Adoption of Micro-Mobility Solutions for Improving Environmental Sustainability: Comparison among Transportation Systems in Urban Contexts
Abstract
:1. Introduction
- Conceive and design cities as places for parked cars (parking lots rather than roads need to be built);
- Increase the utilization of cars through car-sharing solutions (to change their primary use) and/or reduce the number of parked vehicles in urban centres by car-pooling (to increase the number of passengers carried by each vehicle);
- Adopt micro-mobility solutions as an alternative means to cars, either directly (in the case of trips made entirely with micro-mobility vehicles) or indirectly (in the case of adduction system to increase the attractiveness of public transport with respect to private cars).
2. Literary Review on Emission Factors of Different Transport Modes
3. Methodologies for Comparing Transportation Systems in a Sustainable Perspective
4. Application to a Real Urban Context
- Phase 1: one or more models were set up to describe user behaviour in terms of e-scooter trips;
- Phase 2: the environmental performance of e-scooters was compared to that of other transportation systems.
4.1. Model Definition to Describe User Behaviour
- Day 1 real data are very similar to those of Day 2, as they were similar days in terms of weather conditions, which provides a similar value of trips in terms of both quantity (number of trips) and distribution (trip density).
- The model data reproduce both Day 1 data (calibration data) and Day 2 data (validation data) well, confirming the heat maps’ ability to reproduce the physical phenomenon.
4.2. Comparison among Transport Modes
5. Conclusions and Research Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Transport Mode | Range Values for Unit Emission Factors [gCO2-eq/(pax*km)] | |
---|---|---|
Use Phase | Life Cycle Assessment (LCA) | |
Walking | [20–30] | – |
E-scooter | [5–6] | [70–80] |
Bike | [15–20] | [20–25] |
E-bike | [5–10] | [15–20] |
Moped | [50–90] | [80–180] |
E-moped | [25–35] | [50–75] |
Internal combustion car | [120–170] | [200–270] |
Hybrid car | [80–120] | [60–160] |
Electric car | [40–100] | [80–150] |
Internal combustion bus | [10–70] | [20–35] |
Hybrid bus | [9–50] | [25–75] |
Electric bus | [10–60] | [20–30] |
Calibration Task (Day 1 Data) | Validation Task (Day 2 Data) | |||||||
---|---|---|---|---|---|---|---|---|
Parameter | Value | t–Value | Threshold | Confidence Level | t–Value | Threshold | Confidence Level | |
Parameter tests | α1 | +0.00171016 | 9.59 | 2.21 | 94.5% | 8.73 | 2.21 | 94.5% |
α2 | +0.00759030 | 49.89 | 2.21 | 94.5% | 45.41 | 2.21 | 94.5% | |
α3 | –0.00564263 | 2.24 | 2.21 | 94.5% | 2.04 | 2.21 | 94.5% | |
Test Name | Value | Threshold | Confidence Level | Value | Threshold | Confidence Level | ||
Function tests | R2 | 0.577 | - | - | 0.592 | - | - | |
R2adj | 0.576 | - | - | 0.591 | - | - | ||
F–test | 776.8 | 5.94 | 99.9% | 641.9 | 5.94 | 99.9% |
Calibration Task (Day 1 Data) | Validation Task (Day 2 Data) | |||||||
---|---|---|---|---|---|---|---|---|
Parameter | Value | t–Value | Threshold | Confidence level | t–Value | Threshold | Confidence level | |
Parameter tests | β1 | +0.00164309 | 9.34 | 2.21 | 94.5% | 8.52 | 2.21 | 94.5% |
β2 | +0.00792477 | 52.79 | 2.21 | 94.5% | 48.13 | 2.21 | 94.5% | |
β3 | −0.00708747 | 2.85 | 2.21 | 94.5% | 2.60 | 2.21 | 94.5% | |
Test Name | Value | Threshold | Confidence Level | Value | Threshold | Confidence Level | ||
Function tests | R2 | 0.612 | - | - | 0.597 | - | - | |
R2adj | 0.611 | - | - | 0.597 | - | - | ||
F–test | 883.1 | 5.94 | 99.9% | 731.0 | 5.94 | 99.9% |
Calibration Task (Day 1 Data) | Validation Task (Day 2 Data) | |||||||
---|---|---|---|---|---|---|---|---|
Parameter | Value | t-Value | Threshold | Confidence level | t-Value | Threshold | Confidence Level | |
Parameter tests | γ1 | +0.00335325 | 9.59 | 2.21 | 94.5% | 8.73 | 2.21 | 94.5% |
γ2 | +0.01551506 | 51.99 | 2.21 | 94.5% | 47.30 | 2.21 | 94.5% | |
