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This study presents a bibliometric review focusing on the utilization of water electrolysis as a means of generating hydrogen as an energy carrier. The analysis includes research conducted over the past decade, covering from 2014 to 2023 (till August). Several key aspects are highlighted, including publication trends, the leading country in research output, journal rankings, and a citation analysis of papers related to water electrolysis. The findings reveal a notable increase in publication trends, with China emerging as the leading contributor in this research area. Furthermore, the International Journal of Hydrogen Energy is identified as the highest-ranked journal in terms of both publication number and citation impact. Additionally, the top ten most cited research articles and review papers are investigated to determine their influence within the field. To gain a deeper understanding of water electrolysis methods, the three primary approaches: alkaline water electrolysis (AWE), proton exchange membrane (PEM) electrolysis, and solid oxide electrolysis (SOE) is examined. The analysis emphasizes that PEM electrolysis is the most prevalent method for hydrogen generation, particularly when integrated with renewable energy sources such as solar and wind power due to its rapid response to electrical input fluctuation. Finally, the challenges and future directions of water electrolysis in hydrogen production are highlighted, including an exploration of economic and environmental considerations at large scale, offering insights into the path forward for advancing this technology sustainably. • A comprehensive bibliometric analysis of water electrolysis for hydrogen production. • Publication trends, journal rankings, and citation analysis are highlighted. • China is identified as the leading contributor in water electrolysis publications. • International Journal of Hydrogen Energy: the highest-ranked journal in this field. • Specific electrolysis methods, including AWE, PEM, and SOE are examined. [ABSTRACT FROM AUTHOR]

The ongoing transition from fossil fuel-based electricity generation toward green energy relying on the intermittent renewable energy sources indispensably requires development of novel energy conversion and storage methods. Depending on a particular location and local resources, pumped storage, lithium- or sodium-ion batteries, compressed air energy storage, as well as the so called power-to-gas (P2G) technologies may be applied. Taking into account the abundant solar energy resources available in the northwestern China, such as Ningxia hui autonomous region, and local water resources coming from the Yellow River, a power-to-hydrogen (P2H2) distributed energy conversion system is proposed in this paper, which is based on a high-efficiency reversible Solid Oxide Cell (rSOC). The conducted studies comprise tests with simulated current based on the actual photovoltaic power station data, and with usage of the as-obtained Yellow River water for the electrolysis mode without any pretreatment. It is documented that the reversible operation is beneficial, and the tested cell can operate for a prolonged time. Of importance, Na, Ca, Mg, and Si elements remain in an evaporator, and do not contaminate the cell, while higher than the initial boron concentration can be found in the condensed exhaust gases. The respective operational parameters of the system correspond to an electrolysis current density loading up 433.3 mA cm−2 for 4 h, as well as fuel cell operation mode with a current density of 8.3 mA cm−2 for 12 h. It is found that the average voltage losses in the electrolysis cell and fuel cell mode are 0.27 %/cycle and 0.886 %/cycle, respectively, indicating that the lifetime of the cells can be more than 800 h. The reported studies demonstrate validity of applying rSOC-based P2H2 system, especially if abundant photovoltaic power and water resources are available. [Display omitted] • The Yellow River water was utilized by rSOC for the first time. • The designed test current was modeled with local photovoltaics. • The P2H2 nearby consumption system can reversibly operate for over 800 h. • The degradation rates in SOFC mode are lower than those in SOEC mode. • The Yellow River water has no significant effect on the degradation rate. [ABSTRACT FROM AUTHOR]

Regional integrated energy systems (RIES) can economically and efficiently use regional renewable energy resources, of which energy storage is an important means to solve the uncertainty of renewable energy output, but traditional electrochemical energy storage is only single electrical energy storage, and the energy efficiency level is low. Hydrogen energy storage has the advantages of large energy storage capacity, long storage time, clean and pollution-free, and can realize the synergistic and efficient utilization of electricity and thermal power. Based on this, this paper proposes a synergistic planning method for an integrated energy system with hydrogen storage taking into account the coupled use of electric-thermal energy, which effectively reduces the system carbon emission and improves the comprehensive energy efficiency level. Firstly, this paper constructs an electric-thermal coupling model of the hydrogen energy storage unit and proposes an optimization strategy for the integrated energy system containing hydrogen storage taking into account the utilization of electricity and thermal power. Secondly, a planning optimization model with the lowest economy and carbon emission and the highest comprehensive energy efficiency was constructed. Third, the CSPO-GE optimization algorithm is proposed for solving the problem, which significantly improves the solution efficiency. Finally, a planning optimization simulation of RIES for a residential community W in northern China verifies the effectiveness of the model and method proposed in this paper. The comparative analysis of the three schemes shows that compared with the integrated energy system with conventional electrochemical energy storage and heat storage tank as the main form of energy storage and the integrated energy system with only hydrogen storage, the integrated energy system with hydrogen storage and heat storage tank can reduce carbon emissions by 43.8% and 7.61%, respectively, and improve the integrated energy efficiency level by 337.14% and 14.44%. • An electrothermal coupled production strategy based on dynamic equilibrium feedback regulation is proposed. • A hydrogen-containing RIES planning and optimization model considering economy-environment-efficiency is constructed. • A multi-objective chaotic particle swarm algorithm (CPSO-GE) based on greedy and elite retention strategies is proposed • The impact of cost variation of hydrogen storage devices on the economics of integrated energy systems is considered. [ABSTRACT FROM AUTHOR]

