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This study aims at exploring the role of water investment, agricultural land expansion, economic growth, and renewable energy in improving environmental quality (CO2 emissions) in China under the linear and nonlinear Autoregressive Distributed Lags (ARDL and the NARDL) methods covering the 1994–2019 period. The motivation for choosing China for empirical research is that optimum control of pollution in China can improve global environmental quality and combat global warming. The empirical results provide strong evidence for a cointegration relationship between the variables considered. Our study contributes to the existing literature by rejecting two hypotheses. First, we demonstrate from the literature that water investments in China can improve environmental quality. Second, our findings suggest that agricultural land expansion can help reduce greenhouse gas emissions. Our findings suggest that Chinese policymakers should further promote "green agriculture" and encourage the use of renewable energy through "taxes" to control polluting activities and "subsidies," establishing a "punishment and relief" strategy. [ABSTRACT FROM AUTHOR]

Background: The processes of transport decarbonisation are complicated. In this paper, we adopt the Activity-Modal Share-Energy Intensity-Carbon Intensity of Fuel (ASIF) approach and propose a conceptual framework on the direct and indirect impact of COVID-19 on transport CO2 emissions. In the Chinese context, changes of carbon emissions associated with passenger and freight transport (including urban, rural, and inter-city transport) across different transport modes are estimated. Scenario analysis is then used to estimate the impact of COVID-19 on total transport carbon emissions up to 2030. Four scenarios, from minimal to significant behavioural changes and global recession associated with COVID-19, are generated. Results: Under the pandemic, the transport system in China was estimated to have produced 28% less CO2 emissions (1044.2 Mt) in 2020, when compared to 2019. Compared with the business-as-usual scenario, the estimated total transport carbon emissions in 2030 would drop by 6%, 15%, and 21% and 23% under the minimal-impact, low-impact, moderate-impact, and severe-impact scenarios, respectively. Conclusions: The results suggest that the processes triggered by COVID-19 alone will not be sufficient to meet the ambitious transport decarbonisation targets. To meet China's pledge under the United Nations Framework on Climate Change, the medium-term effects of COVID-19 must be combined with strong transport decarbonisation measures of modal shift and new energy applications. With these additional measures, it may be possible to advance the transport carbon peak before 2030. Lessons are relevant to other developing countries. [ABSTRACT FROM AUTHOR]

The number of studies on COVID-19 has rapidly grown in recent years. The pandemic has caused widespread disruption, including in renewable energy research. This study aims to examine the impact of the COVID-19 pandemic on renewable energy research by conducting a bibliometric analysis from the Scopus indexing database from 2020 to 2023. This study employs a bibliometric approach to analyze the authors, affiliation, publication source, keywords, thematic map, and trend topic. The analysis will focus on the comprehensive overview of the published research and identify areas where future research is needed. The results show that the number of research studies related to COVID-19’s effect on renewable energy will continue growing and a solid collaboration network exists. China has had a significant presence on the impact of COVID-19 on renewable energy studies, including organization and university affiliations, number of single and multiple country publications, funding sponsors, and most cited countries. The finding also shows niche themes, research trends, and thematic evolution shifting. This finding will contribute to a better understanding of the impact of the COVID19 pandemic on renewable energy research and a new direction for future research. [ABSTRACT FROM AUTHOR]

Currently, both regulated and deregulated power trading exist in China’s power system, which has caused imbalanced funds in the electricity market. In this paper, a simulation analysis of the electricity market with wind energy resources is conducted, and the calculation methods of unbalanced funds are investigated systematically. In detail, the calculation formulas of unbalanced funds are illustrated based on their definition, and a two-track electricity market clearing model is established. Firstly, the concept of the dual-track system is explained, and the specific calculation formulas of various types of unbalanced funds are provided. Next, considering the renewable energy consumption, the market clearing model based on DC power flow is constructed and solved; by combining fitting methods of mid- and long-term curves, the unbalanced funds are calculated based on clearing results and formulas. [ABSTRACT FROM AUTHOR]

