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China has pledged to peak its carbon emissions before 2030 and achieve carbon neutrality before 2060. New energy decarbonization will be a major focus area for achieving the 'dual carbon' goals since it accounts for about 20 percent of China' s total energy consumption. The Xiong' an New Area' s geographical advantages and natural endowments have brought abundant wind and solar energy resources and have huge development potential in the field of new energy development. This paper summarizes the Xiong' an New Area' s resource endowment and achievements in developing new energy, and comprehensively analyzes the opportunities and challenges faced by this area in the process of building a national new energy demonstration zone in the context of achieving the 'dual carbon' goals. It sorts out the new energy development policy of the Xiong' an New Area and its history of new energy development and then points out that the current development of new energy in this area lies in power project construction, power grid construction, green energy consumption, energy storage development, and new energy equipment. This study believes that the Xiong' an New Area will experience three development stages of carbon neutrality: initial carbon neutrality, fluctuating carbon neutrality, and stable carbon neutrality, because this area has carried out green power planning simultaneously in the initial construction stage and has more green power supply. With the expansion of population and economic scale, the rapid increase in energy demand would lead to the decline in green power supply ratio, causing carbon neutrality to fluctuate and change over time. With the population and economic structure in a stable development stage, the proportion of high-value-added and high-tech industries would increase, and energy utilization technologies such as green electricity would make progress, resulting in the arrival of the stable carbon neutrality development stage. On the other hand, from the perspective of carbon sink potential corresponding to carbon sources, the planned proportion of the blue-green space and the construction land in the Xiong' an New Area would keep its carbon sink capacity in a relatively stable state for a long period of time. Therefore, in addition to the value-added of the natural carbon sink system, carbon capture, utilization and storage technologies and other means of artificial carbon sink can be used as adjustment tools under the carbon neutrality system. [ABSTRACT FROM AUTHOR]

Comprehensively understanding how the policy influences the sustainability transition of the automotive industry is a critical focus in recent years. This paper focuses on the dual-credit policy, an underexplored but critical policy in China, and examines how it would influence the transition of automotive industry. To achieve this goal, an SD model is developed to simulate the effect of policy parameters on the policy objectives by considering the interaction of production, technology and market. The findings reveal that this policy can in general promote the sustainable development of the automotive industry by greatly decreasing its overall energy consumption. However, its impact on the market share of new energy vehicles is limited. Based on the results, some policy suggestions are proposed so as to improve the effectiveness of the dual-credit policy. [ABSTRACT FROM AUTHOR]

China's automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles' whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China's automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China's automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations' research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality. [ABSTRACT FROM AUTHOR]

Chinese coal enterprises are facing severe challenges due to low-carbon economy requirements and capacity reductions. This paper adopts a dynamic SBM model to compare the mining efficiency of each mining area of a coal company in China. We use total excavation footage, number of working platforms, and machine quantities as input indicators, and coal sales and CO2 emissions as output indicators. It was found that (1) both high efficiency and low efficiency mines maintained their production status each year and did not actively improve over time; (2) energy consumption was the primary indicator affecting comprehensive mining efficiency; and (3) while changes in the market environment did not have any significant impact on coal mining efficiency, coal mine characteristics were found to have some correlations with efficiency. [ABSTRACT FROM AUTHOR]

Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th-50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces. [ABSTRACT FROM AUTHOR]

To achieve the goal of 'Net-zero Europe' by 2050, the European Union (EU) has set up detailed policies in the fields of energy and climate. This paper summarizes the main characteristics of relevant policies in the EU and UK and their implications for China by reviewing these policies in the achievement of the carbon neutrality goal and other goals in the energy and climate fields. The characteristics of these energy and climate policies include: ① Top-down and bottom-up approaches are adopted to distribute the EU' s overall emission reduction targets to member states in accordance with the principle of fairness, and each member state formulates and adjusts its own goals to ensure the realization of the overall goals. ② Detailed transition plans are formulated for the power, industry, transportation, and building sectors; importance is attached to the application of new and renewable energy technologies such as solar, wind, and hydrogen energy; specific goals are set concerning coal power transition, nuclear energy development, energy efficiency improvement, carbon capture and storage, electrification, and building renovations; and the importance of energy security in transition planning is stressed. ③ A sound and mature emissions trading system and a fair transition mechanism are established to support the realization of the transition goals, and developing countries are helped to reduce emissions through technical dialogues and climate financing. To reach peak carbon emissions and achieve carbon neutrality, China should develop relevant roadmaps at national, sectoral, and provincial levels and strengthen R&D of key decarbonization technologies. At the same time, China should enhance its regulatory regime, and improve the carbon emissions trading system, the green financial system, and the just transition mechanism. [ABSTRACT FROM AUTHOR]

