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ID:
126531
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| Publication |
2013.
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| Summary/Abstract |
With "soft" costs accounting for well over 50% of the installed price of residential photovoltaic (PV) systems in the United States, 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 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 3000 PV installations across 44 California cities in 2011. Results suggest that cities with the most favorable 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 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.
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| 2 |
ID:
166450
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| Summary/Abstract |
Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a “parameterization + calibration” approach to address both biases and estimate customers’ WTP for PV. After collecting survey data on respondents’ WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.
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