This study examines the long-run impact of coal usage decline from a disaggregated energy consumption perspective by considering the role of coal usage on carbon dioxide (CO2) emissions and economic growth. For this purpose, the study focuses on China and India as the leading coal-user countries; considers coal, oil, natural gas, and renewable energy consumption as explanatory variables; uses yearly data from 1990 to 2021, and applies a novel dynamic autoregressive distributed lag (DYNARDL) simulations. Also, the Kernel-based regularized least squares (KRLS) approach is performed for robustness checks. The outcomes show that (i) there is cointegration between coal usage and CO2 emissions, and between coal usage and economic growth in the long run; (ii) coal usage is the most important indicator among explanatory variables; (iii) coal usage has a statistically significant impact on CO2 emissions in both China and India; (iv) however, coal usage is not significantly effective on economic growth for both China and India based on DYNARDL simulations; (v) negative counterfactual shocks in coal usage have a decreasing impact on CO2 emissions; (vi) KRLS results show that coal usage has a causal impact on CO2 emissions and the results of the DYNARDL simulations are robust. Hence, counterfactual shocks present important insights about the future impacts of coal usage decline on both CO2 emissions and economic growth. Based on the empirical results, policy implications, such as decreasing the amount of coal usage on and increasing the amount of alternative energy sources in the total energy mix, are also proposed. [ABSTRACT FROM AUTHOR]

The climate commitment to achieving carbon neutrality before 2060 in China has been announced recently. In the context of pursuing carbon neutrality, sharing similar sources as greenhouse gases, aerosol particle and precursor emissions are projected to substantially decrease in China, which can potentially have a great impact on climate. Here, we investigate the effects of future aerosol reductions, because of achieving carbon neutrality, on solar and wind energy in China by using an earth system model. We show that significant reductions in aerosol emissions, particularly in eastern China, lead to increases in the surface downwelling shortwave radiation, surface air temperature and wind speed, which can further enhance the potential of solar and wind energy production. The findings underline that the pursuit of carbon neutrality can yield co‐benefits of not only mitigating climate change and air pollution but also fortifying the stability of renewable energy sources. Plain Language Summary: In response to China's commitment to achieve carbon neutrality by 2060, our study examines the potential impact of reducing aerosol emissions and greenhouse gases on solar and wind energy generation. Employing an earth system model, we observe significant reductions in aerosols, particularly in eastern China, resulting in increases in surface downwelling shortwave radiation, surface air temperature, and wind speed. These changes have promising implications for enhancing solar and wind energy production. Our research underscores the interconnected benefits of pursuing carbon neutrality, encompassing climate change mitigation, air quality improvement, and the enhancement of renewable energy reliability. Key Points: The effects of future aerosol reductions in China due to achieving carbon neutrality on solar and wind energy are investigatedAerosol emission reductions, especially in eastern China, increase surface downwelling shortwave radiation, temperature and wind speedClimate changes resulting from emission reductions under carbon neutrality enhance the potential of solar and wind energy [ABSTRACT FROM AUTHOR]

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China's city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China's city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes. [ABSTRACT FROM AUTHOR]

China is now the world leader with green capital actually creating renewable energy technologies. We address the possibility of whether China can emerge a new ecological civilization, as well as become the global leader to reach climate security. In particular, China has the potential to implement a solar energy infrastructure in the deserts of the Middle East as part of the Belt and Road Initiative, mainly in the form of concentrated solar power. The creation of this renewable energy capacity could supply electricity to much of the world, as well as power the direct air capture (DAC) of carbon dioxide with a permanent burial in the crust, in particular, using the ultramafic deposits in Oman. This realization would increase the chances of not breaching the International Panel on Climate Change (IPCC) warming target of 1.5 ℃, of course only if coupled with the termination of utilizing fossil fuels as early as possible and their replacement with renewable energy supplies globally. In this context, our pioneering model establishes that coupling a DAC-driven drawdown of atmospheric carbon dioxide with a rapid increase in the renewable energy capacity is more than capable of producing enough clean energy to eliminate energy insecurity globally (to all 8–10 billion humans) and to provide energy for the drawdown for the rest of the 21st century, thereby reducing atmospheric carbon dioxide to safe levels. This contingency will likely only be realized with a shift to demilitarization of the global economy. Likewise, this potential emergence of China as the global leader for climate security would likely inspire the push for a global Green New Deal to make this goal possible. [ABSTRACT FROM AUTHOR]

