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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 It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards.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]

Faced with the difficult but urgent task of reducing carbon emission intensity, considering urban agglomeration as a fundamental spatial unit for this purpose has become increasingly important. However, as a long-standing and pivotal phenomenon in the development of urban agglomerations, the potential role of spatial functional division in urban agglomerations in reducing carbon emission intensity has been largely overlooked. To fill this gap, this paper explores the relationship between spatial functional division in urban agglomerations and carbon emission intensity, relying on a dataset of 19 urban agglomerations in China. The main results reveal an inverted U-shaped relationship between spatial functional division in urban agglomerations and carbon emission intensity. Spatial functional division in urban agglomerations increases carbon emission intensity in the initial stage; once it reaches a turning point, spatial functional division in urban agglomerations can reduce carbon emission intensity. This conclusion remains robust after conducting various robustness checks and addressing endogeneity concerns. This paper identifies productivity enhancement as an underlying mechanism that shapes this relationship. Moreover, the relationship between spatial functional division in urban agglomerations and carbon emission intensity exhibits heterogeneity based on function types and urban types. These findings emphasize that spatial functional division in urban agglomerations plays a unique role in reducing carbon emission intensity, which offers a new strategy for reducing carbon emission intensity from an individual city level to an entire urban agglomeration level. • An inverted U-shaped relationship exists between SFD and CEI. • Productivity enhancement derived from SFD is a crucial influencing mechanism. • Function types and urban types influence the relationship between SFD and CEI. • Improving SFD adds a novel urban agglomeration-level strategy to reduce CEI. [ABSTRACT FROM AUTHOR]

Promoting energy transformation in rural areas, building a Rural Integrated Energy System, and developing multi-energy complementary and comprehensive utilization of rural energy in accordance with local conditions are important paths to support global low-carbon transformation and rural revitalization. However, how to consider the coupling synergy of multiple devices and plan the capacity of multiple devices in rural energy systems in an economical, low-carbon, and reliable manner is an urgent issue facing the development of Rural Integrated Energy System. This paper proposes a bi-level programming optimization method of Rural Integrated Energy System based on coupling coordination degree of energy equipment. Firstly, a multi-dimensional rural user energy utilization framework is designed that comprehensively considers agriculture, animal husbandry, industry, and lifestyle. Secondly, the bi-level optimization model of the Rural Integrated Energy System based on the coupling coordination degree of energy equipment is constructed. The upper-level planning model puts forward the equipment coupling coordination degree, and determines the equipment selection scheme for different types of Rural Integrated Energy System planning. The lower-level planning model considers economy, low carbon, and reliability, and uses the improved multi-objective Harris Hawk optimization algorithm to solve the capacity of different energy equipment in the Rural Integrated Energy System. Finally, the effectiveness and scientific nature of the model and method proposed in this paper are verified by the Integrated Energy Systems of a rural area in the north of China. In addition, by comparing the equipment selection scheme with the traditional method, it can be found that the optimization scheme in this paper that considers the coupling coordination of energy equipment reduces the annualized total cost by 29.34 %, reduces the annual carbon emissions by 60.43 %, and increases the reliability by 15.85 %. The collaborative planning of energy economy, low carbon energy and reliable energy has been realized. • A framework for the utilization of energy by multiple users in rural areas was designed. • A bi-level programming optimization method of RIES based on coupling coordination degree of energy equipment was proposed. • An improved multi-objective Harris Hawk optimization was constructed. [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 park-level integrated energy system characterized by electricity heat cooling storage is regarded as a viable solution to energy and environmental crises due to its cleanliness and high efficiency. However, decision-makers encounter challenges in picking the optimum construction scheme. The paper introduces the matter element extension into the investment decision-making of the park-level integrated energy system. To begin, the paper analyzes the composition of the evaluation index system from the basic composition of the park-level integrated energy system, and establishes a standard system for comprehensive performance analysis, including economic, environmental, energy, security, and intelligence. Second, the subjective weight is calculated using the fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight. The matter element extension is used to evaluate the alternatives' performance. Finally, an industrial park in Jiangsu Province of China is selected as a case to verify. The results show that good comprehensive performance can be obtained by using photovoltaic, wind turbines, electric energy storage, ice storage, and heat pumps. The decision-making results will not be affected by the change in subjective and objective weight and the change in index weight within 20 %, but changes in matter-element classification would. • Intelligent performance criterion is introduced. • The evaluation system includes economy, environment, energy, security, intelligence. • A decision-making framework for the park-level integrated energy system is established. • A variety of sensitivity analyses were performed. • Using renewable energy and energy storage can obtain good comprehensive performance. [ABSTRACT FROM AUTHOR]