In coastal cities, a large amount of cold energy is released into the nearby seawater during the process of regasification of liquefied natural gas (LNG) at the receiving terminal, and meanwhile, a large amount of exported and/or imported foods need to be frozen by bulk cold energy near the sea. In this paper, a new multistage cold energy recovery/utilization system is investigated to link the LNG cold energy directly to supply the coastal cold store. This design uses a realistic case in Ningbo LNG receiving station in China, and for the very first time considers how to utilize the LNG cold energy in an efficient manner regarding the technical, economic, and environmental benefits. A three‐stage LNG cold energy utilization system is designed and evaluated by considering different temperatures, pressures, and heat exchange parameters. Compared to conventional electric‐driven cold stores, the proposed multistage cold energy recovery/utilization system has a much cheaper capital cost during the initial investment stage, and also avoids the utility power consumption during the daily operation period. In the case study, from Ningbo LNG receiving station to a 10 MW class cold store, the proposed multienergy system can save the energy of 20.05 kilotons of standard coal, and the carbon emission reduction is up to 1.49 × 108 kg per year. [ABSTRACT FROM AUTHOR]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

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

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

At present, China is the world's largest energy consuming country and industrial manufacturing country, especially industrial enterprises are the main energy consumers. The steel industry occupies an important position in China's national economy, and has the characteristics of high energy consumption, high pollution and high emissions. Therefore, based on the perspective of energy management, this paper selects 54 large-scale steel companies from China Iron and Steel Association from 2009 to 2017 as a sample, and applies the SBM super-efficiency model that includes undesired output and the differences-in-differences (DID) method to explore the impact of companies implementing energy management systems on corporate performance. The empirical results show that: the implementation of energy management system has a positive impact on enterprise performance. Moreover, the impact of energy management system on economic performance is greater than that on environmental performance. Through the analysis of regional heterogeneity and ownership heterogeneity, it is further found that the implementation of energy management system in eastern region enterprises and state-owned enterprises has a more obvious impact on enterprise performance. The results of this paper can encourage those managers who want to apply energy management system in enterprises to achieve sustainable development, so as to improve the efficiency and results of energy management. [ABSTRACT FROM AUTHOR]

As China reaffirms its commitments to limit the climate change risk and improve air quality, a combination of new policy and technology opportunities is presenting itself. A national emission trading scheme (ETS) begun in 2017 and is showing great promise, creating decentralized incentives for cost effective pollution mitigation. At the same time, sustained energy sector innovation is driving the costs of renewable electric power generation below those of fossil fuel substitutes. Early economic evidence of ETS measured pollution reductions, but has not addressed important economy-wide net benefits. This paper focuses on technology and energy cost savings and is the first to account for three multiplier effects—shifting consumption patterns, job growth, and the benefits of induced productivity growth—that would result from accelerated renewable electricity deployment in China. Results from a dynamic recursive computable general equilibrium model for 2017 to 2030 show that expanding renewable deployment can interact with the ETS to slash GHG emissions, as well as energy costs, stimulating significant long-term economic growth. These results suggest that China should accelerate its clean energy transition, not only for the air-quality and climate benefits, but to fulfill the energy sector's potential to promote innovation, employment, and economic growth. [ABSTRACT FROM AUTHOR]

In order to explore the comprehensive assessment method of air quality of pollutant emission of energy consumption in China's urban internal industrial parks, based on the full consideration of the actual situation of air pollution control in Nanjing HX Industry Park, this paper selects 15 air quality assessment indicators. According to the national air quality standard, the air quality standard of Nanjing and the air quality standard of Nanjing HX Industry Park, the air quality assessment standard is determined about pollutant emission of energy consumption in Nanjing HX Industry Park. Through the technical treatment of the selected assessment indicators, the niche suitability model is introduced, and based on this, the spatial niche suitability model is reconstructed to comprehensively assess the air quality of pollutant emission of energy consumption in Nanjing HX Industry Park. The study found that the air quality of Nanjing HX Industry Park has been improved from level III in 2012 to level I in 2020. According to the assessment results, the differences of assessment results of different niche suitability models are discussed, which fully reflects the advantages of spatial niche assessment methods. The change law and influencing factors of the assessment results are further discussed. [ABSTRACT FROM AUTHOR]

In this paper, we forecast the self-sufficiency rate of China's total energy and energy compositions by two-hybrid gray models: the improved non-homogeneous gray model and nash non-linear gray multivariable model, and provide some significant data for energy policy. At first, we propose newly the improved one variable gray model (INHGM-Markov) which combines the non-homogeneous gray model (NHGM(1,1)) with Markov chain model and forecast the production and consumption amount of China's total energy. The results show that the production and consumption amount increase slowly after 2012 and the values are 3660.10 and 4584.04 million tons of SCE in 2022. Next, the production and consumption percents of the energy compositions are simulated with the improved nash nonlinear gray multivariable model (INNGM(1, N)). In 2022, the production percents estimated to be 51.56, 7.55, 8.54, and 32.35%, respectively, for coal, crude oil, natural gas and others energy as the energy compositions, while the consumption percents 47.01, 21.82, 8.72, and 22,45%. The production and consumption amount of coal energy have a maximum values around 2013 and 2014, and then decrease until 1887.19 and 2154.88 million tons of SCE in 2022. Finally, the forecasting values of the self-sufficiency rates of the total energy and the energy compositions are determined to be 0.79, 0.88, 0.27, 0.78, and 1.15, which means that China government should focus on securing energy resource, especially crude oil and natural gas energy, for the sustainable development of the economy. [ABSTRACT FROM AUTHOR]

