skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Knowledge Spillovers and Cost Reductions in Solar Soft Costs

Abstract

Despite the commonly acknowledged importance of knowledge spillovers in reducing solar soft costs, we are only beginning to answer a fundamental question: who learns what (knowledge acquisition), from whom (knowledge production), and how (spillover mechanisms)? Until recently, this important topic has been largely unexplored in the case of solar soft costs. Thus, this project set out to identify how knowledge spillovers affect soft costs in the U.S. photovoltaic (PV) installation industry, specifically how important spillovers are, what types of knowledge are most likely to spillover, and how networks of actors affect spillovers. Our findings offer insights for designing solutions that address problems associated with knowledge spillovers and that leverage spillovers to reduce solar soft costs. Recognizing the ambiguity in the definition of soft costs, i.e., “non-hardware costs,” and variability in soft cost categories, we developed the Solar Soft Cost Ontology (SSCO) to systematically identify key concepts related to soft costs, network actors, learning processes, and the relationships between them. This ontology served as a foundational organizational structure for the methodology of the remaining tasks: case studies, surveys, pricing analysis, patent analysis, network analysis, and project integration across tasks. While there is substantial learning among installers that is reducing the softmore » costs for PV installations, most of that learning is retained by firms rather than spread across the industry. The positive relationship between experience accumulation and cost reductions is typically explained as learning by doing (LBD), but we find that LBD effects are mediated by other learning mechanisms, including learning by searching and learning by interacting. Knowledge spillovers have significant potential to reduce solar PV soft costs, but successful knowledge spillover pathways are complex and non-trivial. There are a wide variety of ways to construct an installation business, thus categories of firms that can effectively cross-learn directly are small and what knowledge is relevant to whom is challenging and costly for firms to assess. This fragmentation limits the critical mass needed for spillover related soft cost reductions. Knowledge does not flow directly between installers. Indirect knowledge transfer pathways are critical: distributors, software providers, collaboratives, and hiring. Furthermore, diverse, more integrated knowledge networks tend to promote successful learning by organizations and across the system as a whole. Accordingly, we find the need to supporting the whole ecosystem using an integrated policy and programmatic approach to support installers, distributors, complementary sector, and facilitators. Overall, a deliberate policy-mix design is needed to reduce the solar PV deployment barrier in terms of installation cost reductions, because deployment policies could potentially interact with policies that facilitate network-building and technological innovation. A combination of deployment policies, innovation-support policies, and network-facilitating policies could potentially lead to a more desired market outcome through achieving higher joint learning rates from firms’ cumulative experiences developed in a more integrated production and deployment ecosystem.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Univ. of Texas, Austin, TX (United States)
Publication Date:
Research Org.:
Univ. of Texas, Austin, TX (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1772546
Report Number(s):
DOE-UT-EE0007658
DOE Contract Number:  
EE0007658
Resource Type:
Technical Report
Resource Relation:
Related Information: Beck, A. L., & Rai, V. (2019). Solar soft cost ontology: a review of solar soft costs. Progress in Energy, 2(1), 012001.
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; solar; soft costs; installers; knowledge spillover; learning by doing; balance of system; knowledge network; ontology; knowledge diversity; cost reduction

Citation Formats

Rai, Varun, Beck, Ariane, Nemet, Gregory F., Gao, Xue, Hannah, Douglas, Henry, Adam, and Funkhouser, Erik. Knowledge Spillovers and Cost Reductions in Solar Soft Costs. United States: N. p., 2020. Web. doi:10.2172/1772546.
Rai, Varun, Beck, Ariane, Nemet, Gregory F., Gao, Xue, Hannah, Douglas, Henry, Adam, & Funkhouser, Erik. Knowledge Spillovers and Cost Reductions in Solar Soft Costs. United States. https://doi.org/10.2172/1772546
Rai, Varun, Beck, Ariane, Nemet, Gregory F., Gao, Xue, Hannah, Douglas, Henry, Adam, and Funkhouser, Erik. 2020. "Knowledge Spillovers and Cost Reductions in Solar Soft Costs". United States. https://doi.org/10.2172/1772546. https://www.osti.gov/servlets/purl/1772546.
@article{osti_1772546,
title = {Knowledge Spillovers and Cost Reductions in Solar Soft Costs},
author = {Rai, Varun and Beck, Ariane and Nemet, Gregory F. and Gao, Xue and Hannah, Douglas and Henry, Adam and Funkhouser, Erik},
abstractNote = {Despite the commonly acknowledged importance of knowledge spillovers in reducing solar soft costs, we are only beginning to answer a fundamental question: who learns what (knowledge acquisition), from whom (knowledge production), and how (spillover mechanisms)? Until recently, this important topic has been largely unexplored in the case of solar soft costs. Thus, this project set out to identify how knowledge spillovers affect soft costs in the U.S. photovoltaic (PV) installation industry, specifically how important spillovers are, what types of knowledge are most likely to spillover, and how networks of actors affect spillovers. Our findings offer insights for designing solutions that address problems associated with knowledge spillovers and that leverage spillovers to reduce solar soft costs. Recognizing the ambiguity in the definition of soft costs, i.e., “non-hardware costs,” and variability in soft cost categories, we developed the Solar Soft Cost Ontology (SSCO) to systematically identify key concepts related to soft costs, network actors, learning processes, and the relationships between them. This ontology served as a foundational organizational structure for the methodology of the remaining tasks: case studies, surveys, pricing analysis, patent analysis, network analysis, and project integration across tasks. While there is substantial learning among installers that is reducing the soft costs for PV installations, most of that learning is retained by firms rather than spread across the industry. The positive relationship between experience accumulation and cost reductions is typically explained as learning by doing (LBD), but we find that LBD effects are mediated by other learning mechanisms, including learning by searching and learning by interacting. Knowledge spillovers have significant potential to reduce solar PV soft costs, but successful knowledge spillover pathways are complex and non-trivial. There are a wide variety of ways to construct an installation business, thus categories of firms that can effectively cross-learn directly are small and what knowledge is relevant to whom is challenging and costly for firms to assess. This fragmentation limits the critical mass needed for spillover related soft cost reductions. Knowledge does not flow directly between installers. Indirect knowledge transfer pathways are critical: distributors, software providers, collaboratives, and hiring. Furthermore, diverse, more integrated knowledge networks tend to promote successful learning by organizations and across the system as a whole. Accordingly, we find the need to supporting the whole ecosystem using an integrated policy and programmatic approach to support installers, distributors, complementary sector, and facilitators. Overall, a deliberate policy-mix design is needed to reduce the solar PV deployment barrier in terms of installation cost reductions, because deployment policies could potentially interact with policies that facilitate network-building and technological innovation. A combination of deployment policies, innovation-support policies, and network-facilitating policies could potentially lead to a more desired market outcome through achieving higher joint learning rates from firms’ cumulative experiences developed in a more integrated production and deployment ecosystem.},
doi = {10.2172/1772546},
url = {https://www.osti.gov/biblio/1772546}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 29 00:00:00 EDT 2020},
month = {Thu Oct 29 00:00:00 EDT 2020}
}