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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]

Green energy transition from fossil fuels to wind and solar is inevitably driven by environmental and climate policy. How the emission trading scheme (ETS) influences energy transition in China deserves to be analyzed in the context of carbon neutrality. This study analyzes the impact of ETS on neutral and biased technological progress of China's power generation sector by decomposing the power sector by generation technology in a translog cost function. It then analyzes how the ETS pilot policy brings about biased technological progress between different generation technologies using difference-in-differences and synthetic control methods. The results show that ETS pilot policy contributes to green energy transition by bringing about neutral technological progress of China's power generation sector, utilization rate decrease of thermal power capacity and stimulating generation units' upgradation of the thermal power sector. Although China's energy transition is remarked by substitution of thermal power sector with variable renewable energy sector, there is no evidence that ETS pilot policy has contributed to this trend. However, it inhibits innovation of variable renewable energy in pilot regions. It is necessary for the government to take measures to strengthen ETS policies' impact on energy transition through supporting innovation and expansion of the VRE sector. • ETS pilot policy leads to neutral technological progress of power sector in China. • ETS is biased to save input cost of solar and wind power generation. • ETS stimulates investment and upgradation of thermal power generation sector. • Pilot regions with higher carbon prices are inhibited in green innovation due to profit loss. [ABSTRACT FROM AUTHOR]

This paper compares the macroeconomic impacts of different decarbonization storylines until 2050 using two Computable General Equilibrium models. The modeling shocks are harmonized across models to simulate four decarbonization scenarios through three main drivers: technical development, societal attitude, and policies. The study explores the contribution of each of these drivers to the European decarbonization. The results show that the decarbonization scenarios have moderate effects on GDP; decarbonization scenarios rather result in sectoral shifts. The impact of temperature on labor productivity minimally alters expected growth levels, since larger differences in temperature between the scenarios are only expected to occur in 2100, not yet in 2050. Electricity demand is increasing in all scenarios, particularly with stronger political guidance leading to additional tax and subsidy measures to encourage the use of electricity and discourage fossil-based fuels. When society is a driving factor, it is found that circular business models result in an increase in the service industry but might have a slightly negative impact on overall growth due to stronger spillover effects linked to the manufacturing industry. Technology appears to be the only driver to facilitate decarbonization while maintaining steady economic growth. • We address the macroeconomic effects of five climate scenarios using two CGE models. • We focus on the effects of changes in society, technology, and policy. • We incorporate climate change effects through labor productivity. • Effect of decarbonization on GDP is moderate under given technological improvement. • Decarbonization scenarios result in relatively large sectoral shifts. [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]

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development. • Digital infrastructure construction markedly enhances industrial energy efficiency. • There is a non-linear picture of the impact of digital infrastructure construction on industrial energy efficiency. • The effects of green technology innovation and advanced industrial structure are crucial channels. [ABSTRACT FROM AUTHOR]

As a zero-carbon energy, hydrogen energy is widey recognized as playing an important role in reducing carbon emissions and addressing climate change. Considering the resource and industrial conditions in China, the coal-to-hydrogen coupled with carbon capture and storage (CTHCCS) may be a necessary choice in the early stage of low-carbon hydrogen energy development. This study used real option models to analyze the investment benefits of the representative CTHCCS technology at the national and regional levels in the current and future development scenarios in China, and discusses the impact of uncertainties in carbon price, technological progress, and policy incentives on the CTHCCS investment. The results show that (1) The opportunity for immediate investment in the CTHCCS is not available in China unless the cost of carbon capture and storage (CCS) technology decreases and the carbon price increases significantly. (2) The current CTHCCS investment critical carbon price is 353.3 RMB/t, which will decrease by 45.0%, 73.9% and 57.3% when using the initial investment subsidy, carbon emission reduction subsidy and clean fuel subsidy, respectively. (3) The CTHCCS presents a relatively high investment benefit in Xinjiang, Inner Mongolia, and Shaanxi, highlighting that these areas are key pioneers for "blue hydrogen" production in China. • The real option model is used to evaluate investment decisions for CTHCCS projects. • Uncertainty of carbon price, technology progress and policy subsidy are considered. • Carbon price of 353.3 RMB/t could trigger immediate investment for CTHCCS projects. • Policy incentives lead to a 45–74 % reduction in the CTHCCS critical carbon price. • Xinjiang and Inner Mongolia could be priority areas for blue hydrogen production. [ABSTRACT FROM AUTHOR]

The current study is concerned with the analysis of the determinants of the transition to green energies in Tunisia with a focus on institutional and technological factors. In fact, it affords a deep assessment of the asymmetric effects of technological innovation, government stability, and democracy along with economic growth and non renewable energy on clean energy in Tunisia. The analysis actually covers the 1984/2020 period. Therefore, to perform such analysis the Nonlinear Autoregressive Distributed Lag (NARDL) approach along with the spectral causality is used. Actually, the political context is of great importance in Tunisia, because this country has been going through a political transitional stage since the 2010 revolution. The main upshots bring out that a positive variation in government stability has no significant effect on clean energy, however a negative variation decreases renewable energy by 0.521% and 0.535% in both the long and short run, respectively. Similarly, a negative variation in democracy reduces green energies. Moreover, regarding technological innovation, the results suggest that a positive shock boosts green energy while a negative one lessens it in the long run. Furthermore, a positive variation of the Gross Domestic Product (GDP) positively affects green energy while a negative variation has a negative impact, in the long run. Finally, a positive shock on fossil fuel consumption decreases renewable energies and a negative shock positively affects them, in the long run. Moreover, the results of the causality confirm the presence of a one way causal link from renewable energy to government stability and GDP. In addition, there is bidirectional causality between democracy and renewable energy. Finally, there is a one way causal link from technological innovation and fossil fuel energy to green energy. Therefore, it is crucial that the political parties integrate environmental issues and mainly renewable energies in their political agenda. In addition, policymakers should encourage the creation of research centers devoted to renewable energies, which represent a link between scientific research and green energy industry. • Renewable energy is driven by technological innovation and income. • The democratic transition and government stability in Tunisia have not yet achieved an acceptable level to accelerate the transition to clean energy. • The robustness check is adopted by considering the effect of geopolitical risk on renewable energy. [ABSTRACT FROM AUTHOR]

