| City dashboard (2022) | |
|---|---|
| Population | 8.8 million |
| Surface area (km2) | 1,583 |
| Mobility demand (km) | 74 billion |
| Mobility demand per person per day (km) | 23.0 |
| Mobility emissions (CO2e) | 6.1 megatons |
| Emissions per person per day (CO2e) | 1.91 kilograms |
Urban mobility global warming impact (2030)
0.0°C
Emissions reductions required to reach the 1.5°C target by 2030
-0%
0.0MtCO2e
Introduction
London offers a great mix of mobility services, with a balanced modal share between cars (50%), public transit (30%), walking (18%), and other modes. Mobility demand in London accounted for 74 billion kilometers (46 billion miles) traveled in 2022, generating 6.1 MtCO2e. Half of the distance traveled in 2022 in London was by car, contributing to 82% of mobility emissions despite the Ultra Low Emissions Zone (ULEZ) in central London, where polluting vehicles pay a daily toll to drive, and congestion charges.
Public transport ridership, returned in 2022 to almost pre-pandemic levels but additional recovery and growth are critical to achieve emission goals.
Current situation
Based on city plans, mobility demand is expected to grow by 9% by 2030 while CO2 emissions are forecasted to decrease by 20% due to greater electric vehicle adoption and public transit usage.
During COP26 (Glasgow) in 2021, the United Kingdom government committed to revisiting its 2030 Intended Nationally Determined Contribution (INDC) with a new target to reduce all greenhouse gas emissions by at least 68% by 2030 on 1990 levels.
The ULEZ expanded across all city boroughs, rather than just in central London, in August 2023.
However, London’s commitments to address transport emissions are still roughly 1.1 MtCO2e short of the target, requiring an additional 33% decrease in emissions on top of current government commitments by 2030 to stay within 1.5°C of warming.
Optimization
We explored four different optimization scenarios:
All the optimization scenarios get London to the 1.5°C target, but to do so requires significant shifts in behavior or reductions in demand. For example, the active mobility scenario would require each person to walk an additional 2.5 kilometers, or 1.6 miles, per day. The electrification scenario is the best fit for London because its power grid is transitioning to sustainable energy.
* indicates the scenario that achieves the greatest realistic emissions reduction
When simulating realistic modal shifts, achieving 1.5°C would not be possible. When allowing larger shifts, achieving 1.5°C would require extreme changes: a reduction in total mobility demand of 8% or approximately 2.2 kilometers (1.4 miles) per person per day compared to 2022.
Reduce personal car use:
Increase active mobility such as cycling:
Promote shared mobilities:
When simulating realistic modal shifts, achieving 1.5°C would be possible, requiring a reduction in total mobility demand of 2% or approximately 1.0 kilometers (0.6 miles) per person per day compared to 2022.
Reduce personal car use:
Accelerate electrification of the fleet:
Increase active mobility:
When simulating realistic modal shifts, achieving 1.5°C would not be possible. When allowing larger shifts, achieving 1.5°C would require extreme changes: a reduction in total mobility demand of 7% or approximately 2.1 kilometers (1.3 miles) per person per day compared to 2022.
Reduce personal car use:
Promote shared mobility:
Increase active mobility:
When simulating realistic modal shifts, achieving 1.5°C would not be possible. When allowing larger shifts, achieving 1.5°C would not require a reduction in total mobility demand but would require extreme increases in active mobility compared to 2022.
Reduce personal car use:
Increase cycling:
Increase walking:
