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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

Accurate understanding of the marginal energy-saving costs of energy-using entities is critical for regulatory authorities to formulate and implement energy-saving policies. This article brings out an easy-to-operate analytical framework to measure the marginal energy-saving cost and construct energy conservation supply curve with the help of data envelopment analysis and quantitative model. Furthermore, taking China's pulp and paper industry as an example, the authors discuss the difference of economic costs between industry energy-saving task assignment with and without considering the difference of marginal energy-saving costs of different energy using entities with the help of linear programming model. The main empirical results are as follow. First, there is a big difference in the industrial marginal energy-saving costs in different provinces. The difference between the highest and lowest energy-saving costs is twice as large. Second, the optimal energy-saving task assignment based on an accurate energy conservation supply curve can help to save nearly 35% of total economic cost. • An alternative method of measuring energy conservation cost is provided. • An optimal energy saving task assignment model is constructed. • The problem of trade-off between efficiency and equity in policy making is discussed. [ABSTRACT FROM AUTHOR]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

The core of the energy transformation under the carbon-neutral vision is the gradual replacement of high-carbon energy by zero-carbon and low-carbon energy. However, the large-scale deployment of clean energy technology in turn relies on key minerals such as copper, aluminum, zinc, and nickel. Based on this, this paper conducts research on the demand for key minerals in clean energy transition and the role of key minerals in clean energy transition. In particular, this paper calculated the demand for mineral supply for China's carbon neutrality target, and the impact of some mineral resource constraints on the carbon neutrality path. Relevant results can provide a decision-making basis for the future development and utilization of key mineral resources, as well as the implementation of future carbon-neutral goals. • We evaluate the impact of crucial mineral resource on the carbon neutrality path. • Copper's availability greatly affects the adoption of new energy technologies. • Limited resources hinder early carbon capture and storage development. • Negative emission technologies are necessary for China's carbon neutrality. [ABSTRACT FROM AUTHOR]

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs. • "Opinions" measures encourage NEV to innovate independently, jointly and efficiently. • "Supply-side" tax incentives have a stronger innovation effect. • "Demand side" government procurement has a stronger signal effect. • Government policy and venture capital play an innovative synergistic effect. • There is an innovation failure in policy mix before venture capital intervention. [ABSTRACT FROM AUTHOR]

The scale of pig breeding in China is the largest in the world, and the pollution problem of breeding wastewater is serious. Supercritical water gasification (SCWG) process is a clean and efficient treatment technology for breeding wastewater. This paper involved constructing the molecular model of the primary organic components in pig breeding wastewater. The effects of time, temperature and mass concentration, and the interactions of different components on the SCWG characteristics of pig breeding wastewater were studied by molecular dynamics (MD) simulation, and the decomposition process of lignin and lipid molecules in the SCWG process were analyzed, and the possible intermediates were explored. The results showed that the effect of parameters on carbon gasification efficiency (CE) and H 2 production follows the order of temperature > time > mass concentration. During the gasification of lignin and lipid molecules, the C–O–C bond was broken first, and the gasification rate of lignin molecules was slow due to the benzene ring. Interactions among different organic decompositions hindered the gasification process. This paper will provide theoretical guidance for the selection of operation parameters and the control of products in the actual pig breeding wastewater treatment process. • The MD model of wastewater containing complex organics was established. • The effects of key parameters on SCWG of pig breeding wastewater were studied. • The detailed SCWG mechanisms of pig breeding wastewater were analyzed. • The intermediate products from SCWG of pig breeding wastewater were explored. • The interaction mechanisms of different organics during SCWG were analyzed. [ABSTRACT FROM AUTHOR]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [ABSTRACT FROM AUTHOR]

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [ABSTRACT FROM AUTHOR]

As installed wind power capacity continues to rise, the cost of onshore wind power generation in China has fallen, far exceeding the world average. The purpose of this study is to explore the main factors affecting onshore wind power in China and to identify ways to reduce costs. So as to reduce the cost of wind power and promote the large-scale grid-connected use of wind power. Therefore, this paper summarizes various factors that affect the development of onshore wind power. We also provide a comprehensive and repeatable MFLC (multi-factor learning curve) method to evaluate the factors affecting the cost of onshore wind power. Subsequently, the learning rates of various factors are calculated using this method. The results show that empirical learning by increasing installed capacity/generation, improving technology and selecting sites which are rich in natural resources, and appropriately reducing capital investment and material prices can significantly reduce the LCOE (levelized cost of electricity) of onshore wind power in China. Compared with wind power giants of the United States and Germany, the reduction in the cost of onshore wind power generation in China is more dependent on inputs such as capital investment and raw materials, while experience plays a relatively minor role. The ability to make full use of natural resources and to convert wind energy into electricity more efficiently is crucial to reducing the cost of wind power. • This paper summarizes various factors that affect the development of onshore wind power. • A comprehensive and repeatable MFLC method is built in this paper. • The learning rates of various factors are calculated. [ABSTRACT FROM AUTHOR]

