Data-Driven-Based Study on Sustainable Improvement of the Regional Logistics Industry

Abstract

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.