Small towns with big plans: Municipal adoption of 100% renewable electricity policies

Breetz, Hanna L.; Kunkel, Leah C.; Vallury, Sechindra; Cuiffo, Kathryn V.

doi:10.1016/j.erss.2022.102664

Cited by 4 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…He found that younger residents and residents with higher levels of educational attainment are more willing to pay a premium for renewable energy and energy efficiency investments. Breetz et al (2022) cited similar findings when studying municipal adoption of 100% renewable electricity policies. Through using a matched pair analysis with municipalities that had adopted policies and demographically similar municipalities that had not, they found that 100RE locations of adopters have a lower median age, were 36% more likely to have a four-year college degree, and 17 times more likely to have a research university in their location.…”

Section: Literature Reviewmentioning

confidence: 57%

“…Kunkel et al (2022) found that 100RE commitments were more likely in cities with lower median income and higher poverty rates. Additionally, Breetz et al (2022) found that the poverty rate is 27% higher among municipal adopters of renewable energy than non-adopters. While these studies found that less amounts of wealth are correlated with increased renewable energy, Krause (2011) found that larger populations and higher levels of per capita general revenue are significant enabling resources for climate protection initiatives.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Renewable Energy and Electric Vehicle Conversion: A National Evaluation of the Clean Energy Transition

Baker¹

2022

Preprint

View full text Add to dashboard Cite

This study applies economic modeling to assess the importance of variables thought to benefit or hinder the clean energy transition in all 50 states and one district in the United States. The study will discuss existing theories and literature behind the drivers of renewable energy generation. Linear and logit regression techniques are used to evaluate four different models. The results find that an increase in state population, renewable energy incentives, and average peak sun hours increases renewable energy generation, while an increase in the amount of protected land and people registered as Democrats decrease renewable energy generation. They also find that an increase in state population, the amount of protected land, and people registered as Democrats increase per capita electric vehicle registrations. The study shows that any state can excel in the energy transition given beneficial geographical attributes, increased clean energy incentives, and political accord.

show abstract

Section: Literature Reviewmentioning

confidence: 57%