Search

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]

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]

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]