The impact of city-level permitting processes on residential photovoltaic installation prices and development times: An empirical analysis of solar systems in California cities

Abstract

Business process or "soft" costs account for well over 50% of the installed price of residential photovoltaic (PV) systems in the United States, so understanding these costs is crucial for identifying PV cost-reduction opportunities. Among these costs are those imposed by city-level permitting processes, which may add both expense and time to the PV development process. Building on previous research, this study evaluates the effect of city-level permitting processes on the installed price of residential PV systems and on the time required to develop and install those systems. The study uses a unique dataset from the U.S. Department of Energy's Rooftop Solar Challenge Program, which includes city-level permitting process "scores," plus data from the California Solar Initiative and the U.S. Census. Econometric methods are used to quantify the price and development-time effects of city-level permitting processes on more than 3,000 PV installations across 44 California cities in 2011. Results indicate that city-level permitting processes have a substantial and statistically significant effect on average installation prices and project development times. The results suggest that cities with the most favorable (i.e., highest-scoring) permitting practices can reduce average residential PV prices by $0.27-$0.77/W (4%-12% of median PV prices in California) compared with cities with the most onerous (i.e., lowest-scoring) permitting practices, depending on the regression model used. Though the empirical models for development times are less robust, results suggest that the most streamlined permitting practices may shorten development times by around 24 days on average (25% of the median development time). These findings illustrate the potential price and development-time benefits of streamlining local permitting procedures for PV systems.

Tracking the Sun IX: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States

“…Two of these studies have examined the impact of local permitting processes on residential PV pricing. Dong and Wiser (2013) found that cities in California with the most-favorable permitting practices had installed prices $0.3/W to $0.8/W lower than in cities with the most-onerous practices. Examining a broader geographical footprint, Burkhardt et al (2014) found that variations in local permitting procedures lead to differences in average residential PV prices of approximately $0.2/W across jurisdictions; when considering variations not only in permitting practices, but also in other local regulatory procedures, price differences grew to $0.6/W to $0.9/W between the most-onerous and most-favorable jurisdictions.…”

Policy & Privilege in Photovoltaics: a Community Level Analysis In San Diego County

“…Due to its extensive nature, CSI data have been used as a proxy for PV deployment in California IOU territories in other published research (Dong & Wiser, 2013;Drury et al, 2012;Rothfeild, 2010). The data are biased toward established communities, because it does not include new construction, and do not represent "off-the-grid" systems that may be preferable to rural populations (Schelly, 2014).…”

“…Another growing body of research links reduced costs and permit processing times to increase PV adoption rates (Brown &Chandler, 2008;Burkhardt et al, 2015;Complete Solar, 2014;Dong & Wiser, 2013;Li & Yi, 2014). Confusing, costly, and time consuming permit applications can disincentivize residents who find bureaucratic processes prohibitive.…”

“…Planners, implementing local policy, affect the amount of time and money it takes for an applicant to have their solar system approved and installed. Streamlined permitting processes can shorten the time it takes applicants to get approval by 25% (Dong & Wiser, 2013).…”

“…This research seeks to create an explanatory model of solar adoption in San Diego by scaling down the methodology of two previous studies (Dong & Wiser, 2013;Kwan, 2012 Utilizing a stepwise regression method, the model displayed in Table 6 is the best explanatory fit. Of these explanatory variables, three were statistically significant: lower population density, the proportion of the population over 65 years, and median income.…”