TY - JOUR
T1 - Equity of Incentives
T2 - Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption
AU - Brugger, Heike I.
AU - Henry, Adam Douglas
N1 - Funding Information:
Seeding Less Affluent Communities. Using this strategy, free or low-cost PV systems are given out to a selected number of qualified agents in low-income communities. This has a doubled positive effect on adoption rates: first, strong financial support is especially important for low-income households to support their investment [20, p. 85]; second, it is increasing the visibility of PVs, spurring peer effects within those communities [8, p. 340] (see above on the importance of peer effects in low-income communities). Currently, this form of support is in pilot status only. For example, pilot projects have been implemented in California under the Greenhouse Gas Reduction Fund in collaboration with the Oakland-based nonprofit organization Grid Alternatives.Theprojectispartly financed by California’s cap and trade program and aims at supporting low-income families through free PVs. The peer effects of this project are not yet monitored. In an agent-based model, Zhang et al. [65] show extremely positive effects of seeding for overall adoption rates. Therefore, seeding programs are expected to lead to a faster overall uptake of installations in our models as well (as compared to no incentives). Since the seeding program is only targeting low-income actors, it drastically increases the probability that these agents will adopt solar. Especially in segregated networks, it will be of major importance to spur peer ef fects in currently underserved communities. We therefore expect that seeding programs positively affect the adoption dynamics in segregated networks by decreasing the differences between high-and low-probability actors (as compared to no incentive in segregated networks). In integrated networks, however, the difference in adoption dynamics is not expected
Publisher Copyright:
© 2019 Heike I. Brugger and Adam Douglas Henry.
PY - 2019
Y1 - 2019
N2 - Agent-based models are used to explore how social networks influence the effectiveness of governmental programs to promote the adoption of solar photovoltaics (solar PV) by residential households. This paper examines how a common characteristic of social networks, known as network segregation, can dampen the indirect benefits of solar incentive programs that arise from peer effects. Peer effects cause an agent to be more likely to adopt a technology if they are socially connected to other adopters. Due to network segregation, programs that target relatively affluent agents can generate rapid increases in overall adoption levels but at the cost of increasing disparities in access to solar technology between rich and poor communities. These dynamics are explored through theoretical agent-based models of solar adoption within hypothetical social systems. The effectiveness of three types of solar incentive programs, the feed-in tariff, leasing programs, and seeding programs, is explored. Even though these programs promote rapid adoption in the short term, results demonstrate that network segregation can create serious distributional justice problems in the long term for some programs. The distributional justice effects are particularly severe with the feed-in tariff. Overall, this paper provides an illustration of how agent-based models may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of policymaking.
AB - Agent-based models are used to explore how social networks influence the effectiveness of governmental programs to promote the adoption of solar photovoltaics (solar PV) by residential households. This paper examines how a common characteristic of social networks, known as network segregation, can dampen the indirect benefits of solar incentive programs that arise from peer effects. Peer effects cause an agent to be more likely to adopt a technology if they are socially connected to other adopters. Due to network segregation, programs that target relatively affluent agents can generate rapid increases in overall adoption levels but at the cost of increasing disparities in access to solar technology between rich and poor communities. These dynamics are explored through theoretical agent-based models of solar adoption within hypothetical social systems. The effectiveness of three types of solar incentive programs, the feed-in tariff, leasing programs, and seeding programs, is explored. Even though these programs promote rapid adoption in the short term, results demonstrate that network segregation can create serious distributional justice problems in the long term for some programs. The distributional justice effects are particularly severe with the feed-in tariff. Overall, this paper provides an illustration of how agent-based models may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of policymaking.
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U2 - 10.1155/2019/4349823
DO - 10.1155/2019/4349823
M3 - Article
AN - SCOPUS:85062335664
VL - 2019
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 4349823
ER -