Resilience through Regeneration: The economics of repurposing vacant land with green infrastructure

Galen NEWMAN, Dongying LI, Rui ZHU, Dingding REN

PDF(14171 KB)
PDF(14171 KB)
Landsc. Archit. Front. ›› 2018, Vol. 6 ›› Issue (6) : 10-23. DOI: 10.15302/J-LAF-20180602
papers
papers

Resilience through Regeneration: The economics of repurposing vacant land with green infrastructure

Author information +
History +

Abstract

Many urban areas affected by flood disasters are also becoming increasingly ecologically and socially fragmented due to the accumulation of vacant properties. While redevelopment is often viewed as the primary objective in regenerating vacant properties, they can also potentially provide ecological and hydrological land uses. Rather than chasing developmentbased incentives for regenerating vacant lots in high flood-risk communities, a balance should be sought between new developmental land uses and green infrastructure to help counteract stormwater runoff and flood effects, or “Resilience through Regeneration.” This paper uses landscape performance measures to evaluate the economic and hydrologic performance of green infrastructure regeneration projects for three marginalized neighborhoods in Houston, Texas, USA. Each project site is characterized by excessive vacant lots and flood issues. Results suggest that, when using green infrastructure to regenerate vacant properties, 1) flood risk continually decreases, 2) upfront economic costs increase in the short term (when compared to conventional development), and 3) the long-term economic return on investment is much higher.

Keywords

Urban Regeneration / Community Resilience / Landscape Performance / Green Infrastructure

Cite this article

Download citation ▾
Galen NEWMAN, Dongying LI, Rui ZHU, Dingding REN. Resilience through Regeneration: The economics of repurposing vacant land with green infrastructure. Landsc. Archit. Front., 2018, 6(6): 10‒23 https://doi.org/10.15302/J-LAF-20180602

RIGHTS & PERMISSIONS

2018 Higher Education Press
AI Summary AI Mindmap
PDF(14171 KB)

Accesses

Citations

Detail

Sections
Recommended

/