Data-Driven Eco-Efficiency Analysis and Improvement in the Logistics Industry in Anhui

Abstract

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.

Manufacturing Process Optimization in the Process Industry

“…Emerging techniques, such as artificial intelligence (AI), the internet of things (IoT), cloud technology, machine learning, and big data, are driving the fast development of contemporary technological innovation (Sun & Liu, 2023). The fields of science and business are being revolutionized at lightning speed by scientific computing in particular (Hu et al, 2023).…”