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And boosting public transportation, cycling, and pedestrian infrastructure is the way to do it. Do you have $100 trillion worth of love for your car?

We Could Save $100 Trillion If We Ditched Private Car Ownership By 2050

BY Sydney Brownstone2 minute read

This week, more than 100 world leaders met in New York City at the United Nations’ climate summit, where they echoed the dire warnings we’ve been hearing about climate change for years. Countries pledged to work together to come up with an international agreement, but left that deadline for next year. For many, the summit yielded more questions than answers.

But world leaders might do well to consider something else in the meantime: a new report shows that enormous gains could come out of shifting modes of transportation in cities alone. According to a new study from the University of California-Davis presented at this week’s climate conference, prioritizing non-car transportation systems in cities could save the world some $100 trillion in public and private spending on urban transit by 2050.

Flickr user Wonderlane

The study, called A Global High Shift Scenario: Impacts And Potential For More Public Transport, Walking, and Cycling With Lower Car Use, imagines two scenarios for 2050. The first scenario, a baseline, assumes that U.S. emissions will decrease as the population ages and private car ownership declines. At the same time, the baseline also projects enormous increases in private car ownership in China, India, and several African countries. The second scenario (the “High Shift”) imagines a world in which countries have enforced policies that beef up public transportation, cycling, and pedestrian infrastructure.

A few highlights from the report:

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  • The baseline scenario assumes that most cars’ fuel economies will improve by 2050. But the High Shift scenario dictates a 50% cut in fuel use per kilometer by 2030. This would cut 700 megatons of carbon dioxide emissions a year, and taken together with increased reliance on modes of transportation, this could bring emissions down to pre-2010 levels by 2050.
  • Nearly 1.4 million premature deaths caused by pollution from fuel exhaust pipes could be prevented by better vehicle emission controls. “Cleaner buses alone would account for 20 percent of these benefits,” the report authors write.
  • Boosting public transportation, cycling, and walking options in the High Shift scenario would provide better access to mobility across all incomes. Continued growth in private car-ownership would exacerbate income inequality.
  • Abandoning a reliance on cars also means investing heavily in cyclist infrastructure, like bike lanes. The High Shift scenario imagines a world in which most cities reach rates of European cycling.
  • The High Shift scenario imagines twice as many miles of rapid public transit than projected for the business-as-usual baseline.
  • In order to rein in cars, the report notes that countries would have to implement specific policies to mandate higher occupancies and lower rates of use.

Perhaps the most striking comparison between the baseline and High Shift scenarios is that both assume that people will be traveling the same distances. Ditching cars doesn’t necessarily mean ditching convenience, or even confining ourselves to smaller geographic areas in cities. The major difference between the two is simply a restructuring of transportation priorities, to mighty effect.

“The study concludes that unmanaged growth in motor vehicle use threatens to exacerbate growing income inequality and environmental ills, while more sustainable transport delivers access for all, reducing these ills,” the report authors write. “This report’s findings should help support wider agreement on climate policy, where costs and equity of the cleanup burden between rich and poor countries are key issues.”

Read the whole thing here.


ABOUT THE AUTHOR

Sydney Brownstone is a Seattle-based former staff writer at Co.Exist. She lives in a Brooklyn apartment with windows that don’t quite open, and covers environment, health, and data More


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