The carbon emissions of the power industry account for over 50% of China's total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China's energy transition, economic development, and the power sector's energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China's energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector's generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended. [ABSTRACT FROM AUTHOR]

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like "renewable energy sources", "optimization", and "simulation" are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

Carbon emissions, ecological pollution and a steadily rising global temperature have been widely acknowledged as the most severe risks to human survival in the last few decades. Alarming increases in global temperature and sudden climatic shifts are nature's way of warning us to curb the use of fossil fuels and adopt more sustainable practices. Therefore, the present study investigates the impact of financial stability, environmental regulations and uncertain economic policies on carbon emissions and investment in renewable energy. The study used a nonparametric DEA‐DDF technique to fulfil this objective using a balanced panel dataset comprising 28 Chinese provinces from 2011 to 2021. Overall results demonstrated that financial stability reduces carbon emissions and accelerates investment in renewable energy projects. The findings imply that a financially stable economy like China encourages businesses to invest in cutting‐edge, environmentally friendly technology to boost productivity while reducing carbon emissions. Likewise, results show that stringent ecological regulations decrease carbon emissions and promote investment in renewable energy. Hence, stakeholders are keen to comply with environmental regulations in China to reduce carbon emissions by investing in renewable energy resources to avoid penalties. Finally, results suggest that uncertain economic policies increase carbon emissions and restrict access to credit from financial institutions for investment in renewable energy purposes in China. Findings imply that uncertainty in economic policies could lead to less environmentally friendly production practices that may increase carbon emissions and reduce the demand for renewable energy products. [ABSTRACT FROM AUTHOR]

Despite having 22% of the world's population, China only has 7% of arable land, making it difficult for the country to be self-sufficient in food production and increasing food security. Therefore, it is imperative to investigate the factors that can enhance food production in China, considering the country's significant population size juxtaposed with limited arable land. In light of this, the present study examines the impact of farm mechanization, financial development, and renewable energy on China's food production. Using the Quantile Autoregressive Distributed Lag technique, which explores the nonlinear relationship between variables exhibiting heterogeneity across different quantiles of the dependent variable, we analyze the short and long-run effects of these factors on food production. The findings reveal significant long-term impacts of farm mechanization, renewable energy, and financial development on food production, while the short-run results exhibit some variation. Based on these results, the research suggests various strategies to promote the utilization of renewable energy, farm machinery and improve farmer access to financing, thereby fostering enhanced food production in China. [ABSTRACT FROM AUTHOR]

This paper presents a study of the features, problems, and prospects of implementing knowledge and information systems in the fight against climate change, which are based on the digital management of knowledge and information systems. We demonstrate practices that are applied in Norway and China and determine the key directions for implementing these technologies. We confirm the offered research hypothesis on the effect of such components as finances, infrastructure, and digital readiness of the workforce for the implementation of new ICT. We also establish the prospects for an increase in the effectiveness of the realisation of the consideration process in the conditions of mass implementation of sectorial technologies and the focus on global climate integration in China and Norway. The objective of this research is to identify the directions for tackling climate change in the digital economy that are focused on green management of knowledge and information systems. The methods used in this work include the method of scientific abstraction, comparison, statistical analysis, and factor analysis. The scientific novelty of this research consists in the establishment of the directions for digitalization of environmental growth in the conditions of climate change and the quick emergence of innovative ICT. [ABSTRACT FROM AUTHOR]