γ3 | −0.01273010 | 2.57 | 2.21 | 94.5% | 2.34 | 2.21 | 94.5% | |
Test name | Value | Threshold | Confidence Level | Value | Threshold | Confidence Level | ||
Function tests | R2 | 0.602 | - | - | 0.604 | - | - | |
R2adj | 0.602 | - | - | 0.603 | - | - | ||
F-test | 850.0 | 5.94 | 99.9% | 701.3 | 5.94 | 99.9% |
Transport Mode | Life Cycle Assessment | |||
---|---|---|---|---|
Day 1 | Day 2 | |||
Total Emissions [gCO2-eq] | Percentage Variation [%] | Total Emissions [gCO2-eq] | Percentage Variation [%] | |
Walking | ||||
E-scooter | 180.83 | 100% | 193.76 | 100% |
Bike | 54.25 | 30% | 58.13 | 30% |
E-bike | 42.19 | 23% | 45.21 | 23% |
Moped | 313.43 | 173% | 335.85 | 173% |
E-moped | 150.69 | 83% | 161.47 | 83% |
Internal combustion car | 566.59 | 313% | 607.11 | 313% |
Hybrid car | 265.21 | 147% | 284.18 | 147% |
Electric car | 277.27 | 153% | 297.10 | 153% |
Internal combustion bus | 66.30 | 37% | 71.05 | 37% |
Hybrid bus | 120.55 | 67% | 129.17 | 67% |
Electric bus | 60.28 | 33% | 64.59 | 33% |
Transport Mode | Use Phase | |||
---|---|---|---|---|
Day 1 | Day 2 | |||
Total Emissions [gCO2-eq] | Percentage Variation [%] | Total Emissions [gCO2-eq] | Percentage Variation [%] | |
Walking | 57.87 | 436% | 62.00 | 436% |
E-scooter | 13.26 | 100% | 14.21 | 100% |
Bike | 42.19 | 318% | 45.21 | 318% |
E-bike | 18.08 | 136% | 19.38 | 136% |
Moped | 168.77 | 1273% | 180.84 | 1273% |
E-moped | 72.33 | 545% | 77.50 | 545% |
Internal combustion car | 349.60 | 2636% | 374.60 | 2636% |
Hybrid car | 241.10 | 1818% | 258.35 | 1818% |
Electric car | 168.77 | 1273% | 180.84 | 1273% |
Internal combustion bus | 96.44 | 727% | 103.34 | 727% |
Hybrid bus | 71.13 | 536% | 76.21 | 536% |
Electric bus | 84.39 | 636% | 90.42 | 636% |
Transport Mode | Externalities Cost [€] | User-Generalised Cost [€] | Total Cost [€] | Percentage Variation [%] |
---|---|---|---|---|
Walking | 23.67 | 3013.79 | 3037.46 | 185% |
E-scooter | 5.42 | 1636.18 | 1641.60 | 100% |
Bike | 17.26 | 1205.52 | 1222.78 | 74% |
E-bike | 7.40 | 1636.18 | 1643.58 | 100% |
Moped | 69.04 | 1865.02 | 1934.06 | 118% |
E-moped | 29.59 | 1865.02 | 1894.61 | 115% |
Internal combustion car | 143.01 | 8487.01 | 8630.03 | 526% |
Hybrid car | 98.63 | 8487.01 | 8585.65 | 523% |
Electric car | 69.04 | 8487.01 | 8556.05 | 521% |
Internal combustion bus | 39.45 | 5611.10 | 5650.55 | 344% |
Hybrid bus | 29.10 | 5611.10 | 5640.19 | 344% |
Electric bus | 34.52 | 5611.10 | 5645.62 | 344% |
Transport Mode | Mass [kg] | Service Life [years] | Im [kg/year] | I′m [kg/year*pax] |
---|---|---|---|---|
E-scooter | 16 | 2 | 8 | 8 |
Bike | 15 | 15 | 1 | 1 |
E-bike | 25 | 5 | 5 | 5 |
Moped | 150 | 6 | 25 | 22.73 |
E-moped | 150 | 6 | 25 | 22.73 |
Internal combustion car | 900 | 15 | 60 | 46.15 |
Hybrid car | 900 | 15 | 60 | 46.15 |
Electric car | 900 | 10 | 90 | 69.23 |
Internal combustion bus | 15,000 | 15 | 1000 | 12.5 |
Hybrid bus | 15,000 | 15 | 1000 | 12.5 |
Electric bus | 15,000 | 10 | 1500 | 18.75 |
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D’Acierno, L.; Tanzilli, M.; Tescione, C.; Pariota, L.; Di Costanzo, L.; Chiaradonna, S.; Botte, M. Adoption of Micro-Mobility Solutions for Improving Environmental Sustainability: Comparison among Transportation Systems in Urban Contexts. Sustainability 2022, 14, 7960. https://doi.org/10.3390/su14137960
D’Acierno L, Tanzilli M, Tescione C, Pariota L, Di Costanzo L, Chiaradonna S, Botte M. Adoption of Micro-Mobility Solutions for Improving Environmental Sustainability: Comparison among Transportation Systems in Urban Contexts. Sustainability. 2022; 14(13):7960. https://doi.org/10.3390/su14137960
Chicago/Turabian StyleD’Acierno, Luca, Matteo Tanzilli, Chiara Tescione, Luigi Pariota, Luca Di Costanzo, Salvatore Chiaradonna, and Marilisa Botte. 2022. "Adoption of Micro-Mobility Solutions for Improving Environmental Sustainability: Comparison among Transportation Systems in Urban Contexts" Sustainability 14, no. 13: 7960. https://doi.org/10.3390/su14137960