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the N RMSE by 3.82 % and N MAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system. • Propose a method to extract the most relevant trend component form wind speed. • Propose a novel method for wind speed correction and power calibration. • Propose a sample condition generation method based on GAF and CVA. • Propose an improved clustering distance into the wind speed correction process. [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]

Clarifying the relationship between new energy and economic growth is an essential prerequisite for achieving the goal of carbon peaking and carbon neutrality. This paper utilizes a combination of nonlinear analysis methods, including transfer entropy, Bai–Perron (BP) structure point breakpoint detection, and complex network theory, to examine the coupling relationship between China's new energy and economy across different stages and sectors. An analysis of the network's topological properties shows that a complex coupling driving relationship exists between the new energy industry and the other 11 economic sectors. In particular, the agricultural sector exerts a significant driving force on the new energy industry, with the most extended duration of influence. Besides, the IT and construction industries also play a driving role in the new energy industry, and the latter's impact is earlier than that of the IT industry. The empirical results can guide the government and makers to formulate long-term sustainable action plans for different economic sectors. • Transfer entropy, BP structure break point detection and complex network are used. • 33 net transfer entropy symbol mode complex networks are constructed. • The coupling relationship between China's new energy and economy is complicated. • The agriculture industry has a large driving force on new energy industry. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind and solar, can reduce environmental pollution and promote the transformation of the energy structure. However, the intermittency and uncertainty of new energy sources such as wind and photovoltaics pose challenges to large-scale grid integration. Effective utilization of intermittent energy sources requires large-scale energy storage facilities. Salt rock is recognized as an excellent medium for underground large-scale energy storage with a wide range of applications. This paper identifies the potential of salt caverns to be used for large-scale energy storage by analyzing the distribution of wind and solar energy resources in China, taking into account the grid-connected infrastructure of the Chinese power grid and the geographic location of salt mines. The overlapping areas are identified for utilization in compressed air energy storage and hydrogen storage. Through meticulous categorization and analysis of the factors influencing salt cavern underground storage site selection, the AHP-EWM method is introduced to establish an objective hierarchy model for a comprehensive evaluation system of salt cavern underground storage site selection. The comprehensive planning of China's 20 major salt mines is conducted, and the research findings hold significant reference value for facilitating the low-carbon energy transition in China. • The ways which salt mines can be used for renewable energy peaking are summarized. • Renewable energy religions combined with grid-connected infrastructure and salt mines in China were studied. • Objective hierarchical modeling of subsurface salt cavern siting was developed using a combinatorial assignment method. • Energy Storage Planning for 20 Major Salt Mines in China. [ABSTRACT FROM AUTHOR]

The exploration and promotion of new energy constitute a significant initiative for numerous countries in their pursuit of sustainable development and efforts to alleviate climate change. However, the influence of a multitude of uncertain factors on the evolutionary path of new energy is currently poorly studied. By establishing a dynamic evolution model for new energy under polymorphic uncertainty, this paper analyses the complex interaction between new energy, economic growth, and carbon trading over the period 2020–2060, using China as a case study. We employ numerical simulation and sensitivity analysis to investigate the evolutionary trajectories of the new energy system under various parameter value scenarios. We show that the evolutionary trajectories will undergo substantial fluctuations before 2030, subsequently stabilizing from 2030 onwards. The study highlights that an excessively rapid pace of new energy development is detrimental to new energy systems, whereas energy storage system deployment has the opposite effect. There exists a significant risk of the system collapsing as a result of slow new energy development, over-building energy storage facilities, or over-intervening in carbon trading. We also find that overdevelopment of energy storage system may have greater negative impacts on the economic system than underdevelopment. [Display omitted] • Dynamic evolution model of new energy under polymorphic uncertainty is constructed. • New energy production elasticity and energy ratio are innovatively constructed. • Excessive intervention in carbon market can lead to the collapse of new energy system. • Overdeveloped energy storage hinder the synergy of new energy and economic growth. [ABSTRACT FROM AUTHOR]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