China still faces relatively serious energy poverty, and technological innovation is considered effective at alleviating this issue. This paper empirically investigates the impact of regional technological innovation on energy poverty based on panel data from 30 provinces in China from 2001 to 2020. This paper explores the effect of industrial structure distortion in the process of technological innovation on energy poverty. This paper conducts asymmetry analysis and investigates the heterogeneous impact of technological innovation on energy poverty in different areas. The research conclusions are as follows: (1) Regional technological innovation can effectively alleviate energy poverty. (2) Industrial structure distortion moderates the impact of technological innovation on energy poverty, and it weakens the effect of technological innovation in alleviating energy poverty, although the moderating effect is not significant in coal-producing regions. (3) The effect of technological innovation on alleviating energy poverty is more obvious in regions where energy poverty is prominent. (4) The role of technological innovation in alleviating energy poverty shows spatial heterogeneity, and this alleviation effect is more obvious in the western and non-coal producing regions. We also propose several policy implications for alleviating energy poverty. • Regional technological innovation can effectively alleviate energy poverty. • Industrial structure distortion moderates the impact of technological innovation on energy poverty. • Industrial structure distortion hinders the ability of technological innovation to alleviate energy poverty. • Technological innovation is more effective in alleviating energy poverty in regions where energy poverty is severe. • Technological innovation is more effective at alleviating energy poverty in the west and non-coal-producing regions. [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]

Currently, wind power prediction has so many problems in the ultra-short-term time scale (0–4h), which is difficult to improve the deterministic prediction and probability prediction accuracy of the wind farm cluster because it can not fully explore the spatio-temporal correlation between the physical change process and the wind farm. In this paper, a method of ultra-short-term deterministic and probability prediction of wind farm cluster power based on Graph Convolutional Network (GCN) considering fluctuation correlation (FC) is proposed. Firstly, the adjacency matrix is constructed based on the power sequence fluctuation information of each wind farm, and the GCN is designed. Secondly, the spatio-temporal features of power and Numerical Weather Prediction (NWP) wind speed are extracted and fused based on the network model of dual-channel and dual-adjacency matrix. Thirdly, in order to effectively improve the prediction accuracy, a trend switching mechanism is designed based on the effective trend of NWP. When the fluctuation information of NWP is not accurate, the graph structure is constructed by the adjacency matrix based on geographical location to achieve effective prediction. Finally, the method proposed in this paper is applied to wind farm clusters in three provinces of China, compared with some commonly used methods, the average RMSE is reduced by 1.34 %, 1.62 %, 2.07 %, respectively, and the average CWC is reduced by 6.12 %, 4.49 %, 6.62 %, which verifies the effectiveness of this method. • A method for calculating FCC is proposed. • Combined with FCC, the graph structure of wind farm cluster is constructed. • A two-module GCN is designed. • A trend-switching mechanism is designed. [ABSTRACT FROM AUTHOR]

The low-carbon transformation is the key to China's high-quality economic development. This paper uses the panel data of 278 Chinese cities to measure low-carbon total factor productivity (LCTFP) and examines the impact of environmental protection tax (EPT) reform on LCTFP. EPT significantly promotes LCTFP. The promotion effect is more significant in the lower administrative level and resource-based cities in central and western regions. EPT promotes LCTFP through three channels including green technology innovation (GTI) effect, industrial structure upgrading effect and forcing effect. EPT has a negative spatial spillover effect on LCTFP in neighboring areas. This paper provides important empirical evidence for further advancing the EPT in China. • EPT significantly promotes LCTFP. • The promotion effect is more significant in low administrative level and resource-based cities in central-western region. • EPT promotes LCTFP through green technology innovation, industrial structure upgrading and forcing effect. • EPT has a negative spatial spillover effect on LCTFP in neighboring areas. [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]

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO 2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO 2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO 2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO 2 , while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO 2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO 2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market. • A novel method for estimating CO 2 MAC has been developed. • Ignoring inefficiency will result in an overestimation of CO 2 MAC. • Three different emission reduction pathways are considered in the estimation. • The heterogeneity of the lowest cost abatement pathways has been identified. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

The natural hydraulic connections between cascade hydropower may make hydraulic and electric connections mismatch after the market clears. Aiming at relieving the contradiction between water surplus and water shortage after market clearing, this paper proposes a new market clearing method considering the coupling relationship between water and electricity. And the method is verified by the simulation of a virtual spot market consisting of eight stations in Sichuan Province, China. The main contributions of this paper lay on three aspects: firstly, a new index called water-electricity coupling degree (WCD) and its calculation method are proposed to measure the overall situation of water shortage and water abandonment in the clearing results. Secondly, a multi-objective clearing model for cascade hydropower stations is established with the goal of minimizing power purchase cost and maximizing WCD. Thirdly, a solution method combined with the characteristics of the clearing model is developed. The case results demonstrate that the built model increases the WCD value by 1.57 % and reduces the insufficient output and surplus water by 45.18 % without increasing the power purchase cost of the power grid; The proposed solution strategy lowers the complexity of the model and improves the solution efficiency, where the search space reduction and time period merging strategies can reduce the calculation time by about 3 % and 8 % respectively. The new clearing method provides a new idea for solving the uncoupling problem of water-electricity-price in the cascade hydropower spot market. • Propose a new index called water-electricity coupling degree (WCD). • Establish a multi-objective clearing model for cascade hydropower market. • Develop a model dimensionality reduction strategy for model solution. [ABSTRACT FROM AUTHOR]

