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Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. [Display omitted] • The role of artificial intelligence in the energy transition process is studied. • Stage differences in the role of AI are analyzed. • The nonlinear relationship between AI and energy transition is investigated. • Provide insights into the factors affecting the relationship between AI and energy transition. • This research helps the government to advance the energy structure transition. [ABSTRACT FROM AUTHOR]

Highlights • A nonlinear relationship between industrial agglomeration and energy efficiency. • A threshold effect exists when industrial agglomeration affects energy efficiency. • When agglomeration degree is less 1.3865, it will promote energy efficiency. • Future policy for energy efficiency in China's textile industry is suggested. Abstract Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested. [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

The Chinese government has committed to reduce its carbon dioxide emissions per unit gross domestic product by 60–65% from 2005 levels by 2030. In order to achieve this commitment, various measures should be taken. Due to the complex relationship between carbon emission and economic development, it is critical to quantify the impacts of different measures. Under such a circumstance, this paper is to identify the effects of carbon tax on the sectoral competitiveness in Shanghai by 2030 under unequal imposition of carbon tax between Shanghai and the rest of China by using a two-region dynamic computable general equilibrium (CGE) model. Research results show that different carbon tax rates in different regions would affect GDP of Shanghai and the outcomes are different under different scenarios. In general, a lower carbon tax rate can lead to a higher output and these output changes could further influence the sectoral competitiveness. Under the tax44 scenario, sectors such as agriculture, textile, transport equipment, and electronics are the winners in Shanghai, with outputs increased by 2.02%, 1.47%, 1.08% and 3.05%, respectively; while food production, petrol oil, chemicals and non-metal are the losers, with outputs decreased by 2.02%, 0.87%, 0.03% and 2.39%, respectively. This study also reveals that price and scale effects are the key factors influencing the changes of sectoral outputs. Such findings provide useful insights to those policy makers to allocate appropriate carbon reduction targets to different regions. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the effect of energy construction adjustment on the energy-saving and emission-reduction (ESER) dynamical evolution system. Based on the nonlinear dynamics theory, the dynamic behavior of the novel system is discussed. The quantitative coefficients of the actual system are identified with the aid of genetic algorithm-back propagation neural network. Scenario analysis results show that, energy construction adjustment could effectively control energy intensity. To clarify this further, an example of 4D ESER system with new energy constraints is demonstrated. Investigation results show that, government control can effectively control energy intensity, while brings inhibiting impact on economic growth and people’s livelihood. Economic investment is the key variable affecting energy construction adjustment and ESER, the ESER system will crash when the investment is too low. Energy construction adjustment could effectively reduce energy intensity. However, the ESER system will also crash if the development of energy construction adjustment is too fast. The interesting thing is that the ESER system should be pulled back to steady state as the investment getting bigger. Energy intensity could be controlled in expected range by taking adequate measures. Full use of the role of energy structure adjustment should be made to promote the development of new energy, while government control is used only when necessary. [ABSTRACT FROM AUTHOR]

Regional imbalance in development is amongst the most important challenges for China. As the country’s leading economic engines, three major economic circles representing 6.5% of the national land territory contributed to more than 2/5 of national gross domestic product (GDP). This paper focuses on the impact of these three growth poles in China’s regional development, particularly for their embodied energy uses in light of multi-regional input-output analysis. Significantly, the three developed areas generated huge demands for embodied energy, 1237.52 Mtce and 40.1% of the total domestic supply in 2010, but far from self-sufficient. In Yangtze-River-Delta , Pearl-River-Delta and Jing-Jin-Ji , 93.1%, 85.1% and 63.4% of their embodied energy uses respectively can be traced back to the energy-supply provinces such as Shanxi, Inner Mongolia and Shaanxi and other trade partners. Over a half of the national interregional transfers of embodied energy via domestic trade were induced by the three economic circles’ final demand, largely resulted from their large-scale infrastructure construction, upgraded consumption structures, high export dependency and ongoing urbanization process. The results indicate that demand-driven embodied energy is a comprehensive indicator to reflect the real energy use patterns of developed regions or megacities and reveal their industrial positions in domestic and even global supply chains. Understanding the temporal and spatial energy transition along with regional development from consumption-based insights is a vital step toward appropriately targeted policy making for the nationwide energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