As cities face escalating resource and environmental challenges, advancing green economic performance (GEP) emerges as a critical pathway for sustainable urban development. While China's urban new energy transition strategy presents a promising paradigm shift in the realm of green energy, there exists a knowledge gap regarding its tangible impact on green economic grown. Drawing on city-level panel data from 2006 to 2020, this research assesses the GEP of 260 Chinese cities utilizing the non-radial distance function methodology. Subsequently, the difference-in-differences method is applied to offer quasi-experimental insights into the effects of the New Energy Demonstration City (NEDC) policy on urban GEP. Our findings indicate that the NEDC policy has both economically and statistically significant ramifications for urban GEP, leading to an enhancement effect of approximately 2.0 %. This outcome remains consistent even after rigorous robustness checks. A heterogeneity analysis reveals that the NEDC policy exerts a more pronounced influence on the GEP of central, western, and non-resource cities. Mechanism analysis suggests that the positive policy effect is primarily driven by the advancement of technological innovation, industrial upgrading, and green finance development. The NEDC policy holds the potential for a symbiotic balance between environmental and economic outcomes, setting a precedent for urban green growth. • The impacts of New Energy Demonstration City policy are evaluated. • Use the non-radial distance function method to calculate green economy performance. • The NEDC policy improves urban green economy performance by a significant 2.0 %. • Three specific impact mechanisms are proposed and quantitatively analyzed. • The policy impacts vary greatly among cities with different characteristics. [ABSTRACT FROM AUTHOR]

This study focuses on examining the mechanisms of industrial agglomeration on industrial carbon productivity. A balanced panel dataset of 281 cities in China is used, covering the period from 2004 to 2020, to assess the combined economic and environmental effects of industrial agglomeration. The main research findings are: (1) Industrial agglomeration significantly raises industrial carbon productivity; (2) Industrial agglomeration indirectly increases industrial carbon productivity through technological progress and labor mismatch reduction; (3) Environmental regulations have a negative moderating role in the correlation between industrial agglomeration and industrial carbon productivity; (4) The relationship between industrial agglomeration and industrial carbon productivity is non-linear. One of the possible reasons for the non-linear relationship between industrial agglomeration and industrial carbon productivity is that the level of industrial agglomeration is too high and its effect on promoting technological progress is not significant. The innovation of this paper is that it focuses on the industrial sector, quantifies the learning and matching effects of industrial agglomeration, empirically analyzes how industrial agglomeration affects industrial carbon productivity, and explores the reasons for the nonlinear relationship between industrial agglomeration and industrial carbon productivity. These findings are valuable in guiding the industrial sector on improving industrial carbon productivity through agglomeration effects. • Industrial agglomeration indirectly affects industrial carbon productivity by promoting technological progress and reducing labor mismatch. • Environmental regulation plays a negative moderating role in the relationship between industrial agglomeration and industrial carbon productivity. • The relationship between industrial agglomeration and industrial carbon productivity is non-linear. [ABSTRACT FROM AUTHOR]

Improving energy efficiency and securing employment are critical challenges for sustainable growth, especially in developing countries. Using an econometric model and panel data from China's prefecture-level cities from 2006 to 2019, this study provides empirical evidence of the impact of increased energy efficiency on employment. Multiple robustness test results reveal that a 1% rise in the energy intensity reduction rate leads to a 2% increase in the employment growth rate. According to heterogeneity tests, cities with low energy efficiency and depressed employment rates, compared to flourishing cities, may greatly benefit from increased energy efficiency. Furthermore, this study examines the influence mechanism of energy efficiency on employment. The outcome indicates that booming trade and technological progress magnify the positive impact of increased energy efficiency on employment. In addition, decreasing regional investment in the energy industry is one of the mechanisms by which improved energy efficiency promotes employment. Finally, this study proposes certain energy-saving policies at the city level to guide future policy refinement. • Energy efficiency improvement contributes to employment growth by creating jobs. • The regional differences exist in the impact of energy efficiency on employment. • Trade development and technology progress are identified as the impact mechanisms. • The energy dependence reduction caused by efficiency growth plays a mediating role. [ABSTRACT FROM AUTHOR]

Particularly for developing regions like Sub Saharan Africa, energy intensity is a critical policy issue and usually affected by four challenges: substitution between energy and other factors, technological change, changes in energy composition, and changes of economic growth. This paper examines energy intensity, output elasticity, energy-capital-labor substitution possibility, and factors contribution rates. The study applies a Translog production approach with data spanning 1990–2014 and further, applies ridge regression technique as a robust rectification to achieve unbiasedness in the findings. There is expected evidence of inverse relationship: higher energy intensity in lower per capita economy, evidenced by upward energy tariffs with lower energy elasticity. Factors are inelastic and have average elasticity substitution possibility for capital-energy with their inferential rebound effects' challenges. However, other combinations indicate complementarity at the current development stage. We further, observe that if higher energy intensity not managed, would translate into higher CO 2 intensity, because 99% of total energy is sourced from hydrocarbons of 51% and 48% of biomass. Based on the findings, policymakers and implementors are encouraged to integrate and optimize the whole energy system towards improving energy efficiency through increasing renewable energy composition from the current 1% to at least 10% whiles increasing quality-energy from nonrenewable. • Translog production with Ridge Regression analysis on energy intensity. • Higher energy intensity in lower per capita economy, and low energy elasticity. • The presence of energy substitution has implication for rebound effect on efficiency. • 99% of total energy consumption originates from hydrocarbons and biomass. • Energy poverty and inefficiency as a result of low energy technology innovations. [ABSTRACT FROM AUTHOR]

