The Energy and Environmental Impacts of Free-Floating Shared E-Scooters: A Multi-City Life Cycle Assessment
Bibliographic record
Abstract
Free-floating shared e-scooters (FFSE) have been promoted as a sustainable urban mobility solution, yet their true energy and environmental impact remain contested. This study conducts an attributional life cycle assessment (aLCA) across 32 cities in Europe and North America to evaluate the fossil energy consumption and greenhouse gas (GHG) emissions of FFSE systems. By integrating real-world operational data—including vehicle lifespan, daily turnover rates, and city-specific modal substitution patterns—we quantify the direct and net environmental impacts under varying rebalancing and charging scenarios. Results indicate that FFSE systems do not inherently provide net energy and environmental benefits. Instead, achieving net reductions in greenhouse gas emissions and fossil energy consumption depends on systems operating beyond specific thresholds of service life and total travel distance. These thresholds vary dramatically across cities, influenced by modal substitution patterns and local operational efficiency (i.e., rebalancing intensity, daily turnover rates, and trip distance). Cities with high car displacement and efficient operations achieve net GHG and energy savings at lower life cycle mileages, whereas systems that replace walking or public transit often have negative impacts. Additionally, the distribution of energy and environmental impacts across the life cycle shifts fundamentally with vehicle longevity. When the travel distance exceeds 4000–5000 km, it transitions from being manufacturing-dominated to operation-dominated, with rebalancing and electricity use becoming the primary contributors. The research provides evidence-based guidance for policymakers and operators seeking to maximize the contribution of shared micromobility systems to energy conservation and emission reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".