As one of the key projects of the top ten targeted poverty alleviation projects, Photovoltaic Poverty Alleviation Projects (PPAP) has played an important role in poverty alleviation and eliminating absolute poverty. This paper analyzes the impact of the PPAP on the consumption and poverty vulnerability of impoverished households and reveals the characteristics of the targetedness, effectiveness and sustainability of PPAP based on the data of the previous poverty-stricken households recorded in four townships in Wuwei County, Wuhu City, Anhui Province, China. The results show that under the extreme poverty standard line, PPAP can significantly reduce the poverty vulnerability of impoverished households, and the effect of the PV poverty alleviation project has a strong sustainability; It has a significant heterogeneous impact on families of different poverty types, among which, it plays a significant role in alleviating the poverty vulnerability of impoverished households due to the loss of labor ability, and low-income families, with strong targetedness; For families with a high degree of vulnerability to poverty, the PPAP can significantly alleviate their vulnerability, indicating the effectiveness of PPAP to play a bottom-up role for the poor families. • Effect of China's Photovoltaic Poverty Alleviation Policy. • The targetedness, effectiveness, and sustainability of poverty reduction effects. • Micro-level statistical data obtained from on-site surveys on impoverished households. [ABSTRACT FROM AUTHOR]

As a promising energy source, natural gas hydrate production has ignited scientific research in multidisciplinary fields globally, generating a vast amount of literature. A review in multidisciplinary and international perspectives will contribute to a comprehensive understanding about this field. This paper maps the two-decade spatiotemporal landscapes of scientific research on natural gas hydrate (NGH) production with a bibliometric analysis. The landscapes suggested that the study on gas hydrate production covers multiple research fields and international collaboration. Research topics on NGH production experienced three stages involving resource exploration, productivity enhancement, and environmental issues. An analysis of the research with the most activity showed that Energy & Fuel, Chemical Engineering, and Multidisciplinary Geoscience were the most active, with Thermodynamics, Geological Engineering, Ocean Engineering, and Civil Engineering experiencing fast growth. In terms of the landscape of international collaboration, the United States and European countries were the main contributors in the early stage, followed by China after 2008. The breakthrough of the commercial production threshold relies on the international collaboration and the multidisciplinary research among Thermodynamics, Geological Engineering, Ocean Engineering and Civil Engineering to characterize the energy consumption, gas production rate, and reservoir stability. • A two-decade spatiotemporal landscape of scientific research on NGHs production is delineated with a bibliometric analysis. • Research landscapes visually show the evolution of topics, interdisciplinary intersection, and global collaboration pattern. • NGHs' commercial promise is assessed considering resource potential, production techniques, and environmental concerns. The reference co-citation network is a valuable tool for tracking the evolution of research topics. It highlights links between two references that are concurrently cited in a third article, with the link strength positively correlated to the number of articles that cite both references. Subsequently, the strongly lined references are grouped in a cluster with a topic, which is determined by extracting a phrase from the titles, keywords, and abstracts of the citing documents using the log-likelihood ratio (LLR) method. As a result, the co-citation network is divided into clusters with specific topics, providing insight into the topic evolution and the research frontiers. [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]

Electricity market-oriented reform and carbon emissions trading, as the main policies implemented in China's power sector, have a profound impact on China's green and low-carbon transition by promoting resource allocation efficiency and internalizing external environmental costs, respectively. Therefore, this paper constructed a dynamic recursive CGE model to measure the energy, economic, and environmental impacts of carbon emissions trading and electricity market-oriented reform from electricity market structure and pricing mechanism. The results show that the two policies have a synergistic effect on industrial structure optimization, energy consumption structure optimization, and carbon emission reduction. Specifically, the share of value-added of the tertiary sector, the growth rate of coal consumption, the share of thermal power consumption, and carbon emissions in 2030 are reduced by 5.70 %, 18.29 %, 2.55 %, and 2.838 trillion tons, respectively, compared with the scenario without carbon emissions trading and electricity market-oriented reform. However, imperfectly competitive market structures outperform perfectly competitive market structures in energy consumption structure and carbon emission reduction. Electricity price regulation is also more beneficial to GDP growth and increase in residential disposable income compared to price liberalization. To this end, returning emission trading scheme revenues to residents can effectively mitigate the adverse impacts caused by market-oriented reforms. • EMR is involved in market structure, pricing mechanisms, and market regulation. • EMR and CET have a synergistic effect on carbon reduction. • Energy consumption structure is better in imperfectly competitive market. • Electricity price regulation is more beneficial to GDP growth. • Returning ETS revenue to residents increases per capita disposable income. [ABSTRACT FROM AUTHOR]