Abstract China has the largest textile industry with the complete industrial chain and also the largest textile exporter in the world. The study analyses the energy substitution effect of technological progress of China's textile industry using a macroeconomics approach. In order to predict future energy saving potential, we examine the relationship between energy intensity and its five main factors (technological progress, enterprise scale, labor productivity, dependence on foreign trade and industrial electricity price) by co-integration technique. Empirical results indict that electricity shows alternative features to other energy sources in the context of technological progress in China's textile industry. Besides, there exists a long-run equilibrium among energy intensity and the five main factors. Monte Carlo method was applied for risk analysis to ensure the reliability of forecast. Further, future energy saving potential and CO 2 emission reduction of China's textile industry was predicted using scenario analysis. The result shows that energy conservation potential of China's textile industry is 16.16–27.53 million tons of standard coal equivalent in 2025. Additionally, it was revealed that the CO 2 emission reduction caused by the energy conservation will be 32.63–55.60 million tons in 2025. Finally, future policy priorities for energy conservation of Chinese textile industry are suggested. Highlights • We study the elasticity coefficients of influential variables of energy intensity. • Electricity shows alternative features to other energy sources. • Energy conservation potential of China's textile sector will be 27.53 Mtce by 2025. • CO 2 emission reduction potential is estimated to be 55.60 million tons by 2025. [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]

Natural gas is a crucial transitional energy for Pakistan to attain the emission reduction goal for sustainable economic growth. To imply environmentally-friendly fuel and save foreign exchange, huge indigenous resources can be used. Under sustainable policies, Pakistan's natural gas is predicted to adequately rise, which will significantly impact energy security, economy and environment. The objective of the study is to analyze NGC factors' decomposition in Pakistan from 1996 to 2020, applying the logarithmic mean Divisia index method to provide evidence of the effect of key factors, including NGC intensity, income and population effects. The results show that: (a) during 1996–2020, natural gas is raised maximally due to the rise in income and population levels. (b) Population effect was the second key driver of gas rising, while the intensity effect was added to slow down during the maximum intervals. With the rising trend of a population between 0.12 and 0.22 billion, it is the only leading factor that is increasing the NGC to maximum level. (c) The sectorial outcomes influenced population growth with positive income, evidenced by the per capita. Looking forward to the energy targets, prediction highlights that sustainability and energy security could be guaranteed by modifying technological progress and environmental policies. • Natural gas intensity, income and population are studied under planning period. • Logarithmic mean Divisia index method is employed to evaluate NGC factors. • Income and population factors are the key driver of NGC growth. • NGC intensity was contributed to slow down during the maximum intervals. • Sectorial differences impacted the population growth with positive income growth. [ABSTRACT FROM AUTHOR]

Technological progress is frequently prioritized to resolve the contradiction between large resource consumption and rapid economic growth. However, in previous energy literature, it is frequently discussed as a whole without considering the heterogeneity. This paper divides the technology into production-oriented and energy-conservation types. Subsequently, a provincial dataset in China between 2000 and 2018 is applied to explore the influence of technology on energy demand. Data on the stock of different patents are employed to identify various technology types. When discussing technology in entirety, empirical regressions indicate that technology does not serve to be a major contributor to the evolution of energy demand. Conversely, when dividing technology into production-oriented and energy-conservation types, both were considered as major forces influencing energy demand but with different effects. The channel analysis suggests that production-oriented technology drives energy demand mainly by increasing individuals' income and expanding the industrial production scale. The energy-conservation technology is beneficial in controlling the energy demand. The spatial analysis indicates that compared to local technology, technology spillovers from neighbouring regions are essential in influencing energy demand. • Technology has not exerted a significant role in the evolution of energy demand. • Energy-conservation type of technology curbs the energy demand. • Production-type of technology drives the energy demand through more economic activities. • Technology spillovers are important to explain the evolution of energy demand. [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]

We study the optimal sequence of investment in renewable energy when technology improves over time and the productivity of deployed capital differs with site quality. Our perspective is that of a price- and technology-taking individual or firm. We begin with a model where the price of output produced with the technology is a known constant and technology improves according to a known differential equation. We specify an optimization problem that allows for the solution of the optimal date of initial investment and the dates for optimal replacement. We then develop models where output price evolves according to geometric Brownian motion and technology evolves deterministically or stochastically, with up-jumps (breakthroughs). The possibility of breakthroughs will further delay initial investment compared to the model where technology evolves deterministically. Our analysis is relevant for the initial investment in renewable energy (wind or solar) and determining when and where to replace capital that is inefficient relative to current technology. [ABSTRACT FROM AUTHOR]

Technological progress plays a key role in promoting energy efficiency. In order to find the suitable path of technological progress to improve energy efficiency, this study adopts the growth-accounting method to investigate the effects of two types of technological progress, namely, the Hicks-neutral and the capital-embodied technological progress on the changes in energy productivity. Furthermore, dynamic panel data models are applied to investigate the various effects of these two types of technological progress on energy productivities 30 Chinese provinces from 1997 to 2012. The main results are: (1) the Hicks-neutral technological progress directly contributes to energy productivity improvement, and its indirect contribution comes from the optimization of manufacturing structure induced by technological catch-up; (2) the capital-embodied technological progress has a direct contribution to energy productivity improvement, while the indirect contribution is seen through its interaction with the upgrading of the manufacturing structure; (3) the energy-saving performance from the capital-embodied technological progress is poor because of energy rebound effect. These findings suggest that the capital-embodied technological progress is effective for energy-saving in China; which can be made possible only by implementing energy price reforms. [ABSTRACT FROM AUTHOR]

