The rising levels of global GHG emissions underpin climate change, hence, taking an appropriate inventory of the drivers and patterns of anthropogenic emissions remains crucial to mitigating global climate effects. However, there are conflicting views in the literature on the relationship between respective drivers and GHG emissions due to the lack of robust analysis that accommodates the interaction of all significant drivers. We use novel estimation techniques to decipher the 26-year inventory of GHG occurrences and simultaneous assessment of interactions in 50 countries stratified based on socioeconomic developments over the period 1990–2018. This study highlights different drivers of GHG emissions under broader categories such as population, economic development, forest density, and agricultural practices. Non-parametric estimations roughly confirm the magnitude of the influence of forests, agriculture, and land-use intensity on GHG emissions, ultimately tracking the most significant emission sinks.
Magazzino, C., Cerulli, G., Haouas, I., Unuofin, J.O., Sarkodie, S.A. (2023). The drivers of GHG emissions: A novel approach to estimate emissions using nonparametric analysis. GONDWANA RESEARCH [10.1016/j.gr.2023.10.004].
The drivers of GHG emissions: A novel approach to estimate emissions using nonparametric analysis
Magazzino, Cosimo
;Cerulli, Giovanni;
2023-01-01
Abstract
The rising levels of global GHG emissions underpin climate change, hence, taking an appropriate inventory of the drivers and patterns of anthropogenic emissions remains crucial to mitigating global climate effects. However, there are conflicting views in the literature on the relationship between respective drivers and GHG emissions due to the lack of robust analysis that accommodates the interaction of all significant drivers. We use novel estimation techniques to decipher the 26-year inventory of GHG occurrences and simultaneous assessment of interactions in 50 countries stratified based on socioeconomic developments over the period 1990–2018. This study highlights different drivers of GHG emissions under broader categories such as population, economic development, forest density, and agricultural practices. Non-parametric estimations roughly confirm the magnitude of the influence of forests, agriculture, and land-use intensity on GHG emissions, ultimately tracking the most significant emission sinks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