Enhancing carbon emission mitigation and carbon utilization have become necessary for the world to respond to climate change caused by the increase of greenhouse gas concentrations. As a result, carbon capture, utilization, and storage (CCUS) technologies have attracted considerable attention worldwide, especially in China, which plans to achieve a carbon peak before 2030 and carbon neutrality before 2060. This paper proposed six priorities for China, the current world's largest carbon emitter, to achieve its dual carbon strategy in the green energy transition process. We analyzed and summarized the challenges and potentialities of conventional carbon utilization (CU), carbon capture utilization (CCU), and CCUS. Based on the current development trend, carbon dioxide capture, circular utilization, and storage (CCCUS) technology that integrates carbon circular utilization and partial sequestration, with large-scale underground energy storage were proposed, namely biomethanation. Technically and economically, biomethanation was believed to have an essential contribution to China's renewable energy utilization and storage, as well as the carbon circular economy. The preliminary investigation reveals significant potential, with a corresponding carbon storage capacity of 5.94 × 108 t~7.98 × 108 t and energy storage of 3.29 × 1012 kWh~4.42 × 1012 kWh. Therefore, we believe that in addition to vigorously developing classical CCUS technology, technical research and pilot projects of CCCUS technology that combined large-scale underground energy storage also need to be carried out to complete the technical reserve and the dual-carbon target. [ABSTRACT FROM AUTHOR]

Despite market-oriented reforms, China's energy sector is subject to energy price distortions, which are believed to be a crucial determinants of energy efficiency in China. This paper investigates the impact of energy price distortions on energy efficiency in China from the perspective of spatial correlation. Using the nonradial directional distance function approach, we first estimate the provincial-level energy efficiency in China. Paying attention to spatial correlation among the provinces of China, in stage two, we identify the determinants of energy efficiency. Our empirical results suggest that price distortions have a significant impact on energy efficiency in China. This impact holds when the cross-region effect is considered, i.e., besides its own energy price distortion, a region's energy efficiency is also correlated to the adjacent provinces' energy price distortions. Furthermore, we found that the levels of energy efficiencies in adjacent provinces are highly correlated. This spatial relationship can be decomposed into the 'spillover effect' and 'warning effect'. These two effects work together, determining the spatial relationship among the province-level energy efficiencies. [ABSTRACT FROM AUTHOR]

The introduction of the new round of electricity reform policies has made the electricity sales companies' trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China's current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market. [ABSTRACT FROM AUTHOR]

• A novel application mode of CSP with HEHP technology is proposed. • The flexible operation benefits of CSP with HEHP technology in the IES are analysed and quantified. • The carbon emission trajectory of the IES containing CSP with an HEHP device is depicted. • CSP with HEHP technology increases the utilization of solar energy. • A rational combination of the hydrogen and thermal energy storage capacities are necessary. The efficient integration of renewable energy with hydrogen storage is an important means for China to achieve carbon neutrality. Concentrated solar power (CSP) is an emerging technology for solar energy utilization that combines the advantages of power generation and energy storage. However, CSP has not been fully exploited because of the high generation cost and an incomplete market mechanism in China. The heat collected by CSP can be utilized for high-temperature electrolytic hydrogen production (HEHP), which is an efficient technology for hydrogen production and storage, and reducing carbon emission. Thus, a low-carbon operating mode for an integrated energy system (IES) consisting of wind, solar and hydrogen energy is proposed in this paper. First, an optimal operation model of electricity-heat-hydrogen coupling for an IES containing CSP and HEHP is established. Second, the effect of CSP with HEHP on the total operation performance of the system is evaluated from the aspects of the system economy, renewable energy utilization, and carbon emission. Finally, the model is validated by a case study of an island IES, and an optimal sizing for hydrogen energy storage and thermal energy storage of CSP is determined. The results show that CSP with HEHP can effectively improve the utilization rate of solar energy and reduce the carbon emissions of the system, offering an approach for carbon reduction planning and the operation of CSP in electric-hydrogen energy systems. [ABSTRACT FROM AUTHOR]

Saving energy is an important strategy to address the current energy crisis and environmental degradation. Regarding the pilot policy of the energy quota trading as a quasi-natural experiment by employing a difference-in-differences method, the purpose of this paper is to investigate the effect of this pilot policy on energy saving and its mechanisms based on city-level data in China from 2006 to 2020. We find that the energy quota trading policy can reduce the total energy consumption and energy consumption intensity of pilot cities, and the effect of the policy can gradually strengthen over time. The market-oriented reform of energy factor allocation can effectively promote energy saving and economic growth. These results are convincing through a series of robustness checks. The heterogeneity test shows that the energy quota trading has a significant energy saving effect on economically developed cities, densely populated cities and southern cities, but not in economically underdeveloped cities, sparsely populated cities and northern cities. Further mechanism inspection suggests that the pilot policy of energy quota trading mainly achieves energy conservation through industrial structure upgrading and green technology innovation. Our findings provide a valuable insight for China to control energy consumption and promote the high-quality development of the energy economy. [ABSTRACT FROM AUTHOR]