To reduce the financial burden brought by the feed-in tariff (FIT) policy, China is shifting its renewable energy support policy to the renewable portfolio standard with tradable green certificates (RPS/TGC). Moreover, the two policies are being implemented in parallel in the transitional period. In this context, this study investigates the game between renewable energy power plants (REPPs) regarding electricity outputs and tradable green certificate (TGC) prices under different renewable energy support policies practiced in China. We focus on analyzing the impact of policy changes on the competing REPPs' decisions and the renewable electricity market with TGCs. The results show that implementing the RPS/TGC policy inhibits the development of renewable electricity unless the subscription of TGCs is promoted. Government subsidy arrears significantly affect electricity outputs and TGC subscriptions. The low subscription rate of TGCs is caused not by the subsidy-based TGC pricing mechanism but by government subsidies. This suggests that the government should implement the RPS/TGC policy, and demand-side power consumption responsibility weights should also be strengthened to promote TGC subscriptions. [ABSTRACT FROM AUTHOR]

The world faces several problems related to natural gas resource rents and energy production from renewable sources. One of the main problems is the influence of energy imports, manufacturing exports, and alternative energy sources on natural gas and electricity production from renewable sources. Energy imports, manufacturing exports, and alternative energy sources can impact natural gas and electricity production. This paper examines natural gas resource rents and electricity production from renewable sources nexus from 1971 to 2021, using energy imports, manufacturer's exports, and alternative energy sources in China. Electricity production from renewable sources and manufacturing exports are negatively associated with natural gas resource rents. Energy imports and alternative energy sources positively relate to natural gas resource rents in China. These results suggest that the energy sector in China is highly interconnected and that policies that seek to promote renewable energy sources and other alternatives can positively affect natural gas resource rents. China needs to develop an energy policy considering the policy implications of energy imports and natural gas resource rents. Such a policy should focus on increasing domestic production, reducing energy imports, and ensuring adequate revenue from natural gas resource rents. Additionally, regulations could be implemented that support the development of alternative energy sources, such as requiring utilities to purchase a certain percentage of their power from renewable sources. [ABSTRACT FROM AUTHOR]

Coal was first used in China for ornaments starting about 6000 years ago. Its use as fuel for households and light industry began about 2000 years ago, but coal production and consumption remained low until the end of the nineteenth century due to weak industrialisation. Thereafter, a series of political events drove progressive industrialisation that required the increasing exploitation of the nation's vast coal resources. These events were the Treaty of Shimonoseki in 1895, the creation of the state of Manchuguo by Japan in 1932, the Communist accession to power in 1949 and the start of opening up in 1978. Advances in mining technology and expanding transport networks allowed coal to become a major source of primary commercial energy, which it remains today. Since 2012, the slowing rate of economic growth combined with the rapid expansion of renewable energy has steadily reduced the share of coal in the energy mix. Standards of safety, resource management and environmental protection were low throughout most of the long history of coal in China. Moreover, mine workers were marginalised and many local officials were directly involved in the mining. Only in this century has the government achieved some success in addressing these challenges. [ABSTRACT FROM AUTHOR]

With the development and refinement of the carbon emissions trading market, the relationship between the carbon market and the stock market has grown increasingly intertwined. This has led to a surge in research investigating the interactions between the carbon market and related sectors. This study examines the intensity and direction of spillover effects among ten industries associated with carbon emissions, spanning traditional and emerging energy sectors. Through static analysis, we find that spillover effects between industries in the carbon and stock markets are bidirectional and asymmetric. Dynamic analysis reveals that the carbon market, acting as the primary recipient of spillover effects, is notably influenced by traditional energy industries such as coal and oil, followed by photovoltaics, new energy vehicles, and others. The magnitude of these spillover effects is subject to fluctuations influenced by energy crises and events like the COVID-19 pandemic, while policy interventions can alter the overall trends in net spillover effects across various industries. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