This paper proposes a total-factor energy consumption performance index (TEPI) for measuring China's energy efficiency across 30 provinces during the period 1997 to 2012. The TEPI is derived by solving an improved non-radial data envelopment analysis (DEA) model, which is based on an energy distance function. The production possibility set is constructed by combining the super-efficiency and sequential DEA models to avoid “discriminating power problem” and “technical regress”. In order to explore the impacts of technological progress on TEPI and perform statistical inferences on the results, a two-stage double bootstrap approach is adopted. The important findings are that China's energy technology innovation produces a negative effect on TEPI, while technology import and imitative innovation produce positive effects on TEPI. Thus, the main contribution of TEPI improvement is technology import. These conclusions imply that technology import especially foreign direct investment (FDI) is important for imitative innovation and can improve China's energy efficiency. In the long run, as the technical level of China approaches to the frontier, energy technology innovation and its wide adoption become a sustained way to improve energy efficiency. Therefore, it is urgent for China to introduce measures such as technology translation and spillover policies as well as energy pricing reforms to support energy technology innovation. [ABSTRACT FROM AUTHOR]

This paper applies the real options method to analyse the defer option value and optimal investment timing for solar photovoltaic projects in China. The main purpose of this paper is to examine investment behaviours under different market systems and support schemes. This paper further investigates the interaction of technological progress and support schemes. Four scenarios are designed, and the corresponding real options models are established. In the case study, we find that electricity market reform enhances the defer option value in the short term but makes the owners of solar PV projects postpone their investment. Nevertheless, the government can stimulate investment by implementing appropriate support schemes. Additionally, the impacts of different support schemes vary according to the market system. The impacts of feed-in tariffs and price premiums are similar in a regulated market but are different in a free market. The price premium scheme greatly promotes the defer option values in the short term, but the feed-in tariff scheme excels in the long term. A feed-in tariff has a greater impact on reducing the expected execution time and its variance than the price premium. In addition, more attractive support schemes are required when the technological level is improved. [ABSTRACT FROM AUTHOR]

Many countries have considered energy subsidies, with both successes and failures. Energy subsidies often have failed to achieve their stated objective of advancing national economic, social, and technological progress. Budgetary savings from the removal of energy subsidies and the resulting upsurge in energy prices may induce the adoption of innovative technologies in a country. The economic impact of energy subsidies has been analyzed for many countries in the literature. However, this issue with energy technology innovation using panel analysis has not yet been thoroughly investigated. Therefore, this study is intended to discover the effect of energy subsidies on energy technology innovation in the 25 highest energy subsidy-providing countries for the period of 2010–2020, employing advanced econometric approaches. The results confirm that energy subsidies and energy technology innovation are significantly cointegrated, and the removal of energy subsidies stimulates technological innovation, which induces the adoption of a renewable-based polygeneration system. The policy implication of this paper suggests going for further innovations in energy subsidy reforms for sustainability and technological innovation for polygeneration that may contribute to clean, sustainable, and affordable energy for all (SDG-7). [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]

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing. • The energy rebound effect of Beijing are quantified at the level of overall economy, industry and sector. • The overall economy of Beijing appears backfire effect and partial rebound effect. • The energy rebound effect of the tertiary industry is higher than that of the secondary industry in most years. • Sectors with low energy efficiency may have higher rebound effects. [ABSTRACT FROM AUTHOR]

India is the world's third-biggest carbon-emitter after China and the United States. Most of the mixed energy has been dominated by oil, coal, electricity, and gas. This study has attempted to investigate the output elasticities, the possibility of factors substitution, and technological progress between energy and non-energy factors. Ridge regression has been employed to investigate the parameters because of multicollinearity issue in the data. The outcomes illustrate that all energy inputs are substitutes. Gas contributes to only a small proportion of energy, while gas, oil and electricity replace coal in the production process. This will provide the opportunity to substitute coal gas-emitting with renewable energy resources without any risk. Maximum substitution between coal-gas and coal-electricity is found, with lower possibilities to replace coal for oil. The results support that lessening the coal for electricity provides the potential for primary interest. Moreover, the outcomes evidence that the significant role of coal in India converges over future, although slowly. Though, the technological progress is measured to be between −0.0076 and +0.053 among the different input pairs with capital as superior technical progress to energy and labor, which can meet anytime and substitute over time. Finally, these results have broader implications for energy conservation, energy substitution, capital enhancement, and carbon reduction policies in emerging nations. Furthermore, by analyzing technical progress, insights are provided on future carbon mitigation. • Analyzing fuel substitution possibilities and the technical progress in India. • Ridge regression has been employed to investigate the parameters during 1990–2019. • All the energy inputs are substitutes. • Coal-gas and coal-electricity show higher substitution. • Capital has superior technical progress to total energy and labor. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to scrutinize the linkage between technological advancement and energy security in the presence of global uncertainty, financial development, globalization, international cooperation in the form of trade and infrastructure for the panel set of the newly industrialized countries. This paper contributes to the empirical literatures by using robust econometric techniques and the main estimation technique employed is cross sectional autoregressive distributed lag model (CS-ARDL). A battery of robustness tests is further put to use to test the underlying model specifications. To explore the causality nexus, the [1] panel causality test is also employed. The long-run estimation results reveal that technological advancement reduces the risk associated with energy security. But world uncertainty induces energy security risk. Furthermore, financial development and globalization affect energy security risk negatively. The causality test results demonstrate bidirectional causality between financial development and energy security, international cooperation and energy security, uncertainty and financial development, international cooperation and financial development, infrastructure and financial development. The empirical outcomes from this study offer salient aspects for designing policy strategy on energy security in the newly industrialized countries against the backdrop of attainment of the sustainability goals. [Display omitted] • Factors of Energy Security are examined for Newly Industrialized countries. • Robust second generation econometric models are employed. • Technological growth reduces energy insecurity. • Global uncertainty induces energy security risk. [ABSTRACT FROM AUTHOR]

This study attempts to fill important knowledge gaps to explore the distinct symbolic influences of subsidy and tax rebate policies on green firm research and development (R&D) efficiency using the global Malmquist–Luenberger (GML) index method, while focusing on green industries in China. Our empirical results demonstrate that the subsidies and tax rebates received from local and central governments have significant U-shaped impacts on green firm R&D efficiency at the 10% significance level, and subsidies and tax rebates have significant and positive interactions with green firm R&D efficiency under the U-shaped effects of subsidies and tax rebates at the 1% significance level. Furthermore, subsidies and tax rebates play distinct transmission roles in influencing green firm R&D efficiency by stimulating R&D investment, boosting technical efficiency change, and facilitating technical progress change. • This article explores the fiscal policy-R&D efficiency nexus in green firms. • Subsidies and tax rebates have a U-shape relationship with R&D efficiency. • Tax rebates and subsidies have a positive interaction with R&D efficiency. • 4.Subsidies and tax rebates produce effective transmission channels on R&D efficiency. [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]