As the world' s largest energy consumer and carbon emitter, China faces a serious situation in energy conservation and emission reduction. However, China' s digital economy is still developing rapidly. In order to explore how the development of the digital economy affects energy consumption and CO2 emissions, this paper took the panel data of 30 provinces in China from 2011 to 2019 as a sample, used the entropy method to analyze the digital economy level of each province, and built a double fixed effect model to empirically analyze the 'positive U-shaped' or 'inverted U-shaped' relationship between the digital economy and energy or between the digital economy and CO2 emissions. The results showed that: (1) There were inter-provincial and regional differences in the level of digital economy in China. Guangdong Province and China' s eastern region had the highest level of digital economy, while Qinghai Province and the western region had the lowest level of digital economy. (2) Both the digital economy and ECSC and the digital economy and CESC had significant ' positive U-shaped' relationships, while the digital economy and ECST, the digital economy and ECEF, and the digital economy and CEEF all had significant 'inverted U-shaped' relationships. The energy-saving effect of the development of the digital economy was greater than the carbon emission reduction effect. (3) There was heterogeneity in the nonlinear relationship between the digital economy and ECSC, ECST, and ECEF, the digital economy and CESC, and CEEF in the eastern, central, western, and northeastern regions. (4) Digital infrastructure, digital innovation environment, digital user scale, and digital industry scale had different nonlinear effects on ECSC, ECST, and ECEF, as well as on CESC and CEEF. Accordingly, to provide empirical support and a theoretical basis for China's development of the digital economy while taking into account the effects of energy conservation and carbon emission reduction, this study puts forward relevant policy suggestions from three aspects: narrowing the gap in the level of digital economy, formulating digital economy development policies, and focusing on key points for the development of the digital economy. These suggestions are also conducive to boosting China's goals of reaching peak carbon emissions and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

To clarify whether there is a spatial correlation problem among China's provincial energy consumption, economic growth, and ecological environment, this paper adopts the panel data of 31 provinces in China from 2008 to 2019 and uses a spatial data analysis method. This work studies economic growth and the spatial distribution pattern of the ecological environment, as well as the spatial effects of its energy consumption and economic growth on the ecological environment. Moreover, the spatial autocorrelation model, spatial-temporal transition method, and generalized moment estimation method are adopted to conduct empirical analysis on the research object. The results show that (1) there are spatial correlations among energy consumption, economic growth, and the ecological environment in China's provinces, and there are obviously different agglomeration areas in spatial distribution; (2) energy consumption, economic growth, and the ecological environment level show different growth trends, and the differences between different provinces in provincial units are shrinking; and (3) regarding energy consumption, the spatial impact of economic growth on the ecological environment is obvious, and there are obvious "path dependence" characteristics in the spatial distribution and positive correlations. Finally, this paper puts forward research conclusions and relevant suggestions on the basis of empirical analysis. [ABSTRACT FROM AUTHOR]

To explore the effect of financial development on energy efficiency is of guiding significance for grasping China's energy situation and making fiscal policies. First, this paper considers the environmental pollution into the output variables, and builds an SBM model to evaluate the energy efficiency. Second, this paper explores the dual effect of financial development on energy efficiency, and based on the interaction characteristics of multiple factors, the configuration of energy efficiency is discussed by using fsQCA method. The results are as follows: there are obvious differences in energy efficiency in different regions, with a declining trend from eastern to western. Regions that achieve high energy efficiency tend to have better financial development and industrial structure. When financial development and industrial structure are relatively good, regions should consider the role of FDI and give full play to its spillover effect. Regardless of financial development, regions should steadily achieve economic growth, attach importance to innovation input and correctly guide foreign investment. [ABSTRACT FROM AUTHOR]

With resource shortage and environmental pollution becoming more and more serious, the development of new energy vehicles (NEVs) plays an important role. In this paper, the hybrid method of best-worst method (BWM), Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Evaluation based on Distance from Average Solution (EDAS) is used to evaluate new energy vehicles (NEVs) and select the best new energy vehicle. BWM method is used to obtain the subjective preference weight, MULTIMOORA method is used to integrate the objective data with the subjective weight to evaluate new energy vehicles, and the final ranking of alternatives is obtained by the EDAS method. The paper collect the data of 22 representative new energy vehicle types in China, the validity and feasibility of the method is verified. [ABSTRACT FROM AUTHOR]