The cross-regional renewable hydrogen supply is significant for China to resolve the uneven distribution of renewable energy and decarbonize the transportation sector. Yet the economic comparison of various hydrogen supply routes remains obscure. This paper conducts a techno-economic analysis on six hydrogen supply routes for hydrogen refueling stations, including gas-hydrogen tube-trailer, gas-hydrogen pipeline, liquid-hydrogen truck, natural gas pipeline, MeOH truck, and NH 3 truck. Furthermore, the impacts of three critical factors are examined, including electrolyzer selection, transportation distance, and electricity price. The results indicate that, with a transport distance of 2000 km, the natural gas pipeline route offers the lowest cost, while the gas-hydrogen tube-trailer route is not economically feasible. The gas-hydrogen pipeline route shows outstanding cost competitiveness between 200 and 2000 km, while it is greatly influenced by the utilization rate. The liquid-hydrogen truck route demonstrates great potential with the electricity price decreasing. This study may provide guidance for the development of the cross-regional renewable hydrogen supply for hydrogen refueling stations in China. • The techno-economics of six renewable hydrogen supply routes for hydrogen refueling stations are studied in China's context. • The cost impacts of electrolyzer selection, transportation distance, and electricity price are explored. • Natural gas pipeline H 2 blending route offers the lowest cost at 2000 km distance. • Gas-hydrogen pipeline route's cost is strongly influenced by its utilization rate. • Liquid-hydrogen truck route has the best economics when the electricity price is below 0.1 CNY/kWh. [ABSTRACT FROM AUTHOR]

• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Urban energy supply includes more renewable energy, but renewable energy, such as wind and photovoltaic, is intermittent and connected to the grid and impacts the safe operation of the power system. For urban multi-type energy dispatching, this paper proposed a day-ahead multi-energy robust optimization dispatching method for an urban power grid with a high proportion of renewable power. Firstly, the power supply and load operation scenarios of the urban energy supply system are constructed. Multiple typical scenarios are formed based on fully considering the uncertainties caused by renewable energy and load prediction. A robust optimal scheduling model with system constraints was constructed based on the tuning capacity of adjustable generator sets and energy storage devices in the power grid, with multi-system functional cost optimization as the objective function. Extending the augmented Lagrange multiplier Method, the Alternating Direction Method of Multipliers (ADMM) is proposed to solve the constructed model. The effectiveness of the proposed method is verified by some examples based on the data of an actual power grid in China. [ABSTRACT FROM AUTHOR]

Strengthening environmental regulation (Reg) is an important measure to solve energy and environmental problems, then to realize the profit sustainable growth (Gro). Taking 116 listed renewable energy enterprises in China from 2010 to 2019 as samples, this paper uses FE and SYS-GMM models to examine the moderating effect of three types of Reg in the relationship of factor market distortions (Dist) and the Gro. The conclusions are as follows: For the direct impact, capital and labor market distortions (DistK and DistL) hinder the Gro. Specifically, the absolute value of the coefficient of DistK in state-owned enterprises (SOEs) is greater than that of the DistL, which indicates that the inhibitory effect of DistK in SOEs is more significant. For the indirect impact, the coefficients of economic and supervised environmental regulation (RegE and RegS) are positive and significant, and promote the Gro of SOEs and non-SOEs, while legal environmental regulation (RegL) is the opposite, which shows that Reg has significant "cost effect" and "rebound effect". Moreover, Reg has a positive moderating effect, which is mainly reflected in SOEs, and alleviates the inhibitory effect of DistK and DistL on Gro to a certain extent. Finally, this paper puts forward some targeted policy suggestions. [ABSTRACT FROM AUTHOR]

Renewable energy is regarded as an effective method to alleviate environmental pollution and accelerate the green transition in China. This paper systematically investigates the impact of financial inclusion on renewable energy demand by using S-GMM model based on provincial data in the green finance era. This paper also analyzes the asymmetry, heterogeneity, and mediating mechanism in the nexus between financial inclusion and renewable energy demand, and highlights the following findings: (1) Improved financial inclusion can promote renewable energy development in China and present the characteristics of green finance; (2) the impact of financial inclusion on renewable energy demand is asymmetric and heterogeneous; specifically, this impact is much higher in northern China and the regions with higher renewable energy development; and (3) wind and photovoltaic power generation are effective mediators between financial inclusion and renewable energy industry, while the mediation effects of hydropower and nuclear power are not significant. New evidence in this study is presented for the achievement of green transition in China, as well as practical policy implications for improving financial inclusion and increasing renewable energy consumption. For instance, fiscal and tax incentives and green investment subsidies in the renewable energy sector need to be further improved. • This study explores the impact of financial inclusion (FI) on renewable energy demand (RED) in China. • Asymmetry, heterogeneity, and the mediating mechanism are further discussed. • Increased FI can promote RED , which presents the characteristics of green finance. • The impact of FI on RED is asymmetric and heterogeneous. • Wind and photovoltaic power are effective mediators between FI and RED. [ABSTRACT FROM AUTHOR]