Prefabricated buildings (PB) are assembled using prefabricated components, known for their cost-effectiveness and high efficiency. They are seen as an effective means to achieving sustainable development goals and mitigating climate change. This paper examines the impact of prefabricated buildings on urban green total factor energy efficiency (GTFEE) using panel data from 278 cities in China. The analysis is conducted through the application of a super-efficiency slack-based measure (SBM) model and a difference-in-differences model. The results of the study indicate that the promotion of PB can significantly improve urban GTFEE; there is heterogeneity in the improvement of urban GTFEE by PB; the innovation effect, industrial effect and marketization effect of PB are the main ways to improve urban GTFEE. Specifically, PB exerts an innovation effect by promoting technological innovation. It influences consumer and investor demand, leading to increased activity in the secondary industry, thereby creating an industrial effect. Moreover, PB facilitates the marketization effect by phasing out outdated industries, encouraging autonomous enterprise innovation, and fostering the growth of new advanced enterprises. This study reveals the critical role of PB in urban GTFEE improvement. It clarifies its mechanism of action, which provides a reference for urban sustainable development strategies and energy efficiency. • Prefabricated buildings can improve green total-factor energy efficiency. • Urban green total-factor energy efficiency has been measured. • PB improves GTFEE through innovation, industry and marketization effect. [ABSTRACT FROM AUTHOR]

Government auditing plays a crucial role in enhancing energy efficiency and advancing green and low-carbon economic development. Given that audit oversight's quality and efficiency are influenced by the independence of auditing institutions, this study utilizes the vertical management reform of government auditing institutions as an exogenous shock, and empirically examines its impact on urban energy efficiency (UEE) using a Difference-in-Differences model (DID). We find that the audit reform improved UEE by 0.023 which is both economically and statistically significant. Concurrently, the audit reform and the development of the digital economy play substitutive roles in enhancing UEE. Finally, the results of the mediation analysis using the stepwise regression method indicate that the improvements in audit efficiency, green technology innovation, and energy structure optimization have mediated the promotion of UEE in the audit reform process, accounting for respective mediation effects of 4.35%, 4.35%, and 8.69%. • We investigate the audit independence from the level of audit management systems. • This paper finds a substitution effect of digital economic development. • The study discusses the heterogeneity in audit oversight from a global perspective. [ABSTRACT FROM AUTHOR]

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply. • Cluster analysis to extract steam user load characteristics. • Divide peak/valley times based on network storage and user traits. • Create a multi-objective Time-of-Use model for steam pricing optimization. • Carry out a case validation in an actual industrial park in Jiangsu. [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]

Faced with the challenges posed by the Paris Agreement targets, biomass co-firing offers an efficient measure to decarbonize coal-fired power generation. Although economic burden of biomass co-firing has severely limited the large-scale application, effective CO 2 emission trading system has the capability to subsidize and incentivize. Existing studies related to biomass co-firing under CO 2 emission trading system fail to consider multiple decision makers and uncertain decision-making environment. To address these limitations, this paper proposes a bi-level model for coal power decarbonization via biomass co-firing considering CO 2 emission trading system in an uncertain decision-making environment. It considers the interactive relationship between multiple decision makers. The upper-level model focuses on the formulation of CO 2 emission trading system, and the lower-level model decides biomass co-firing and emission reduction. Fuzzy set theory is employed to describe the uncertain parameters and convert them into exact values. To calculate the model, a bi-level interactive method based on satisfactory solution is developed. A case study is conducted to illustrate the effectiveness and practicality of the proposed model. Results show that the price of initial quotas fluctuates between 38.01 and 48.7 CNY/tonne, and the lowest carbon intensity is 0.741 kg/kWh. Management recommendations are provided to support CO 2 emission reduction. • A bi-level model for biomass co-firing under carbon emission trading is developed. • The hierarchical decision process between multiple stakeholders is considered. • Fuzzy set theory is employed to effectively handle real-world uncertain information. • A case study in China is conducted to demonstrate the practicality of the method. • The method effectively achieves carbon reduction and reaches Stackelberg equilibrium. [ABSTRACT FROM AUTHOR]