Spatio-temporal heterogeneity and rebound effect are significant issues on evolution of energy intensity. This paper analyzed the local effect on the energy intensity evolution cross the thirty provinces of China from 1995 to 2011 applying the Geographically and Temporally Weighted Regression Model, calculated the rebound effect in both national and provincial levels during the same sample period above, and examined the effect of technological progress on energy intensity when there exists rebound effect. The results show that heterogeneity exists significantly in the evolution of China's energy intensity. On the point of view of the spatial and the temporal dimension, the identification of heterogeneity helps to comprehend the situation of the energy intensity which has been improved in higher degree during the period after 2000 than before, and more obviously in eastern region than central and western region. This paper also proves that the effect of technological progress on decreasing energy intensity is restricted because of the rebound effect. Therefore the spatio-temporal variation in the path of energy intensity's evolution should be fully considered, and corresponding policies are necessarily made to reduce rebound effect, which will lead to make the balanced development of energy consumption in different regions of China. [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]

Improving the energy efficiency of economies is the core of promoting global sustainable development. At present, improving energy efficiency is a common problem faced by all countries. In this paper, the Three-stage DEA model improved by radial slack based measure (SBM) model is used to evaluate the energy efficiency and influencing factors of Regional Comprehensive Economic Partnership (RCEP) members. Meanwhile, the dynamic changes of energy efficiency of each country are further explored by the Malmquist index model. The empirical analysis shows that the energy efficiency of China, Japan, and Australia are all 1, which is at the forefront of efficiency, while the energy efficiency of Vietnam is the lowest, which is 0.136. China's technological progress change index is 1.013, leading other countries. The results of the study are helpful to understand the trend of energy efficiency changes in various countries, to transform their energy efficiency advantages into the common advantages of each member state, and to improve the comprehensive governance capacity of energy efficiency of each country. • Energy efficiencies of 11 RCEP countries are quantified from 2000 to 2019. • The three-stage Undesirable SBM DEA model yields more accurate energy efficiency. • External factors affecting energy efficiency are examined. • External circumstances and random errors can cause overestimation of energy efficiency. • The total factor energy efficiency of each country can be analyzed by the Malmquist index model. [ABSTRACT FROM AUTHOR]

As a hub of the world's energy challenge, efficient energy use in Sub Saharan Africa (SSA) can improve energy security, environmental sustainability and boost socio-economic conditions towards the "Africa We Want". In this regard, considering group heterogeneity, we analyzed the trend of energy productivity changes under Meta frontier Malmquist framework (MMEPI) based on Shephard energy distance function over 2000–2016 year period in SSA. Additionally, external factors of energy productivity changes are explored using Tobit regression model. The results indicate improvement in energy productivity, but the average distribution density shows a backward trend. The growth is principally driven by technological progress, which signals a greater potential in improving technical efficiency changes. Also, there are significant disparities in energy productivity changes across income groups. This suggests that employing Meta frontier technology overcomes the potential of underestimating productivity levels. The regression results depict that energy price, GDP growth, and population density positively support improvement in MMEPI. We support the view that each country should examine its local features, design and implement specific techniques and collectively build a regional sustainable energy development level. • We analyze energy productivity changes and determinants in Sub-Sahara Africa (SSA). • The Meta frontier Malmquist analyses indicate the presents of group heterogeneity. • Energy productivity improved, but average distribution density shows a backward trend. • Technological advances play a pivotal role in energy productivity improvement. • Energy price, GDP growth, and population density positively support energy productivity. [ABSTRACT FROM AUTHOR]

The paper innovatively divides technological progress's actual emission mitigation effect into technical emission mitigation effect, rebound effect, and substitution effect. Then, taking the power sector as an example, the paper applies a dynamic recursive multi-sector computable general equilibrium model to simulate technological progress and explore its effects. The results indicate that the level of the rebound effect somehow is the trade-off between emission reduction and economic growth and social welfare. Price regulation in the power sector can significantly reduce the rebound effect, economic growth, and social welfare. We find that technological progress could affect economic indicators through commodity and factor markets in mechanism analysis. If the commodity market is distorted/regulated, the factor market will be the only market that directly reacts to the progress. We cannot hold that technological progress has been offset because of the rebound effect. To some extent, the benefits of technological progress may not just be emissions mitigation. Based on our conclusion, several specific implications are proposed. • We provide a wider perspective on how to evaluate the impact of rebound effect. • The rebound effect is the result of maximum behavior of the activities. • Controlling the commodity price may significantly reduce rebound effect. • Rebound effect seems to be a trade-off between economy and energy consumption. [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]

As issues concerning the environment and energy security have become extremely serious, investment in technological research and the development of China's industrial sector has been markedly increasing in an effort to control energy consumption. Energy rebound effects may offset the effects of the energy saved as a result of technological advancement. The present study assessed 30 provinces in China from 2001 to 2017 and analysed the relationship between the heterogeneous technological progress rates and energy productivity of China's industrial sector. This study also assesses the energy rebound effect of China's industrial sector using elasticity analysis. The energy rebound effect in China's industrial sector was significant. Furthermore, the energy rebound effect serves as a regional characteristic of progressive growth from the eastern, central and western regions in China. Moreover, an increasing variation with time was observed in the energy rebound effect of most provinces, indicating that China's industrial development is still ridden with the rough developmental pattern in the sample interval. Finally, according to the characteristics of the energy rebound effect of China's industrial sector, possible explanations and related policy recommendations have been provided from the perspective of energy market reforms and environmental regulations. • Technological advances in China were assessed for technological progress. • Elasticity analysis was used to measure the rebound effect of energy consumption. • Short- and long-term and regional differences were considered. • Energy conservation should be encouraged along with market-oriented reform. [ABSTRACT FROM AUTHOR]