Industrial chain structure optimization and digital transformation are crucial factors for achieving carbon emission reduction goals. Based on China's non-competitive input-output tables, this paper constructed industrial chain panel data using the structural path analysis method. We explored the influence of industrial chain division (ICD) on CO 2 emission intensity through panel fixed effects model, and examined the moderating effect of digitization. The research found that: (1) The ICD can significantly reduce CO 2 emission intensity, the results are robust after some endogeneity tests. (2) The emission reduction effect brought by ICD will be more obvious in high-stage, high-energy consumption, and high-intelligence industrial chains. There is still room to improve the emission reduction brought by the division network effect. (3) Digitization plays a positive moderating role on this effect. Furthermore, digital manufacturing and digital services exhibit heterogeneity in various types of industrial chains. (4) The ICD can reduce carbon emission intensity through pathways such as intelligent transformation, technological innovation, and energy efficiency, and digitization has positively moderated these impact pathways. Based on research results, targeted policy suggestions are provided for optimizing industrial chain structure and promoting digital transformation. • This paper constructed industrial chain carbon emission intensity panel data. • This paper assessed the industrial chain digitization by digital intermediate input. • This paper constructed industrial chain synergistic division (ICSD) index. • This paper explored the influence of ICSD on CO 2 emission intensity. • Digitization plays a positive moderating role on carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

The proposed targets to achieve peak carbon before 2030 and carbon neutrality before 2060 would require China to accelerate the development of natural gas infrastructure. The forecasted future of natural gas consumption in China will not only help learn about the future usage of natural gas but also guide government departments to conduct more scientific planning and management of natural gas production and pipeline channels. Therefore, this paper fully considers the characteristics of natural gas consumption affected by seasonal factors and uses the 2018–2021 quarterly natural gas consumption time-series data to analyze whether natural gas consumption contains trends and seasonality. It is noticeable that seasons impact China's natural gas consumption, so the moving average method is applied to calculate the seasonal index. Based on this, China's natural gas consumption from 2022 to 2035 is forecasted. As results show, China's gas consumption will continue to grow over the next 14 years but at a slower rate. By 2035, it will double from the 2021 level, reaching 765.4 billion cubic meters, but the growth rate will drop to below 4%, entering a period of low growth. Finally, the paper illustrates current situation of natural gas development combined with rationalized suggestions for future natural gas production and energy security in China. • Natural gas consumption is influenced by seasonal factors. • A seasonal forecasting method is applied. • China's gas consumption will continue to grow by 2035. [ABSTRACT FROM AUTHOR]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

The essence of industrial chain structure optimization is to strengthen the synergy and division ability among sectors. It is a crucial factor in reducing energy intensity and achieving carbon neutrality goals. This paper completed the China's non-competitive input-output table from 2000 to 2020, constructed industrial synergy and division index using structural path analysis method and examined its development trend. Combining with a two-way fixed effects model, we explored the influence of industrial synergy and division on energy intensity. The research found that China's industrial synergy and division development level shows an upward trend. The proportion of the low-layer industrial chain decreases, and the peak contribution rate moves to the high-layer industrial chain. Secondly, industrial synergy and division significantly reduces sectoral energy intensity, the results are robust after endogeneity test. Finally, heterogeneity analysis showed that industrial synergy and division can reduce the energy consumption intensity of all sectors, and it is more obvious for sectors with high energy dependence. This research can provide targeted policy suggestions for China to optimize its industrial structure. It also has a reference significance for other countries. • The impact of industrial synergy and division on energy intensity is explored. • This paper extracts China's non-competitive IO table from the global MRIO table. • SPA is applied to construct industrial synergy and division index. [ABSTRACT FROM AUTHOR]

This paper proposes a novel management scheme, namely, the distribution system operator (DSO) as a key player participating in China's electricity markets owing to its high value-added capability, high customer loyalty and robust risk resistance. In light of this, this paper discusses the maximal marginal revenue for DSO in uncertain electricity markets with vehicle-to-grid operation incorporated. Initially, considering different benefit expectations of electric vehicles (EVs) owners, DSO could provide preferential contracts, including the discount price contract (DPC) and revenue-sharing contract (RSC), in exchange for the management rights of EVs. Then, the stochastic optimization model considering the spatiotemporal uncertainties of EVs and electricity markets is built up, and the optimal bidding and coordinating strategy are proposed to maximize the marginal revenue of DSO. Comprehensive case studies are conducted in a 31-node distribution system of a university campus under the electricity markets in Guangdong, China. Results reveal that different distribution of EVs parking lots has a significant impact on the profit of DSO, and the revenue for DSO is 13,477 yuan more than that for EVA by scheduling 1400 EVs' charging/discharging behavior properly. Additionally, the DSO with RSCs is exposed to 25% less market volatility risk than that with DPCs. • Comparing EV aggregator(EVA) and distribution system operator(DSO) characteristics. • Discount price contracts and revenue-sharing contracts are compared. • The advantages of DSO over EVA in market participation are analyzed. [ABSTRACT FROM AUTHOR]

