Our Approach To Meeting The Paris Agreement With Urban Mobility
How Our Report Helps Reduce Urban Mobility Emissions
This report was created to offer city governments a feasible blueprint to lower their carbon footprint. With some estimates suggest that cities are responsible for 75% of global CO2e, and the transport sector placed among the largest contributors, the weight of this research is clear. This report narrows its focus to cities rather than national governments, given the size and severity of their carbon footprints. Cities can also act with more agility than national governments – the former having more control to enforce mobility policies within their jurisdiction than national governments and are more intimately aware of their demographic makeup.
Inclusive and affordable transportation and innovation are the cornerstones of this report’s findings. For many cities, whether they’re in developing economies like Lagos or boast mature mobility networks like Berlin, offering new or expanded suites of transit modes for all is essential to reduce emissions from personal car use – a lever that will do much of the work in bringing cities to their goal. Expanded and electric public transit fleets, new shared mobility providers, or 15-minute city concepts can all make mobility more sustainable and available to all. And whether cities are transitioning from energy grids reliant on fossil fuels or building more charging infrastructure, innovation and public-private partnerships will be key to success.
Cities must think innovatively and not solely rely on commonly referenced solutions, like electrifying mobility fleets or encouraging more cycling. While these methods are still very much worthwhile, they are not the blanket solution for cities to reach their Paris Agreement goals. This report calls on cities to explore other, novel solutions and to encourage major – or even required – behavioral changes.
This report was created with sustainability at its core
Our web development and design teams employed sustainable methods to bring this research to life. We used Ecograder to assess the 2022 edition of our Urban Mobility Readiness Index to grade how we currently develop reports and web pages and where we can improve. Ecograder examines how a web page performs based on factors like coding scripts, load times, and the image file sizes. These factors are important to consider for sustainable web design, given how readers engage with a website can influence the carbon emitted from their devices.
The analysis led us to take the following actions:
- Enhance the user experience and website functionality. To achieve this, we minimized the need for excessive scrolling, thereby further decreasing emissions produced when users navigate through the pages. Our design approach ensures that users have all necessary information conveniently accessible on a single screen.
- Decrease the usage of images and videos, favoring darker colors to create lighter web pages. This reduction in multimedia content translates to reduced emissions. Using a 3D grid rather than a resource-intensive video, for example, reduces multimedia loads.
- Use “green servers” that power their data centers with renewable energy.
The Oliver Wyman Forum’s “Road to 1.5” research focuses on urban mobility within global cities. The selected cities exhibit diverse geographic and mobility characteristics, ranging from car-centric cities like Dubai and Los Angeles, to multi-transport cities with well-balanced mobility offerings like Paris and San Francisco, and emerging cities like Lagos. These cities, each with their unique challenges and solutions. embody the global mobility trends that can limit global warming to 1.5°C by 2030.
In developing the research, five passenger mobility modes were considered – car, public transit, walking, micromobility, and motorbike/mopeds. Each category was broken down into subgroups by powertrain and usage type. This enabled us to map out urban mobility demand and estimate the distance traveled by calculating the emissions per kilometer with more accuracy and granularity.
Modes, Powertrains, and Type/utilization covered in the report
We mapped the demand projections from 2022 to 2030 with a robust range of sources and data. Sources include reports from the United Nations and government agencies, online databases like Statista, industry reports, academia, news outlets, and internal Oliver Wyman databases. With emission reduction targets established by the United Nations Intergovernmental Panel on Climate Change and the Climate Analytics national pathways, we estimated the global warming impact of the transportation sector at city-level. The optimization model integrates these components to generate an optimized mix of transportation types (modal mix) for each city, guiding them toward or achieving the 1.5°C target by 2030 within each defined scenario.
An optimization model was developed to help identify how each city can best capitalize on its mobility demand to reduce emissions. The model takes into account a selected city’s current and projected 2030 urban mobility makeup based off its current plans in order to allocate the emissions reduction budget best suited to the city’s needs.
Urban Mobility Scenarios
Each city has a unique set of ambitions and priorities, and this model considers four different urban mobility scenarios that are available for each city to reach climate targets:
- A default scenario reduces emissions with minimal mobility behavioral changes
- An electrification scenario accelerates the transition from internal combustion engine-powered to electric vehicles
- A multimodal scenario encourages the use of shared mobility and public transit
- An active mobility scenario promotes walking and cycling as alternative modes of transport to replace short-distanced trips travelled by cars
The model’s modal mix optimizations consider a multitude of factors in determining a solution’s feasibility, including affordability, accessibility, and sustainability. We arrived at the optimized outputs by testing within the following four constraints:
- Distance: the maximum percentage increase or decrease of the total distance travelled within each trip size (micro, small, mid, large) for each mode of transportation
- Cost: the maximum percentage increase of the cost of each mode of transportation
- Time: the maximum percentage increase of the time spent by an average person for each mode of transportation
- Capacity: the maximum percentage increase or decrease of the total distance travelled for each mode of transportation
The initial set of optimization constraints enables the model to find the best mobility mix for each city within reasonable limits, simulating feasible shifts in human behavior. Unfortunately, most cities cannot hit the 1.5°C target using this set of constraints, requiring more drastic changes.
We therefore established a second set of relaxed constraints, enabling each city to reach the 1.5°C target by 2030. These outputs help demonstrate the extensive and dramatic changes that would be necessary for each city to achieve 1.5°C.
This report would not have been possible without the contributions of Fernanda Baron, Ludovic Cartigny, Elena Chiappa, Oliver Cordeiro, Kyle Dickens, Tara Donston, Jodie Gadd, Dan Kleinman, Dustin Irwin, Selena Lu, Jilian Mincer, Heather No, Francisco Nodarse, Ramona Pillai, Chloe Rosenberg, Sophie Shaw, Steffen Rilling, Adrien Slimani, Weronika Talaj, Niel Yocom, Ai Peng Thoo, Brahma Polavarapu, Cheryl Kar Yean Lai, Cynthia Perez, Eva Lim, Kinga Pawelec, Ozzie Santana, Shams Rzayeva, and Wai Leong Hoh.
For more information, contact us at OWForum@oliverwyman.com.