This paper uses panel data on renewable energy in China. Thus, the current analysis fills the gaps by examining the relationship between green technology innovation and renewable energy and CO 2 emissions from 1990 to 2018. The current study examines all concerns connected to panel data analysis with advanced panel estimators, such as cross-sectional dependence, stationarity, variation in slope parameters, and structural break. Our findings show that test results reveal that green technology innovation and renewable energy have a negative and considerable influence on CO2 emissions in the long run. Moreover, the short-run relationship of green technology innovation, on the other hand, is not significant—as evidenced by the findings of robustness tests such as AMG and CCEMG. Other empirical results revealed that GTI, REN and NEN significantly reduced local CO 2 emission, while POP and PI had no obvious reduction effects on CO 2 emissions. Different GTIs had the same spatial "symbiotic effects" on CO 2 emission reduction in the short term, showing positive spatial spillover reduction effects. Lastly, we conclude that the use of green technology innovation has positive externalities. Some policies to assist green innovation technologies and renewable energy resources have been suggested for the Chinese government to enact. • This paper uses panel data on renewable energy in China. • The current study examines all concerns connected to panel data analysis with advanced panel estimators. • The short-run relationship of green technology innovation is not significant. • We conclude that the use of green technology innovation has positive externalities. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from 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 existing evaluation methods of renewable energy technical plans. [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]

Hydrogen, as a clean and efficient energy source, is important in achieving zero-CO 2 targets. This paper explores the potential of hydrogen geologic storage (HGS) in China for large-scale energy storage, crucial for stabilizing intermittent renewable energy sources and managing peak demand. Despite its promise, HGS faces challenges due to hydrogen's low density and viscosity, and its complex interactions with geological formations and microorganisms. This review offers a comprehensive overview of the current research status for HGS, with a particular focus on highlighting the main challenges confronting China. These difficulties and challenges primarily arise from complex geological conditions and the absence of fundamental parameters within potential reservoirs, including depleted oil/gas fields, salt caverns, and brine aquifers. Additionally, we have synthesized the current applications of machine learning (ML)as a potential solution. Key challenges are examined, such as the effects of operational parameters (e.g., cyclical injection-production and injection rates) on HGS efficiency, which can influence phenomena such as hydrogen fingering and caprock integrity. This review also looks into the insufficiently understood hydrogen-water-rock geochemical reactions under diverse temperatures and pressures, a gap that hampers the development of predictive numerical simulations and raises concerns about hydrogen leakage due to changes in porosity and permeability. Additionally, the paper addresses the limited knowledge about the metabolic mechanisms of subsurface microorganisms under extreme conditions, highlighting potential risks of hydrogen leakage and groundwater contamination. These microorganisms can metabolize hydrogen, producing gases such as CH 4 and H 2 S, which may cause steel corrosion. Furthermore, the study assesses the distribution and prospects of three primary methods for pure hydrogen storage in China, considering the current state of hydrogen development and relevant government policies. The role of ML in advancing HGS is also discussed, offering insights into future research directions. This review not only scrutinizes the scientific challenges of HGS but also underscores its potential, guiding future simulation and practical engineering applications in this field. • HGS is feasibility in China through policy examination and potential site analysis. • Geochemical and microbial reactions jeopardize to reservoir integrity and H 2 quality. • Elucidating the present application and future prospective for ML in HGS. • Summarizing the key challenges associated with achieving large-scale HGS. [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]

The fundamental way for China to achieve carbon peak and carbon neutrality is to continuously improve the penetration of renewable energy (RE) in the power system and establish an electricity mix dominated by RE power. According to China's 14th Five-Year Plan, the proportion of non-fossil energy power generation should reach about 39% by 2025. However, the impact of the increase in share of RE power on carbon emissions of the electricity sector remains to be estimated. To this end, the purpose of this paper is to capture the nonlinear characteristics of RE on electricity-related carbon emissions by using panel smooth transition regression (PSTR) model and provincial panel data covering the period 1991–2020 under the background of provincial RE quota system. The estimation results indicate that, (i) in the four transition variables of PSTR model such as the installed capacity of power generation, substation equipment capacity, total electricity consumption and total power generation, the nexus between RE deployment and electricity-related carbon emissions was detected as nonlinear. (ii) The heterogeneous RE carbon abatement effect is reflected in its increasing penetration in provincial power systems. (iii) Carbon abatement performance of RE generation decreases in the negative range with the increase of transition variables. (iv) The estimation coefficient of RE is positive at the higher value of the transition variable in the PSTR model of carbon emissions per capita. As a result, the RE quota with the best emission abatement performance should be designed based on the provincial power resource endowment and the medium and long-term expected electricity consumption. [ABSTRACT FROM AUTHOR]