The influence of green finance (GF) on renewable energy development efficiency (REDE) has garnered considerable attention from both academia and society. To study the changes in China's REDE in recent years and GF's effect on it, this research first evaluates 30 provinces' REDE from 2010 to 2019 using a non-radial directional distance function (NDDF) based on global technical assumptions. Second, we evaluate the direct impact of GF on REDE. Finally, we explore the internal mechanism for how GF influences REDE. The results of this study are as follows. (1) GF has a significant promoting effect on REDE. (2) The improvement of renewable energy (RE) technological innovation level and the increase of foreign investment strengthen GE's promoting effect on REDE. (3) The positive effect of GF on REDE is prominent in regions with lower environmental regulations and carbon intensities and higher financial development levels. This paper offers relevant policy suggestions to improve REDE and to enhance GF development. [ABSTRACT FROM AUTHOR]

The post-COP26 era provides policy directions for economies to track the ways of achieving sustainable environment as a lasting solution to the ravaging impacts of global warming. The call for global sustainability heightens the criticality of digital economy which is recently evolving in environmental empirics. This study provides the first empirical evidence advancing the symmetric effects of eco-digitalization, green technology, green finance, and renewable energy on environmental sustainability in China within the novel STIRPAT model. The study explores the ARDL model to evaluate the stated hypotheses based on quarterly data from 1995Q1 to 2019Q4. The preliminary analyses reveal the vulnerability of the Chinese environment to surging greenhouse gas (GHG) emissions. Besides, the structural break unit root anchored on Zivot–Andrew reveals prominent breakpoint years. The essential roles of urbanization and affluence are empirically investigated. Robustness checks on the long-run impacts are conducted based on FMOLS, DOLS, and CCR coupled with the causality analysis examined based on Toda–Yamamoto estimator. The findings uncover that eco-digitalization, green technology, green finance, and renewable energy promote environmental sustainability in China by mitigating CO2 emissions and ecological footprint. Conversely, urbanization and affluence escalate both pollutants. Further, causalities running from bidirectional to unidirectional nexuses among the variables are well documented. Policy insights that drive the paths to sustainability in China are suggested based on the findings. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]

This paper intends to look into the time-varying dynamic impact of US fuel ethanol, one of the renewable energy sources, on the prices of agricultural products (specifically corn, soybeans, rice, and wheat) in China based on monthly price data from January 2000 to January 2023. To achieve this, a time-varying parameter vector autoregressive (TVP-VAR) model is employed, which takes into account structural changes in emergencies through time-varying parameters. The empirical results show that the equal-interval impulse responses of price fluctuations in agricultural commodities are primarily positive to variations in fuel ethanol prices and production. And the intensity and direction of the effects vary at distinct time lags. Additionally, the magnitude of these responses is most pronounced in the short term for all agricultural commodities except for corn, and the duration of the impulse responses at different time points is generally longer for corn prices compared to other commodities. The study also reveals that the influence of US fuel ethanol on Chinese agricultural commodity prices is not substantial on the whole. Therefore, there is a necessity to advance the growth of biofuels and provide policy support and financial subsidies for agricultural products earmarked for food production. These actions could shed insights into the progression of Chinese renewable energy and food policies, ensuring the stability of the market in the long run. [ABSTRACT FROM AUTHOR]

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning. [ABSTRACT FROM AUTHOR]

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the "Three North" region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads. [ABSTRACT FROM AUTHOR]

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided. [ABSTRACT FROM AUTHOR]

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality. [ABSTRACT FROM AUTHOR]

