With a renewed global scientific and technological revolution and industrial reform, the digital economy, with data resources as the key element, has rapidly developed. This study proposes a data-driven measurement and evaluation method to promote the coordinated development of the digital economy and logistics industry. An evaluation index system is constructed, which comprehensively considers the index dimensions that reflect the development level of the digital economy and logistics industry. A Z-score standardisation method is applied to data processing, to carry out dimensionless standardisation processing of the original index data. A collaborative degree model is constructed to evaluate the collaborative development level of the digital economy and logistics industry composite system. We demonstrate the implementation process of these models using data from Anhui province from 2013 to 2020. The results verify the feasibility of the research method and emphasise that the development level of the composite system of the digital economy and logistics industry in Anhui province shows a fluctuating growth trend, with variations between the types and degrees of collaboration policy; suggestions are made accordingly. This study provides theoretical and methodological support for the coordinated development of the regional digital economy and logistics industry.

This study explores the spatial effect of infrastructure development on real estate prices in the Yangtze River Delta. It constructs an evaluation system of the infrastructure development level across five dimensions (i.e., transportation, water supply and drainage, energy and power, postal communication, and ecological environment), analyzes the development characteristics of urban infrastructure in the Yangtze River Delta, and uses a spatial panel model to explore how urban infrastructure development affects real estate prices. Results indicate that (1) the overall development level of urban infrastructure in the Yangtze River Delta region shows an upward trend. Significant regional differences exist as the development level of urban infrastructure in the eastern region is ahead of that in the central region; (2) Spatial autocorrelation and real estate prices in the Yangtze River Delta region in infrastructure development and overall levels, respectively, are high; (3) Infrastructure directly affects local real estate market demand and improves the vitality of the housing market in adjacent areas; and (4) Infrastructure construction can significantly promote the rise of urban real estate prices in the eastern region, while this driving effect is not significant in the central region. This research will help the government promote the coordinated development of urban infrastructure and formulate relevant policies for the macro-control of the real estate market in urban agglomerations.

The digital transformation of the logistics industry is the current trend of development. In order to promote the integrated development of the logistics industry (LI) and the digital economy (DE), we propose a data-driven method which can be used to measure, evaluate, and identify the coupled and coordinated development (CCD) of the LI and DE. On the basis of data collection, we use the entropy weight method to measure the comprehensive development level of the LI and DE. A coordination model is then used to evaluate their CCD level. Finally, an obstacle degree model (ODM) is used to identify the key factors inhibiting the coordinated development (CD) of the two. This method is then applied to gauge the integration development of the LI and DE in Anhui Province. The results show that energy consumption and the lack of logistics employees are the main obstacles to the development of the LI in Anhui Province. The main obstacles to the development of the DE are the low development level of the electronic communications equipment manufacturing industry and the limited digitization of enterprises. Accordingly, this study puts forward corresponding countermeasures and suggestions to provide decision support for the CCD of the LI and DE.

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).