The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in reducing pollution remains a topic of ongoing debate. The purpose of this paper is to examine empirically the effects of various environmental tax categories (energy, pollution, resource and transport) on CO₂ emissions in 34 OECD countries between 1995 and 2019. The dynamic panel threshold regression developed by Seo and Shin (2016) is implemented to assess whether the impact of environmental taxes on CO₂ emissions depends on a given threshold level. The locally weighted scatterplot smoothing analysis provides evidence for a nonlinear association between environmental taxes and CO₂ emissions. The analysis indicates the existence of one significant threshold and two regimes (lower and upper) for all environmental tax categories. The dynamic panel threshold regression reveals that the total environmental tax, energy tax and pollution tax reduce CO₂ emissions in the upper regime, i.e., once a given threshold level is reached. The threshold levels are 3.002% of GDP for the total environmental tax, 1.991% for the energy tax and 0.377% for the pollution tax. Furthermore, implementing taxes on resource utilization may be effective but with limited environmental effects. Based on the research results, it is recommended that countries in the OECD implement specific environmental taxes to reduce greenhouse gas emissions.

Climate change represents one of the most serious threats to the well-being of humanity. In recent decades, there has been a significant increase in the amount of research dedicated to analysing the economic impacts of climate change. Nevertheless, the spatial aspect of climate change has not been addressed. This research is the first to empirically assess both direct and indirect (spillover) effects of climate change, as measured by temperature variations, on economic growth. The empirical analysis is based on a balanced dataset for 86 countries between 1980 and 2019. The preliminary analysis suggests the presence of spatial autocorrelation and the suitability of the dynamic spatial autoregressive model to assess the spillover effects of climate change. The results for the full sample reveal that there are no substantial short- or long-run effects of climate change on economic growth. When the sample is decomposed by income, the analysis indicates that climate change has direct and indirect spillover effects on economic growth only in low–middle-income countries over the short- and long run. The decomposition according to the climate regime also yields interesting findings as climate change exerts adverse direct and indirect spillover effects on economic growth only in the hottest countries over the long run. These findings are robust since they hold regardless of whether the contiguity weight matrix or the inverse distance weight matrix is used. The research advocates for international collaboration in the design and implementation of climate change mitigation and adaptation strategies.