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. The quality of Numerical weather prediction (NWP) has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it' s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and division of transitional weather process. The combined prediction method was first used to correct the NWP wind speed, and then we use the double clustering method to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSE is 5.93% and in MAE is 4.82% compared to the various comparison methods in the four seasons. The USTWPP method combining wind speed correction and double clustering division of transitional weather scenarios can significantly improve accuracy of USTWPP. • Propose a new NWP correction mechanism to improve the NWP forecasted errors. • Propose a multiple clustering method to improve the wind power prediction accuracy. • Propose weighted evaluation indicators of sequential and non-sequential clustering. • It provides new ideas for wind power prediction under different weather scenarios. • Proposed power prediction idea reduces the RMSE and MAE on average 5.93% and 4.82%. [ABSTRACT FROM AUTHOR]

The photovoltaic poverty alleviation (PV-PA) policy is a promising policy innovation to achieve sustainable development in China. Based on first-hand field investigation data of 18 prefecture-level cities and 110 counties in China's Henan Province, this paper first evaluated the efficiency of PV-PA policy and analyzed its spatial correlation characteristics by adopting data envelopment analysis, spatial autocorrelation analysis and social network analysis methods. The results show that: (1) the comprehensive efficiency of the PV-PA policy in Henan Province is good, and that of the prefecture-level is better than the county-level, with great differences; (2) comprehensive efficiency of the PV-PA policy shows a significant spatial agglomeration phenomenon at the county-level, and regional development imbalance exists; and (3) PV-PA policy network linkages at prefecture-level are relatively weak. Finally, this paper makes policy recommendations for optimizing project scale, promoting regional balanced development, strengthening supervision and management, and enhancing the cooperation between policymakers. • DEA was used to evaluate the efficiency of the PV-PA policy in China's 110 counties • Explored the spatial correlation patterns of the PV-PA policy efficiency • Constructed a spatial correlation network for the PV-PA policy efficiency • Comprehensive efficiency of PV-PA policy needs to be enhanced • Unbalanced development and regional uncoordinated situation needs improvement [ABSTRACT FROM AUTHOR]

Accurately predicting the coal demand in China's key coal-consuming industries is crucial for coal exit path planning. This paper selects the chemical, building materials, steel and thermal power industries as research objects, organically integrates data-characteristic-driven and decomposition-ensemble ideas to construct combination forecasting models for coal demand in each industry, and empirically analyzes the influencing factors and evolutionary trends of coal demand in these industries. The results show that: First, the prediction accuracy and stability of models constructed in this paper based on data characteristics are significantly superior to other traditional combination models and single models. Second, the coal demand of all four industries is influenced by industrial policies and economic fluctuations, additionally, that of thermal power industry is increasingly influenced by energy-saving and emission reduction policies. Third, during 2021–2025, the overall coal demand in four industries will fall and then rise, reaching 2.88 billion tons by 2025; different industries will exhibit significant heterogeneity in coal demand trends, among which coal demand of thermal power industry will decrease and then rise, that of building materials industry will remain stable, while that of chemical and steel industries will show a fluctuating downward trend and the speed of decline: steel > chemical. • We propose a data-characteristic-driven integrated approach to predict coal demand. • The proposed model significantly outperforms other widely developed baselines. • Factors affecting coal demand in each industry are analyzed on multiple timescales. • Coal demand trends in different industries will show significant heterogeneity. • Overall coal demand will first fall and then rise, up to 2.88 billion tons in 2025. [ABSTRACT FROM AUTHOR]

Under the background of "carbon neutrality" and "carbon peak" concepts, China desires to develop a new power system based on renewable energy sources (RES), which will be the primary energy support in prospective China. Thus, it is imperative to improve the peak shaving capability of power system to address the problem of random fluctuation and intermittency of RES by developing various energy storage systems. An innovative approach to enhance the flexibility of the conventional thermal power unit (TPU) through the utilization of flywheel energy storage array (FESA) is presented, simulated, and evaluated in this paper. Using the relatively practical and specific model of TPU and FESA, modified frequency modulation and automatic generation control (AGC) instruction tracing operation scenario models are constructed. A comprehensive control strategy of FESA and a well-designed operation coordinated control method for TPU with FESA are proposed and applied. The simulation results are distinctively processed and evaluated by the methods in "two documents", aiming to assess the real-world operation performance of the model proposed in this paper. The comprehensive analysis of the indicators demonstrates that the FESA can improve the flexibility and economy of the units. This research may guide the usage of FESA to improve the operational flexibility of TPU. • Composition and Control Methods of the flywheel energy storage array are provided. • A coordinated control scheme for the thermal power unit with flywheel energy storage array is proposed. • Frequency modulation and AGC instruction tracking scenario models are constructed and simulated. • AGC regulation indicators are conducted and analyzed to evaluate the unit's performance. [ABSTRACT FROM AUTHOR]

