<p>Reducing commuting from the actual to the minimum level is designed to prioritize environmental sustainability in order to reduce carbon emissions and alleviate traffic congestion, but does it also ensure social equity and sustainability? We study the social inequity of reducing commuting using commuting flow data defined over a combination of multiple housing and socio-demographic attributes in Chicago, Illinois. Social inequity may manifest itself as limited commute reduction benefits or commute increase liability for socially vulnerable groups compared to advantaged groups. We find that (1) 11.8% of Chicago’s workers face commute increase liability after optimization, (2) high poverty Black neighborhoods are the most spatially disadvantaged group after optimization because they have one of the highest commute increase liabilities and one of the lowest commute reduction benefits, (3) the highest reduction benefit and lowest increase liability accrues to higher income and Whites neighborhood groups, and (4) that the amount of commute increase liability is higher in neighborhoods with jobs-housing imbalance. Our analysis provides a method to help decision makers evaluate optimal commuting in terms of social inequity.</p>

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Social inequities in the transition to environmentally optimized commuting

  • Michał A. Niedzielski,
  • Yujie Hu

摘要

Reducing commuting from the actual to the minimum level is designed to prioritize environmental sustainability in order to reduce carbon emissions and alleviate traffic congestion, but does it also ensure social equity and sustainability? We study the social inequity of reducing commuting using commuting flow data defined over a combination of multiple housing and socio-demographic attributes in Chicago, Illinois. Social inequity may manifest itself as limited commute reduction benefits or commute increase liability for socially vulnerable groups compared to advantaged groups. We find that (1) 11.8% of Chicago’s workers face commute increase liability after optimization, (2) high poverty Black neighborhoods are the most spatially disadvantaged group after optimization because they have one of the highest commute increase liabilities and one of the lowest commute reduction benefits, (3) the highest reduction benefit and lowest increase liability accrues to higher income and Whites neighborhood groups, and (4) that the amount of commute increase liability is higher in neighborhoods with jobs-housing imbalance. Our analysis provides a method to help decision makers evaluate optimal commuting in terms of social inequity.