transformation of AI's short-term effects into long-term effects as well as emphasizing the negative role of green technological advances and accelerating the transformation of green technological achievements, among other measures. [Display omitted] • A dynamic indicator system for regional AI development in China is constructed. • AI effects on energy conservation and emission reduction are explored. • A dynamic spatial Durbin model shows regional and temporal heterogeneity. • Green technological progress has negative effects that should be emphasized. • Mature market element development can realize the positive green effects of AI. [ABSTRACT FROM AUTHOR]

• ·The three-stage super efficiency SBM-DEA model and Malmquist index was used to measure carbon emission efficiency, index and decomposition terms of land use. • ·LUCE and its spatial and temporal pattern of urban agglomerations in the YREB were discussed from static and dynamic perspectives respectively. • ·Environmental variables such as level of economic development, urbanization rate, and industrial structure all have significant effects on input slack value. • ·The PTE of the MRYR was relatively low due to insufficient resources in low-carbon technology, while the low TE of the CC is due to its insufficient SE. • ·TC was the main factor promoting the improvement of LUCE in the whole basin, and the trend changes of LUCE in the three urban agglomerations was closely related to their management level, technological level, and adjustment of element structure. Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

This study introduces three production technology shocks (Energy manufactures, Brown enterprises, and Green enterprises) by constructing different models, namely, the environment sector (baseline) model, the carbon emissions trading scheme (ETS) model, and the carbon emissions rights trading mechanism. The fluctuation trend of China's macroeconomic and environmental quality before and after the establishment of carbon market is compared and analyzed. Additionally, the study examines the welfare of the implementation of carbon market policies. The carbon trading market policy can promote the synergistic efficiency of China's pollution reduction and carbon reduction using energy and green production technologies. From the perspective of social welfare, the optimal range of the initial carbon quota ratio issued by the government to enterprises is [0.7,0.8]. The findings of this study provide theoretical support and contribute toward understanding the impact mechanisms of carbon market and technological progress on synergizing the reduction of pollution and carbon emissions in China. [ABSTRACT FROM AUTHOR]

Digital development is critical for achieving coordinated regional economic growth, energy conservation, and emissions reduction, which makes it a viable method for achieving carbon neutrality and peaks. This study investigates the impact of the digital economy on urban total factor productivity and carbon emissions, as well as its mechanism, from 2011 to 2019 by developing a digital economy evaluation index system. We find that the growth of the digital economy can increase urban total factor productivity while lowering carbon emissions and also has a significant spatial spillover effect on regional economic growth, energy conservation, and emissions reduction. The main influencing mechanism is that the digital economy accelerates marketization by encouraging industrial structure upgrades and technological progress, which results in higher total factor productivity and lower energy consumption. In terms of the spatial distribution, the digital economy promotes total factor productivity more in the central and western regions and large cities and promotes carbon emission reduction less in the eastern regions and smaller cities. The findings extend the benefits of the digital economy beyond economic and social welfare benefits and serve as a model for achieving a win–win situation in terms of both economic growth and carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Energy mix diversification (EMD) is recognized as a plausible strategy for emerging economies to achieve energy security targets and meet net zero carbon emissions goals by 2050. Thus, this research explores the determinants of EMD utilizing a sample panel for seven major emerging (E7) economies, including Brazil, China, India, Indonesia, Mexico, Russia, and Turkey, observed from 1995 to 2018. The Pedroni panel cointegration test investigates the long-run equilibrium, and the long- and short-run determinants are identified using a panel autoregressive distributed lag model. The findings reveal a long-run equilibrium linkage among EMD, carbon emissions, financial development, economic growth, technological progress, energy efficiency, and globalization. The long-run estimates show that energy efficiency and carbon emissions are driving forces for EMD, whereas technological progress, financial development, and globalization have insignificant effects. However, all variables - except economic growth are found to be the barriers to EMD in the short-run. The findings presented in this research are novel and add value to the energy literature by identifying the long- and short-run determinants of EMD, specifically for E7 economies. These economies are expected to take appropriate actions to stimulate the adoption of clean and green energy sources; thus, appropriate implications of the findings are discussed. • Short-run and long-run determinants of energy mix diversification are examined in this study. • Sample is a panel data for seven major emerging economies observed from 1995 to 2018. • A cointegrating relationship exists among the variables considered. • Energy efficiency and carbon emissions are drivers in the long-run. • All the variables are barriers in the short-run. [ABSTRACT FROM AUTHOR]

Industry is the pillar of China's national economy. The energy-saving and emission reduction in the industrial sector directly bear on the sustainable development of the whole country. This paper sets up an evaluation index system for industrial carbon emissions performance (ICEP), and then evaluates the ICEPs in 30 Chinese provinces from 2003 to 2017, using the stochastic block model (SBM). Based on the regional difference of the ICEP, a Tobit model was established to identify the driving factors of the ICEP. The main results are as follows: (1) There are obvious provincial differences in the ICEP: coastal provinces (e.g. Zhejiang, Shanghai, Beijing, Jiangsu, and Guangdong) had relatively high ICEPs, while inland provinces (e.g. Yunnan, Guizhou, Ningxia, Shanxi and Qinghai) had undesirable ICEPs. (2) The three regions of China can be ranked as eastern region, central region, and western region in descending order of the ICEP. The central and western regions should be focused to promote energy-saving and emission reduction in the industrial sector. (3) The ICEP clustering shows that the ICEPs were medium and low in most provinces, and high in very few provinces. (4) Regression results of the Tobit model demonstrate that different driving factors have varied effects on the ICEP: economic development, technical progress, and foreign trade have significant positive effects on the ICEP, while energy structure and environmental regulation have significant negative effects on the ICEP. [ABSTRACT FROM AUTHOR]

