| City dashboard (2022) | |
|---|---|
| Population | 6.0 million |
| Surface area (km2) | 6,340 |
| Mobility demand (km) | 54 billion |
| Mobility demand per person per day (km) | 24.5 |
| Mobility emissions (CO2e) | 3.7 megatons |
| Emissions per person per day (CO2e) | 1.71 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
Singapore offers a wide variety of mobility services, with a balanced modal share between cars (36%),
public transit (42%), walking (18%), and other modes. Mobility demand in Singapore accounted for
54 billion kilometers (34 billion miles) traveled in 2022, generating 3.7 MtCO2e.
And while cars are used for only about a third of trips made through Singapore, they account for almost 60% of the city’s mobility emissions. Reducing car footprint is a key lever to reaching the Paris Agreement target, but Singapore is already less car reliant than many other cities due to its balanced mix of mobility services.
Current situation
Based on city plans, mobility demand is expected to grow by 11% by 2030 while CO2 emissions
are forecasted to decrease
by 6%, driven by greater electric vehicle adoption and increased metro ridership.
And yet, Singapore remains off track to reaching the 1.5°C target by 2030. Singapore’s carbon-intensive energy sector is heavily reliant on natural gas, and the city-state had a late uptake of electric vehicles despite a goal to phase out of gasoline-powered cars by 2040. Limited land availability is a challenge for Singapore’s ability to scale renewable energy, although hydrogen imports are being considered.
Its Green Plan 2030 demonstrates Singapore’s ambitious and concrete agenda to improve its carbon footprint trajectory. Goals include a 75% public transit modal share, a 50% EV bus penetration, a 360-kilometer (224 miles) rail network expansion, and broadened solar energy usage.
However, Singapore’s commitments to address transport emissions are still roughly 2.2 MtCO2e short of the target,
requiring an additional 60% 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:
Singapore has strong public transit infrastructure which should be an effective lever to reduce the personal car modal share. However, because of the carbon-intensive electric grid, the city’s ability to further optimize will be limited. Within our optimization paradigm the only way for Singapore to achieve the 1.5°C target by 2030 is by pairing demand reduction with significant modal shifts. Reducing demand is not an easy option and may not be realistic. Without a sharp reduction of the power grid footprint, Singapore would need to reduce mobility demand by building denser housing or a 15-minute city. To avoid going down that path, the city should consider pursuing a lower carbon grid paired with an electrification of both public and private transport.
* 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 39% or approximately 10.3 kilometers (6.4 miles) per person per day compared to 2022.
Aggressively reduce personal car use:
Decarbonize the power grid:
Promote 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 39% or approximately 10.3 kilometers (6.4 miles) per person per day compared to 2022.
Aggressively reduce personal car use:
Decarbonize the power grid:
Accelerate electrification of the fleet:
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 39% or approximately 10.3 kilometers (6.4 miles) per person per day compared to 2022.
Aggressively reduce personal car use:
Decarbonize the power grid:
Promote 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 6% or approximately 2.4 kilometers (1.5 miles) per person per day compared to 2030 projections.
Aggressively reduce personal car use:
Decarbonize the power grid:
Promote active mobility:
