China is one of the world’s largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China’s carbon emissions by constructing a system dynamics model of China’s carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China’s carbon emissions. The contribution rates of the five factors to China’s carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China’s carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China’s carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small.

According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015–2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

In the light of the shortcomings of the analytic hierarchy process and other common regional sustainable development evaluation methods, this paper proposes the use of a combination of subjective and objective weights to generate input/output indicators using the Data Envelopment Analysis (DEA) method. Using this methodology, we construct a comprehensive evaluation index which is useful in expanding the application of Data Envelopment Analysis (DEA) in the comprehensive evaluation of sustainable development. Moreover, this paper addresses the shortfalls of the traditional DEA evaluation model and uses the Super-Slack Based Measure (SBM)-Undesirable and DEA-Malmquist evaluation models, which are based on traditional DEA model optimization, to analyze the spatio-temporal characteristics of sustainable development on regional scales. Using China’s Yangzte River Economic Belt as an example, an empirical analysis is carried out. We show that analysis results are virtually identical to the extant situation and can objectively reflect the status and abilities of sustainable development in each subregion. Additionally, from the angles of input, output and technological progress, this paper uses the DEA evaluation method to analyze the reasons behind the slow development in several provinces and municipalities along the Yangzte River Economic Belt (YERB). The regional characteristics of each province and city within our study are combined to explore the optimal mechanisms for sustainable development.

Scientifically justifiable spatial structure can not only promote the efficient use of regional resources, but can also effectively avoid “urban diseases”, such as traffic congestion, housing shortage, resource scarcity, and so on. It is the “regulator” and “booster” of regional development. Firstly, this paper measures the spatial structure of the Yangtze River Economic Belt from the four dimensions of scale distribution, central structure, spatial connection, and compactness: Gini coefficient of urban scale, urban primacy, regional economic linkage strength, and spatial compactness. Secondly, the optimized Super-Slack Based Measure-Undesirable model is used to evaluate the sustainable development status of the Yangtze River Economic Belt. Finally, a sustainable development correlation analysis model based on regional spatial structure is constructed. Based on the overall perspective of the Yangtze River Economic Belt and the individual perspective of 11 provinces and cities, the relationship between the spatial structure of the Yangtze River Economic Belt and sustainable development is analyzed. It is found that the impact of the four spatial structure indicators on the sustainable development level of the Yangtze River Economic Zone is relatively stable in five different periods. The ranking results are as follows: Gini coefficient of urban scale > urban primacy > regional economic linkage strength > spatial compactness.

A profound analysis of China’s research achievements in the realm of carbon emissions holds the potential to furnish insightful references for analogous endeavors and inquiries in other nations. Employing the CiteSpace tool, this paper identifies five major focal points in Chinese scholars’ research on carbon emissions: carbon emission computation and prediction, influencing factors of carbon emissions, carbon footprint, carbon emission efficiency, and differential analysis of carbon emissions. Subsequently, this article systematically scrutinizes and dissects the outcomes of Chinese scholars’ endeavors in the aforementioned five focal points, culminating in recommending China’s forthcoming research on carbon emissions. (1) The research findings reveal a diversified evolution in the methods employed for calculating and predicting carbon emissions in China. However, due to the limited exploration of delineating carbon emission boundaries, instances of overlap and deviation in carbon emission quantification have emerged. (2) Factors influencing carbon emissions can be categorized into five major classes: economic, demographic, energy-related, policy-driven, and others. Yet, studies investigating industry-specific influencing factors remain relatively scarce. (3) Overcoming challenges associated with cross-boundary measurements, comprehensive effects, and policy applications is imperative in carbon footprint research. (4) Significantly disparate levels of carbon emission efficiency prevail across distinct regions or industries, with intricacies characterizing the influencing factors and a notable dearth of micro-level investigations. (5) The analysis of carbon emission differentials primarily encompasses regional disparities, industrial differentials, and temporal variations, lacking sustained tracking studies on the nuances of carbon emission disparities.

The rapid development of the digital economy (DE) has provided innovative solutions for the transformation and upgrade of the construction industry. Leveraging technologies such as intelligent management, the Internet of Things, and artificial intelligence effectively enhances the construction industry’s green total factor productivity (GTFP). Based on data from 30 Chinese provinces spanning 2012 to 2022, this paper systematically investigates the mechanisms through which the DE influences the GTFP of the construction industry from multiple dimensions, including direct effects, indirect effects, and threshold effects. The findings reveal that the DE significantly promotes the improvement of GTFP in the construction industry. The DE indirectly enhances GTFP through technological innovation and environmental regulation, with the mediating effect of technological innovation being more pronounced. Urbanization exhibits a significant single-threshold effect in moderating the relationship between the DE and GTFP, with the impact of the DE on GTFP following a “U-shaped” trajectory.