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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]

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 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]

To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models. • A novel combined framework is proposed to predict short-term wind power. •Linear and nonlinear feature extraction strategies are applied to analyze the character of NWP data. •A variance combined strategy is used to allocate the optimal weight of the primary combined model. •The wind power prediction error analysis is conducted by multiple-error performance metrics. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

The increasing generation of renewable energy and its concurrent technological innovation are driving the transformations of the energy sector, but little effort has been made at the regional level to specify the heterogeneous impacts of different types of renewable energy development on multi-dimensional energy security. Therefore, this paper establishes a four-dimensional energy security assessment system based on the current energy market situation and economic context of China. Renewable energy generation and technological innovation were simultaneously used as the core proxies for renewable energy development. Then, the panel quantile regression method was used to analyze the heterogeneity of the impact of solar, hydro, and wind energy on energy security, covering 30 provinces in China from 2006 to 2019. The empirical results show that renewable energy power generation generally promotes energy security, but it cannot be effectively applicable to all levels of energy security. Renewable energy technology innovation has surprisingly hindered energy security. These results can serve as a policy and practical guide, suggesting that provincial governments in China should implement energy security and renewable energy policies in line with local characteristics. • The redefined provincial energy security presents obvious spatial heterogeneity. • Panel quantile regression was applied to balanced provincial panel data. • Hydropower contributes only to medium and low levels of energy security. • Wind and solar power contribute differently to all levels of energy security. • Renewable energy technological innovation impedes energy security. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the world's biggest energy consumers and CO 2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO 2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO 2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO 2 emissions. This indicates that curbing CO 2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal. • Renewable energies, coal consumption, economic growth, and CO 2 we examine. • We investigate the causal predicted relationship on China, India, and the USA. • An advanced methodology in Machine Learning is used. • India has a prediction of an increase in emissions of CO 2. • China and the U.S. present a more promising path to sustainability than India. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind and solar power in China, the consumption of these intermittent renewable energies is severely restricted by the capacity of the transmission channel, which leads to massive renewable energy curtailment. Therefore, it would be beneficial to use limited transmission channels to absorb as much renewable energy as possible. In this paper, we propose a chance constraint-based multistage nested hydro-wind-solar coordinated optimal scheduling model to aid peak shaving while ensuring maximum power generation. First, multistage partitioned section chance constraints are introduced to mitigate the power congestion of the transmission network. Then, based on the principle of using hydropower to complement the uncertainty of wind and solar power, the compensation chance constraints are considered. To quantify the uncertainty of wind and solar power, their prediction errors are analyzed using the Gaussian mixture model. Finally, the model is recast and linearized into a mixed integer linear programming model. A case study of a hydro-wind-solar base in Southwest China demonstrates that the proposed model can effectively leverage the regulation ability of hydropower to coordinate multiple power sources with the restrictions of multistage transmission sections, effectively alleviating the congestion of transmission channels and reducing the curtailment of renewable energy. [ABSTRACT FROM AUTHOR]

Abstract: The People''s Republic of China foresees a target of 30 GW for installed wind power capacity by 2010 (2008: 12 GW). This paper reports on the technical and economic potentials of wind power, the recent development, existing obstacles, and related policies in China. The barriers to further commercialization of the wind power market are important and may deter the 100 GW capacity target of the Chinese government by 2020. The paper concludes that the diffusion of wind power in China is an important element for not only reducing global greenhouse gas emissions, but also for worldwide progress of wind power technology and needed economies of scale. [Copyright &y& Elsevier]

To achieve the carbon-neutral goal set for 2060, China has promoted renewable energy development for more than two decades. Meanwhile, China's electricity market is undergoing a new round of reform by introducing more market-oriented mechanisms and changing the market structure. This paper investigates the impact of three electric utility market structures experimented with in China on promoting renewable energy through a simulation model. The three market structures are vertical monopoly, weak monopoly (separation of power generators and grids), and monopolistic competition (Cournot-Nash competition). The model is calibrated with the actual data of the regional utility network in Beijing, Tianjin, and Tangshan areas. The simulation results show that the electricity market structure plays a vital role in renewable electricity, and the competition among utilities tends to benefit renewable energy development (wind power here). The study's conclusions are helpful to our understanding of further reforms in the electric utility industry and the long-term clean energy target in China. [ABSTRACT FROM AUTHOR]

n recent years, the annual curtailment ratio of wind power in China has been around 15%. Although the priority dispatch policy for wind power has been put in place in the country, it is not well implemented. One of the reasons for this is that wind power is viewed by some power system operators as “garbage power” due to its inherent intermittency nature. The improvement of social awareness of the value of wind power deployment is crucial for the effective implementation of the priority dispatch policy. In this paper, we analyze the impact of the priority dispatch of wind power on energy conservation and environmental improvement, and then compare the generation cost between wind power and coal-fired power while considering environmental externalities. We conclude that the energy intensity in the system decreases as the proportion of wind power generation increases, and the energy intensity is minimized when the proportion reaches 15%–20% provided a small amount of wind power curtailment is allowed. Meanwhile, we confirm that the wind power priority dispatch has significant positive impacts on environmental improvement. Further, we find that wind power generation cost is very close to coal-fired power cost when environmental cost is taken into account. [ABSTRACT FROM AUTHOR]