The rapid development of China's economy has driven the growth of energy consumption. However, the domination of the energy structure by fossil energy not only increases carbon emissions but is also an important source of fog and haze in China. This will cause global warming and increase people's lung diseases. To alleviate these problems, renewable energy (RE) development is crucial in reducing CO 2 emissions, promoting economic development, and reducing fog and haze in China. Industry accounts for the highest proportion of energy consumption, so an efficient adjustment of the industrial structure will greatly contribute to overall energy development, economic development, and environmental protection. By analyzing the sales output value of China's industrial subsectors, together with energy consumption and CO 2 emissions, this paper determines which industries should be encouraged and which industries should be restricted in China. Finally, RE strategies and industrial policies are proposed for Chinese government references. The contributions of the paper are three-fold: (1) propose a novel framework for the development of China in the aspects of energy, economy, and environmental policies; (2) provide valuable information for the Chinese government to enable it to understand which industries should be encouraged to improve its economic competitiveness; and (3) use hybrid methods (e.g., entropy method and weighted sum method) to rank the energy and CO 2 productivity, and the recommendations will provide strategic guidance for China's energy development. [Display omitted] ● China's Renewable Energy Strategy and Industrial Policy. ● Which industries should be encouraged or discouraged in China. ● Energy Productivity and CO 2 Productivity. [ABSTRACT FROM AUTHOR]

• The solar potential in different residential blocks is simulated. • The influences of block morphological parameters on solar potential are varified. • Four parameters that impact on the block solar potential more than 70% are verified. • The facade PV capacity in high-rise and hybrid residential blocks is more than 66%. • The average PV installation rate of roof, south and west facade is 96%, 32% and 39%. Block-scale application of photovoltaic technology in city is becoming a viable solution for renewable energy utilization. Rapid urbanization process has made urban buildings have huge development potential for solar energy in China, especially in residential areas that occupy large parts of the city. However, the residential blocks in China show differences in the morphological layout and parameters, which means they have diverse potential for solar energy development. This paper studies the effect of quantified morphological parameters on solar potential for residential blocks in Wuhan, China. The distribution characteristics of solar potential in different block environments are discussed. Various strategies are evaluated in terms of the installation position and height of PV modules. The results showed that floor area ratio, building density, average building height and building interval have significant impact on solar potential for residential block, and their correlation can reach 75%, 71%, 78% and 72% respectively. The average PV installation ratio of roof, south facade and west facade can reach 96%, 32% and 39% respectively. The results of this paper not only provide a reference to the block-scale application of PV technology in urban environment, but also stimulate the implementation of photovoltaic policies on various residential blocks in China. [ABSTRACT FROM AUTHOR]

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption. [ABSTRACT FROM AUTHOR]

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people's everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China's carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper. [ABSTRACT FROM AUTHOR]

• Bibliometrics overview of ZEB publications. • Draw of the collaboration networks from ZEB research publications. • Brought out thematic trend of the development ZEB over the time. • Discussions of the main challenges and solutions for ZEB studies. Zero Energy Building (ZEB) is gaining more attention as a solution to significantly mitigate the environment concerns. However, a bibliometric analysis of the topic, a very useful method to analyze the database of published publications and reviews, is lacking. Especially, to provide when and how this concept has been brought out, insights into the state-of-art technology, the findings of the collaboration networks and the challenges of ZEB. To address this gap, in this paper, ZEB studies have been reviewed using multiple perspectives and analytical tools, such as VOSviewer and Biblioshiny to analyze the collaboration networks and the thematic trends of ZEB publications. The original data was retrieved in Scopus, a total of 1051 documents including research articles and reviews published from 1976 to 2020. The analysis of the number of publications over the years shows that the development of Zero energy building research has gone through three stages: a slow development period from 1076 to 1999, followed by a development from 2000 to 2010 and finally a rapid development from 2011 to 2020. The study also reveals different rankings, including the most widely collaborated scholars, the most cited scholars, the most published countries, etc. Therein, the United State, United Kingdom and China have the most research on ZEB. Based on the analysis of co-occurrence network and evolution of thematic keywords, it is found that energy efficiency and life cycle assessment (LCA) are the main challenges for the achievement of ZEB target. Finally, these challenges have been discussed and solutions provided. [ABSTRACT FROM AUTHOR]

The current legal and policy system of renewable energy in China has set up a framework of policy instruments, which provides an important foundation for dealing with climate change and promoting the healthy development of the renewable energy industry with the goal of "carbon neutrality" and "emission peak". However, the current policy instruments cannot meet the needs for rapid development of the renewable energy industry. This paper investigates, analyzes, and concludes the status of renewable energy development along with related policy instruments in different renewable energy sources by literature and points out the problems of current policy instruments, and puts forward corresponding countermeasures. Moreover, from the perspective of energy consumption market transformation, the authors carried out an analysis of China's challenges of policy instruments, pointing out that China's renewable energy policy instruments have the potential to take one step further in the areas of the regulatory system, legal system and legal security system. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system's evolution mechanism based on Haken's synergetic theory to supplement a systematic perspective's empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution. [ABSTRACT FROM AUTHOR]

