Efficiency evaluation of China's transportation system considering carbon emissions: Evidence from big data analytics methods

• Published in: The Science of the total environment Vol. 922; p. 171031
• Main Authors: Liu, Jia-Bao, Liu, Bei-Ran, Lee, Chien-Chiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.04.2024
• Subjects: Carbon emissions; Malmquist-Luenberger index; Principal component analysis (PCA); SBM-DEA; Tobit model; Transportation system efficiency; Tobit model; Transportation system efficiency; Malmquist-Luenberger index; SBM-DEA; Principal component analysis (PCA); Carbon emissions
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.171031

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness. [Display omitted] •The redundancy of transportation inputs is reduced by the PCA method.•The super-efficiency SBM-DEA model is combined with the SFA method.•China's transportation efficiency has long been at a low level.•Regional heterogeneity exists in China's transportation efficiency.•Regional insufficient technical capacity mainly restricts transportation efficiency.