Big-data-driven approach and scalable analysis on environmental sustainability of shared micromobility from trip to city level analysis

Artikel i vetenskaplig tidskrift, 2024

Shared e-scooters (SES) have recently gained popularity, but their environmental sustainability remains debatable. This study develops a data-driven and scalable method based on big data and data fusion from multiple sources to comprehensively analyze substitutions and the environmental impacts of SES from trip to city level analysis. Field trip transaction data in three major Swedish cities (Stockholm, Gothenburg, and Malmö) are leveraged for empirical analysis considering mode choice behavior. The results reveal that most SES trips (86.7% in Stockholm, 85.6% in Gothenburg, and 85.3% in Malmö) replace walking or public transport, while the proportion substituting private car and taxis is less than 12%. On average, each SES trip increases in CO2−eq emissions (34.58 g in Stockholm, 21.18 g in Gothenburg, and 24.07 g in Malmö). Only a limited percentage of SES trips (19.20% in Stockholm, 24.22% in Gothenburg, and 23.94% in Malmö) and a small percentage of urban areas with SES (8.3% in Stockholm, 7.48% in Gothenburg, and 2.02% in Malmö) demonstrate positive environmental effects from SES. The substitution and environment impacts of SES vary significantly across different trips spatially and temporally, emphasizing the importance of conducting trip-level analyses. The analysis provides quantitative insights into the sustainability of SES in Nordic contexts, offering potential support for sustainable management in a variety of urban contexts.