Much has been written on the rooftop solar photovoltaic (PV) adoption in the U.S., but granular economic assessment at large scale is missing. We provide household level PV economic assessment for a medium size city in North Central Florida, and analyze the economic viability of these installations. Results show that a large number of households will not benefit from solar installations. Further, economic viability is heavily reliant on incentives whose future is uncertain at best. Our analysis did not reveal significant variations in economic viability across different household values --- a proxy we used to differentiate household wealth. Yet, building permits and installation locations indicate economically disadvantaged communities have much lower installation rates as has been the main conclusion in the earlier literature. We argue economic assessment for PV should extend beyond simple benefit--cost analysis. A more nuanced approach should be taken in PV feasibility assessment, and structuring incentive schemes.
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Moving targets: When does a poverty prediction model need to be updated?
A key challenge in the design of effective anti-poverty programs is determining who should be eligible for program benefits. In devel- oping countries, one of the most common criteria is a Proxy Means Test — a simple decision rule that determines eligibility based on basic information about each household (for example, the number of rooms in the household, the number of children, whether there is indoor plumbing, and other observable characteristics) [1, 3, 4, 7]. At the core of each Proxy Means Test (PMT) is a machine learning algorithm that uses the short list of household characteristics to pre- dict whether the household should be deemed poor, and therefore eligible, or non-poor, and therefore ineligible [5, 6].
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- Award ID(s):
- 2125913
- PAR ID:
- 10535743
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701498
- Page Range / eLocation ID:
- 117 to 117
- Format(s):
- Medium: X
- Location:
- Cape Town South Africa
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3588001.3609369