Judging the carbon emissions of various agricultural inputs in crop planting and clarifying the decoupling level between grain production and carbon emissions from crop planting can provide data support and policy references for agricultural carbon emission reduction. Considering six carbon emission sources (plowing , irrigation , fertilizer , pesticide , agricultural film (agrifilm), and diesel fuel (diefuel)). This research accounted the carbon emissions from crop planting in 31 provinces in China from 2000 to 2021. Using the Tapio model to explore the relationship between grain production and carbon emissions from different types of agricultural inputs. The regulating effects of planting scale, technological progress, and their covariates on carbon emission reduction were explored using two-way fixed-effect regression. The results showed that the growth rate of carbon emissions from crop planting in China had a fluctuating declining trend from 2000 to 2021, and it was weakly decoupled from grain production in most years, and that an overall strong decoupling was achieved in 2021. A 1% increase in planting scale leads to 1.0%, 0.68%, and 0.31% increases in carbon emissions from plowing , irrigation , and diefuel , respectively. The synergistic effect of the planting scale and technological progress can significantly reduce the carbon emissions of crop planting, and thus promote the decoupling of grain production and carbon emissions of crop planting. This study provides valuable references for policy formulation in economies to promote synergy between grain production and carbon reduction targets to a greater extent. • The growth rate of carbon emissions from crop planting decreases. • The decoupling level in China is improving. • Technological advances for carbon reduction in plowing, irrigation, fertilizers and pesticides. • The interplay of planting scale and technological advances favors carbon mitigation. [ABSTRACT FROM AUTHOR]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effects of technological advancements, energy efficiency, and renewable energy utilization in Türkiye's fossil fuel sector on CO 2 emissions and to test the applicability of the Environmental Kuznets Curve (EKC) hypothesis. The EKC hypothesis has been confirmed for Türkiye, revealing an inverse U-shaped relationship between CO 2 absorption and per capita income. Furthermore, all variables display negative coefficients and show statistical significance in the long term. Thus, it has been established that technological progress, energy efficiency, and the adoption of renewable energy have a positive impact on reducing CO 2 emissions. Given Türkiye's status as an energy importer heavily dependent on fossil fuels, prioritizing technological advancements in the energy sector is crucial since complete abandonment of fossil fuels in the near future is unlikely. Moreover, efficient energy utilization plays a vital role in reducing energy imports, a substantial contributor to Türkiye's current account deficit. Furthermore, an increase in renewable energy will have a positive effect on Türkiye's economic and energy outlook. The uniqueness of these findings lies in the utilization of these significant variables, which have not been previously employed in the context of the Türkiye economy. [Display omitted] • Energy technology, energy efficiency and the use of renewable energy reduce CO2 emissions in Türkiye. • The efficient use of energy is the most important factor in improving environmental quality in Türkiye. • This study uses both standard and Fourier-based methods to analyze the data. • The Environmental Kuznets Curve (EKC) is valid for Türkiye. [ABSTRACT FROM AUTHOR]

Technological progress is an effective tool for controlling carbon emissions. However, because the connection between technological progress and carbon emissions remains under-theorized, the literature often discusses the overall effect of technological progress, ignoring the heterogeneity of technology. To better understand the driving factors of carbon emissions, we develop a new theoretical model based on the Green Solow model, in which technology is divided into production, energy conservation, and energy substitution technologies. Dynamic panel models are then applied to show the driving influence of different factors affecting carbon emissions in the Yangtze River Delta (YRD) region between 2000 and 2017. The results indicate that total technological progress is conducive to reducing carbon emissions. The heterogeneity analysis indicates a positive reduction effect on carbon emissions for energy substitution and energy conservation technologies, but not for production technology. Interestingly, production technology significantly drives carbon emissions. However, the results also suggest that to control carbon emissions, it is necessary to promote production technology. Further analysis suggests that production technology plays an important role in the effectiveness of energy conservation and energy substitution technologies in decreasing carbon emissions. • A theoretical framework for understanding carbon emissions is constructed. • An empirical analysis is conducted for the YRD region. • Production technology drives the carbon emissions. • Energy conservation and substitution technology decreases the carbon emissions. [ABSTRACT FROM AUTHOR]

Existing studies on carbon emission efficiency seldom discuss ocean carbon emission efficiency, and few studies on ocean carbon emission efficiency hardly discuss its regional differences. To fill this research gap, this paper innovatively measures and evaluates the ocean carbon emission efficiency of 11 Chinese coastal provinces from 2001 to 2019 using the super-efficiency SBM-GML model, and empirically analyzes the dynamic link between ocean carbon emission efficiency, trade openness and financial development by constructing a PVAR model based on an endogeneity perspective. Meanwhile, another major innovation of this study is to divide China's 11 coastal provinces into two coastal areas, north and south, with the Huaihe River as the boundary, in order to investigate the regional heterogeneity of ocean carbon emission efficiency and its influencing factors. The results show that (i) China's average ocean carbon emission efficiency has improved significantly, which is mainly due to the driving effect of technological progress. (ii) China's ocean carbon emission efficiency generally presents a spatial pattern that is higher in the south and lower in the north. Technological progress is the main source of the improvement in ocean carbon emission efficiency in the two regions. (iii) Significant regional heterogeneity exists in the impact of trade openness and financial development on ocean carbon emission efficiency, that is, trade openness and financial development both promote and hinder ocean carbon emission efficiency in the southern region than in the northern region. Finally, targeted policy recommendations are proposed. [Display omitted] • Investigating the regional differences in ocean carbon emission efficiency. • The super-efficiency SBM-GML technique and the PVAR approach are used. • China's average ocean carbon emission efficiency has improved significantly. • The ocean carbon emission efficiency spatial pattern that is higher in the south and lower in the north. • Regional heterogeneity of the impact of trade openness and financial development on ocean carbon emission efficiency. [ABSTRACT FROM AUTHOR]

This study evaluates the effects of military expenditures (MEX) on environmental pollution, as well as the moderating role of factors such as financial development and technological progress for 15 NATO member countries under the environmental Kuznets curve (EKC) hypothesis. Using the CS-ARDL estimator, the study analyzes the effects of MEX, income, energy consumption, financial development, and technical progress on carbon emissions spanning the period from 1991 to 2018. Additionally, interaction terms are employed to regularize the moderating effects of financial development and technical advancement. The results show that income, energy consumption, and MEX all contribute to a rise in emissions. It is also found that the financial sector does not eradicate the detrimental ramifications of MEX on the environment, but that technological progress has a moderating effect. A 1% increase in the interaction of technological progress with the military sector reduces environmental degradation by 0.36, but a 1% augment in the interaction of the financial sector with the military sector increases carbon emissions by 0.41%. For this reason, it is imperative to evaluate and revise the financing of MEX of NATO countries from an environmental perspective for sustainable development. Furthermore, as long-run income elasticity in NATO countries is lower than the short-run one, the EKC hypothesis is valid. Based on the overall findings, the study suggests that NATO allies should incorporate the benefits of income growth and technological development into their environmental policies to offset the negative effects of MEX. [Display omitted] • Employs second-generation panel data approaches for NATO countries. • Examines the moderating role of technological progress and financial development. • NATO countries damage the environment with their military expenditures. • Technological progress has an environmental moderating role in the military sector. [ABSTRACT FROM AUTHOR]

