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In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

China and the United States are both major countries in the world in the production of and trade in wind energy products, and they are also important trading partners. Therefore, it is of great practical significance to analyze and evaluate the Sino-US bilateral wind power complementarity for re-examining their respective comparative advantages and re-understanding the benefits that trade liberalization brings to both countries, in an attempt to strengthen the trade relations between China and the United States and enhance trade complementarities in wind energy products under the global circumstances of fierce competition. In this paper, based on the trade data from the UN Comtrade database in 2007-2016, a quantitative analysis was conducted on the complementarity of Sino-US wind energy products, by adopting the quantitative research methods of trade combination degree index (TCD), export similarity index (SI) and trade complementarity index (TCI). The results shows that: i) the total trade volume of wind energy products between China and the United States has increased rapidly, and the trade surplus between China and the United States has been expanding year by year; ii) the overall trade balance of wind energy products trade between the two countries is generally stable, and the dependence of China's wind energy products on the United States is much higher than that of the U.S wind energy products on China; iii) the Sino-US wind energy products between China and the United States are highly competitive in the international market; iv) Sino-US wind energy products have different degrees of complementarity in bilateral trade. Finally, this paper proposed policy implications based on the findings of this study. [ABSTRACT FROM AUTHOR]

In order to assess the viability of wind energy projects in China, this paper takes a real options approach by accounting for flexibility over deployment of the technology in response to a stochastic non-renewable energy cost. The difference between the latter and the R&D-enhanced cost of renewable energy operation may be thought of as a benefit from using wind. On the other hand, the Chinese government provides feed-in tariffs to encourage further adoption of wind, which should be included as a cost. This work, thus, shows that there is substantial options value to China's wind energy program, which is bolstered if the cost of carbon dioxide emissions is internalized. Moreover, by varying the rate of feed-in tariffs, the model may be used to show plausible ranges for the feed-in tariffs, which could be a useful policy insight. Furthermore, the study hypothesizes that with the likelihood of renewable energy to replace non-renewable energy, wind power in China can potentially reduce CO 2 emissions by approximately 6.4% by 2020 comparing to the 2005 level and highlights the significant role of wind power in China's emission targets and energy security. This is a relevant paper, especially given the importance of a sustainable energy transition for China. More generally, using the real options approach from a policy perspective could provide insights about the viability of renewable energy programs and robustness of feed-in tariffs rates. [ABSTRACT FROM AUTHOR]

In recent years, China has successively issued relevant policies to support the development of distributed renewable energy sources such as dispersed wind power (DWP) and distributed photovoltaic (DPV). The capacity of DPV grows fast, but the progress of DWP deployment is limited. In this paper, the improved levelized cost of electricity (LCOE) model and the internal rate of return (IRR) are employed to evaluate the economics of DWP and DPV for case regions. We find that, under current market regime, the IRR of DWP in eastern and central regions is 8.81%–10.34% for the grid side, and which of DPV is 9.08%–12.8% for the user side. When the power market is restructured, the IRR of DWP for industrial and commercial user side increases to 12.73%–14.86%, higher than that the return of DPV. Policy implications on grid-connection, land expropriation, and financial support, planning and approval procedure are proposed to promote the development of DWP. Overall, this paper covers the research gap on the development of DWP in China with a technical/institutional economics perspective and a comparison with DPV. Furthermore, our efforts make a significant contribution to DWP policy by identifying the institutional factors that hinder the development of DWP. • Explore the technical & institutional factors around DWP deployment in China. • DWP and DPV gain comparable economic returns under current planning regime. • DWP can have better economic returns than DPV with electricity market reform. • Identify institutional factors that hinder DWP and propose policy implications. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]