The development of information and communication technology (ICT), a subset of many functions such as visual communication, can positively affect renewable energy development in different countries. In this paper, focusing on China as the world's largest emitter of greenhouse gases, the relationship between visual communication and the deployment of renewable energy is investigated using the dynamic ARDL (autoregressive distributed lag) method and annual data from 1990 to 2021. Regarding the empirical estimations, a 1% increase in green finance market size leads to an improvement of sustainable energy deployment by 0.01%. In addition, the ICT development index has a significant and positive effect only in the long term. Furthermore, there is evidence of uni-directional causality from ICT to renewable energy deployment in the medium and long term. The recommended practical policies by the research are developing green financing tools, green economic growth policies, and creating virtual portals for financial interactions. • There is uni-directional causality from visual communication to renewable deployment. • ICT development index has a positive effect in the long term. • Effect of GDP on the development of renewable energy consumption is negative. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, the traditional energy storage operation based on individual framework —users own and operate independently mode may become impracticable due to conflicting interests, insufficient utilization, and poor interoperability. Riding on the wave of the proliferation of sharing economy, storage energy sharing expands the existing storage energy without requiring costly and time-consuming infrastructure investments. However, the development path of shared energy storage (SES) mode is not clear due to the asymmetric decision-making of the owners of energy storage systems under bounded rationality. In this paper, the diffusion of the business model of SES among multiple renewable energy stations (the owners, RES) and its key factors are analyzed based on the evolutionary game. The goal is to maximize social welfare and ensure the continuous growth of the sharing market. The numerical simulation is carried out based on the data from northwest China to interpret the development path. The result shows that, in renewable energy cluster the stations with intermittent output or with the higher prediction accuracy are more willing to participate in sharing. The energy storage sharing mode fails when the energy storage capacity ratio of RES is less than 10%. While the high-level ratio (more than 30%) is not conducive to the diffusion of the sharing model in RESs with low power generation prediction accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards (RPS) is a mandatory institutional change to promote low-carbon energy transformation and renewable energy development. Its successful implementation requires the scientific design of relevant incentive mechanisms and rules. To make up for the defects in the current RPS mechanism design and the problems exposed in the Tradable Green Certificate (TGC) market, this paper takes China as an example and uses the system dynamics method to study the supply side incentives. The results show that: (1) Scientific setting the relevant system parameters of RPS (such as quota, benchmark price and penalty) and promoting the electricity market reform will effectively stimulate the power generation and investment on the supply side, and improve manufacturers' profit. (2) Higher quotas and lower benchmark prices can increase the manufacturers' power generation, profit and investment, and stimulate the supply side to conduct TGC transactions. However, when the penalty is set at different multiples of the benchmark price, the incentive effect on supply side power generation, investment and profit differ. (3) Marketed on-grid prices can significantly stimulate the supply side and promote renewable energy development. This paper can provide reference for the design of RPS incentive mechanism and institutional parameters. [ABSTRACT FROM AUTHOR]

Renewable portfolio standard is an effective administrative tool to guide the behavior of electricity producers and impact the renewable energy diffusion. However, currently the renewable energy quota is generally set to be static and the efficiency is limited. In this paper, we proposed a dynamic renewable portfolio standard policy and we aimed to analyze how it will impact the renewable energy diffusion in China. The connections between electricity producers were simulated under both the perfectly competitive market and monopoly market. And a networked evolutionary game approach is applied considering dynamic linking between the electricity producers. The results show that dynamic quota policy is effective in promoting the proportion of wind power generation from 20% to 59.17% and promoting the proportion of solar power generation from 20% to 79.25%. With the sensitivity analysis, we found that renewable energy quota and the on-grid price are the most influential parameters. The proportion will reach 76.47% if quota is 0.7 and 68.1% if on-grid price is 630 Yuan/MWh. And the influence of tradable green certificate prices and generation cost is limited in promoting renewable energy diffusion. This paper offers the Chinese government a potential implementation for the perfection of national renewable energy diffusion. [Display omitted] • Propose a novel dynamic renewable portfolio standard scheme. • Analyze the influences of different kinds of the electricity market. • Investigate parameters affecting the wind power and solar power generation. • Establish dynamic networked evolutionary game models. • Dynamic linking is considered when the connections of players change. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to evaluate the impacts of green finance on green energy deployment in China's tourism industry. To this end, the annual data over 1992–2021 through the ARDL (Autoregressive Distributed Lag) approach for the case of China are analyzed. The findings confirm that the issued green bonds positively impact the green efficiency of China's tourism industry in the long term. In contrast, the coefficient of issued green bonds is insignificant for the short-term approach. In addition, in the short and long term, the consumer price index also has a significant adverse effect on the green efficiency of China's tourism industry. The recommended practical policies are improving executive regulations and transparent market information, implementing green fiscal policies, green construction in hotels and tourist accommodations, green globalization, operational phases of innovation, adaptation, and stabilization (IAS), and eco-tourism regionalism. • Green bonds have a positive impact on the green efficiency of China's tourism. • Consumer price index has a negative effect on green efficiency of China's tourism. • Economic globalization index has a significant coefficient on green efficiency. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [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]