Transport industry is one of the major contributors to energy-related carbon emissions in China, thus becoming a topical area of concern where efforts to improve carbon emission efficiency need to be prioritized. By establishing a Static-Dynamic Efficiency (SDE) framework and using panel data from 30 provinces in China from 2010 to 2019, we develop a super slack-based measure model to assess the static efficiencies and potential for improvement of the TICEE at the provincial level, adopt the global Malmquist-Luenberger index to examine the dynamic changes in TICEE and the underlying driving factors, and propose improvement strategies by integrating the static and dynamic perspectives. We observe: (1) severe carbon emission inefficiency in transport industry in China, particularly in western, central and north-eastern regions; (2) a slow downward trend in static efficiency due to the slow progressing technological innovation; and (3) high potential for saving energy and reducing emissions in the provinces that fall into the "inefficient-deterioration" category. Our research findings extend the literature by bringing together the static and dynamic aspects into the SDE framework, as well as assist policy makers in better understanding the way to improve TICEE. [Display omitted] • We assess the carbon emission efficiency of transport industry in Chinese provinces. • Static efficiency and improvement potential of the TICEE in China are quantified. • Underlying drivers of the varying TICEE are identified by decomposing GML index. • Improvement strategies are proposed by linking the static and dynamic perspectives. [ABSTRACT FROM AUTHOR]

As an important attempt to promote intensive development, new-type urbanization (NU) pilot programs have received increasing attention. However, most related literature does not clarify the relationships between the NU pilot, paths of technological progress, and carbon emissions. Thus, we constructed a difference-in-differences model to compare the differences in carbon emissions between NU-pilot and non-NU areas before and after implementing the pilot, with significant results. The NU pilot policies primarily affected carbon emissions through domestic R&D activities and intercity and FDI technology spillover. A sub-sample analysis considering city heterogeneity confirmed that the NU pilot had heterogeneous impacts on cities with different locations, administrative levels, population sizes, and sea front conditions. The pilot policies' technological action paths varied with changes in urban characteristics. The results imply that policymakers should tailor policies to pilot policy action paths and cities' local conditions. [ABSTRACT FROM AUTHOR]

To study the key drivers of China's carbon dioxide emissions, this study combines the structural decomposition analysis method and the input-output subsystem analysis method to construct a decomposition model of the factors influencing the amount of change of the carbon dioxide emissions in China. From the perspective of final demand, this decomposition model (own internal component, feed-back component, spillover component, scale component) is used to analyze the amount of change of China's carbon dioxide emissions in the two periods of 2012–2017 and 2017–2018. Four scenarios are set up to predict the carbon dioxide emissions of China before 2035. The results show that in 2012, 2017 and 2018, the carbon dioxide emissions generated by final demand were respectively 9,018,810, 8,715,226, and 8,803,201 kt, of which the spillover component accounted for the largest share, namely 85.5%, 85.6% and 84.7% respectively. This indicates that the increase of carbon dioxide emissions in China is due to intersectoral pulling effects, and the activities of the Construction sector have had pulling effects on the carbon dioxide emissions of other sectors to a greater extent. The energy intensity effect is the significant influencing factor that inhibits carbon dioxide emissions and economic development is the main driver of the increase in carbon dioxide emissions; this implies that changing the current energy structure is necessary to reduce carbon dioxide emissions. The technical progress scenario (QN) and the energy structure optimization scenario (GN) are predicted to achieve the carbon peak in 2025 and 2030, respectively. [Display omitted] • Total carbon emissions generated by final demand are decomposed into four subsystems, with a structural decomposition. • The spillover components account for the largest proportion of carbon emissions. • Construction industry has pulled the carbon emissions of other sectors to a greater extent. • Under GN and QG scenarios, it is expected to achieve carbon peak in 2025 and 2030 respectively. • Technical improvements are effective in most sectors. [ABSTRACT FROM AUTHOR]

Bangladesh has traditionally relied on fossil fuels for meeting its energy demand whereby this major South Asian economy has not been able to safeguard its environment from greenhouse gas emission-related adversities. Moreover, by ratifying several international environmental agreements, especially the Paris Accord and the Sustainable Development Goals, the government of Bangladesh has expressed its solidarity in abating greenhouse gas emissions through the deployment of relevant environmental policies. Hence, this study assesses the impacts of petroleum consumption, economic growth, and technological innovation on carbon emissions in Bangladesh using quarterly frequency data from 1972Q1 to 2020Q4. Overall, apart from confirming the cointegrating relationships among the variables, the regression findings reveal that higher petroleum consumption and economic growth stimulate environmental degradation by boosting carbon dioxide emissions while technological innovation reinstates environmental well-being by curbing the Bangladesh's emission figures. Additionally, technological innovation is seen to moderate the relationship between petroleum consumption and carbon emissions by jointly reducing the emissions with petroleum consumption. Lastly, the causality analysis shows that petroleum consumption, economic growth, and technological innovation causally influence carbon emissions. Based on these key findings, it is recommended that Bangladesh mitigates its petroleum dependency, blends environmental objectives into its economic growth policies, and develops its technological stock. • Determinants of environmental well-being in Bangladesh are analyzed. • Petroleum consumption and economic growth boost carbon emissions. • Technological innovation reduce carbon emissions. • Technological innovation moderates the petroleum consumption-carbon emissions nexus. • Petroleum consumption and technological innovation jointly reduce carbon emissions. [ABSTRACT FROM AUTHOR]

Calcium sulfoaluminate (CSA) cement can be used as a replacement for Portland cement to reduce CO 2 emissions. However, the performance of CSA cement remains unknown with regard to its hydration mechanism, mechanical and long-term performance. Therefore, it would be beneficial to consolidate the literature on CSA cement to facilitate its use in the construction industry. To this end, recent progress and technical challenges of using CSA cement are discussed in this paper. We begin with the introduction of the different types of CSA cement and the manufacturing process. This is followed by a detailed discussion on the hydration mechanisms and phase assemblage, mechanical performance, and long-term durability of CSA cement. Finally, the applications of CSA cement are discussed. [ABSTRACT FROM AUTHOR]

• Urban-rural energy-carbon performance (ECP) gap was explored. • An income-based meta-frontier decomposition model was developed. • Complex network of ECP gap was analyzed. • ECP in the urban areas were generally greater than that in the rural areas. • Efficiency loss and innovation effects were drivers widening the ECP gap. While extensive studies focused on income inequality between the urban and rural, the corresponding energy-carbon performance (ECP) gap which describes differences in energy consumption and CO 2 emission simultaneously, is ignored to a large extent for an emerging economy in particular. This could hinder formulating policy towards energy saving and emission reduction in the residential sector as well as public welfare improvement. To fill the gap above, the study explored the source and spatial pattern of urban–rural ECP gap in 30 China's provinces over 2005–2019, by developing income-based meta-frontier decomposition analysis and social network analysis. We found that ECP in the urban areas were generally greater than that in the rural areas, partly supporting energy consumption ladder theory in China. Urban-rural ECP gaps in most provinces were becoming increasingly obvious. The efficiency loss effect and the innovation effect were important causes of widening the urban–rural ECP gap while the technological progress of energy-carbon use in various regions was unbalanced. Further, the original hierarchical ECP gap spatial correlation structure has been improved while provinces exhibited significant heterogeneity. We suggest that policymakers can focus on narrowing the urban–rural ECP gap by vigorously developing the digital economy, promoting urban–rural integration and energy efficiency with advanced technology. [ABSTRACT FROM AUTHOR]

