
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Income inequality and CO2 emissions in the G7, 1870–2014: Evidence from non-parametric modelling

Abstract We examine whether income inequality effected carbon emissions in what are now the world's wealthiest countries over the period 1870–2014. Employing a non-parametric panel estimation method with cross-sectional and time-varying coefficients, we find that the relationship between income inequality and CO2 emissions is highly non-linear. In terms of signs and significance, the nonparametric coefficient function for income inequality is found to vary over the period 1870–2014. Income inequality exhibits a significant positive effect from 1870 to 1880 and a significant negative impact from 1950 to 2000 on CO2 emissions. We also find that for extended periods between 1881 and 1949 and between 2000 and 2014, there is no significant relationship between the two variables.
- Monash University Australia
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).98 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