The carbon market has been playing an increasingly important role in mitigating carbon emissions. However, with the tightening of the carbon allowance, where the carbon market will evolve is still unclear. Abstracting three agents—Government, Carbon-supplier('CS'), Carbon-buyer('CB'), this paper intends to investigate the operation mechanism of carbon market from the perspective of the stakeholders. Based on the simulation analysis, it is suggested that: 1) the carbon market in China may become a major "seller's market" when carbon quota experienced dramatic reduction; 2) when the carbon price is constant, CS with excessive carbon productivity will crowd out and exploit CB in the carbon market competition; 3) strict administrative penalties and information disclosure would help promote proactive participation of CS and CB in the carbon market; and 4) the interaction between CS and CB in different strategic scenarios is influenced by the carbon price and the coefficient of demand variation for carbon quota. In contrast with previous studies, this paper has uncovered a dynamic strategic evolution mechanisms of stakeholders in the carbon market at the micro-level, contributing to carbon market governance and providing stakeholders with tailor-made climate policy tools and strategic choices. • The carbon market in China may evolve into a "seller's market" with a scarcer allowance. • We first identify the phenomenon of carbon exploitation in the carbon market. • Improving the carbon price under buyer's market would facilitate positive participation of three agents. • Carbon accounting, carbon disclosure, and strict administrative penalty help regulate non-compliance actions by enterprises. • The equilibrium of agents' strategy are influenced by dual aspects—parameters and other stakeholders' choices. [ABSTRACT FROM AUTHOR]

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers. • Carbon awareness based integrated optimization model for data center in China is proposed. • Layout, scale, cooling technology and power supply mix are simultaneously considered. • 25% of newly established data center electricity related emissions in China can be avoided. [ABSTRACT FROM AUTHOR]

China's textile industry has developed rapidly in recent years. China's cotton textile spindles account for half the world's total, while China's textile trade volume accounts for one-third of the world's total. China has become a veritable textile superpower. The present paper establishes a new application of the logarithmic production function model and performs the preliminary analysis of output elasticity and the elasticity of substitution. Results show that the output elasticities of capital, labor, and energy of the textile industry are positive for the period 1980–2012. In the textile industry, the output elasticity of energy is greatest, followed by that of labor and that of capital. A stable and significant substitution relationship exists among capital, labor, and energy, and the elasticity of substitution remains at a value of around 1. The elasticity of substitution between labor and energy is largest and remains above 1.01 during the sample period, which indicates that the technical level of the textile industry remains relatively backward and results in the high substitution of labor for energy. On the basis of empirical analysis, the paper discusses policy implications and proposes countermeasures for the future development of the textile industry. • The elasticity of substitution between labor and energy is the largest. • Output elasticity of each factor and substitution elasticity are analyzed. • The output elasticities of capital, labor, and energy are positive. • Future policy for energy substitution in China's textile industry is suggested. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the decoupling status and mitigation potential of CO2 emissions from China's transport sector over the period 1991–2015. Firstly, this paper utilizes the Log-Mean Divisia Index (LMDI) technique to identify influencing factors, which govern CO2 emissions from China's transport sector. Over the study period, the CO2 coefficient effect (Δ C c f t ) was the main factor for decreasing CO2 emissions. However, the economic growth effect (Δ C g t ) was the main factor for increasing CO2 emissions. Then, the decoupling indicator is also constructed based on the above influencing factors, which can be utilized to reflect the dependence degree of economic development on CO2 emissions from China's transport sector. During 1991–2015, the CO2 emissions from China's transport sector presented expansive coupling (EC) with economic development. Weak decoupling (WD) only appeared in the sub-period 1991–1995. The CO 2 emissions only presented 3 decoupling grades in all sub-period and the period 1991–2015: Ⅱ, Ⅲ, and Ⅳ. Furthermore, the above influencing factors are also used to establish a theoretical mitigation model, which is used to reflect the mitigation potential of CO2 emissions from China's transport sector. Finally, the occurrence of CO2 mitigation only appeared in 3 sub-periods: 1991–1995, 2005–2010, and 2010–2015. • The LMDI technique is used to identify influencing factors. • The decoupling indicator is constructed based on the influencing factors. • The influencing factors are used to establish a theoretical mitigation model. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have witnessed rapid development. However, with the characteristic of numerous complexity links, there are many uncertainties exist in the supply chain which will cause high risks to the electric vehicles. Thus, carrying out a risk assessment is essential for the supply chain. The purpose of this paper is to identify and assess the potential risk factors for China's electric vehicle supply chain under uncertain circumstance. Firstly, this paper establishes a risk assessment index system for electric vehicle supply chain, which consists of three aspects and associated 15 indexes. Secondly, a combined hesitant fuzzy linguistic term set with fuzzy synthetic evaluation is developed. Thirdly, the risk assessment on China's electric vehicle supply chain is conducted. The results show that the risk level of the electric vehicle supply chains in China is between "general" and "high". And the technical aspect and market aspect should be noted, mainly involving the information sharing risk and the assembly line setting risk. This paper can help managers of electric vehicle supply chain not only identify potential risks but also develop appropriate risk prevention measures. • Risk assessment is carried out in the field of electric vehicle supply chain. • A risk assessment index system of electric vehicle supply chain is established. • A risk assessment model is established based on fuzzy theories. • An empirical study of China's electric vehicle supply chain is provided. • Risk prevention suggestions are put forward based on the obtained results. [ABSTRACT FROM AUTHOR]

Experiencing extremely severe and persistent haze pollution in the past several years, China is among the world's most polluted countries with air pollution. As a relatively clean fossil fuel, natural gas is considered as a transitional energy for China's green and low-carbon development. Can natural gas use really improve air quality effectively? It is an important question but remains unanswered because of an estimation endogeneity. A major challenge in estimating this unwatched pollution reduction is how to accurately and cleanly identify the air quality impact from natural gas utilization in urban environments. This paper tries to provide comprehensive estimates for this problem. Based on a national policy of trans-regional natural gas allocation, this paper employs differences-in-differences method to identify the net effects of natural gas utilization on air quality in Chinese cities. Our estimates imply that PM 2.5 concentrations in treatment group cities decreased by about 12% after the natural gas pipeline construction. The mechanism analysis shows that natural gas utilization has a substantial impact on urban haze pollution mainly through the improvement of the industrial energy mix and the change in urban household gas fuels. Furthermore, natural gas utilization can bring a co-benefit of reducing other typical air pollutants. The findings can provide new insights to the role of natural gas utilization in China's green and low-carbon development. • Natural gas utilization's effect on air quality is studied. • A quasi-natural experiment is constructed using the West to East Pipeline Project. • Natural gas utilization has a significant reduction effect on haze pollution. • A co-benefit of reducing other air pollutants is found in natural gas utilization. • The expansion of natural gas utilization is of great significance to current China. [ABSTRACT FROM AUTHOR]

This paper examines the long-term effects and related short-term movements of energy security (ENS), economic policy uncertainty (EPU), and government ecological expenditure (GEE) on China's carbon dioxide (CO 2) emissions by applying the quantile autoregressive distributed lag approach. The empirical findings demonstrate that ENS and EPU have prominent negative and positive impacts, respectively, on CO 2 emissions in the long term for the majority of quantiles, while their short-term influences are less obvious. In addition, the effect of GEE is short-term and concentrated in high quantiles. The following are the article's main contributions. This study builds special energy security indicators based on the actual situation in China, providing a basis for better understanding the interaction mechanism between ENS and CO 2. Considering that China is the largest oil importer and greenhouse gas emitter, this study explores for the first time the linkages between ENS and CO 2. Both short- and long-term impacts are observed, and long-run effects are dominant in the linkages among variables, suggesting that the level of CO 2 emissions is mainly driven by long-term shocks. In addition, to clarify the nexus between ENS and CO 2 , time- and quantile-varying analyses are used, which take into account not only varied CO 2 emission levels but also distinct time periods for events. The findings emphasize the importance of market participants to form a better understanding of how ENS, EPU, and GEE affect CO 2 under different emission levels. Some detailed policies, including optimizing the energy consumption structure, making prudent economic policy adjustments, and strengthening ecological protection expenditures, are provided to curb pollution. • Energy security and carbon neutralization are main targets for China. • Energy security negatively affects carbon dioxide in the long-term. • Time-varying and quantile-varying features are captured for Energy security. • The quantile autoregressive distributed lag model is utilized in this paper. [ABSTRACT FROM AUTHOR]

Microgrid is a promising way to integrate renewable distributed generation into the electric power sector, which has received widespread attention. In this paper, a uniform capacity-constrained optimization model under different power purchase and sale price contracts is developed to explore the optimal green investment strategy for grid-connected microgrid (GCM) with three configuration types. The impact of renewable energy source endowment and incentive policy is focused when macrogrid's generators emit more pollution than GCM's non-renewable generators, such as in China and India. The results show that: (1) GCM with renewable generation can only be invested in a site with relatively abundant renewable endowment; (2) carbon tax effectively promotes the deployment of GCM with renewable generation and achieves carbon emission abatement, but excessive carbon tax cannot incentive investment in the most desired GCM type; (3) a subsidy trap is found under feed-in tariff (FIT) subsidy, which implies that the fade-out or exit of FIT subsidy is beneficial for microgrid's healthy development; (4) and the comparisons under different price contracts emphasize the importance of macrogrid providing non-discriminatory access to feed-in and feed-out services to microgrid. • Investment strategy is explored for grid-connected microgrid (GCM) with three types. • Renewable source endowment and incentive policy determine the investment for GCM. • Excessive carbon tax cannot incentive investment in the most desired renewable GCM. • The fade-out or exit of FIT subsidy is beneficial for healthy GCM deployment due to the subsidy trap. • Emphasizing the importance of macrogrid providing non-discriminatory services to GCM. [ABSTRACT FROM AUTHOR]

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction. • The balance between environmental resources and economic development is considered. • The optimal economic growth path under the low-carbon goal is measured. • Calibration of regional statistics with the nighttime light dataset "NPP-VIIRS-like". • The error correction model is built as a short-term regulation mechanism for power prediction. • Predicted electricity demand for optimal economic growth in the PRD under different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach that utilizes aircraft seat capacity and its route flight time to predict carbon emissions for aircraft manufacturers during the aircraft design stage. To address data fluctuations arising from the human-aircraft-environment-management system throughout the production and operation of the future-designed aircraft, we employ the interval-valued data type, replacing the traditional point data type, and construct an interval-valued regression model. Within the parameterized method framework, our programming model incorporates additional constraints to ensure the intersection of each interval-valued sample. This addresses the challenges significant data fluctuations pose, leading to improved fitting performance. Furthermore, the model is demonstrated to satisfy Kuhn-Tucker conditions for solvability, and the obtained regressed parameters exhibit favorable small-sample properties. An empirical study using airline operating data from China validates the prediction effectiveness of the programming model. Finally, based on these predictions, we provide applications and suggestions for decision-makers aimed at reducing CO 2 emissions. • Estimated aircraft carbon emissions for Chinese routes. • More precise estimations based on an improved parameterized model. • For narrow-body aircraft, increasing seat capacity and controlling flight time are crucial for carbon emissions reduction. • For wide-body aircraft, controlling seat capacity and long-distance flights benefits carbon emission reduction. [ABSTRACT FROM AUTHOR]

For multi-family buildings with radiator heating system (MFBRHS) in China, on-demand heating is an important route to achieve energy saving, emission reduction, and improve user satisfaction. The current researchs are mainly focused on load forecasting and achieving heat balance by adjusting supply temperature or flow rate. However, the time-delay of disturbances factors and control parameters caused by the building envelopes and hydraulic coupling of the network is often ignored, which results in the failure to truly achieve heat balance. In this paper, a linearization method is proposed for the MFBRHS, and the frequency domain approach was employed as an analytical tool to gain a deeper understanding of the dynamic characteristics of the system. The results show that the selected building is equivalent to a low-pass filter. The average response speed of supply temperature and flow rate is slower than that of the solar radiation and outdoor temperature, which indicates that each control parameter should be delayed for adjustment to truly achieve heat balance. Additionally, the uncertainty of the building envelope only affects the magnitude of the disturbances while has a limited effect on its response speed. • The linearization method for multi-family buildings. • System controllability analysis in the frequency domain. • Impact of disturbances under uncertainty in thermal parameters of the building envelopes. [ABSTRACT FROM AUTHOR]

In the past 20 years, China has undergone a rapid process of electrification, which has contributed to the modernization of people's lives. This paper associated household electricity consumption, appliance service function, total cost of electricity consumption to life well-being for the first time, and explained the structural characteristics of these changes. The welfare index from appliance services is defined as the result of the interaction between electricity consumption and appliance operation, and is used as an indicator to assess the improvement of household well-being. We sorted out 9 types of service functions of appliances, estimated the structure of household electricity consumption, total electricity cost and welfares received, and analyzed the factors that enhance welfare and their contributions. The results show that the electricity consumed by 12 main household appliances per household per year increased from 358 kWh in 2000 to 1419.5 kWh in 2020, and the total household electricity cost increases from 1315.74 yuan (RMB) to 6772.68 yuan. The quantity and structure of both have changed greatly. The service functions provided by appliances are increasingly diversified, have expanded from the basic services such as lighting, watching TV, refrigerated food to nine aspects including communication and transportation. The quality of life of residents continues to be improved, and the welfare index has increased from 2.48 to 7.82. There is a good linear relationship between the index and the three independent variables. The unit contributions of household disposable income, total electricity cost and household population size to the welfare index were 0.0836, 0.1195 and −1.6161, respectively. To accelerate electrification and enhance people's well-being, China needs to further improve the power and communication infrastructure, promote the application of intelligent technology, and implement precise encouragement policies. • Electricity consumption and appliance services cloud be linked to residential welfare. • The changes of household electricity structure and cost are quantitatively measured. • The welfare index from home appliance services are estimated for the first time. • The well-being is influenced by ability to pay, willingness to pay and family size. • The domestic electrification has greatly improved people's quality of life. [ABSTRACT FROM AUTHOR]

Accurate and robust wind power forecasting (WPF) is great significance to ensure the safe and stable operation of the power system and promote the transformation of low-carbon energy. However, the high randomness and intermittency of wind power bring great challenges when designing reliable forecasting models. In this paper, a novel spatial-temporal attention graph convolutional network model is proposed. Firstly, the spatial attention mechanism is used to aggregate and extract the spatial correlations of the raw wind power data. Secondly, the temporal attention mechanism is applied to capture the temporal correlations. Then, the extracted spatial-temporal correlations were put into the temporal convolution network and the spatial convolution network to further obtain the temporal and spatial dependencies. Finally, the wind power forecasting results is output through the full connection layer. The proposed method is verified by using wind power data from real wind farm in China. The experimental results reveal that the proposed depth spatiotemporal prediction model has more significant advantages than other advanced models in terms of prediction accuracy and stability. • The spatiotemporal attention mechanism is used to learn the dynamic spatiotemporal correlations. • The spatiotemporal convolution mechanism is applied to enhance feature extraction of wind power. • The proposed model can improve the accuracy of wind power prediction by experiments. • Real datasets from wind farm in China is employed for the modeling study. [ABSTRACT FROM AUTHOR]

This paper takes 30 provinces in China as the research subjects and constructs a real options model to explore the impact of carbon emissions trading market, energy storage subsidies, and their synergy on the optimal investment decision of household PV-ESS projects. The results show that a single factor has a catalytic effect on project investment, and both together have a better effect. And, project investment benefits are better in areas with abundant light resources and higher than average electricity prices. In addition, a sensitivity analysis is conducted on the initial investment cost, CO 2 price, and energy storage subsidies. The results show that immediate investment in all provincial projects can be achieved when the initial investment cost is reduced by 50%, or the CO 2 price is increased by about 33 times, indicating that the current environment is insufficient to attract investors. The results provide a reference framework for investment in household PV-ESS projects. • Use real options methods to assess investment decisions for household PV-ESS projects. • Explore the role of carbon trading market and energy storage subsidies on investment. • Research the impact of different regional power grids on investment value. [ABSTRACT FROM AUTHOR]

To meet the active regeneration requirements of the diesel particulate filter (DPF), the outlet temperature of the diesel oxidation catalyst (DOC) must be maintained within the range of 585 °C–615 °C. Firstly , this paper explores the DOC system using a set of partial differential equations that describe the coupled flow and heat transfer of both the gas phase and solid phase carriers. Subsequently, the DOC model is significantly simplified, and the equations are solved based on their characteristics. Secondly , a Luenberger observer is designed, and the appropriate range of values for the observer gain is calculated. By minimizing the cost function and adhering to constraints within a finite prediction horizon, the optimal sequence for injecting hydrocarbon(HC) is determined. Thirdly , in this study, a Worldwide Harmonized Transient Cycle test is conducted on the engine, following the China VI emission standard. The computational results demonstrate that the mean absolute errors of the DOC outlet temperatures obtained from the model prediction and the observer are 0.535 °C and 1.994 °C, respectively. Finally , a nonlinear model predictive control is implemented to ensure that the DOC outlet temperature can be maintained between 585 °C and 615 °C. [Display omitted] • The DOC gas phase and solid phase temperature differential equations were simplified. • The Backward Differentiation Formulas method was used to solve the DOC model and the parameters were identified by the Levenberg-Marquardt algorithm. • The Luenberger observer was designed and the Lyapunov function was constructed to prove its stability. • The DOC model was combined with the minimised cost function and NMPC was implemented to optimise the HC injection strategy. [ABSTRACT FROM AUTHOR]

Underground coal gasification is an essential means of meeting the growing energy needs of some countries. In this paper, a new numerical simulation method of thermal-force-chemical-displacement multi-field coupling is developed, and in order to better adapt to the actual working conditions, taking Shanjiaoshu Coal Mine as the geological background, the first gasification working face of No. 12 coal seam is simulated based on the controlled retraction injection point technology. This method is used to simulate the heating of coal body by the ignition device at the initial stage of UCG, the chemical-thermal changes triggered by spontaneous combustion of the coal body under the continuous backward movement of the injection point, and the simulation of the formation of the cavity after the coal gasification reaction. Finally, the evolution of temperature and displacement fields in No.12 coal seam after gasification reaction is analyzed. Therefore, this study aims to simulate the actual working conditions more realistically, optimize the UCG technology, and produce more clean energy syngas to improve the energy situation in China. • A coupled thermal-force-chemical-displacement multi-field calculation of coal and surrounding rock strata. • Simulating of continuous movement of the gasification point to fit the actual process of CRIP. • The heat flowing into the model is calculated based on the chemical change. [ABSTRACT FROM AUTHOR]

This paper presents the design of a waste heat recovery system for a China VI emission standards (China VI) natural gas engine to address cooling challenges. A heat balance bench experiment was conducted to characterize the waste heat properties of the engine's exhaust, exhaust gas recirculation exhaust (EGR), and coolant. A transcritical dual-loop Rankine cycle system was modeled and analyzed using MATLAB to recover this waste heat. The system utilizes two loops: high-temperature loop to recover exhaust waste heat and EGR waste heat and low-temperature loop to recover waste heat from the coolant and high-temperature loop, improving overall recovery. The low-temperature loop is based on a transcritical cycle, making the system more compact than a subcritical alternative and better suited for vehicle applications. Analysis identified water as the working fluid for high-temperature loop and R32 as the optimal working fluid for low-temperature loop. The Rankine cycle waste heat recovery system was found to increase the engine's power output by approximately 71.29 kW at rated operating conditions, representing a 21.03% gain over the original power. • China VI natural gas engine holds substantial potential in waste heat utilization. • Transcritical dual-loop Rankine cycle is designed for waste heat recovery. • Water and R32 are the optimal working fluid combination. • Engine's power increases by 21.03% at the rated condition. [ABSTRACT FROM AUTHOR]