• We construct a stock-driven dynamic material flow analysis model for materials production and carbon emissions, integrated with Chinese regional power infrastructure material flow account database. • We estimate the material stock and carbon emissions of power generation infrastructure and explore the nexus of material-energy-carbon emission in China from 1993 to 2060. • We identify several spatial and temporal evolution patterns of installed capacity with concern on the stocks of cement, steel, aluminum, and copper, as well as carbon emissions and their intensities from China's power generation infrastructure. • We predict material demand and carbon emissions of Chinese power generation infrastructure under carbon peaking and neutrality goals with perspective of technological progress. • We propose that carbon emissions caused by cement and steel consumption would still play a leading role and the proportion of carbon emissions caused by aluminum consumption would gradually increase. Climate change and energy transformation has made huge alteration in power system, which led to great uncertainty in relation to material, energy and carbon emissions in power generation infrastructure. We aim to estimate material stock and carbon emissions of power generation infrastructure and explore material-energy-carbon emission nexus in China from 1993 to 2060 using stock-driven dynamic material flow analysis model for materials production and carbon emissions. Results indicate that cement is the largest used material and the southwest economic zone in China has highest cement intensity due to hydropower construction, whilst steel intensity is evenly distributed across the country, aluminum intensity the smallest and more distributed in northwest region, and copper stock is the lowest. Southwest and Yangtze River economic zone contributes most to carbon emissions from national power infrastructure. Material per unit installed capacity from 1993 to 2018 has an overall increasing trend, but carbon emissions per unit installed capacity of power generation are expected to decline. Carbon emissions of new installed capacity is expected to peak around 2028. This study provides a dynamic approach to assess resource consumption and carbon emissions intensity of power generation infrastructure for decision-making on China's carbon peaking and neutrality goals. [Display omitted] [ABSTRACT FROM AUTHOR]

This study developed a climatic-economic-technological integrated assessment model named CIECIA-TD-F. Based on model, the implementation effects of low carbon technology financing on climate mitigation, technology promotion, and economic development in the context of climate change cooperation were studied. The results indicate that carbon reduction, under the current pledged climate financing scale, is limited. Global surface warming in 2100 is still around 2.38 °C even when the technologies are globally shared. Low carbon technology financing improves technology R&D and absorptive capacities of the least developed countries. Their carbon reduction rates reach 50 percent under the capacity principle. However, donations impair the R&D inputs of developed countries and delay their advanced technological progress, leading to 1 percent more carbon emissions. This limits the imitations of China and Russia in turn by decreasing their imitation numbers by 1–2 percent. Collecting 800 billion USD per year from 2030 to 2100 is a climatically effective means. It holds global warming to 1.99 °C. However, the donors suffer more than 1 percent of economic losses. That indicates the economic feasibility of this means is weak. On this basis, we proposed a Pareto-improvement scheme which combines financing with technology sharing and national R&D improvements. This policy mix both achieves the 2 °C goal and protect the economic benefits of all countries. Thus, policy mix combining different measures will be more suitable for improving reduction effects and achieving multiple goals. Compared with sole technology financing, it has more significant global carbon governance meanings and is noteworthy in future climate negotiations. • Nine combinations of typical collection and dispensation principles were assessed. • The current scale of climate financing pledge was insufficient. • 2 °C goal can be achieved by improving financing scale dramatically in short term. • Advanced technology innovation was delayed for the economic losses of the funding donors. • A Pareto improving scheme was proposed. [ABSTRACT FROM AUTHOR]

While promoting environmental efficiency is widely perceived as one of the panaceas for solving environmental issues in China, little effort has been made to identify whether environmental efficiency improvement helps to reduce carbon emission intensity (CEI). Therefore, the work presented in this study remedies the inadequacies in the previous literature by investigating the linkages between environmental efficiency and CEI, by explicitly considering its spatiotemporal heterogeneous effect and dynamic measurement. We first measure the environmental efficiency (EE) and its growth (environmental total factor productivity) at prefecture level in China during 2003–2016 by incorporating a recently modified epsilon-based data envelopment analysis technique in the global Malmquist–Luenberger index. A spatial panel regression approach is then applied to reveal the impact mechanism of EE and its growth on the CEI. The empirical results confirm that promoting EE and environmental technology efficiency can help mitigate the urban CEI. However, the impact of economic scale efficiency on the CEI reduction has not been verified, indicating great room for CEI mitigation by optimizing the urban production scale and reducing production redundancy. Evidence from the spatiotemporal econometric analysis shows a spatially heterogeneous effect of EE on CEI, and the strength of influence tends to decrease over time. The findings also reveal that enhancing EE matters in reducing CEI for southern and western cities. Finally, several control variables have negative direct and indirect (spillover effect) effects on CEI reduction, including urbanization, industrial structure, and foreign direct investment. Our findings suggest that strengthening the synergistic effect between environmental pollution governance and carbon emission control by enhancing EE to promote low-carbon development in Chinese cities. [Display omitted] • An extended epsilon-based measure and global Malmquist–Luenberger index is proposed to measure environmental efficiency and its growth. • Enhancing environmental efficiency has positive effects on CEI reduction in China. • The technological progress change and pure technical efficiency change help to reduce CEI. • The positive impact of environmental efficiency on CEI mitigation is gradually weakening. • The impact of explanatory variables as well as their spatial spillovers on CEI are geographical heterogeneity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Establishes an integrated dynamic emission assessment model by coupling end-use energy model and system dynamics model. • Explores the dynamic interaction mechanism between macrolevel and microlevel factors as well as technical options. • Commercial buildings will probably hit the emission peak at 1.28Gt CO 2 in 2037 under the BAU scenario. • Lighting and cooling will contribute over 70% to the carbon emission growth. • Technological progress, clean energy, and electrification rate play significant roles in boosting emission peak and peaking time. Carbon-mitigation in the commercial building sector is critical to carbon peaking and carbon neutral commitment. However, there has been little scientific focus on long-term evolutionary trajectories and peak path in this sector. This study innovatively develops an integrated dynamic emission assessment model (IDEAM) by combining the system dynamics model with the bottom-up end-use energy model. Moreover, the IDEAM is combined with the scenario analysis approach to model the dynamic evolution of Chinese commercial building carbon emissions toward 2060. The results show that commercial building carbon emissions will peak at 1.28 Gigatons (Gt) of CO 2 in 2037 under the baseline scenario and will advance toward 2029 with an emissions peak of 0.98 Gt CO 2 under the low-carbon scenario. Cooling and lighting are the two end-uses that contribute most to the growth of carbon emissions at over 70%. These two end-uses indicate different carbon-abatement potentials across climate zones. Sensitivity analysis reveals that promoting technological progress, increasing the share of clean energy, and improving low-carbon awareness are major ways to facilitate the early realization of carbon peaks and carbon neutrality. This study provides a deeper understanding of possible emission pathways and could assist policy-makers in devising scientific carbon mitigation plans. [ABSTRACT FROM AUTHOR]

Using the data of 281 representative cities of China, this study designs industrial intellectualization indicators from three dimensions of regional information intelligence, production intelligence, and intelligent innovation. Then the mixed distance EBM-DEA model that includes undesired outputs is employed to calculate the carbon total factor productivity. Further, we examine the impacts, mechanisms and constraints of industrial intellectualization on the regional carbon total factor productivity. The results show that: (1) Industrial intellectualization has a positive effect on carbon total factor productivity, that is, if the level of industrial intellectualization increases by 1 unit, carbon total factor productivity will increase by 0.03 units. (2) Technological progress and industrial structure upgrading are two main channels of the industrial intellectualization affecting carbon total factor productivity, and the impact of industrial intellectualization on carbon total factor productivity is heterogeneous in different regions. Specifically, the carbon total factor productivity effect of industrial intellectualization is significantly positive in the eastern and western regions, positive but not significant in the central region, and significantly negative in the northeastern region. (3) Whether industrial intellectualization can increase carbon total factor productivity is subject to three constraints, namely, the level of information technology, workers' skills, and resource dependence. • Industrial intellectualization indicators are designed to explore the impacts of industrial intellectualization on CTFP. • Industrial intellectualization has a significant increasing effect on CTFP. • Differences in industrial intellectualization and CTFP vary across regions, showing a polarization trend. • The scale of big data and skilled labors are constraints on the performance of industrial intellectualization on CTFP. [ABSTRACT FROM AUTHOR]

As countries pay more attention to the issue of global warming, the solution of carbon emission reduction has important practical value. Most of the existing literature studied the driving factors of embodied carbon emissions. This paper creatively focuses on the impact of technological progress on embodied carbon emissions and on the embodied carbon emissions of different technology-intensive manufacturing industries, and aims to find out the important role of technological progress on embodied carbon emissions. This paper constructs a multi-regional input-output model based on the world input-output table, and analyzes and compares the changes in carbon emission intensity and export-implied carbon emissions of G7 countries from 2000 to 2014. This paper introduces the SDA model to investigate the impact of technology effects on the embodied carbon emissions of G7 countries' exports, and decomposes the technology effects into carbon emission technology effects and intermediate production technology effects. This study divides the manufacturing industries into low-tech, medium-low-tech, medium-high-tech and high-tech industries, and aims to examine the impact of technological progress effect on manufacturing with different levels of technology intensity. The empirical results reveal that, first, the carbon emission technical effect and the technical effect of intermediate production together constitute the technical effect. The carbon emission technology effect has a significant effect on suppressing the embodied carbon emissions of exports, while the impact of the intermediate production technology effect is relatively small, and the direction of action is uncertain. Second, the overall technological effect reduces the embodied carbon emissions of G7 countries' exports, and offsets the embodied carbon emissions brought about by the scale effect to a certain extent. That is, technological progress can effectively reduce the embodied carbon emissions of exports. Third, technological effect has the most significant effect on the low- and medium-tech industries, and the low- and medium-tech industries are also the manufacturing industries with the most embodied carbon exports in G7 countries. Relevant policy recommendations for global carbon emission reduction are put forward at the end of this paper. [ABSTRACT FROM AUTHOR]

Under the requirements of China's regional coordination and green low-carbon development strategy, using provincial balanced panel data from 1998 to 2017, this paper reveals how the spatial functional division affects carbon emissions and discusses the regulatory effect of transportation and communication infrastructure on the relationship between the spatial functional division and carbon emissions. The research conclusions are as follows. First, with the deepening of spatial functional division, the carbon emissions show a significant inverted U-shaped trend, and this result remains robust after taking into account location differences and spatial form. Second, the number of provinces where the spatial functional division exceeds the inflection point gradually increases; however, the level of spatial functional division in most provinces is insufficient. Third, the mechanism analysis indicates that market integration and technological progress are important channels through which spatial functional division affects carbon emissions. Finally, the enhancement of transportation and communication infrastructure contributes to the carbon reduction effect of the spatial functional division. In a context where the link between agglomeration and carbon emissions has been widely explored, this paper provides new evidence and cases for thinking about the synergistic development of industrial agglomeration and carbon emissions reduction from the perspective of functional division. [Display omitted] • The impact of spatial functional division on carbon emissions is explored. • The spatial functional division in several provinces beyond the inflection point. • Market integration and technological progress are important influencing paths. • Transportation and communication infrastructure have moderating effects. [ABSTRACT FROM AUTHOR]

Carbon-reduction effect of the urban residential building sector is crucial to the carbon neutrality target. This study aims to explore the interaction mechanism among influencing factors and simulate the future evolutionary trajectories of urban residential building carbon emissions (URBCE). An integrated dynamic emission assessment (IDEA) model is innovatively established by coupling the system dynamics (SD) model and a bottom-up end-use decomposition model. Combining with the scenario analysis approach, this IDEA model is applied in China's urban residential building sector from 2000 to 2060. Results show that under the baseline scenario, the URBCE will fail to peak before 2030 but will peak at 1.19 Bt CO 2 in 2037. In comparison, it will peak at 0.79 Bt CO 2 in 2025 and decline to 0.17 Bt CO 2 in 2060 under carbon-neutral scenario, which can be neutralized by negative carbon technologies. Different end-uses and climate zones show marked discrepancies in emission-reduction potential, with the contribution of heating and appliances being over 50%. Sensitivity analyses shows that the low-carbon awareness, electrification rate, proportion of clean energy generation and technological progress contribute positively to the early peak of URBCE. This study provides a deeper understanding of China's potential peaking paths and can assist policy-makers in better evaluating emission paths for other nations and regions. [Display omitted] • Develops an IDEA model by combining system dynamics model and LEAP model. • Explores the interaction and feedback mechanism among different levels of factors. • Simulates the URBCE by combining the IDEA model and scenario analysis approach. • Urban residential buildings will peak at 1.19 Bt CO 2 in 2037 under the BAU scenario. • Different end-uses and climate zones show marked discrepancies in emission-reduction potential. [ABSTRACT FROM AUTHOR]