As a crucial issue in the wind power industry, it is a tough and challenging task to predict the wind power accurately because of its nonlinearity, non-stationary and chaos. In this paper, we propose a novel hybrid model, which combines an integrated processing strategy and an optimized local linear fuzzy neural network, to forecast the wind power. First, discrete wavelet transform and singular spectrum analysis are used to filter out the noises and extract the trends from original wind power series, respectively. Then, the novel no-negative-constraint-combination theory together with the CS algorithm are adopted to integrate these two subseries obtained from the first step to retain strengths of each method. Based on the phase space reconstruction model, we could determine the most proper structure of the input sets and the output sets. Next, the local linear fuzzy neural network, with the initial rule consequent parameters optimized by the seeker optimization algorithm, is utilized to make wind power forecasts for a selected number of forward time steps. The numerical results from two experiments demonstrate that the proposed hybrid model is an effective approach to predict wind power, and the accuracy of prediction is highly improved compared with conventional forecasting models. [ABSTRACT FROM AUTHOR]

Abstract: To increase the supply of electricity and reduce environmental pollution, China has made an effort to boost the renewable energy. As a renewable energy, wind energy has been maintaining a rapid growth over the last ten years. China has the largest construction market for wind power in the world, so it attracts many local and foreign investors. However, because of the lack of a full understanding of China''s complicated administrative procedure for wind power projects, many foreign investors have encountered difficulties when trying to enter the Chinese wind power market. According to the relevant laws, regulations, policies and project practices, this paper puts forward a main management procedure model for Chinese wind power projects as well as four subdivided process models, including the Project Approval Process, the Land Application Process, the Design Process and the Licensing and Construction Process. And then the administrative requirements, milestones and main points of works are identified and analyzed within the framework of the four processes. The results open a window through which both the local and foreign investors can perceive a panoramic procedure for wind power project construction in China. [Copyright &y& Elsevier]

Abstract: The Chinese government has made an important effort to diversify the country’s energy mix and exploit different sources of renewable energy. Although China’s installed wind power capacity has experienced a dramatic expansion over the past five years, electricity generation from wind power has not increased as expected. This paper aims to present the current status of wind generation in China and analyze the causes of the large discrepancy between installed capacity and generation. We find that this is mainly caused by the inadequacy of the power transmission grid, the absence of economic incentives to transmission and backup generation providers, and the lack of a generation-based renewable portfolio standard. [Copyright &y& Elsevier]

Abstract: Chinese electric power industry has adopted Build–Operate–Transfer (BOT) approach in a number of projects to alleviate the pressure of sole state-owned investment. The Chinese government has taken enormous efforts to create an environment to facilitate the application of BOT approach in electric power projects. Moreover, the growing attention on the sustainability issues puts the traditional major source of electricity – thermal power project under more strict scrutiny. As a result, various renewable energy projects, particularly the wind power projects have involved private sector funds. Both thermal power and wind power projects via BOT approach have met with a varying degree of success. Therefore, it is imperative to understand the factors contributing towards the success of both types of BOT power projects. Using an extensive literature survey, this paper identifies 31 success factors under 5 categories for Chinese BOT electric power projects. This is followed by a questionnaire survey to exam relative significance of these factors. The results reveal the different levels of significance of success factors for BOT thermal power projects versus wind power projects. Finally, survey results were analyzed to explore the underlying construction and distributions among the identified success factors. This study provides a valuable reference for all involved parties that are interested in developing BOT electric power projects in China. [Copyright &y& Elsevier]

Abstract: A reliable supply of energy is essential to maintain and to improve human being''s living conditions. Compared to the conventional coal-fired approach, renewable energy (RE) helps to mitigate the impacts of greenhouse gas emissions to a large extent. As a newly developed sector with large resource potential and good commercial prospects, China''s wind power industry is exceeding expectations. By 2008, installed capacity of wind power in China totaled over 12GW, making China the fourth largest wind market in the world. However, China has to admit the gap with foreign leading countries, mainly in technical R&D for large-scale wind turbine generation units. This paper attempts to formulate an analytical model for studying and assessing factors that have significant impacts on the local industry. An improved dynamic Diamond Model is developed to help the international community to understand the status quo of the Chinese wind power industry. In order to build a stronger wind power industry, the elements identified in the model need to be strengthened. [Copyright &y& Elsevier]