Due to the requirement of a cleaner, more sustainable form of energy production and the rapid development of DT, nuclear energy needs to be digital transformed. This paper aims at the time series prediction module of DT in NPP. Despite the widespread application of time series forecasting in various domains, the inherent uncertainties within the data and the selection of neural network hyperparameters pose a formidable challenge to accurate predictions. This study proposes a coupled multivariate prediction method and conducts a comparative analysis of three popular time series forecasting models: LSTM, CNN-LSTM, and Transformer. PSO hyperparameter optimization is also employed, accompanied by multiple error calculation metrics for model evaluation. It is noteworthy that the experimental data employed in this study are derived from the real operation process of a Gen II + reactor in the Chinese mainland, which is in the off-site power loss accident condition. Simultaneously, adopting a smaller training dataset proportion contributes to striking a balance among resource utilization, model performance, and experimental efficiency in the research. Experimental results unequivocally demonstrate that among these models, Transformer excels in multi-input multi-output time series forecasting. Moreover, uncertainty analysis using Bayesian generates a forecast band, proving the robustness of Transformer. • The study relies on real operational data from an operating Gen II + reactor in China, ensuring the authenticity of the dataset. • A multiple-input multi-output Transformer model based on PSO optimization is developed for accident prevention. • Transformer is superior both in point prediction and uncertainty analysis of interval prediction. • Employing a smaller training dataset proportion aids in optimizing the equilibrium among resource efficiency, model performance, and experimental efficiency in research. [ABSTRACT FROM AUTHOR]

Multimodal transport is an effective method to reduce the energy consumption and carbon dioxide (CO 2) emissions in transportation sector. Evaluating the low-carbon performance is a prerequisite for developing multimodal transport. This paper establishes the new cross-efficiency (CE) network data envelopment analysis (DEA) approach, and applies it to evaluate low-carbon efficiency of railway-waterway intermodal transportation (RIT) in China from 2017 to 2022. The proposed approach takes advantage of network DEA and considers the internal structure in the RIT system as well as the interconnections between various transportation modes. This article comprehensively analyzes the evaluation results in terms of the overall situation, geographic distribution characteristics, internal stages and links, external influencing factors, and efficiency prediction, and the results find that: (1) the low-carbon efficiency of China's overall RIT needs further improvement. (2) The geographical distribution shows a trend that the efficiency in the Northern Region is higher than that in the Southern Region. (3) The level of urban industry, external transportation conditions, and foreign trade development will all affect the performance of RIT. Finally, according to the empirical results, management strategies are proposed. The main contributions include: proposing a network-structured CE-DEA method focusing on internal states; The advantages of CE network DEA are utilized to study CO 2 emissions of multimodal transport; The low-carbon efficiency of RIT in China is comprehensively analyzed. This study enriches the research on DEA methods, and the results can provide a basis for decision-making by the government and transport enterprises, help promote the development of low-carbon transport in China, and provide a reference for the optimization of multimodal transport in other countries. • Constructed a cross-efficiency DEA approach. • Analyzed the low-carbon efficiency of multimodal transport. • Discussed the distribution and influencing factors of efficiency. [ABSTRACT FROM AUTHOR]

Accurately predicting natural gas consumption is essential for developing a clean, low-carbon, safe, and efficient energy system. Therefore, this paper integrates the idea of the extended logistic model into multivariable grey prediction theory. Considering the delay effect and nonlinear characteristics of the system, a novel dynamic nonlinear multivariate grey delay prediction model is constructed. Meanwhile, the algorithmic framework for solving the parameters of the new model is provided to identify and optimize the parameters of the time-delay effect of the model. The genetic algorithm was identified as the best matching optimization algorithm for the new model. Furthermore, the new model is applied to fitting and forecasting China's natural gas consumption, and the validity of the model is fully verified. Compared with other forecasting models, the new model has a lower average relative error in the fitting and forecasting stage, and, in particular, achieves a better forecasting effect with the lowest MAPE pre value (0.001 % for one-step ahead, 0.005 % for two-step ahead, and 0.746 % for three-step ahead). Moreover, the model is employed to forecast China's natural gas consumption for the next five years, providing a robust foundation for government policies related to the supply of natural gas. The highlights of this article are as follows. • A novel dynamic nonlinear multivariable grey delay model is proposed. • The optimum algorithm matching the novel model is determined from four alternatives. • GA with the optimal parameter group has been selected for searching coefficients. • The validity of the model is verified by theoretical deduction and case analysis. [ABSTRACT FROM AUTHOR]

