| City dashboard (2022) | |
|---|---|
| Population | 24.9 million |
| Surface area (km2) | 6,340 |
| Mobility demand (km) | 198 billion |
| Mobility demand per person per day (km) | 21.8 |
| Mobility emissions (CO2e) | 13.3 megatons |
| Emissions per person per day (CO2e) | 1.46 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
Shanghai offers a wide variety of mobility services, with a balanced modal share between cars (36%), public transit (40%), walking (21%), and other modes. Mobility demand is robust, as 198 billion kilometers (123 billion miles) were traveled in 2022, generating 13.3 MtCO2e.
And while cars only make up about a third of trips made through Shanghai, they account for 60% of the city’s mobility emissions. With new car-free zones and a large rail network boasting more than 20 lines, 870 kilometers (545 miles) of track, and 500 stations, there are avenues available to lower car emissions.
Current situation
Based on city plans, mobility demand is expected to grow by 30% by 2030 while CO2 emissions
are forecasted to increase by only 5% thanks to greater electric vehicle adoption and increased metro ridership.
Current mobility trends predict that electric vehicles will continue to increase in market share because Shanghai has a $10,000 licensing fee for gasoline-powered vehicles, and China requires all new cars to be New Energy Vehicles (NEV) by 2035. The transition will be eased by the broad availability of affordable electric vehicle options for residents, as China has exempted NEVs from the purchase tax. Shanghai has also invested in charging infrastructure.
And yet, Shanghai’s commitments to address transport emissions are still roughly 8.6 MtCO2e short of the target, requiring an additional 63% decrease in emissions on top of current commitments by 2030 to stay within 1.5°C of warming.
Optimization
We explored four different optimization scenarios:
Shanghai has strong public transit infrastructure, which should be an effective lever to reduce the personal car modal share. However, because of its carbon-intensive electric grid, the city’s ability to further optimize will be limited. Within our optimization paradigm the only way for Shanghai 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, Shanghai 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 29% or approximately 6.7 kilometers (4.2 miles) per person per day compared to 2022.
Reduce personal car use:
Decarbonize the power grid:
Increase motorbike and moped use:
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 13% or approximately 3.3 kilometers (2.0 miles) per person per day compared to 2022.
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 29% or approximately 6.7 kilometers (4.2 miles) per person per day compared to 2022.
Reduce personal car use:
Decarbonize the power grid:
Promote shared 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:
Decarbonize the power grid:
Increase walking:
Increase cycling:
