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Facing the problem of energy shortage and the responsibility of carbon reduction, to achieve sustainable regional economic development, renewable energy must be vigorously developed. Technology can not only boost the development of renewable energy but be a new driving force for the economy. However, the development of the economy, technology, and renewable energy are interrelated, and the coupling and coordination among them lack sufficient evaluation and analysis. Therefore, an evaluation index system is constructed for the economy–technology–renewable energy ternary group. The evaluation model is applied to measure the coupling coordination degree, and a geographically weighted regression model is used to analyze the influence of key factors and their spatial differences. 30 provinces in China are used as examples for the study, and the results show that the coupling coordination degree is low in most regions, except for Guangdong, which has been maintained at an extremely coordinated level, and most provinces are in a fluctuating upward trend during the decade. Additionally, the results of the geographically weighted regression report that the influence of factors has spatial heterogeneity. The study provides a basis for relevant policy formulation in China, and the evaluation and analysis ideas can provide a reference for other countries or regions. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development goals, China has further increased its goal of reducing carbon intensity and has made digitalization an important support for sustainable development. However, the impact of digitalization on carbon intensity reduction is still unclear. In this context, this paper first evaluates the digitalization level of 30 provincial regions in China and then constructs a spatial Durbin model for two stages, 2012–2015 and 2016–2019, so as to explore the spatial spillover effects of carbon intensity in different stages and the important roles of digital infrastructure and digital inputs in carbon intensity reduction. The main findings are as follows: (1) the current digitization level of each province in China is widely disparate, with the region showing a high level in the east and a low level in the west; (2) carbon intensity reduction has a significant spatial spillover effect, as shown by a 1% reduction in local carbon intensity and a 0.21% reduction in neighboring regions; and (3) digitalization has a more significant positive impact on the reduction in carbon intensity in stage 2. The research results are strong demonstration that digitalization drives sustainable development. [ABSTRACT FROM AUTHOR]

In the current context of pursuing carbon neutrality and carbon peaking, many countries are accelerating the construction of carbon trading markets. Accurate prediction of carbon prices can enable national carbon trading markets to play a role in carbon emission reduction as soon as possible. However, current research is limited mostly to point forecasting of carbon prices, which makes it difficult to guarantee the stability of forecasting results in an increasingly complex market. Therefore, this paper proposes a three-stage vertical carbon price interval prediction framework. The contributions of this paper are as follows: the selection process of the decomposition model is regarded as an important process of prediction; a backpropagation neural network optimized by the sparrow search algorithm (SSA-BPNN) is used for the point prediction of carbon prices as a first attempt; and the kernel density estimation (KDE) model is used for interval estimation based on the point prediction results, which improves the confidence of the prediction. To validate the framework, this paper uses Shenzhen SZA-2014 products as the sample. The results show that the root mean square error of the predicted result with the improved complete ensemble empirical mode decomposition model (ICEEMD) is reduced by 29.7% and the use of SSA increases the predicted R2 by 8.5% compared with other optimization algorithms. In addition, the prediction interval coverage probability of interval prediction reaches 86% under 70% confidence. These results show that the proposed framework is not only more effective in point prediction but also performs well in interval prediction. • The weekly carbon price is affected mainly by Monday's carbon price. • A vertical forecasting framework of carbon prices is proposed. • A hybrid ICEEMD-SSA-BPNN model is used for point prediction. • The KDE model is used for interval estimation. [ABSTRACT FROM AUTHOR]