Distributed energy systems are becoming increasingly popular worldwide. The 100% renewable energy system can be an important energy supply alternative to reduce the carbon emissions and address energy shortage, especially for remote areas. However, the performance of 100% renewable energy system (RES) at community level needs further detailed analysis, which is affected by the climates, availability of renewable energy and local energy markets. This paper proposes a design optimization framework of 100% renewable energy systems for low-density communities and investigates the system performance. To investigate the integration and performance of 100% RES, thirty typical cities located in different regions of China were chosen considering different climates, geographical features, and renewable energy distributions. By taking the economic performance as the optimization objective, the optimal design for the 100% RES is obtained, and the energy and economic performance is analyzed. Results show that, under the current energy market conditions, the 100% RES is feasible for low-density communities in most regions of China. The payback periods of the systems in most cities are less than six years. When the cost of PV is reduced by half considering future technological developments, the payback period of a 100% RES can be reduced by 30%–60%. This paper would provide design suggestions and application recommendations in regard to promoting 100% RES in China. • Application of 100% renewable energy system at community level is studied. • 30 zones are selected considering climates, renewable energy sources and markets. • Solar energy and biomass can contribute to over 90% of the total energy supply. • Investment of 100% RES can be paid back in 6 years for most regions. • Payback periods can be reduced by 30%–60% when PV technology is further developed. [ABSTRACT FROM AUTHOR]

In the context of sustainable development, the renewable energy technology innovation (RETI) may have a potential impact upon the industrial structure upgrading, while green finance will play a moderating part. This paper studies the role of green finance and renewable energy technological innovation on industrial structure in China. We found that RETI has contributed significantly to the adjustment of industrial structure. The research results of PVAR (Panel Vector Autoregressive) model further prove this dynamic and continuous positive promotion. The threshold regression results indicate that with the improvement of green finance, there is a non-linear relationship between RETI and industrial structure. In areas where green finance is relatively low, RETI can inhibit the upgrading of industrial structure. When the degree of green finance development is relatively high, the influence of RETI on industrial structure is remarkably positive. Heterogeneity analysis shows that in eastern China, renewable energy technological innovation has significantly promoted the industrial structure upgrading. Therefore, policy makers should take various measures to expedite the green finance development and strengthen the green finance support to renewable energy technology innovations. Chinese government has the need to continue to implement related green credit policies and expand the source of green funds. • Study the impact of renewable energy technology innovation on industrial structure. • Green finance is a moderating variable. • Threshold regression model is used to study nonlinear relationship. • Renewable energy technology innovation positively impacts industrial structure. • Green finance has a positive moderating effect. [ABSTRACT FROM AUTHOR]

Green economic growth and sustainable development are inextricably linked, and encouraging green economic growth has practical implications in today's world. In this context, this study examines the impact of environmental regulation on renewable energy development and China's green economy from 1990 to 2018. We look at how environmental regulations affect green economic growth and renewable energy technologies. Using the Meta frontier SBM model's super-efficient data envelopment analysis, it is possible to determine the relative level of green economic growth in China's provinces based on their energy efficiency. Environmental regulations were also assessed using an improved fuzzy inclusive appraisal method. Finally, we investigate the possible mechanism and discuss our findings. The study discovered an unreliable indicator of green growth during the research period, attributed to the non-serious nature of government strategies. The results show that the DEA method consistently confirms how the piece is put together. On the other hand, the sub-results models invariably affect countries with high GDP per capita. Finally, the impact of environmental regulations on the growth of renewable energy sources was studied. Green economic growth depends on environmental regulation policies, which contribute significantly to renewable energy development. Population size, economic development level, technological innovation, industrial structure, urbanization, environmental regulation, and FDI influence green economic growth, but the spatial effect of each influencing factor varies. The findings of this paper form the basis for China's pursuit of green economic growth and the development of renewable energy policies. • Proposed a novel framework on R & D expenditures, low carbon energy transition and green economic growth. • Study analyzes the effects of the multiple R & D expenditures in the context of Belt & Road Initiative economies. • Dynamic linking is considered when the connections of players' change. • Established vibrant empirical model and suggested implications for green economic recovery. [ABSTRACT FROM AUTHOR]

As a result of the COVID-19 epidemic, a worldwide economic slump has reduced the depletion of natural resources, lowering their costs. The loss of renewable energy profitability might hinder the attainment of specified goals of sustainable development goals. By using Chinese provinces data from 1995 to 2020, this research examined the relationships between renewable energy investment (REI), green finance (GFI), growth of the economy (GDP), renewable energy production (REP), and private sector participation (PSP) in China. According to this analysis, REI, REP, and GFI are more variable throughout the given period than GDP. And PSP. A bidirectional substantial causal correlation between REI and REP was identified using the Generalized method of moments (GMM). Still, the regression coefficient between GDP and REI and REI. and GFI has a one-way causal relationship. There was no evidence that the PSP directly impacted the REI. Based on the empirical findings of this research, we propose that policies be designed to reduce volatility in REI GFI, and REP and increase support to improve sustainable economic development and environmental and renewable energy production. Examine between green financing and environmental conservation for long-term environmental quality. • There is a dire need to measure the examined the relationships between renewable energy investment (REI), green finance (GFi). • Bidirectional substantial causal correlation between REI and REP was identified using the Generalized method of moments (GMM). • There was no evidence that the PSP had a direct impact on the REI. • This paper improve sustainable economic development as well as environmental and renewable energy production. [ABSTRACT FROM AUTHOR]