Many developing countries are facing the difficulty of choosing between economic growth and energy conservation and emission reduction (ECER). China has strengthened the implementation of ECER by setting environmental accountability as the development goal of local governments, hoping to have better governance effects. To evaluate the actual intervention effect of this approach, this paper constructs panel data covering 46 countries from 1995 to 2014 and uses the difference-in-differences (DID) method and the composite control method to quantitatively analyse the policy effect. The results show that China can effectively curb energy consumption and carbon emission intensity per unit of GDP by adding ECER targets to the government's five-year plan, which has significant effects on ECER. Furthermore, we use an intermediary mechanism to test and identify low-carbon alternatives and an ECER promotion mechanism for technological advancement. The conclusion shows that economic development is compatible with low carbon and energy consumption. Combined with China's long-term goals for ECER, it can be considered that on the road to achieving carbon peaking and carbon neutrality in the future, the economy and tertiary industry should be rationally developed, the degree of urbanization should receive more attention, and the proportion of thermal power generation should be reduced. [ABSTRACT FROM AUTHOR]

In order to explore effective ways for energy conservation and emission reduction, seven correlated variables are selected in this paper according to the government statistics with the extended STIRPAT model, and the impact of China's industrialization and urbanization on carbon emission intensity of energy consumption is studied. The study found that urbanization rate and urban population employment rate are the main factors affecting per capita carbon emission of energy consumption in China;the industrialization has a reverse impact on per capita carbon emission of energy consumption, indicating that China's industrialization is relatively low and is not the main factor affecting per capita carbon emission of energy consumption; the service industry added value rate and the agricultural industry added value rate also have reverse effects on China's per capita carbon emission of energy consumption, and the degree of impact is relatively small. Therefore, China is supposed to develop industry vigorously in the future, to appropriately control the development speed of urbanization and to increase the agricultural industry added value rate moderately, so as to achieve effective control of per capita carbon emission intensity of energy consumption. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model to forecast China's future energy consumption in 2022–2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions. [ABSTRACT FROM AUTHOR]

Based on the panel data from 30 provinces in China from 2010 to 2020, this paper employs the panel vector autoregression (PVAR) model to investigate the dynamic relationship between a green economy and energy utilization level. The results show that: (1) the green economic development level and energy utilization level of 30 provinces in China have been continuously enhancing in recent years, the increase in green economic development level is higher than energy utilization level, and the geographical characteristics of both levels are significant, decreasing from East to West; (2) the causal relationship between the green economy and the energy utilization level has passed the Granger test; the two have formed a relatively high-level balanced relationship over a long period, showing a benign and orderly development trend; (3) the green economy generates a positive pulse response on energy utilization level in the initial period, but the impulse response weakens in the period of lag 1, entailing the relationship needs to be further strengthened in the long term; (4) in the process of continuous enhancement of energy utilization level, it is difficult to drive economic benefits due to high input costs in the initial stage, but it can form a two-way interaction relationship with a green economy as time goes by. [ABSTRACT FROM AUTHOR]

In order to achieve China's energy conservation and emission reduction goal of peaking carbon dioxide emissions around 2030, it is of great significance. An important means of building energy conservation and emission reduction is the fine management of building energy consumption, which is based on the accurate prediction of building energy consumption, so as to support the optimal management of building operation and achieve the goal of energy conservation and emission reduction. This paper puts forward the evaluation indexes of the results of the building energy consumption prediction model, uses MAPE and RMSE indexes to evaluate the accuracy of the prediction results of the model, and uses the prediction time and input parameter dimensions to evaluate the time cost of the prediction model. Then, using the three building energy consumption prediction models based on machine learning algorithm established above, the prediction of energy consumption of four types of public buildings in different seasons is completed, and the prediction results are evaluated and analyzed. According to the prediction results and the requirements of related work on the accuracy of building energy consumption prediction model, the adaptation relationship between different types of buildings and different machine learning algorithm prediction models is summarized. [ABSTRACT FROM AUTHOR]

Energy efficiency policies can help to reduce energy use and mitigate the effects of climate change. This paper examines the U.S. and Chinese economies by applying HEM to both. Key sectors absorbing more energy from other sectors by purchasing intermediate products from them and, simultaneously, transferring more energy by selling products to the rest of the sectors are identified. This provides insights for decision-making on appropriate energy efficiency policies. It appears that the U.S. economy should focus on the service sector, and the Chinese should focus on the industrial sector. [ABSTRACT FROM AUTHOR]

Energy saving is one of the main technique routes for net zero carbon emissions. Air compressor systems take up a large part of energy consumption in the industrial field. A pre-cooling air compressor system was proposed for energy saving by cooling the air before it flows in a compressor. The energy efficiency of the proposed system was analyzed. As additional energy consumption is required for air cooling, the feasibility of the pre-cooling method for energy saving was analyzed. As the efficiency of the pre-cooling air compressor system is mainly influenced by the environment temperature and humidity, applicability of the system in different regions and at different seasons was discussed. A pilot project was performed to verify the technical feasibility and economics of the proposed system. When the precooling temperature of the pilot system was set to 2 °C, the annual pneumatic-electrical ratio of the system can be increased by approximately 2% in several regions of China. This paper shows the pre-cooling air compressor system is feasible for energy saving. [ABSTRACT FROM AUTHOR]

