2018
DOI: 10.1016/j.enpol.2018.08.044
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The influence of extractive activities on public support for renewable energy policy

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Cited by 63 publications
(25 citation statements)
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“…We found evidence suggesting that respondents from the same county/districts felt somewhat similarly toward the various energy sources, though our model did not account for what specific local factors might be responsible for this. Previous work has found that local significance of energy industries can favorably influence residents' perceptions of those energy sources, and even unfavorable influence their perception of new or different energy sources [72,87,89,92]. In this study, we found that perceived local economic importance of energy industries was related to all energy types except nuclear, though we caution that our measure was based on respondent perception.…”
Section: Discussionmentioning
confidence: 54%
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“…We found evidence suggesting that respondents from the same county/districts felt somewhat similarly toward the various energy sources, though our model did not account for what specific local factors might be responsible for this. Previous work has found that local significance of energy industries can favorably influence residents' perceptions of those energy sources, and even unfavorable influence their perception of new or different energy sources [72,87,89,92]. In this study, we found that perceived local economic importance of energy industries was related to all energy types except nuclear, though we caution that our measure was based on respondent perception.…”
Section: Discussionmentioning
confidence: 54%
“…Using mixed effects modeling (also known as multilevel modeling) allowed us to account for possible unobserved heterogeneity at different spatial levels (known as random effects), in this case, county (or Canadian district) and state (or Canadian province). A mixed effects modeling approach helps account for similarities (if there are any) between respondents living in the same county/district or state/province [118] and is an increasingly common approach for analyzing spatially-dependent survey data [72,92,[119][120][121][122][123]. We used ordinal logistic regression modeling instead of ordinary least squares linear regression modeling because our dependent variables were categorical and not normally distributed (Supplementary Materials).…”
Section: Discussionmentioning
confidence: 99%
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