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

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

As the integration of renewable energy sources (RES) progresses and energy technologies advance, integrated energy systems (IES) have emerged as a practical solution for leveraging RES and balancing local demand–supply dynamics. This paper investigates the planning and optimization of operations for an electricity-hydrogen integrated energy system (EH-IES), considering degradation and multi-timescale operations of storage devices to minimize the life cycle costs. To mitigate computational challenges, the planning model adopts a Representative Time Period (RTP) framework, and a novel intertemporal operation model is developed to coordinate the operations of battery and hydrogen storage. Furthermore, we propose a two-stage approach to derive representative weeks and hours from meteorological and load data using fewer data points, ensuring chronological coherence and preserving long-term patterns to validate storage device constraints and improve planning accuracy. Case studies in southeastern China's IES verify the effectiveness of our planning approach, which lowers equivalent annual costs by 4.5% and reduces battery capacity needs by 12.8%, thereby highlighting its significant economic benefits. Additionally, numerical analyses reveal that our two-stage method surpasses conventional RTP selection techniques, delivering at least 10% less average error with an equal number of operating points. • A novel planning and intertemporal model of EH-IES based on representative time periods is established. • The multi-timescale operations of the battery and hydrogen storage system is considered. • An efficient and accurate representative time-period selection method is developed. [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]

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias. [ABSTRACT FROM AUTHOR]

With the growing share of the climate-sensitive renewable energy market supply in China, estimating future energy drought risk (ED) is essential. In this paper, based on CMIP6, energy demand and power generation are estimated from 2023 to 2100 under SSP2-4.5 and SSP5-8.5 climate scenarios, and ED is defined from high demand (HD), low wind-solar generation (LWS), and residual load (RL). The results show that the ED exacerbation is relatively high under the SSP5-8.5. Between 2023 and 2100, the duration and severity of HD and RL notably increase, while LWS changes are relatively weak. We find that HD and RL exhibit high summer risks in the south and high winter risk in the north. The return periods vary significantly and extends over time in most regions. Moreover, there are few ED when the installed power generation capacity (IPGC) is increased. When the IPGC increases by 2%–5%, the LWS will decrease at least 20%–72%. With a 10% increase, the LWS will be mitigated by at least 80%, whereas the mitigation for RL is limited, approximately 3–17% at different rates. This work performs the first evaluation of future ED and provides the useful references for risk mitigation in China based on CMIP6. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

The increasing importance of innovation-driven development requires empowering enterprises as the main drivers of innovation in emerging energy technologies. The existing literature primarily emphasizes government support for renewable innovation, with limited research on external collaborations. This paper searches and collects announcements of listed renewable energy companies regarding strategic alliances during 2009–2021 to explore whether renewable energy alliances promote innovation and their mechanism to motivate innovation efforts, using the Two-way Fixed Effects Model. The main results suggest that renewable energy alliances increase innovation output significantly. The mechanism analysis reveals that establishing renewable energy alliances indirectly enhances innovation output via transaction cost savings and alleviates financing constraints. The heterogeneity analysis indicates that factors such as firm ownership, size, knowledge absorption capacity, green investor concerns, and enterprise life cycle significantly impact the innovation incentives from renewable energy alliances. This paper enriches the research on the drivers of renewable energy innovation and provides policy insights for encouraging autonomous innovation in renewable energy enterprises. [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]

Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [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]

Poverty alleviation is an important work of the Chinese government in recent years. According to the international poverty standards of the world bank, China's poverty reduction population accounts for more than 70% of the world. Photovoltaic poverty alleviation is a significant way for regions rich in solar energy resources to transform the advantages of renewable energy resources into the driving force of social and economic development. It is also an effective means for China to implement power poverty alleviation. While promoting emission reduction in these regions, it accelerates economic development and implements targeted poverty alleviation. This paper analyzes the comprehensive benefits of typical market entities of photovoltaic poverty alleviation projects, and establishes the environment, economic and social benefit evaluation models for poor households, the state and government, photovoltaic enterprises and grid corporation. In the model, we creatively put forward an index system to characterize the poverty alleviation effect, and consider the additional industrial benefits brought by photovoltaic poverty alleviation projects. Finally, a typical photovoltaic poverty alleviation project in Haiyuan County, Northwest China is selected to verify the feasibility of this model. [ABSTRACT FROM AUTHOR]