As most power is consumed domestically, the environmental efficiencies of electric power industries in different countries may serve as a benchmark to evaluate their emission reduction efforts. Taking the power generated and carbon emissions as desirable and undesirable outputs respectively, this study adopts the environmental Malmquist index, which is based on a SBM-DEA (slack based measure data envelopment analysis) model, to investigate the environmental TFP (total factor productivity) index of electric power industries in 26 OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries from 1996 to 2010. In addition, we employ the Tobit regression model to analyze the influence of related factors on TFP and its decompositions. The empirical results indicate that the dynamic environmental efficiency or TFP provides a good perspective for evaluating emission reduction efforts of electric power industries in different countries. Fuel structure change and technological progress are the main driving forces to promote dynamic environmental efficiency. Furthermore, the economic situation and energy price changes also affect dynamic environmental efficiency significantly. Therefore, taking the energy structure and affordability into consideration, environmental TFP may indicate climate change mitigation efforts to a large extent. [ABSTRACT FROM AUTHOR]

China has promised that by 2030, its carbon intensity will be cut down by about 60%. The purpose of this article is to investigate the main influencing factors of the heavy industry's carbon intensity. Different from the existing related literature, this article uses a semiparametric model to empirically analyze the carbon intensity of China's heavy industry. The main results are as follows: (1) the relationship between spatial agglomeration and carbon intensity shows a non-linear pattern, mainly caused by the cross-regional flow of production factors. (2) The impact of technological progress on carbon intensity also displays significant non-linear characteristics, mainly due to the staged differences in research and development investment. The following three factors are linearly related to carbon intensity. (1) Energy consumption structure has a more significant linear impact on the carbon intensity in the central and eastern regions, due to the heavy coal consumption of the heavy industry in these two regions. (2) The linear impact of environmental regulations on the carbon intensity in the eastern region is more significant, owing to the region's considerable investments in environmental governance. (3) The industrial structure has the most significant linear impact on the carbon intensity in the eastern region, since the scale of the tertiary industry in this region is larger. The conclusions can provide empirical support for local government managers to formulate differentiated energy policies. • The impact of environmental regulations on carbon intensity in the eastern region is even greater. • The nexus between spatial agglomeration and carbon intensity shows a non-linear pattern. • Energy structure has a greater impact on carbon intensity in the central and eastern regions. [ABSTRACT FROM AUTHOR]

Green economic growth and sustainable development complement each other, promoting green economic growth has important practical significance. However, the existing research focuses on qualitative analysis and less on quantitative analysis of the green economic growth. This study constructs a comprehensive index system of green economy, and uses entropy weighted TOPSIS method to evaluate the green economic growth from 2000 to 2017 in China. This study shows that:(1) Since the 21st century, China's green economy has been constantly improved. Ecological civilization and social progress are the main support for green economic growth. Economic development is secondary, and innovation-driven is lagging behind. (2) There are obvious regional differences in green economic growth. The eastern region is obviously better than the central and western regions, but the overall regional differences are shrinking. At the same time, the green economic growth shows obvious spatial agglomeration effect. (3) Population size, economic development level, technological innovation, industrial structure, urbanization, environmental regulation and FDI have impacts on the green economic growth, but the spatial effect of each influencing factor is different. In the process of developing the green economy, policy-makers should fully combine the spatiotemporal evolution of the green economy and its influencing factors, and make targeted policies. • This paper measures China's green economic growth. • Entropy weighted TOPSIS framework is employed. • China has made great progress in green economic growth. • China's green economic growth has obvious regional heterogeneity. • China's green economic growth has a spatial effect. [ABSTRACT FROM AUTHOR]

With the increase of building area, energy consumption of building and carbon dioxide (CO 2) emissions have been increased. Reducing the energy consumption of buildings has an enormous impact on the sustainable development of the world economy and social. Therefore, this paper proposes a novel energy consumption analysis and saving method of the building based on static and dynamic input-output models. The input-output based data envelopment analysis (DEA) is used to analyze the energy consumption of the building statically. Then the slack variables obtained by the DEA are used to improve efficiency values of ineffective days in months of buildings. Moreover, the total factor productivity (TFP) with technical efficiency (EFFCH), technical progress (TECHCH), pure technical efficiency (PECH), scale efficiency (SECH) and the total factor productivity change (TFPCH) of the building is dynamically analyzed by the Malmquist index. Finally, the static and dynamic energy consumption analysis and saving method is used to improve the energy efficiency of the building. Furthermore, when the technical progress by 1%, the total factor productivity change of the building can be improved by 1.042%. [Display omitted] • Novel static and dynamic energy consumption analysis method is proposed. • Energy saving and efficiency improvement of buildings is established. • Energy consumption of ineffective days is reduced by slack variables. • The total productivity of the building can be improved. [ABSTRACT FROM AUTHOR]

China receives great global attention for its energy consumption. To implement effective and fair policies to save energy, it is critical to understand what drives China's energy consumption and regional disparities. Most of previous studies failed to include indirect factors embodied in the inter-sectoral and regional input-output flows. Therefore, a hybrid structural decomposition analysis is conducted to explore drivers of energy consumption and regional disparities. The results present: (1) energy, directly or indirectly, is flowing from coastal regions to Northwest, Southwest and Central China. (2) The growth of residents' income to contribution of energy consumption is by 106.7% and 169.8% during 2002–2007 and 2007–2012, respectively. Meanwhile, the positive impact of income on energy consumption is larger in urban than rural areas, and the structure effect has a significantly negative impact on energy consumption in Northwest and Southwest regions. (3) By factors, the substitution effect non-energy to energy inputs in industry sector might bring more energy consumption. By sectors, the technological advancement of the non-industry sectors decreases energy consumption by 31.7% and 174.6% during 2002–2007 and 2007–2012, respectively. Technological advancement of industry sectors plays the most important role in increasing energy consumption across China. [Display omitted] • Developing a hybrid structural decomposition analysis to capture drivers of energy consumption and regional disparities. • Income effect is more important than structure effect to explain increase of energy consumption. • The impacts of technical progress on energy consumption vary by regions, factors and sectors. • The industry and income effect contribute most to the increase in the energy consumption. [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]

