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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]

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

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

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

The significance of renewable energy as a pivotal green industry in decarbonizing the global economy and addressing the climate change is profound. The rise of China's regional renewable energy serves as a testament to its transition from an industrial powerhouse to a leader of sustainable growth, offering valuable insights for a sustainable future worldwide. This study analyzes renewable energy efficiency, alongside the factors that influence it, in Qinghai Province over 2000–2021. It uses a combination of the Logarithmic Mean Divisia Index factor decomposition, Data Envelopment Analysis of Super-SBM models, and field research with a rounded approach. The study finds a robust increase in renewable energy capacity, particularly after 2016. Efficiency in renewable energy generally has risen, although periods of inefficiency were noted from 2014 to 2019. Despite fluctuations, the overall impact of factors such as renewable energy structure, electricity intensity, energy security, energy intensity, carbon productivity, and carbon emissions, all contribute positively to the development of renewable energy, notably in recent years. The paper concludes with a discussion of green industrial policies with wider implications for the renewable energy sector. • The rise of China's regional renewable energy offers valuable insights for sustainability. • Renewable energy capacity in Qinghai increased robustly over 2000-2021. • Renewable energy efficiency has risen but with inefficiency from 2014 to 2019. • Despite fluctuations, the overall impact of influencing factors promoted renewable energy, notably in recent years. • There is a need for stable green industrial policy, diverse energy mix, strengthening grid infrastructure, and promoting sustainable energy. [ABSTRACT FROM AUTHOR]

Based on 196 prefecture-level cities in China from 2006 to 2021, this paper constructs a weight matrix of urban economic geography and uses the spatial differential model to verify the impact of smart city policies on urban green energy efficiency. The results show that the efficiency value of green energy fluctuates in Chinese cities. Different test results show that smart city policies can effectively improve urban green energy efficiency, but mainly from the time effect rather than the individual effect. It means the improvement in urban green energy efficiency comes more from the fast-growing of the Chinese economy and urban construction in this period than from the country's smart city pilot policy. In addition, from the spatial spillover effect result, China needs to design a better urban spatial development plan to improve the capacity of smart city energy services and vigorously develop intelligent technology represented by information and communication science. [ABSTRACT FROM AUTHOR]

With the comprehensive progression of China's electric power reform, the participation of hydropower in the spot market has become inevitable. For hydropower-dominated power grids, the tight hydraulic and electric coupling relationship between cascade hydropower plants and the policy requirement of clean energy consumption greatly increase the challenge of optimal scheduling. Based on the market characteristics and actual demand of the hydropower-dominated power grids, this paper discusses the day-ahead spot market trading mechanism to promote the guaranteed consumption of renewable energy. On this basis, a day-ahead optimal scheduling model is established which aims to minimize the comprehensive power purchase cost of the power system. The nonlinear factors in the scheduling model, such as the relationship between the water level and storage capacity of hydropower plants, the relationship between the water level and water consumption rate, and the constraints on the start-up and shutdown durations of thermal power units, are linearized through multiple linearization techniques. The original nonlinear model is transformed into a mixed integer linear programming (MILP) model and is then solved using a CPLEX solver. The developed model is verified by taking 10 thermal power plants and 22 hydropower plants in Yunnan Power Grid as a case study. The calculation time is 183.6s, the comprehensive power purchase cost of the system is 155.35·million CNY, and no abandoned water is generated in the three basins. The simulation results show that the proposed model has high solution accuracy and efficiency, and maximizes the hydropower consumption under the premise of meeting the minimum comprehensive power purchase cost. The findings of this study can provide a scientific basis for the development of the day-ahead spot market of hydropower-dominated power grids. [Display omitted] • A day-ahead spot market trading mechanism to promote consumption of renewable energy. • Day-ahead optimal scheduling of hydropower-dominated power grids under a spot market environment. • A novel MILP approach is employed to solve the model. • Proposed model has high solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]