As the world's largest energy consuming country, it is important to study the mechanisms of improving renewable energy and energy efficiency to achieve green economic growth in China and globally. Therefore, this study investigates the impact of environmental regulations on green economic growth and renewable energy development in China, respectively. First, this study measures the level of green economic growth in Chinese cities by the Metafrontier-Global-SBM super-efficient data envelopment analysis (DEA) model from the perspective of energy efficiency. And the environmental regulation intensity was measured using an improved fuzzy comprehensive evaluation method. Second, a spatial econometric model based on longitudinal data was constructed to examine the non-linear effects of environmental regulations on green economic growth and its spatial spillover and feedback effects. The results indicated a U-shaped non-linear effect of energy efficiency-related environmental regulation on green economic growth as well as a spatial spillover effect and spatial feedback effect. Finally, the impact of environmental regulation on renewable energy development was investigated. The results show that renewable energy development, an important component of green economic growth, is equally dependent on the implementation of environmental regulation policies and that environmental regulation makes a significant contribution to renewable energy development. The findings of this paper provide a basis for the realization of green economic growth and the formulation of renewable energy policies in China. • Impact of environmental regulation on renewable energy development was investigated. • A spatial econometric model of 286 Chinese cities from 2003 to 2018 was constructed. • Environmental regulation has a significant U-shaped effect on green economic growth. • Non-linear effects include spatial spillover and spatial feedback effects. • Environmental regulation promotes green growth earlier in resource-based cities. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between fiscal decentralization and renewable energy demand by including income inequality, economic growth, urbanization, and economic globalization as the main determinants in the energy demand function for the case of China. Quarterly frequency data for the period 1980–2018 are used for empirical analysis. Our empirical results show fiscal decentralization positively impacts the demand for renewable energy; in other words, fiscal decentralization can help increase renewable energy demand. In addition, income inequality is negatively linked to renewable energy consumption, while economic growth is positively associated with renewable energy demand. Urbanization and economic globalization add to renewable energy consumption. This study presents Chinese authorities with new guidelines for using fiscal decentralization as an economic tool to formulate effective energy policies for long-term development of the use of renewable energy sources. [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]

• Design a hydrogen energy storage system-carbon capture and utilization system-based micro energy grid. • Proposed a carbon emission-green certificate equivalent interaction mechanism for multi-energy conversion and storage system. • Construct a two-stage optimal dispatching framework including day-ahead robust dispatching model and real-time rolling model. • Proposed an entropy-Shapley-based benefit allocation method considering multi-dimension impact factors. • Carried out the effectiveness and applicability analysis for the proposed dispatching scheme and benefit allocation strategy. To fully utilize the abundant renewable energy resources in county-level areas of China, this paper designs a novel structure of micro-energy grid integrating hydrogen energy storage (HES) system and carbon capture and utilization (CCU) system (HES-CCU-based MEG). And a carbon emission-green certificate equivalent interaction mechanism is established. Then, a two-stage optimal dispatching framework is proposed for mitigating the impact of uncertainty variables, including a day-ahead robust dispatching model and a real-time rolling optimization model. Thirdly, an entropy-Shapley-based benefit allocation method is constructed to allocate the benefits in dimensions of energy conservation, carbon emission reduction, and renewable power consumption among various devices. Finally, a micro-energy grid in Henan province, China, is selected as an example for case study. The results show that: (1) HES could transfer surplus renewable power and low-priced power to periods with high power prices, while CCU could achieves the cycle and utilization of CO 2 , leading to a decrease in power purchase costs and carbon emissions by 7.85% and 0.47%, respectively. (2) The two-stage optimal dispatching model gives full play to the flexibility adjustment ability of each device, mitigates the deviations caused by uncertainty variables, and formulates the optimal dispatching strategy. (3) The entropy-Shapley-based benefit allocation method could evaluate the contributions of each device in different dimensions, effectively, ensuring the rationality of benefit allocation results. Overall, the proposed model and method could utilize renewable energy resources in county-level areas, which is better to promote the clean and low-carbon transformation of the overall energy structure. [ABSTRACT FROM AUTHOR]