This paper attempts to explore carbon tax pilot in Yangtze River Delta (YRD) urban agglomerations based on a novel energy-saving and emission-reduction (ESER) system with carbon tax constraints, which has not yet been discussed in present literature. A novel carbon tax attractor is achieved through the discussion of the dynamic behavior of the new system. Based on the genetic algorithm-back propagation neural network, the quantitative coefficients of the actual system are identified. The scenario analysis results show that, under the same tax rate and constraint conditions, the ESER system in YRD urban agglomerations is superior to the average case in China, in which the impacts on economic growth are almost the same. The former’s energy intensity is lower and the shock resistance is stronger. It is found that economic property of YRD urban agglomerations is the main cause for the ESER system of YRD urban agglomerations being superior. In the current YRD urban agglomerations’ ESER system, energy intensity cannot be adjusted to an ideal level by commercialization management and government control; however, it is under effective control of carbon tax incentives. Therefore, strengthening the economic property of YRD urban agglomerations and effective utilization of carbon tax incentives could perfectly control energy intensity, without obvious potential negative impact on economic growth. [ABSTRACT FROM AUTHOR]

Along with rapid development of economy, urbanization and industrialization in China, the transportation sector especially road transport accounts for the quickest growth of energy consumption and greenhouse gas (GHG) emissions across the country. This paper selects four representative cities (Beijing, Shanghai, Guangzhou, and Chongqing) in the north, east, south, and west of China as targets of case study. It predicts future motor vehicle population in various cities using the Gompertz Model, and predicts and analyzes fuel consumption and GHG emissions of different types of motor vehicles in the case cities by 2035. The results indicate that besides gasoline and diesel, in the future uses of various types of vehicle fuels will follow different patterns among these four cities due to diverse resources endowment, economic strength, technology levels and geographical features. Based on predicted vehicle population and fuel consumption, it is found that from 2013 to 2035, GHG emissions from tank to wheel (TTW) and well to wheel (WTW) in all cities will continuously increase yet at different rates. If there is no interference from new policies, around 2020 Chongqing is expected to replace Beijing as the city with the highest volume of GHG emissions of vehicles among four case study cities. Therefore, the four cities especially Chongqing need urgently to develop or adjust low-carbon policies in road transportation sector, in order to achieve China’s future greenhouse gas reduction targets. Some policy implications to reduce GHG emissions of the road transportation sectors of the case cities are suggested based on the analysis results. [ABSTRACT FROM AUTHOR]

The so-called post-China countries (PC-16́s), distinguished by low wages and high economic growth, will replace China as the “world’s factory”. The aim of this paper is to assess the effect of these changes on global CO 2 emissions pathways. To achieve this, a counterfactual is proposed wherein China’s trade with the rest of the world is replaced by the PC-16’s trade in a global multiregional input–output context. The emissions savings realized by trade replacement are significant in those scenarios where the current pattern of trade is maintained (−13% on emissions traded and −3.5% on global emissions) and in scenarios where enterprises relocate their production directly or indirectly to the most environmentally efficient countries (ranging from −15.2% to −18.2% on emissions embodied in trade). Nevertheless, the potential savings drop considerably (ranging from −1.5% to −7.1%) if companies and host countries take advantage of cheaper, but more polluting means of production and do not internalize the externalities. Through changes in international trade, there is a possibility of reducing emissions, which have to be included in international, multilateral and bilateral agreements to mitigate climate change if we do not want to lose the opportunities these changes present. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

Facing serious energy constraints and environmental challenges, policy makers in China have set up clear targets to reduce energy intensity in order to secure a sustainable economic growth; however, it is unclear in theory what the determining forces are. The empirical evidence, although intensively discussed in the literature, also remain divided in opinion. This paper contributes to the existing literature full of heated debates using a Bayesian averaging approach to identify robust determinants of energy intensity in China. Using provincial level data, key contributors that help explain the level of energy intensity across China are found. By ranking the relative importance of explanatory variables according to their posterior inclusion probabilities, this study can also offer support to policy makers in designing intensity reduction policies. It is suggested that policies should focus on those robust and relatively more important factors such as fiscal expenditure, infrastructure and economic structure. [ABSTRACT FROM AUTHOR]

The current mode of economic development in China is typified by high growth, high energy consumption, and high pollution characteristics and this has caused great stress on both energy consumption and the environment. This paper focuses on a historical analysis of China’s energy, environmental, and economic (‘E3’) efficiency and the sources of E3 productivity growth therein. A developed slacks-based measure is utilized to evaluate the performance of E3 efficiency and decompose the performance fluctuations into three components: energy, economy, and environmental efficiency fluctuations. By applying a method based on global data envelopment analysis, we also analyze the key factors responsible for the change in E3 productivity during 2002–11 from the point of view of technical progress, production scale, and management level. The results show that China performs well on the economic front, while the energy and environmental performances are not optimistic. Fortunately, energy and environmental efficiency have gradually improved in recent years. Further analysis shows that the trend in E3 productivity in China has begun to follow an ascending path. Technical progress is the most powerful contributor to China’s E3 productivity growth, while falling scale and management efficiency are the two main obstacles preventing improvement in E3 productivity. [ABSTRACT FROM AUTHOR]