As one of the most significant carbon emission departments in China, the power industry will gradually become the core hub of reducing carbon emissions in the process of undertaking carbon emissions transferred from other industries. Therefore, it is of vital importance to predict the power consumption in China's end energy consumption to achieve the carbon peak goal on time. This paper firstly uses the gray relational analysis model to study the relationship between power consumption indicators of the three major industries and some social and economic indicators and obtains the influencing factors with the greatest correlation with the power consumption of the three industries. Then, based on the analysis of socio-economic factors, considering different growth rates, the GMCN(1,N) model of electricity consumption in China's three major industries is established. Forecast data under different scenarios have important practical significance for formulating active and effective energy policies. The data indicate that the secondary and tertiary industries consume the greatest amount of electricity. It is estimated that the power consumption of China's three major industries will reach 10.15 trillion kWh (kilowatt hours) by 2030. [ABSTRACT FROM AUTHOR]

Energy piles have attracted much attention in recent years because they provide a profitable solution for efficient shallow geothermal energy utilization in different climatic regions. However, the long-term performance design of energy piles under different thermal scenarios currently relies on time-consuming and computationally expensive methods such as numerical simulations, which seriously hinders the further engineering application of energy piles. To this end, this paper provides a high-precision and computationally efficient model for predicting the long-term performance of energy pile design through an artificial neural network machine learning process. First, experimentally validated numerical models are developed. Then, they are utilized to generate the training and evaluate datasets for the proposed model by inputting sixty typical thermal load distributions in different regions of China. Finally, the performance of the proposed model is evaluated by comparing its calculations with those obtained using numerical models. The results show that the proposed model can predict the long-term performance of energy piles and enrich the current methods for the long-term design of energy piles. Furthermore, its features that tremendously reduce the computational time and minimum required resources make it an excellent supplement compared with numerical simulations. [ABSTRACT FROM AUTHOR]

This paper constructs a model to accurately estimate the urban CO2 emissions in 2000, 2005, and 2013 in China, using the combined data of DMSP/OLS nighttime light data and the provincial energy statistical yearbook data. We calculate and analyze the growth of urban built-up areas and carbon emissions in different time periods both all over the country and the four economic zones in China. It was shown a good fitting relationship between urban growth and carbon emissions, with the R2 at 0.6188 in 2000, 0.7132 in 2005, and 0.7195 in 2013. The growth rate of developed land area was 13.4% from 2000 to 2005 and 15.9% from 2005 to 2013. During the same period, CO2 emissions had been increasing as well, at an average annual growth rate of 12.2% from 2000 to 2005 and 6.5% from 2005 to 2013. From a spatial point of view, carbon emissions are far greater in the eastern region of China than in western China. The carbon emissions are the highest in major metropolitan cities such as Beijing, Shanghai, and Guangzhou. Per capita carbon emissions are also higher in eastern China, which is consistent with the people's higher living standards. In some cities with large energy and heavy industry concentrations, especially in the northeastern and western regions, the growth rate of carbon emissions has risen faster than in other cities. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance has delivered significant benefits, such as sustainable development and economic growth. We explore the relationship between digital finance and green total factor energy efficiency (GTFEE) for the first time, filling a gap in the existing literature. This paper uses dynamic panel models to explore digital finance's impact on GTFEE at the Chinese city-level panel data from 2011 to 2018. The results show that digital finance can significantly improve urban GTFEE, and the findings remain robust with various tests. Second, the mechanism analysis indicates that digital finance can improve GTFEE by promoting urban green technology innovation and industrial structure upgrading. Further study shows that digital finance has a better effect on the improvement of GTFEE in central and western cities, small cities and non-resource-based cities, but has no significant or small impact on GTFEE in eastern cities, large cities and resource-based cities, reflecting the inclusiveness of digital finance. [ABSTRACT FROM AUTHOR]