Technological progress has long been recognised as one of the most effective tools for resolving the contradiction between economic growth and energy consumption. However, previous studies assessing the impact of technological progress on energy consumption have been mostly comprehensive. Additionally, knowledge of the mechanisms influencing their relationship is limited, and the role of spatial correlation in determining regional energy consumption has been largely ignored. Using Chinese provincial data from 2000 to 2016, this study employs a slack-based version of the Malmquist-Luenberger index to measure and decompose technological progress. A dynamic spatial panel model is applied to account for spatial correlation and explore technological progress from this decomposition perspective. The estimated results show that before decomposition, technological progress is incapable of effectively decreasing energy consumption. After technological progress has been decomposed into efficiency and technical change, only the direct spillover effect of efficiency change is significant. A further decomposition for efficiency change results in a significant effect of reducing energy consumption, mostly from the direct effect of pure efficiency change. Lastly, we offer some specific recommendations for China's government to implement and better employ technological progress as a powerful tool to control energy consumption. • Technological progress is decomposed through the SBM-GML technique. • Effect of technological progress on energy consumption is deeply investigated by DSPM. • Direct effects of TFP, EC and PEC can significantly decrease energy consumption. • The negative effect of SEC on energy consumption is mainly due to the indirect effect. [ABSTRACT FROM AUTHOR]

In this study, the LMDI method is used to decompose the production power consumption changes of the Yangtze River Economic Zone into an advanced technology effect, structural effect, income effect, and population effect. The regional and industry differences in the driving effect of electricity consumption change are also investigated. From the perspective of time differences, the progress of power-saving technology is the main factor restraining the growth of electricity consumption; whereas, economic growth, industry structure adjustment, and population size adjustment promote the growth of electricity consumption; and the other three influencing factors, in turn, decrease. From the perspective of regional differences, regional electricity consumption varies significantly, and various regions have great room for improvement in industry structure. Regional population affects electricity consumption from two aspects: absolute population quantity change and population mobility. From the perspective of industry differences, electricity consumption varies significantly among industries and is mainly reflected in the effect of technological progress and structural effects. As a result, control measures on electricity consumption in the Yangtze River Economic Zone should focus on the promotion of power-saving technology and industry structure adjustment. • LMDI is used to decompose the difference of electricity consumption change. • The paper studies electricity consumption from regional and industrial aspects. • Economic growth is the dominant factor for the increase in electricity consumption. • The effect of technological progress inhibits the growth of electricity consumption. • Industries in the Yangtze River Economic Belt differ greatly in electricity consumption. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a study that combined traditional Factor Decomposition method and System Dynamics (SD) modeling in the quest to predict the pattern of future oil consumption per capita (OCPC). Three factors were used for decomposition, i.e. economic activity, technological progress, and structure of energy consumption. The study comprises three major sections: (1) IPAT Identity and its variants as Factor Decomposition method for OCPC; (2) the hybrid methodology of Factor Decomposition method modified by SD; and (3) scenarios for Chinese OCPC. Three scenarios were contemplated, showing respectively Exponential Growth, S Growth and Smart Exponential Growth (SEG), the latter most likely closest to the future trajectory. Testing the SEG scenario on historical data of China provided significant confidence in the hybrid model structure. According to the SEG scenario, China''s OCPC would increase at 4.6% per year during 2005–2010 and 3.0% during 2010–2025. Values of OCPC are estimated to be 453kgce (kg of standard coal equivalent) in 2010, 585kgce in 2020, and 626kgce in 2025. Not before 2094 would the OCPC of China reach America''s level of 2006. [Copyright &y& Elsevier]

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]

This study employs a Luenberger-Hicks-Moorsteen productivity indicator to measure the total factor energy efficiency (TFEE) of the Yangtze River Economic Belt (YREB) of China from 2005 to 2016. The relative importance analysis method is used to investigate the driving forces of the TFEE. The main results are as follows. (a) The TFEE has an average growth rate of 3.3% during the study period. The TFEE declines first, then rebounds, and declines again. The contribution of technological efficiency changes to the TFEE growth is almost zero which indicating no catch-up effect in the sample period. Technological progress contributes a negative impact to the TFEE, with an average annual decrease rate of 3.4%. The scale efficiency changes, with an average annual growth rate of 6.7%, are extremely important for the growth of TFEE. (b) The TFEE of the western region is narrowing the gap with the central and western regions, while the gap between the central region and the eastern region is increasing. (c) Research investment can improve the growth of TFEE significantly, while government expenditure and industrial structure are not. And government expenditure, economic development, and research investment are top factors to explain the variation of TFEE. • The Yangtze River Economic Belt of China is empirically analyzed. • Total factor energy efficiency and its driving factors are investigated. • A new productivity indicator and a relative importance analysis method is employed. • The average growth rate of total factor energy efficiency is 3.3%. • Government expenditure and economic development are top driving factors. [ABSTRACT FROM AUTHOR]