Hydropower, an excellent power source for peak shaving, can respond quickly to load changes, which helps to overcome the anti-peak characteristics of wind and solar power. However, complementary operation with wind and solar power changes the operating boundaries of hydropower stations, making the original operating rules no longer applicable. Considering daily peak-shaving demands, this paper proposes a methodology to develop operating rules for a hydro–wind–solar hybrid system (HWSHS). First, a multi-objective and multi-timescale operation model is developed to ensure energy generation and peak-shaving performance simultaneously. Second, the feasible and reasonable region of available energy-based operating rules is analyzed, and three rule forms are employed to explore the optimal operating rules. Last, NSGA-II is utilized to optimize the rule parameters. A decoupling method is proposed to handle the shape constraints of operating rules, and the multi-timescale simulation method for a HWSHS is developed as the fitness function. In the simulation method, a load reconstruction-based load shedding (LRLS) method is introduced to determine the hourly output of each hydropower station. A forebay elevation check method is performed to obtain the hourly forebay elevation and correct the hourly output. The Beipan HWSHS in Guizhou Province, China, is selected as a case study, and the optimal operating rules are determined. The results also show that (1) the standard operating rules with hedging and packing increase peak-shaving performance by an average of 4.31% and 2.03% compared to the standard operating rules and linear operating rules, respectively; (2) the peak-shaving performance can be significantly increased (6.70%) with a slight decrease (0.37%) in the energy generation through changing the seasonal distribution of water and energy of the cascade hydropower stations; (3) the LRLS method, which makes the interday energy distribution more uniform, increases the peak-shaving performance by an average of 9.17% compared to the traditional load shedding method; (4) the energy generation and peak-shaving performance of the HWSHS are affected by the transmission capacity, and it is necessary to increase the transmission capacity by 25% to 35% of the installed capacity of wind and solar power stations. • A methodology is proposed to develop operating rules considering peak-shaving demands. • A multi-objective and multi-timescale operation model is developed. • The feasible and reasonable region of available energy-based operating rules is analyzed. • A multi-timescale simulation method is designed to simulate the operation of a HWSHS. • The methodology significantly improves the peak-shaving performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • It analyzes the feasibility of achieving net zero emission in the Chinese power sector by 2050 and the required policy measures. • It is essential to foster the concurrent development of renewable energy, energy storage, and demand response technologies. • The carbon pricing mechanism with exponential growth is more suitable. • Referring to the European offshore wind power investment model is beneficial for the development of offshore wind power in China. The low-carbon transition in China's power sector plays a crucial role in the mitigation of climate change worldwide, and increasing renewable energy penetration rate is widely recognized as the key approach to decarbonize this sector. This paper proposes a renewable energy penetration model to examine the feasibility for net zero emission by 2050 in China's power sector taking into account of economic and social development. It also evaluates the impacts of carbon pricing and financial incentives on renewable energy penetration and carbon emissions. The results indicate that along with economic development, China's electricity demand will continue to grow, but at a gradually slowing rate. It is possible for the power sector to achieve net zero emission by 2050 while meeting this growing electricity demand, with a renewable energy penetration rate of 75%. To ensure the achievement of the goal, we recommend setting the carbon price with the current market price as the baseline and increasing it annually by a relative increment index of 10%. Furthermore, given the high initial cost and late start of offshore wind power, we suggest incorporating associated grid connection cost into the transmission and distribution price to ensure the comprehensive and healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

In this era of global warming the importance of climate risk cannot be understated, and finding ways to reduce climate risk is a topic worthy of study. In this paper, we innovatively examine the role of renewable energy technology innovation on climate risk in China. In addition to their linear relationship, we also investigate the asymmetric and heterogeneous impact, detect the impact channels, and find that (1) increased renewable energy technology innovation can effectively alleviate the climate risk situation. Accordingly, the development of renewable energy technology innovation can make a significant contribution to CR abatement. (2) Pronounced impacts of renewable energy technology innovation on climate risk can be observed in the higher quantiles (75th and 90th) of climate risk. (3) Renewable energy technology innovation is a powerful weapon for eradicating climate risk in provinces with a higher level of government fiscal expenditure. And (4) renewable energy technology innovation has an impact on climate risk by facilitating energy consumption efficiency, which is a necessary mediator. These conclusions inspire us to put forward several corresponding policy implications for better climate risk governance and renewable energy technology innovation development. [ABSTRACT FROM AUTHOR]

To achieve the goal of "carbon peaking and carbon neutrality", China is vigorously developing renewable energy. This paper aims to examine the major contributors to regional inequality in renewable energy consumption by analyzing the 30 provinces in China. We have analyzed the inequality of China's renewable energy consumption with Dagum Gini coefficient, and then use the Generalized Divisia Index Model (GDIM) to decompose the influencing factors contributing to the inequality of renewable energy consumption. The main conclusions are summarized as follows: First, renewable energy consumption was higher in the western region than in the central and eastern regions, and the regional inequality in renewable energy consumption have increased over time. Second, the study reveals that inter-group disparity is the primary source of regional inequality in renewable energy consumption in China. Third, at the national level, the results of the GDIM decomposition show that economic development continues to rely on fossil energy consumption, which negatively impacts the low-carbon transition of energy. Given China's complex geographical conditions, it is crucial for different provinces to develop their own strategies for renewable energy development, taking into account unique resources and challenges. [ABSTRACT FROM AUTHOR]

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism. [ABSTRACT FROM AUTHOR]

The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources. However, the lack of a well-set operational framework and a cost-sharing model has hindered its widespread implementation and large-scale development. This paper introduces three distinct cost-sharing methods, namely the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method, which are laterly applied to develop a combined operational and cost distribution model. The objective is to improve the efficiency of the power generation system by incorporating shared energy storage assistance and allocating the associated costs based on the use of various renewable energy stations. Furthermore, the viability of the suggested operational framework for shared energy storage and the methods for allocating costs are confirmed through numerical simulation. The findings indicate that the uniform allocation method represents the most straightforward approach for distributing costs evenly. In contrast, the second model introduces a cost allocation method that addresses the issue of fairness by considering the predicted generation level of each renewable energy plant. However, the third model takes into account the actual generation level of the plants when determining their respective costs. Sensitivity analysis is further conducted to offer valuable insights into cost-saving policies for four representative regions in China. The proposed operation and cost-sharing model is anticipated to serve as a useful reference for the widespread implementation of shared energy storage in power generation side. • Design three cost-sharing methods to allocate costs among various entities. • Formulate a combined operational and cost allocation model. • Optimize operational and cost allocation strategies for various entities. • Reduce total costs by up to 36% through the dynamic weighted allocation method. [ABSTRACT FROM AUTHOR]