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Economics of climate attitudes in oil-rich regions

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  • Kurronen, Sanna

Abstract

The study aims to identify economic measures that enhance support for climate change mitigation, particularly in oil-rich communities. Using US county-level data, the research shows that the presence of oil reserves is negatively associated with the attribution of human influence to climate change and policies regulating CO2 emissions. Interestingly, a high current dependence on mineral extraction is associated with greater support for climate policies, while a decline in the mining-income share in oil-rich regions does not correlate with increased support for climate action, underpinning our hypothesis of persistence of climate attitudes. This suggests that a region's current economic dependence on mining is not necessarily an obstacle to greater action to mitigate human impacts on climate. While evidence on the impact of extreme weather events on climate attitudes is mixed, we also present evidence that regional economic losses from natural disasters and rising home insurance costs may help convince people of the need for climate policy measures.

Suggested Citation

  • Kurronen, Sanna, 2025. "Economics of climate attitudes in oil-rich regions," BOFIT Discussion Papers 13/2025, Bank of Finland Institute for Emerging Economies (BOFIT).
    • Handle: RePEc:zbw:bofitp:333961
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    References listed on IDEAS

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    1. Guy Michaels, 2011. "The Long Term Consequences of Resource‐Based Specialisation," Economic Journal, Royal Economic Society, vol. 121(551), pages 31-57, March.
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    Keywords

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    JEL classification:

    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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