CO2 consumer tax support and wind turbine exposure

Ecological Economics(2024)

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摘要
With the international commitments to cut CO2 emissions by 50–70% by 2030 and 100% by 2050–2070, the search for cost-efficient tools is continuously ongoing. In theory, CO2 taxes are one of the most efficient and simple tools. However, despite their excellent economic properties, CO2 taxes are not always preferred by the public and can impact social inequality. Another issue is that other CO2 reduction interventions, such as increased renewable energy like wind power, can substitute the CO2 tax. Nevertheless, wind power is also controversial, and the local acceptance of new, mainly onshore, wind power projects can be very low. In this paper, we test how these two issues are related. Using data from a national survey with 2386 respondents, we test how the existing and potential future wind power landscape (number of turbines) relates to the CO2 consumer tax support. The average results show no relations. However, conditional on gender, age, and income, female respondents, older respondents, and respondents from low-income households who can see many turbines from the residence are more positive towards consumer CO2 taxes than male respondents, younger respondents, and respondents from higher-income households who see two or more turbines. We also find that low-income households with knowledge about local wind turbine projects support a consumer CO2 tax more than higher-income houses with the same knowledge. Finally, the density of turbines on the postal area level correlates differently and significantly with the support of a consumer CO2 tax between male and female respondents. First, our results illustrate the dynamic properties of wind turbine exposure and the correlation with the support for CO2 consumer taxes. Secondly, our results also denote the complexity of substitution between acceptance of CO2 consumer taxes and wind power development across generations and household income levels.
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关键词
Carbon Tax,Acceptance,Wind Turbine Exposure,Gender,Age,Income
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