This study is intended to expose the connectedness of green financing and other green policies like renewable energy and technological progress towards identifying the best practice to achieve climate goal cum sustainable environmental development for China within the period 1985–2021. Amid the increased utilization of fossil fuels in China's economic activities and its position as the highest carbon emitting country in the world, China is considered a financial stable economy with access to renewable energy source such as hydroelectric power. For effective research into the objective of this study, the study builds on mechanism of the green finance—renewable—technology and emission nexus with the help of linear dynamics of ARDL bound and VECM granger causality methods. Empirical results from both approaches have given insight to the selected objectives of this study. According to ARDL dynamics: (i) the coefficients of linear (1.802) and non-linear(− 1.860) (i.e. square term) of financial development are positively and negatively connected to the carbon emissions. This confirms the presence of inverted U-shape relationship between the financial development and carbon emissions. (ii) the relationship between hydroelectric power representing renewable energy and CO2 is negative—(− 0.19) and significant at 1% level. This results depicts the reduction power of hydroelectric power on CO2, (iii) also, a negative (− 0.000982) linkage between number of patent application representing technological innovation and environmental development through carbon emissions responses is established. Also, findings from VECM granger causality approach back the findings from ARDL through a nexus between the variables of interest (finance, hydroelectric power, and technology and carbon emissions). Findings point to the ability of green financing to reduce carbon emissions if green financing policies are utilized effectively in regulating carbon emissions. [ABSTRACT FROM AUTHOR]

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model's hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production. [ABSTRACT FROM AUTHOR]

The development of renewable energy has become an important measure for countries worldwide to improve air quality, reduce greenhouse gas emissions, and achieve sustainable development. This trend may have implications for green total factor productivity (GTFP), which represents the economic growth efficiency in terms of resource and energy use. However, the current research on the impact of renewable energy development (RED) on GTFP based on China's provincial data is still insufficient, and it has not yet integrated multiple factors such as economic, social, environmental, and technological aspects to jointly analyze potential nonlinear effects. This paper first calculates the GTFP of 30 provinces in China using a global super-efficiency slack-based measure model. Then, a two-way fixed effects model and a panel threshold regression (PTR) model were used to explore the linear and nonlinear effects of RED on GTFP. The results show that: First, RED has a significant positive effect on GTFP, which remains in effect after a sequence of robustness tests including endogenous treatment. For every 1% increase in RED, GTFP increases by 0.025%–0.039%. Second, the contribution of RED to GTFP shows a clear characteristic of diminishing marginal benefits, with the impact coefficient decreasing from 0.058% to 0.022%. Third, the higher the level of economic activity, social conditions, environmental protection, and technological advancement, the greater the favorable impact of RED on GTFP. The range of coefficient changes under the influence of economic- and social-level factors reaches 0.012%–0.143%, while the range of coefficient changes at the technological and environmental levels is only 0.026%–0.057%. Based on the above conclusions, some policy implications related to the development of renewable energy are proposed. [ABSTRACT FROM AUTHOR]

Even though China has mapped out different policies targeting the mitigation of its environmental degradation, the country still occupies the 1st position in the ranking of carbon emissions due to excessive utilization of non-renewable energy sources in its domestic economic activities. From a theoretical and empirical point of view, factors like economic growth, financial development, export diversification, technological innovation, and renewable energy can be considered to play a vital role in environmental quality. Therefore, this study exposes the environmental performance of China amidst export diversification, financial development, innovation, and clean energy use. Due to the non-availability of data, the annual data of China are converted to quarterly data from 1995Q1 to 2018Q4, and autoregressive distributed lag-(ARDL) and Granger causality approaches are adopted in this study for quantitative and insightful analysis. The findings from both approaches expose the environmental implications of the selected variables (renewable energy, financial development, technological innovation, and export diversification) to China's sustainable development. ARDL approach has confirmed the inverted U-shaped link between financial development and emissions of carbon for China, a negative link between renewables, technologies, and carbon emissions, and a direct association exists between the diversification of export, economic growth, and emissions of carbon. This pattern points toward mitigating environmental dilapidation with renewable energy, technology, and financial development. The Granger causality output lends support to the ARDL findings; hence, a policy initiative that will promote the renewable energy sector and technological innovation through financial programs is advised. [ABSTRACT FROM AUTHOR]

