The reasonable optimization of energy structures and improvement of energy utilization efficiency are the inevitable way to achieve new progress in ecological civilization construction. The Yangtze River Economic Belt, as the leading demonstration area of China’s ecological civilization construction, is of great significance to take the lead in clarifying its energy efficiency situation under the dual-carbon goal. For this purpose, this paper uses the super-efficiency SBM model, ML index and Tobit model considering undesired output to explore the energy efficiency and the main factors affecting it of nine provincial capitals and two municipalities in the Yangtze River Economic Belt from 2003 to 2019. The results show that: (1) During the investigation period, the energy efficiency values of 11 cities are above 1, with a recent trend of decline, and the energy efficiency difference between regions is still expanding. (2) From 2003 to 2019, the overall energy efficiency of the Yangtze River Economic Belt has gradually improved with an average annual growth rate of 4.7%. Technological progress is the main force behind efficiency improvement, technical efficiency plays a small role, and the growth rate of the scale efficiency change index is only 1.3%, which means that the existing scale system of the Yangtze River Economic Belt still needs to be further improved. (3) The impact of industrial structure and government dominance on energy efficiency is significantly positive, while the degree of opening to the outside world is significantly negative. The relationship between economic development level and energy efficiency is an inverted “U” shape.

To investigate the impact of real estate market sentiment on demand forecasting, this paper constructs a Weibo sentiment index incorporating emotional polarity and verifies its predictive advantage for market demand. Based on the Bidirectional Encoder Representations from Transformers - Bidirectional Long Short-Term Memory (BERT-BiLSTM) and the characteristics of China’s housing market, we classify sentiments in crawled Weibo texts and train a sentiment analysis model specifically for the Chinese real estate domain. This model accurately extracts positive, neutral, and negative sentiment features to build a high-frequency sentiment index. Simultaneously, Internet Concern index is constructed using Baidu search data as a non-directional sentiment proxy variable. Further adopting the Autoregressive Distributed Lag Mixed Data Sampling model (ADL-MIDAS), we compare the predictive performance of these two sentiment indices alongside macroeconomic variables on market demand. Experimental results show that: (1) The BERT-BiLSTM model achieves 78.5% accuracy in sentiment classification, with its F1-score outperforming traditional methods (SVM, LSTM, etc.) by over 30%; (2) The Weibo sentiment index yields a Root Mean Squared Forecast Error (RMSFE) of 1.6%-4.7% under the ADL-MIDAS framework, significantly lower than the Internet Concern index (6.7%-7.0%). The study demonstrates that integrating deep learning with high-frequency social media sentiment indiex containing emotional polarity can more effectively capture market expectation fluctuations, while simultaneously yielding superior performance for real estate market demand forecasting.

The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

Under the dual carbon goals, green innovation acts as a key driver for the transformation and upgrading of China’s energy-intensive enterprises, and stands as an inevitable choice to propel China’s shift from a major energy-consuming nation to a powerful scientific and technological nation. To explore the driving mechanism of digital transformation on the green innovation efficiency of energy-intensive enterprises, this paper takes such enterprises from 2011 to 2022 as the research subject, designs three distinct scenarios (environmental protection, enterprise innovation and scientific research innovation), simulates heterogeneous development paths, and forecasts trends to 2030. The results show that green innovation efficiency rises steadily across all scenarios, with the strongest improvement under the scientific research scenario. By 2030, efficiency under this scenario is expected to reach 1.77 times that of the baseline scenario, while the other two scenarios also perform significantly better. These findings confirm the positive role of multi-dimensional policy interventions in strengthening digital-enabled green innovation efficiency. Accordingly, practical recommendations are put forward regarding how digital transformation can effectively drive the improvement of green innovation efficiency of energy-intensive enterprises from the aspects of policy formulation, enterprise management and innovation investment.

This article takes the low-carbon logistics industry in 17 provinces along the Belt and Road from 2015 to 2022 as the research object. Utilizing a super-efficiency SBM evaluation model that accounts for undesirable outputs, and after constructing an evaluation index system, a static analysis of the logistics efficiency in the key provinces is conducted. Dynamically, the Malmquist index model is employed to analyze the logistics efficiency in these provinces from the perspectives of overall productivity in logistics, shifts in technical efficiency and advancements in technological progress. Through spatial autocorrelation analysis, the spatial effects of logistics in major provinces are analyzed to obtain the trend of spatial dependence and agglomeration. The results suggest that the logistics efficiency in the major provinces is markedly altered by external environmental determinants, the size of the logistics still requires further enhancement and improvement, there is insufficient development and utilization of logistics technology, and invested resources are not effectively utilized. Finally, based on the evaluation results, strategies are proposed for how these key provinces can improve their logistics efficiency.

Against the backdrop of global warming, China has set forth its ‘dual carbon’ goals, striving to achieve carbon neutrality by 2060. As a vital engine of economic development, the Yangtze River Delta region has formulated implementation plans, prioritizing carbon emission reduction. The transport industry, a major source of carbon emissions, plays a crucial role through its transition to clean energy, making it pivotal for advancing regional carbon neutrality. This study categorizes carbon emission drivers based on an assessment of current emissions and dynamic evolution analysis, integrating policy evolution and technological innovation trajectories. These drivers are classified into: transport structure, transport intensity, energy intensity, year-end resident population, per capita GDP, and industrial structure. Using the extended STIRPAT-Ridge model, quantitative analysis of carbon emission drivers is conducted. Employing the Tapio decoupling model, the decoupling state between carbon emissions and economic growth is deconstructed. Empirical findings reveal that carbon emissions from the transport industry in the Yangtze River Delta are influenced by multiple factors, with year-end resident population and industrial structure emerging as primary drivers. The decoupling between carbon emissions and economic growth exhibits fluctuating characteristics, but has been progressively strengthened in recent years by government policy initiatives and market mechanisms.