Stated preferences for smart green infrastructure in stormwater management
Name
201031 Meng Hsu LUP OA version.pdf
Description
Accepted version
Size
473.82 KB
Format
Adobe PDF
Checksum (MD5)
b175e739a75d77c8a97679cc4fae58ce
Author(s)
Meng, Ting
Hsu, David
Date Issued
July 2019
Journal
Landscape and Urban Planning
Publisher
Elsevier BV
Citation
Meng, Ting and David Hsu. "Stated preferences for smart green infrastructure in stormwater management." 187 (July 2019): 1-10 © 2019 Elsevier B.V.
Version
Author's final manuscript
Abstract
Many smart technologies have been proposed to improve existing or new infrastructures by adding sensing, controls, communications and computing, but it is not well understood which actual functions and capabilities are desired by potential users. Since water systems in the United States are largely run by public sector agencies, this study conducted a national survey with officials in water utilities and agencies to understand what kinds and capabilities of smart green infrastructure are desired for stormwater management. Analyzing the stated preferences of these users using a discrete choice model, our results indicate that these officials are willing to invest more upfront in smart technologies if they lower the costs associated over time with construction, maintenance, and labor. For example, in a typical rain garden, water agencies are willing to pay 12.1% more for construction to reduce maintenance costs by 20% and would pay 12.9% more to add self-irrigating capabilities. Preferences for smart green infrastructure among agencies are also affected by their characteristics. Agencies with large service areas or prior experience with green infrastructure are more likely to adopt smart green infrastructure. These results should assist in the further design and development of smart technologies and green infrastructure in stormwater management.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.LANDURBPLAN.2019.03.002