Energy and green innovation are important to a country's economic progress, but the externalities of increased pollution as a result of a rise in energy used and economic growth must not be overlooked. Within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, this study aims to check empirically the effect of environment-related technologies (ERT), coal consumption (CC), economic growth (EG), and population density (PD) on the consumption-based CO 2 emissions (CCO 2e) in the BRICS (i.e., Brazil, Russia, India, China, & South Africa), as with fast economic progress BRICS is facing problems of CO 2 emission. The econometric techniques of the cross-sectional dependence test, slope heterogeneity test, and second-generation panel unit root test were used to check the nature of the data. The available balance data over the period 1985–2018 were analyzed via Driscoll and Kraay and a dynamic common correlated effect mean group (DCCEMG). Long-term empirical findings show that ERT has a negative impact on the CCO 2e , while per capita GDP and CC have a positive impact on CCO 2e. Empirical evidence recommends that ERT is important for BRICS countries' long-term sustainability. It is suggested that policymakers should encourage ERT that help in curbing their CO 2 emanations. [ABSTRACT FROM AUTHOR]

In achieving the "double carbon" target, the emission trading system (ETS), as a key market-based emission reduction mechanism, assumes the responsibility of addressing climate change and implementing reform. Most studies focus only on the emission reduction effects of green policy, but weighing the relationship between emission reduction and development is a practical dilemma to be faced. Taking China's pilot ETS as an example, this problem was analyzed using the synthetic control method, and the specific impact channels are discussed. It was found that: (1) Carbon emissions and carbon emission intensity in the pilot ETS areas of Tianjin, Hubei and Chongqing decreased significantly, and economic growth has progressed to a certain extent. Robustness tests basically verified the effectiveness of the policy implementation. (2) The pilot ETS mainly has an impact through the transformation and upgrading, and technological innovation channels, promoting significant improvements in energy structure, industrial structure, energy efficiency and the number of patent applications. This study releases a positive signal for the compatibility of emission reduction and economic development goals at the national level for China's pilot ETS. • The pilot ETS significantly reduced carbon emissions and carbon emission intensity. • The pilot ETS promoted economic growth to some extent. • Transformation and upgrading, and technological innovation are the influencing channel. • The SCM method can synthesize the "ideal" control unit to evaluate the policy effects. [ABSTRACT FROM AUTHOR]

The bottleneck of China's industrial carbon efficiency improvement is that the contribution of carbon emission technology is less than that of energy technology, and that of neutral technology is less than that of capital-based technology. The key to breaking through this bottleneck is to clarify how heterogeneous technological progress enhances carbon efficiency through industrial structural upgrading. The effects of four levels of technological progress on carbon efficiency under two technical classifications based on technology sources and carbon emission processes from energy consumption are studied by using the DEA method. The suitable choice of the path of technological progress to promoting China's industrial carbon efficiency is provided accordingly. The panel data model is used to deeply investigate the effects of these four levels of technological progress on industrial carbon efficiency in China's 30 provincial industries. The main results are as follows: First, in terms of direct effects, progress in energy technology is more conducive to improving carbon efficiency than progress in carbon emission technology, and progress in neutral technology is more effective in improving carbon efficiency than progress in capital-embodied technology. Second, in terms of indirect effects, progress in capital-embodied technology is effective in upgrading industrial structures and enhancing carbon efficiency; and, through green upgrading of industrial structures, progress in energy technology has a positive and significant impact on carbon efficiency. Third, the level of industrial development and government environmental governance have a positive impact on carbon efficiency, and the energy structure has a negative impact on carbon efficiency. • Different technologies impact carbon efficiency through different paths. • NT and CET promote industrial structure grading and carbon efficiency. • Industrial structure grading promotes ET and CT progress. • Promoted by industrial structure grading promotes carbon efficiency. • ET and CT progress promotes carbon efficiency. [ABSTRACT FROM AUTHOR]

• CO 2 emissions in East Africa are growing exponentially at 6.5% per year. • The process of energy clean-up is slow with little emission reduction. • Economic and population growth are the most significant emission drivers. • Emission patterns and drivers vary significantly from country to country. • Locally tailored emission reductions are needed. East Africa is typical of the less developed economies that have emerged since the 21st century, whose brilliant economic miracle has also triggered the rapid growth of energy consumption and carbon dioxide emissions. However, previous carbon accounting studies have never focused on the region. Based on multi-source data, this paper rebuilt the 45-sectors carbon emission inventories of eight East African countries from 2000 to 2017, and used index decomposition analysis to quantify the drivers of growth. Here we found that overall the CO 2 emissions show a 'two-stage exponential growth' pattern, with significant heterogeneity between countries. In terms of the energy mix, technical progress in hydro and geothermal energy was almost offset by a growing appetite for oil and coal, making it the weak and valuable factor driving emissions reduction (−1.4Mt). But it was far from enough to overcome the pressure of economic and population growth, which brought about a 13Mt and 11Mt emission growth respectively from 2000 to 2017. Increasing energy intensity due to industrialization and transport development also contributed to an increment of 6.4Mt. Low-carbon policies should be tailored to local conditions and targeted at the improvement of energy efficiency and use of renewable energy so as to achieve a win-win situation between sustainable economic growth and emission reduction. [ABSTRACT FROM AUTHOR]

Reducing agricultural carbon emissions (ACE) plays a valuable role in China's overall carbon-emission-reduction plan. Using provincial data from 2001 to 2018, this work explores the dynamics of ACE and agricultural eco-efficiency (AEE) in China and the driving factors of AEE growth by DEA–Malmquist–Luenberger and panel data analyses. A previously unexplored aspect is investigated on the different mechanism of AEE growth among three types of agricultural areas. The findings suggest that the trend of ACE shifted from increasing to continuously decreasing after 2015 at the national level, mainly based on the contribution of major grain-producing areas. The results also imply that the AEE growth mechanism changed after 2010, mainly owing to technological progress. The improvement of agricultural infrastructure and human capital positively contributed to AEE growth, whereas public investment in agricultural R&D and agricultural industrial structure undermined it. Hence, green-oriented policies targeted at regional heterogeneity and agricultural public investments are needed for ACE reduction and AEE growth at the same time. [Display omitted] • China's agricultural carbon emissions started to decrease after 2015. • Regions with carbon-emission reduction have lower eco-efficiency growth rates. • Eco-efficiency growth mechanism has changed since 2010, mainly due to tech-progress. • Improved agri-infrastructure and human capital had positive impact on eco-efficiency. • Public investment in agricultural R&D undermined eco-efficiency. [ABSTRACT FROM AUTHOR]

China's carbon emissions have been ranking first in the world. This study filled in the gaps in research, decomposed carbon intensity from the perspective of time, space and industry. A decoupling effort model based on factor decomposition models was constructed to analyze the driving factors of carbon emissions and economic decoupling, which builded a foundation for achieving sustainable economic development. Using the Logarithmic Mean Divisia Index method (LMDI), the paper measured the carbon emission intensity of 29 provinces and cities in China from 1998 to 2019, and decomposed the decoupling effect between GDP and carbon emission on the basis of factor decomposition by tapio. The results showed that: (1) Carbon intensity declined first, then rise lightly, and finally declined steadily. For the primary industry and the tertiary industry, the carbon intensity declined steadily, while the carbon intensity increased accordingly to the overall carbon intensity. In terms of spatial evolution, the regional differences between different provinces decreased correspondingly. (2) The cumulative contribution rates of these three effects, i.e., technological progress, industrial structure and regional scale were 106.3299%, −15.1486% and 8.8188%, respectively. There were obvious differences of these cumulative contribution rates of carbon intensity among different provinces. (3) From the perspective of industrial, technological progress effect is the largest contribution for carbon intensity in the secondary industry. The Industrial structure effect mainly affects the primary and tertiary industries; and no significant difference in regional scale effect. (4) The decoupling effect gradually improved, and technological progress has played an absolute leading role in promoting the decoupling effect. Based on the research results, the key policy recommendation are put forward as follows: (1) Further improve the technological level and support clean technology enterprises. (2) Promote industrial upgrading in backward industrial provinces (3) Promote regional assistance and the introduction of high-quality foreign investment. • A decoupling effort model built based on the Logarithmic Mean Divisia Index model. • Carbon intensity declined first, then rise lightly, and finally declined steadily. • Technological progress effect dominates the carbon intensity and decoupling effect. • Industrial structure effect mainly affects the primary and tertiary industries. [ABSTRACT FROM AUTHOR]

• The link between the airlines' eco-productivity changes and the EU ETS is explored. • The airlines continuously improved eco-productivity over time, but at a slow pace. • Efficiency gain, technology progress respectively led the eco-productivity growth. • Implications are provided for promoting eco-productivity in the airline industry. • Non-convexity is used to form global Malmquist Luenberger productivity index. With the rapid traffic growth, carbon dioxide emissions from aviation have become a growing cause of climate change. Currently, the European Union Emissions Trading System (EU ETS) remains the largest cap-and-trade system tackling aviation emissions. This study examines how the eco-productivity of airlines in the European Economic Area – which are directly affected by the EU ETS – changed during 2012–2019. A non-convex global Malmquist-Luenberger productivity index was developed to provide better estimates of the changes. The study presents three major findings as follows: First, since the inclusion of aviation in the EU ETS, the airlines displayed continuous but slow growth in their eco-productivity. Second, efficiency improvement and technological innovation respectively drove the airlines' eco-productivity growth. Third, the carbon price on the EU ETS was not a strong indicator of the airlines' eco-productivity changes. [ABSTRACT FROM AUTHOR]

• The macroeconomic fundamentals and material flows have non-linear effects on decoupling. • Economic growth is the main driver of CO 2 emissions. • Stock markets and debt decrease CO 2 emissions, in high-regimes of economic growth and debt. • Technology mitigates CO 2 emissions in regimes of robust growth and low debt, in the European context. Overall, the literature strongly validates the Environmental Kuznets Curve hypothesis and the decoupling of the economy regarding energy intensity and raw materials. However, there is no assessment of whether such evidence derives from the non-linearities of macroeconomic fundamentals from considering a simultaneous supply-demand market perspective of material flows. Therefore, using a sample of 40 countries, we estimated a Panel Smooth Transition Regression model for the period between 1994 and 2016. The results showed that economic growth and material flows are the drivers of CO 2 emissions. They also showed that stock markets and debt issuance reduce CO 2 emissions in high regimes, and the preponderant technological progress in the European context, mainly on the demand side and in high-regimes of economic growth and low-debt regimes, contribute to reductions in CO 2 emissions, even though positive in the low-regimes of economic growth. This study considered the efficiency levels and the evolution of macroeconomic fundamentals, providing new guidance for managers and policy-makers seeking to either innovate public policies and market positions or design sustainable asset investment strategies and portfolios. [ABSTRACT FROM AUTHOR]

Rapid urbanization in China has made the construction sector become a significant industry for CO 2 emissions. Accurately measuring the CO 2 emissions performance of the construction industry is a key to CO 2 emissions management and the basis for successful implementation of the national carbon neutral strategy. From a total-factor production perspective, CO 2 emission performance refers to the degree to which the desirable outputs are maximized, and the CO 2 emissions are minimized under a certain level of inputs in production activities. To measure the dynamic evolution of CO 2 emission performance and its reasons more reasonably, a global Malmquist CO 2 emissions performance index (GMCPI) that can evaluate CO 2 emission performance of multi-input and multi-output production process is proposed in this study. By using this index, the CO 2 emissions performance of the construction industry in China's 30 provinces from 2004 to 2016 was investigated with three key findings. First, the overall CO 2 emissions performance was improved by 13.6% during the study period, mainly contributed by technological progress rather than the increase of technical efficiency. Second, an imbalance growth of CO 2 emissions performance was observed in different regions. The performance in the eastern region ranked the best, with an average annual increase of 14.5%. Third, the reasons contributing to different levels of CO 2 emissions performance in different provinces were explained according to the decomposition of GMCPI. Thirteen provinces are the slow-efficiency-growth type with low technical efficiency change(EFFCH) and low technology change(TECHCH). Five provinces are the technology-driven type with high TECHCH and low EFFCH, four provinces are the technology-management dual-driven type with high EFFCH and high TECHCH. Eight provinces are the management-driven type with high EFFCH and low TECHCH. China's construction industry should further improve the overall CO 2 emission performance by strengthening the driving role of technological progress and promoting the exchange of advanced management experience to reduce regional differences. • A global Malmquist CO 2 emission performance index (GMCPI) is proposed. • CO 2 emission performance of China's construction industry is measured. • Provincial spatial and temporal differences in CO 2 emission performance is investigated. • GMCPI was mainly driven by technology change from 2004 to 2016. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's agricultural carbon emission performance (ACEP) has greatly improved. • Technological progress promotes ACEP, but efficiency changes does little. • China's ACEP has an aggregation effect, provinces with lower ACEP are catching up. • China's ACEP is converging and growing at the same time. • China's ACEP will increase further in terms of spatial scope and temporal trends. The spatial characteristics of carbon emissions form the basis for exploring carbon neutrality measures for different regions. As the second largest carbon emission source globally, agricultural carbon emissions should not be ignored. This paper used panel data from 2009 to 2019 to calculate the agricultural carbon emissions of 30 provinces in China, and used the ML index, spatial Morans'I index and three convergence indices to analyze the dynamic changes and spatial pattern of China's agricultural carbon emission performance (ACEP). The results showed that: (i), China's ACEP has increased by 1.5% from 2009 to 2019, following a spatial pattern of eastern (1.041) > western (1.020) > central (0.974);(ii) the contribution of technological progress to China's ACEP was 1.035, exceeding 1, and thus indicating that technological progress can improve ACEP, while the contribution of efficiency changes was only 0.991; (iii) China's ACEP has exhibited a spatial aggregation effect over the last 10 years (P < 0.05), but the value of Moran's I index dropped from 0.265 in 2009 to 0.202 in 2019, which indicates that the spatial aggregation of ACEP is weakening; and (iv) the ML index for 30 provinces in China showed σ convergence, which indicates that the ACEP is improving, while there is absolute β convergence and conditional β convergence, which indicates a "catch-up effect" among regions. The results indicate that China's ACEP is likely to increase further in terms of spatial scope and temporal trends. [ABSTRACT FROM AUTHOR]