Diversified energy power generation is a critical component of China's West-to-East Electricity Transmission (WEET) project and a key driver of China's clean energy strategy. Aiming at the complex non-linear relationship of inter-regional energy system and fully exploring the information of system data, our paper proposes a novel information-enhanced Grey Lotka–Volterra model (IE-GLVM). The novel model consists of Grey Lotka–Volterra equations and universal network terms, which achieves a clever fusion of energy system competitive relationships and data information-driven modeling in the modeling methodology. In addition, the Joint Gradient Descent method is used to optimally search for all the parameters of the novel model, and we theoretically prove the stability of the algorithm. Based on this, the IE-GLVM model is used to analyze the competitive and cooperative relationships among the three provinces of Sichuan, Hubei, and Jiangsu in the middle line of the WEET project in China under multiple energy sources for power generation and to forecast the future power generation. Eventually, IE-GLVM was compared with three benchmark models, and it demonstrated superior performance in most cases. An analysis and summary of the power generation relationships of each regional energy source were conducted based on the quantitative results of the IE-GLVM model. • A novel information enhanced grey Lotka–Volterra model is proposed for diversified energy power generation forecasting. • The new model combines the advantages of mechanism modeling with data modeling and improves prediction accuracy. • The model can analyze the competition and cooperation relationship of the electrical structure and predict future. • The joint gradient descent method is proven to smoothly optimize all parameters of the model. • The model is used to analyze the electrical structure in China's West-to-East Electricity Transmission project. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head. • Long-term operation of cascade energy storage system was modelled. • Operating rules of cascade energy storage system were derived. • 81.6 % renewable curtailment was converted to hydroelectricity. • Upper plant accounted for 96.5 % of hydroelectricity increment. • Water saving and renewable accommodation could be coordinated. [ABSTRACT FROM AUTHOR]

The aggravation of the global warming crisis, due to excessive consumption of traditional energy, urgently calls for the strategic goal of sustainable development. This study addresses endogeneity concerns by employing panel fixed effect and DID models, employing panel data from China's provincial green finance index spanning from 2016 to 2021. The empirical findings indicate that: (1) the impact of the new energy industry on environmental and economic benefits is more pronounced in inland areas characterized by lower GDP and smaller population size; (2) this effect is driven by green technological innovation, government incentive policies, and job creation. Drawing upon these results, this paper offers a range of pertinent recommendations at governmental, industrial, and societal levels to foster sustainable development, thereby enhancing social welfare. • We address endogeneity concerns by employing panel fixed effect and DID models. • New energy industry development contributes to environmental and economic benefits. • The impact of the new energy industry is more pronounced in inland areas. • The effect is driven by CO2 reductions, incentive policies, and job creation. [ABSTRACT FROM AUTHOR]

Achieving an efficient, modern, and sustainable energy transition towards carbon neutrality has attracted worldwide attentions. Composite statistical indices are increasingly recognized as valuable tools for policymakers, offering enhanced understanding of the present condition and anticipated direction of energy transition. In this research, we constructed a comprehensive index to track provincial energy transition in China. With this index, we monitored the energy transition progress of 30 provinces in China. The Provincial energy transition index (PETI) scores exhibited considerable heterogeneity across provinces and throughout different time periods, with eastern provinces having the highest scores, and some northwestern and large coal provinces, like Shanxi, having the lowest scores. These findings indicated that provincial energy transition policies should be tailored to the local energy status and economic structures. The paper delineated conclusions and policy implications derived from the study's extensive evaluation. Specifically, effective ways to further improve a region's sustainable energy transition path include advancing the use of clean energy, vigorously promoting power grid construction, and investing in new energy sectors. The implementation of these strategies is expected to reduce energy consumption and carbon intensity, thereby decreasing carbon emissions, then ultimately accelerating the realization of the dual carbon targets. • We present a provincial energy transition index to track regional energy transition process. • There was significant heterogeneity in PETI scores across different provinces. • There were disparities among the performances in the three dimensions of the PETI. • Energy transition policies ought to give precedence to regions that are lagging in their energy transition. [ABSTRACT FROM AUTHOR]