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Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

Crucial to the green and sustainable development of China's industry are both choosing an appropriate path of directed technical change and optimizing and improving energy consumption pattern. In this paper we incorporate fossil energy, non-fossil energy and environmental factors into the traditional production function and calculate the factor output elasticity, the growth rate of environmental total factor productivity, the factor-biased degree of directed technical change and factor substitution elasticity in China's industrial sector based on stochastic frontier analysis of the transcendental logarithmic production function. The results show that the high output elasticity of capital leads to the highest factor-biased degree of directed technical change of capital although it is accompanied by an increase in energy consumption and environmental degradation. At present, the factor-biased degrees of directed technical change in China's industry, from high to low, are capital, the environment, fossil energy, labor and non-fossil energy; these can be seen as the root cause of, among other things, overcapacity, energy shortages and environmental pollution. Optimizing the path of directed technical change of labor, capital and non-fossil energy is an advisable way to solve the above problems. The patterns of energy consumption in industrial production in most provinces are unsustainable and each province should optimize its local energy consumption pattern according to those technical changes biased towards non-fossil energy and the positive factor substitution relationship between two kinds of energy. • We calculate the growth rate of environmental total factor productivity. • We measure the factor-biased degree of directed technical change. • The factor-biased degrees of capital and environment are relatively high. • The patterns of energy consumption in most provinces are unsustainable. [ABSTRACT FROM AUTHOR]