The electrification of transportation is currently seen as one of the key strategies in the sustainable energy transition. This work aims to identify the role of the electrification of transportation from a perspective of the mid-to-long term. The main focus is on the coupling of the deployment of electric vehicles (EV) and power system decarbonization. The paper combines a detailed bottom-up model for the road transportation sector, which features a detailed classification of vehicle types, with the EnergyPLAN tool, a cross-sector and cross-region energy system model of future scenarios consisting of different planning strategies and vehicle charging modes. A case study of the Beijing-Tianjin-Hebei region shows that without increasing renewable power capacity, a 100 % EV penetration can create energy savings and reduce carbon dioxide emissions by a minimum of 11 % in 2050. Moreover, considering the foreseeable technological progress in the battery, a 25 % reduction in the EV initial investment is feasible in all the investigated future scenarios. • The technical-economic impacts of vehicle electrification on the energy systems are analyzed. • Energy system simulation combines EnergyPLAN and a detailed road transportation model. • The Beijing-Tianjin-Hebei region in China is taken as a case study. • The positive effect of interregional collaboration on energy transition is identified. • A 25 % reduction in the EV initial investment is feasible in all designed scenarios. [ABSTRACT FROM AUTHOR]

In the social context of enhancing environmental quality and mitigating climate change, the improvement of carbon emission efficiency has emerged as a hot topic recently due to its promise as an approach to reduce emissions and improve environmental quality. A large volume of studies has identified technological progress as a critical factor affecting energy efficiency and pollution emission levels. From the perspective of national differences, the contributions of our study lied in constructing a system to measure technological progress based on the outputs and transformations of a country's technical progress level. Secondly, this paper examined the direct effect of technological progress on carbon emission efficiency, and the interaction effect between technological progress and energy intensity on carbon emission efficiency. Thirdly, we taking into account differences in the effect that technological progress can have on carbon emission efficiency in different national contexts. For this purpose, we evaluated the carbon emission efficiency of 59 countries in the period 1998–2016 using a super-SBM model, subsequently employing a national panel quantile regression method to explore the multiple effects of technological progress on the carbon emission efficiency of countries with different levels of efficiency. Our main findings indicate that the national carbon emission efficiency steadily increased across the study period, albeit with slight fluctuations, and that generally countries' carbon emission efficiency still exists great potential of enhancement. Technological progress will drive carbon emission efficiency to improve significantly, while this effect varies between countries with different levels of efficiency. Further, the interaction effects of technological progress and energy intensity are shown to exert complex effects on carbon emission efficiency, providing evidence that scientific and technological achievements need to be converted into productivity in time to offset the negative environmental effects of pollution and emissions. Governmental departments should, as a result, strengthen environmental supervision and urge enterprises to eliminate backward production capacity, introduce new technologies, and improve energy efficiency. Countries may realize the development of low-carbon sustainable societies by transforming their economic growth mode and adopting energy-saving production techniques. [ABSTRACT FROM AUTHOR]

The modernization of the different economic sectors has drastically increased energy consumption and CO 2 emissions in developing countries, leading to a literature stream on the relationship between technological progress and the carbon emissions from the various sectors. However, most related policies do not consider the diversity of technological sources. As such, this study develops a comprehensive model that combines the expanded stochastic impacts by regression on population, affluence, and technology (STIRPAT) and the geographically and temporally weighted (GWTR) models to explore the spatial effects of three technology progress channels (research and development investment, technology spillover related to FDI, and DS) on the CO 2 emissions in China from six sectors during 2000–2017. The results show that research and development investment has an inhibitory effect on the CO 2 emissions from the agricultural, industrial, and wholesale sectors, and a catalytic effect for those from the construction, transportation, and residential sectors. The DS has a negative impact on the CO 2 emissions from the agricultural, construction, and wholesale sectors, but a positive one for those from the industrial, transportation, and residential sectors. Finally, foreign direct investment has a positive effect on the CO 2 emissions from all sectors (except for transportation). Therefore, this study shows that all the effects of the three technological progress channels on the carbon emissions from different sectors display spatial correlations and differences. In conclusion, policymakers should tailor policies to the various sectors in the different provinces. [Display omitted] • We develop a model combining the extended STIRPAT and GWTR models. • We explore the spatial effects of three technology progress channels. • These channels display spatial correlations and differences for the various sectors. [ABSTRACT FROM AUTHOR]

Rising concern regarding traditional non-renewable energy consumption has led policymakers to explore the potential of economical renewable energy sources. In this regard, biomass energy has received considerable attention because previous studies have found mixed results regarding the effect of biomass energy on environmental quality. Together with modern technology, biomass energy may significantly influence environmental quality. This study investigates the impact of biomass energy consumption, education, and technological innovation on environmental quality by controlling for the role of economic growth and financial development in the function of environmental quality. Second-generation econometric methods were used to solve the issues of heterogeneity and cross-sectional dependence in the study variables. The Westerlund and Edgerton (2008) cointegration technique confirmed the existence of a long-run equilibrium among the variables in the presence of structural breaks. The panel quantile regression results indicate that biomass energy use and technological innovation reduce environmental quality. Similarly, economic growth increases carbon emissions in the environment. Education and financial development contribute to reduce carbon emissions. • Role of biomass energy consumption, education, and technological innovation is investigated in carbon emissions. • Panel quantile regression method is employed for APEC countries. • Biomass energy consumption and technological innovation increase carbon emissions. • Education and financial development improve environmental quality by reducing carbon emissions. • Dumitrescu and Hurlin method is used to examine the causality among the variables. [ABSTRACT FROM AUTHOR]