In recent years, there has been an increasing lack of social responsibility, such as low credibility of information disclosure, product quality defects, food safety, and other issues. This has had a certain impact on supply chain performance and has become an important topic of sustainable supply chain research. This study considers the relationship between logistics corporate social responsibility and supply chain performance. Structural equation models were built to explore the relationship between logistics corporate social responsibility, supply chain collaboration, and supply chain performance, and the bootstrap method was used to build path models to explore whether there is a mediation effect between logistics corporate social responsibility and supply chain performance. The results show that logistics corporate social responsibility has a significant positive impact on supply chain coordination, and logistics corporate social responsibility and supply chain coordination also have a significant positive impact on supply chain performance. Supply chain coordination plays an intermediary role between logistics corporate social responsibility and supply chain performance. Based on these results, it is suggested that logistics enterprises improve their awareness of fulfilling corporate social responsibility, improve transparency, strengthen supply chain collaboration, and accept the role of supervision and management at the government level.

The resources and environmental carrying capacity (RECC) of a region are considered the key and the foundation for achieving sustainable development and the benchmark of environmental protection and pollution control. However, to improve the regional RECC, we need to comprehensively consider the data information and correlation of the economy, society, resources, and the environment. Therefore, we propose a data-driven method for RECC measurement and evaluation of the regional RECC. Based on data collection and the application of the pressure-state-response (PSR) framework to reflect RECC, an evaluation index system for the regional RECC is constructed. The technique for order of preference by similarity to the ideal solution (TOPSIS) model with the entropy weight method is used to measure and evaluate the regional RECC. The obstacle degree model is adopted to select and identify the key factors affecting the regional RECC and to propose targeted policy suggestions for data application. The results indicate that the RECC level in three provinces and one city of the Yangtze River Delta region fluctuated slightly from 2010 to 2019, with an overall upward trend. Anhui Province has a relatively weak carrying capacity, and the main obstacles to RECC improvement in the region are the proportion of wetland area and the ownership of water resources. This study provides theoretical and methodological support for regional RECC research and management as well as a basis for formulating policies related to environmental protection and pollution control.

Improving the logistics industry’s resource efficiency (LIRE) is one of the most significant measures for ensuring sustainable development. We offer a data-driven technique for analyzing and optimizing the LIRE to improve it and achieve sustainable development. A LIRE index system is built based on relevant data gathering and a complete examination of the economy, society, and environment. The Super-EBM-Undesirable model was used to calculate the LIRE; the Global Malmquist–Luenberger index model was used to calculate the LIRE’s dynamic change characteristics, and ArcGIS and spatial autocorrelation models were used to analyze the LIRE’s spatial evolution pattern. The LIRE in 30 Chinese provinces and cities from 2011 to 2019 is used to illustrate the method implementation process. The results indicate the following: (1) The overall LIRE is low, with an average value of 0.717, and there are regional variances with a decreasing gradient pattern of “East–Northeast–Central–West”. (2) Changes in pure technical efficiency have a bigger impact in general; increasing technical efficiency is the LIRE’s principal motivator. (3) Improving the LIRE should take spatial spillover and inhibitory effects into account. This study provides theoretical and methodological support for the evaluation and optimization of the LIRE and a theoretical foundation for the logistics industry’s sustainable development (LISD).

The ecological efficiency (eco-efficiency) of a regional logistics industry (RLI) is widely regarded as a key factor affecting sustainability of economic development, environmental protection, and resources utilization. This study applied a data-driven method to evaluate and increase the eco-efficiency of an RLI. Based on RLI-related data, which were converted into proper dimensionless indices, data envelopment analysis (DEA), which assumes that the decision-making units (DMUs) are in the situation of variable returns to scale, the Banker, Charnes, and Cooper (BCC) model, and Malmquist index model were used to assess the eco-efficiency of the RLI from both static and dynamic viewpoints. Then, a Tobit regression model was built to explore the factors that influence eco-efficiency. The effectiveness of this approach was verified by its application to an example from Anhui Province. This study has theoretical and practical value for the assessment and promotion of the ecological eco-efficiency of the RLI. We believe that our approach offers a powerful tool to assist logistics enterprises and local governments in coordinating the relationship between the RLI economy and the ecological environment, facilitating the drive to carbon neutrality.

The rapid growth and development of the logistics industry has brought productivity to commerce and trade and greatly contributed to national economy, but at the expense of vast energy consumption, which significantly affects sustainability. To improve sustainability of the development of the logistics industry (LISD) and identify its potential influencing factors more directly and efficiently, this study proposes a data‐driven‐based evaluation and optimisation method. First, a comprehensive evaluation index system is constructed for LISD from the aspects of economy, society, and environment (including the logistics industry, degree of specialisation, environmental effects, and innovation capability). Second, considering the diversity of dimensions and units, a min‐max standardisation is utilised for data normalisation, providing dimensionless indicators for further weight determination via an entropy value method. Third, two coupling degree models are adopted to evaluate the degree of correlation among subsystems. Subsequently, a degree of obstacle model is applied to analyse the interaction between factors, providing theoretical support for improving regional LISD. Finally, an evaluation of LISD in Anhui Province is used as a case study to validate the practicability and feasibility of the proposed method, establish theoretical basis, and propose policy recommendations for future sustainable development.