With the gradual attention of global climate change, a series of carbon emission plans have been formally implemented in the field of energy. There is a consistent relationship between the carbon market and the electricity market in promoting clean energy development and improving carbon dioxide emission reduction targets. The market design of a low‐carbon power system should be considered as a whole to promote the coordinated development of carbon and power markets. Taking an industrial park in China as the research object, an internal market trading model of an industrial park considering carbon dioxide emission reduction targets is proposed. The proposed trading mechanism considers both carbon dioxide emission reduction targets and social welfare maximization. The proposed market trading model connects day‐ahead and intraday electricity markets, which deal with uncertainty about renewable energy sources. [ABSTRACT FROM AUTHOR]

In China, where energy activities, predominantly driven by fossil fuel combustion, account for nearly 90% of the country's greenhouse gas (GHG) emissions and coal power alone contributes over 40%, the shift towards carbon neutrality is a critical national ambition. This study conducts a comprehensive comparison of the environmental impacts of solar photovoltaic power generation (SPPG) and coal power, employing both life cycle assessment and ecological footprint analysis. We meticulously analyze the complete lifecycle of SPPG, pinpointing key stages of GHG emissions, and offer nuanced, localized policy recommendations. Our findings indicate that a 1 kWp SPPG module emits 1,601.18 kg of GHGs over its lifespan, equating to 1.35 kg/kW-h per unit of electricity produced--substantially lower than the 4.81 kg/kW-h emitted by coal power, thus highlighting the latter's heightened environmental detriment. Additionally, this study assesses the ecological footprint of both energy sources in Ningxia. SPPG emerges with an ecological surplus, showcasing a per capita footprint of 0.0342 hm², compatible with Ningxia's ecological capacity. In stark contrast, coal power exhibits a sustained ecological deficit over the past 5 years, with a growing per capita footprint of 0.6529 hm², underscoring its unsustainability. This research provides a detailed comparative analysis of the environmental impacts of SPPG and coal power in Ningxia, offering valuable insights for energy policymakers and industry stakeholders. It underscores the urgent need for industrial restructuring towards more sustainable and renewable energy sources, aligning with China's broader objectives of environmental preservation and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

The development of renewable energy is indispensable to promoting the low-carbon transition of power systems. Nevertheless, it also brings uncertainty to the reliability of power systems. Herein, the panel model and panel threshold model are established based on the provincial data in China from 2012 to 2020. The results reveal that the negative effect of renewable energy development (RED) on power supply reliability (PSR) is gradually lessening. If the development of renewable energy is a rational way, power supply reliability can be improved. Additionally, energy-exporting regions bear more risks of RED than energy-importing regions. If the coal prices are stable and natural disasters are manageable, the RED can enhance the PSR. However, if they are not stable or controllable, a high proportion of renewable energy in the power system could cause even more severe problems with PSR. Based on these critical results, some suggestions are made to promote the formation of a new power system. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system's short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method's effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate. [ABSTRACT FROM AUTHOR]

This study addresses the objectives currently being pursued through regional power connectivity across Asia. The study contributes to the literature on energy regionalism and serves as a basis for further analysis in Asia as new regional power connectivity initiatives are proposed from China, among others. Results reveal the different weights for each of the objectives, highlighting the importance of economic factors. However, we also find that political, technical, social, and environmental goals are considered high priorities. We discuss the importance of balancing all these goals, and how the existence of multiple power connectivity initiatives can contribute to this. [ABSTRACT FROM AUTHOR]

To improve automatic generation control (AGC) performance and reduce the wastage of regulation resources in interconnected grids including highproportion renewable energy, a multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas. In addition, a cocktail exploration multi-agent deep deterministic policy gradient (CE-MADDPG) algorithm is proposed as the framework algorithm. In this algorithm, the controller and power distributor of an area are combined into a single agent which can directly output the power generation command of different units. Moreover, the cocktail exploration strategy as well as various other techniques are introduced to improve the robustness of the framework. Through centralized training and decentralized execution, the proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple agents and is verified on the two-area LFC model of southwest China and the four-area LFC model of the China Southern Grid (CSG). [ABSTRACT FROM AUTHOR]

Wind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China. Plain Language Summary: China has set an ambitious goal of achieving carbon neutrality by 2060, which requires an accelerated energy transition toward wind power and photovoltaic. However, the sources of wind and solar energy are sensitive to weather and climate, and future potential of wind and solar energy in China under a warming climate remains uncertain, posing a significant concern for the decision‐makers. Based on high‐resolution multiple regional climate models (RCMs) projections, we found that the annual average of wind power density (WPD) and photovoltaic power potential (PVP) are expected to decrease in the future by approximately −11.67% to −1.7% and −4.6% to −1.12%, respectively. There are notable uncertainties among the multi‐RCM projections, and the internal variability is the main source of uncertainty for most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. Our results highlight the advantages of reducing greenhouse gas emissions to enhance and stabilize renewable energy sources in China. Key Points: The annual average wind and solar energy in China are expected to decrease in the future based on a multi‐RCM ensembleThe internal variability plays a dominated role in the uncertainty of WPD and PVP projections in most sub‐regionsThe uncertainty in WPD and PVP projections over the TP is driven primarily by the different GCMs and emission scenarios, respectively [ABSTRACT FROM AUTHOR]

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 building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

The demand for the power grid system's capacity to undergo peak-shaving is increasing as the proportion of renewable energy rises. In China, nuclear power units usually only provide a base load operation in the view of safety and economic considerations, but they do not provide load adjustment services, which undoubtedly increases the pressure of grid load adjustment. In this paper, a novel flexibility load adjustment strategy of the CHP nuclear unit is studied, which is achieved by introducing the thermal storage tank (TST) into the Rankine cycle without changing the output of the nuclear reactor. The AP1000 pressurized water reactor nuclear power unit for combined heat and power is taken as an example, and the thermodynamic model is established through the water vapor equation. Furthermore, the reference system is simulated for the goal of minimizing the imbalance between power supply and demand, and the flexibility–environment–economy benefits are evaluated. The results show that the heat storage/release of the TST may achieve power output flexible adjustment of the nuclear unit, and the power imbalance of the reference energy system is reduced from 1107.99 MWh to 457.24 MWh, a reduction of 58.73%. The introduction of a 600 MWh TST can enable the reference unit to contribute 335 MWh of peak electricity within the reference day. From the perspective of replacing the power generation output increment of coal-fired power units with equal amounts, it can achieve a reduction of 106.09 tons of coal consumption in the case day, which means that 277.73 tons of CO2 emissions can be reduced. The profit of the reference unit can be improved by CHY 70,125 via participating in load adjustment in the case day if following the time-of-use electricity price. [ABSTRACT FROM AUTHOR]

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan. [ABSTRACT FROM AUTHOR]

With the proposed goal of 30•60 carbon peak and carbon neutrality, the proportion of new energy continues to increase, the proportion of conventional power supply capacity continues to decrease, and the flexibility demand of the existing power system continues to increase and the flexibility adjustment ability is insufficient. Therefore, a medium and long term planning method is proposed to flexibly adjust the multi‐time scale coordination of thermal power support wind and solar storage. Considering the long‐term investment decision and the short and medium term operation simulation, the flexible transformation cost and the penalty cost of insufficient flexibility of thermal power units are included in the planning objective, and a multi‐time scale coordinated planning model is established with wind power as the main power source and thermal power and hydrogen energy storage as the auxiliary power source. Through the multi‐stage cycle iteration of investment decision model, medium and long term production simulation and typical daily operation simulation, the flexible transformation resources of wind and solar fire storage and thermal power are optimized. Finally, a provincial power grid in northeast China is taken as an example to verify that hydrogen energy storage equipment assisting thermal power unit flexibility transformation can better support load fluctuation and large‐scale new energy access in new power systems. [ABSTRACT FROM AUTHOR]