With fast economic development, industrialization and urbanization, China faces increasing pressures on carbon emission reduction, and especially on air pollutants (SO 2 , NOx, PM) reduction, particularly the notorious haze issue caused by air pollution in recent years. Pursuing co-benefits is an effective approach to simultaneously respond to both carbon and air pollutant problems. In this paper, the AIM/CGE (Asia–Pacific Integrated Assessment Model/Computational General Equilibrium) model and GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies)-China model are combined together to project future CO 2 and air pollutants emissions in China, as well as reduction costs and co-benefit effects. Considering implementation of carbon mitigation policy and air pollutant mitigation technologies, four scenarios (S1, S2, S3 and S4) are analyzed. Results indicate that by implementing both carbon and air pollutant mitigation (S4), CO 2 emission per GDP can be reduced by 41% by 2020, compared with the 2005 level, and SO 2 , NOx and PM2.5 emissions would change by a factor 0.8, 1.26 and 1.0 of the 2005 level, respectively in 2030. The real co-benefits of emission reductions (S2 minus S4) for SO 2 , NOx and PM2.5 are 2.4 Mt, 2.1 Mt and 0.3 Mt in 2020, and the corresponding cost reduction co-benefits are 4, 0.11, and 0.8 billion €, respectively. Provincial disparity analysis reveals that regions with higher co-benefits are those with higher GDP such as Guangdong, Shandong and Jiangsu, energy production bases such as Inner Mongolia and Shanxi, low coal quality provinces such as Sichuan (for SO 2 ), and industrial base provinces such as Liaoning. Cost-effectiveness is finally discussed for policy implications, which suggests that investment in less developed western regions is more cost-effective and easier in reducing CO 2 or air pollutant emissions than in developed eastern regions. [ABSTRACT FROM AUTHOR]

With rapid economic growth, China is facing tremendous pressures of emission–reduction and serious income inequality issues. The existing research is concerned with the relationships between income inequality and CO 2 emissions in recent years, but little attention has been paid to the regional differences in China. This paper investigated the impact of income and its inequality on CO 2 emissions at the national and regional levels using the panel data from 1995 to 2010 in China. The empirical results show that income growth increases China’s CO 2 emissions. The effects of income growth on CO 2 emissions vary across regions. Further, the impact of income inequality on CO 2 emissions in the Eastern region is greater than that in the Western region. This research reveals that a more equitable income distribution may help control CO 2 emissions in developing China, and there is a win–win situation of income redistribution and emission–reduction. Our findings merit particular attention from policy makers in China. [ABSTRACT FROM AUTHOR]

• We explore whether each of the 30 Chinese provinces will peak emissions by 2030. • We combine STIRPAT models and scenario analyses to predict emissions trajectories. • 26 provinces are likely to peak emissions under at least one scenario, but 4 are not. • Peaking time and cumulative emissions are considered in seeking optimal peaking path. • Provincial results in aggregate show China may peak in 2028–2040 and 2030 is optimal. China has pledged in its Nationally Determined Contributions (NDCs) that its carbon emissions will peak no later than 2030. However, the varying carbon emissions trajectories of individual provinces make it difficult to determine whether China can fulfill this ambitious goal on time. To bring transparency and clarity to this core issue, this paper explores for the first time whether and how all the 30 Chinese provinces will peak their energy-related emissions in future by developing a set of extended STIRPAT models that investigate the driving forces behind each province and integrating the estimates into scenario analysis for peaking simulation. We observe that, despite continued economic growth, 26 provinces are highly likely to reach peak emissions under at least one scenario, whereas the remaining are not. We find it interesting that the earliest peaking scenarios may not be the best option for those provinces whose cumulative carbon emissions are estimated to be considerably large. To ensure a viable reaching of China's peak emissions, much more attention should be paid to seeking for the most appropriate emissions trajectory to peak at the subnational level with relatively low cumulative value. Provincial results in aggregate show that China may peak emissions in the period 2028–2040, with cumulative emissions ranging from 403,607 Mt CO 2 (corresponding to peaking in 2030) to 456,191 Mt CO 2 (corresponding to peaking in 2038), while 2030 would be the optimal year for the country to peak because of the lowest cumulative carbon emissions. Our findings only provide a deeper understanding of China's potential peaking paths by province, but also assist policy makers in better prediction and evaluation of possible emissions trajectory for other nations and the whole world from a bottom-up perspective, and therefore could be of global significance. [ABSTRACT FROM AUTHOR]