Sustainability drivers in road transportation system: Evidence from China

This research explores the sustainability drivers of the Chinese road transportation system in terms of its cargo and environmental productivity levels. A novel Fuzzy Double-Frontier Network Data Envelopment Analysis (FDFNDEA) model is proposed to investigate the relationship between desirable (freight and passenger turnovers) and undesirable (CO2 and NOx emission levels) outputs against the respective power consumed in each one of the 29 Chinese provinces (municipalities and autonomous regions) between 1985 and 2017. The power consumption emerges spatially and temporally as a consequence of the evolution of the road system's productive resources (employees, highway length, number of vehicles, and fuel consumed) at the province level over the course of time. Shannon's entropy is used as the cornerstone to quantify input and output vagueness of this evolution in terms of triangular fuzzy numbers (TFN), thus allowing the building of alternative optimistic and pessimistic double efficiency frontiers. Respective Malmquist Productivity Indexes (MPI) for overall and each stage productivity are regressed against contextual variables related to demography, economic activity, competitor infrastructure, and highway quality using bootstrapped Cauchy regressions. Results confirm the disruptive evolution of the Chinese road transport system over the course of the years and different expansion strategies at the regions. The energy and environmental efficiency of the Chinese road transportation system is affected by these contextual variables. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This research explores the sustainability drivers of the Chinese road transportation system using a novel Fuzzy Double-Frontier Network Data Envelopment Analysis model to investigate the relationship between different outputs and power consumption. Results confirm the disruptive evolution and different expansion strategies of the Chinese road transport system in various regions, with energy and environmental efficiency being influenced by contextual variables.