How Can We Create Effective Regional Planning?

Kaid Benfield looks at shortcomings of the dominant national apparatus for regional planning - the Metropolitan Planning Organization - through the lens of transportation, and pines for new mechanisms for regional cooperation.

2 minute read

May 1, 2012, 9:00 AM PDT

By Jonathan Nettler @nettsj


The opportunities and challenges of contemporary planning and development, from transportation to economic development to climate change, do not stop at municipal borders. However, planning often does. As such, according to Benfield, "Our highly fragmented "system" of local governments makes it nearly impossible to address important issues facing metropolitan America in a rational way."

Using case studies of failed transportation integration in Philadelphia and Washington D.C., Benfield points to the shortcoming of MPOs in integrating cross-jurisdictional planning: "Especially when it comes to land use, MPOs are basically advisory, and their ability to influence what jurisdictions within their purview choose to do with regard to transportation, economic development, and other matters of great import tends to be extremely limited."

So what models for more effective regional cooperation are out there? Benfield looks to Portland's precedent-setting Metro, "the nation's only directly elected regional government with legal authority over regional land use, transportation, and other specified issues in three counties and 25 cities...But Metro's jurisdiction does not extend to those parts of the Portland region across the Columbia River in Washington, which lag behind their Oregon neighbors in addressing these issues."

He also looks to the SB 375, California's "innovative planning law to reduce pollution of greenhouse gases" as "the new frontier for regionalism in the US." The jury is out, however, on the extent to which the economic and regulatory incentives included in the law will be enough to empower the state's MPOs to exercise influence over regional land use and transportation patterns to a larger extent than prior models.

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