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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's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development. • China's Coastal region is the major geographical unit for the future development of China's industrial. • In high-quality economic growth and climate change, clean energy use is vital for sustainable development. • The utilization of wind energy and it's efficiency is predicted using Deep Belief Network (DBN) model. • Empirical results link economic growth with carbon emissions, showing growth promotes emissions offset. • Green innovation changes the economic growth-carbon link, aiding China's carbon reduction efforts. [ABSTRACT FROM AUTHOR]