The previous research has suggested that the existence of technology disparities reduces a contribution of environmental technological progress to combat various environmental issues. To handle the difficulty, we propose a new framework for assessing technology inequality and technology diffusion barriers. To measure them, we discuss the concept of efficiency Gini coefficient. Then, we combine it with Data Envelopment Analysis (DEA), DEA-DA (Discriminant Analysis) and decomposition methods of inequality measurement. Using the combined approach, we perform group-based and source-based decomposition to identify technology diffusion barriers. Empirically, this study has utilized the approach to evaluate technology inequality in Chinese provinces from 2007 to 2018. The main findings are summarized as follows. First, an overall efficiency Gini coefficient tended to increase over time, implying that technological disparity has increased across Chinese provinces. The disparity reduced the contribution of technological progress in addressing various environmental issues. Second, two factors mainly produced the technology inequality. They were cross-group inequality and within-group efficiency Gini coefficient. These results explained growing disparities across Chinese regions. Finally, a considerable difference existed in the within-group efficiency Gini coefficient. • This study puts forward the concept of efficiency Gini coefficient. • It proposes a new approach to measure technology inequality. • Our approach is used to evaluate Chinese provinces. • We performed group- and source-based decomposition analysis. • A substantial technology inequality is found among Chinese provinces. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic panel threshold model to empirically investigate whether the effects of technological progress on metal consumption in the electricity industry differs by the change of industrial structure upgrading level (INSTRU). We consider a panel data set including 41 countries over the period 2000–2016. The estimated results suggests 2.72, 2.39 and 2.84 as the average threshold parameters of INSTRU for basic ferrous metals (Iron and Chromium), basic non-ferrous metals (Aluminium, Copper and Nickel) and critical metals (Indium, Neodymium and Dysprosium) respectively. When INSTRU is lower than the corresponding threshold, domestic technological progress (LnSRD) and foreign technological spillover progress (LnSIM) significantly promote the consumption of the above-mentioned metals. However, when INSTRU is higher than the corresponding threshold, LnSRD and LnSRD will reduce the consumption of basic ferrous metals such as Iron and Chromium, but will continue to increase the consumption of critical metals such as Indium, Neodymium and Dysprosium. Last, we confirm that LnSRD play a more important role in changing metal consumption structure in the electricity industry than that of LnSIM. These findings are of great significance to government policies that ensure the transition to low-carbon electricity systems. • Nonlinear effects of TEC on metal consumption in the electricity industry is examined. • A dynamic panel threshold model was used to estimate the threshold effect of INSTRU. • The effects of SRD and SIM on metal consumption structure depends on INSTRU. • The consumption of basic metals has decreased, while critical metals has increased. • The effects of SRD on metal consumption is stronger than that of SIM. [ABSTRACT FROM AUTHOR]

Based on the perspective of biased technological progress and factor substitution, this paper constructs the trans -log cost function including input factors, neutral and biased technological progress. We study the factor biases of technical progress and the impact on energy intensity in the whole manufacturing industry and three intensive industries from 2004 to 2016. The results show that the relationship between energy and capital, energy and labor is substitution relationship, reflecting that the increase of capital or labor input can reduce the energy intensity in manufacturing industry. In addition, the bias on the energy and non-energy factors from technological progresses presents the heterogeneity. R&D investment shows energy bias and energy-saving effect, which should be enhanced in manufacturing industries. Finally, the impact of technological progress on energy intensity is different, among which the effect of biased technological progress is more significant through factor substitution. The beneficial enlightenments for selecting suitable technological progress to improve factor structure and energy intensity in manufacturing industry are given in the research. • The effect of biased technological progress on energy intensity is significant. • The input structure of the factors can be changed according to the factor price. • The increase of capital or labor input can reduce energy intensity. • R&D investment is the important path to improve energy efficiency. [ABSTRACT FROM AUTHOR]

Among all available solar technologies, solar-assisted heat pump (SAHP) is the most popular technology for low- and medium-temperature applications, i.e., drying, space heating, and water heating. This paper presents a critical review of the SAHP, leading to identification of the key technological challenges with the current SAHP technology, including (1) poor energy performance at low ambient temperatures; (2) difficulty in coupling solar thermal panels with heat pumps to achieve increased operational time and reduced electrical energy usage; (3) mismatch between heat demand of the building and heat supply of the SAHP system; and (4) lack of multi-functional modelling tool. A recently developed SAHP technology is introduced, which can effectively tackle the challenges with the current SAHPs, thus achieving enhanced solar efficiency, improved heat pump energy efficiency, and fast responsive time to the heat demand of buildings. The future research directions of the SAHP were identified: (1) multi-source heat pump; (2) heat pump defrosting capacity using waste heat and renewable energy; (3) advanced heat storage and exchange unit (HSEU) technology; and (4) advanced machine learning and multi-objective evolutionary optimisation models for performance prediction and optimisation of the SAHP using big datasets. • Current development of Solar Assisted Heat Pump was investigated. • A recently developed SAHP technology was introduced. • The new SAHP has 30% higher COP and 8–15% higher solar efficiency. • The new SAHP achieves the fast response to building's heat demand. • The future research directions of the SAHP were identified. [ABSTRACT FROM AUTHOR]

This paper selected the relevant data of China's thirty administrative regions from 2005 to 2016, and constructed a geographically and temporally weighted regression model of technological progress and energy intensity, to fully analyze the heterogeneous impact of technological progress on energy intensity. The results showed that: (1) The energy intensity of China's different regions, in the long run, had an obvious spatial agglomeration effect and a significant positive correlation in spatial. (2) The spatial heterogeneity of the impact of technological progress on energy intensity changed smoothly and steadily in the temporal dimension, and the dominant factors affecting the energy intensity gradually shifted from foreign direct investment, opening-up degree, human capital to research and development funds from 2005 to 2016. (3) The temporal heterogeneity of the effect of technological progress on energy intensity varied greatly from different spatial dimensions, specifically, the influence of research and development funds on energy intensity was high in the western regions and low in the eastern regions, but that of the foreign direct investment was the opposite, the spatial distribution of the effects of opening-up degree, human capital on energy intensity was dispersed and had no obvious aggregation phenomenon. • GTWR model between technological progress and energy intensity was constructed. • Energy intensity in China had significant spatial agglomeration effect and spatial positive correlation. • Spatial heterogeneity of the impact of technical progress on energy intensity changed steadily. • Temporal heterogeneity of the impact of technical progress on energy intensity varied greatly. • Since 2010, R&D funds became the most effective factor for the reduction of energy intensity. [ABSTRACT FROM AUTHOR]