This paper explores opportunities for effective policy intervention for residential energy savings in southern China, by analyzing achievable potential of residential energy savings in the city of Xiamen as a case study. In contrast to static technical and economic potential analyses, the achievable potential analysis is based on dynamic energy consumption projections, which consider two real-world factors: (1) gradual ramping-up adoption process of advanced technical measures; and (2) consumers’ adoption of these measures. A bottom-up type Residential Energy Consumption (REC) projection model specifically tailored for southern China was developed in this paper, based on the general logic and calculation principles utilized in the U.S. EIA’s “National Energy Modeling System (NEMS),” and the adoption theory of advanced technical measures proposed by Kastovich. This REC projection model was then used as a policy analysis tool to quantitatively evaluate the impact of various policies on residential energy savings in the case study city of Xiamen. The analysis of the Xiamen case shows that although there is a significant technical potential for residential energy savings in the city (about 20.9–24.9%), the maximum achievable potential (MAP) in 2020 is only about 8.3–8.4% of that year’s business-as-usual baseline consumption. Moreover, with existing policies only about one-fourth to half of the calculated MAP is likely to be achieved by 2020. These findings indicate that in order to realize a larger share of achievable potential for residential energy savings in China, additional and more effective polices are needed. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in 'Type III' solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve "elite capture." • We analyze the changes of energy accessibility and affordability in rural China. • We quantify the energy services effect of solar PV projects (SPVPs). • SPVPs perform well in improving family energy consumption cleanliness. • The energy services effect of solar PV projects has spatial heterogeneity. • The implementation of China's rural SPVPs does not involve "elite capture.". [ABSTRACT FROM AUTHOR]

[Display omitted] • PCM energy storage and PV/T coupled multi-source heat pump systems were combined. • The thermal performance, energy efficiency, and economy of the system were analyzed. • The performance of paraffin C17 and water as energy storage materials were compared. • The performance of six low-temperature PCMs applied to the system were analyzed. • Compared to the existing system, it saves 56 % of energy and 27.7 % of operating costs. To improve the application of renewable energy in the heating zone, as well as to address the limitations of air and water source heat pump. The study presents a PV/T (photovoltaic/thermal) coupled air–water source heat pump system integrated with phase change energy storage. A theoretical analytical model and TRNSYS simulation model of the system were developed to evaluate the thermal performance, energy efficiency and economics of the system. The performance of phase change energy storage was compared with that of water storage, and the effect of different phase change materials on the system characteristics. The results show that the coupled system achieves a seasonal performance factor of 2.3, a 56 % reduction in energy consumption, and a 27.7 % reduction in operating costs compared to a conventional gas boiler system. Comparative analysis with the water storage system showed that the system equipped with phase change materials performed well in terms of energy consumption, operating costs and heating stability. In addition, the density of the phase change material has a biased effect on the overall performance of the system. This paper can provide a reference value for the study of renewable energy heating systems in cold zone. [ABSTRACT FROM AUTHOR]

The battery electric vehicle (BEV) industry, commanding over half of the global market share in China, has garnered significant attention in response to escalating concerns about climate change. NCM (LiNi x Co y Mn z O 2) batteries, prized for their high energy density, are pivotal in this industry. However, their production relies on critical metals, posing environmental challenges. Recycling processes aim to address this issue by recovering critical metals from end-of-life batteries. The industry is updating from NCM 111 to NCM 811 batteries to enhance energy density and reduce costs, but the key factors influencing the material flows and environmental impacts remain uncertain. This paper aims to identify the key factors contributing to the potential differences in material flows and environmental impacts introduced by the NCM battery upgrading in China. Following the new energy vehicle sales projections in China until 2035, a dynamic material flow analysis was employed to calculate material demands, recovered amounts, and substitution rates for various upgrading scenarios. The study reveals that the majority of retired batteries until 2035 were sold between 2022 and 2029. Material substitution rates prove to be more sensitive to the upgrading scenario than the lifetime and growth rate of BEVs. Faster NCM 811 penetration results in over 2.5 times more saved cobalt demand compared to additional nickel demand. A life cycle assessment of NCM batteries reveals a 39% lower global warming impact for NCM 811 compared to NCM 111, highlighting environmental benefits from increased energy density and reduced cobalt consumption. The energy density improvement contributed 85%, while the alteration in material composition contributed 15% to the global warming reduction in NCM 811. Recycling emerges as a crucial strategy in mitigating environmental impacts across all categories, particularly addressing eutrophication, previously deemed challenging due to the difficulty in recovering Li 2 CO 3. [ABSTRACT FROM AUTHOR]

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

This paper focuses on energy poverty dynamics by answering one question: Does energy poverty trap exist in Chinese cities? For this purpose, we examine the co-evolution between per capita energy consumption and income for 286 prefectural-and-above level cities in China over the period 2002 to 2014. A nonparametric joint-distribution dynamics approach is employed in the analysis. Both static and dynamic distribution analyses demonstrate significant evidence of club convergence in energy consumption across Chinese cities, and there are multiple equilibria between per capita energy consumption and income in the long-run distribution. The results also suggest that considerable cities fall into energy poverty trap, a chronic state with low income and low energy consumption. Therefore, we can argue that energy poverty is not a transitory state for those cities and support the existence of energy poverty trap in China. Our findings highlight the need to tailor policies aimed at reducing energy poverty in low-income cities. • We examine the co-evolution between per capita energy consumption and income in China. • A dataset of China's 286 prefecture-level cities during 2002–2014 is explored. • The results argue club convergence in energy consumption across Chinese cities. • Considerable cities fall into energy poverty trap. • The study supports the existence of energy poverty trap in China. [ABSTRACT FROM AUTHOR]