The drivers of GHG emissions: A novel approach to estimate emissions using nonparametric analysis
| dc.contributor.author | Magazzino ,Cosimo | |
| dc.contributor.author | Cerulli ,Giovanni | |
| dc.contributor.author | Haouas,Ilham | |
| dc.contributor.author | Unuofin, John Onolame | |
| dc.contributor.author | Sarkodie, Samuel Asumadu | |
| dc.date.accessioned | 2024-02-29T05:28:27Z | |
| dc.date.available | 2024-02-29T05:28:27Z | |
| dc.date.issued | 2023-10-18 | |
| dc.description | Human needs for goods and services are indefinite, while the Earth only provides finite resources for humans to exploit. This sit uation creates an imbalance between demand and supply, espe cially amid the continuously rising population and the shortening availability of land to produce raw materials. During the last dec ade, the world’s population has increased by 12 %, from 6,922 bil lion in 2010 to 7,753 billion in 2020. This growing population supplies more labour to the global economy, which in turn drove the global Gross Domestic Product (GDP) by 38 %, from US$ 66,163 trillion in 2010 to US$ 84,705 trillion in 2020. | |
| dc.description.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. Keywords: Lasso regression, Climate change mitigation, GHG emissions, Land use ,Forestry | |
| dc.identifier.citation | Magazzino, C., Cerulli, G., Haouas, I., Unuofin, J. O., & Sarkodie, S. A. (2024). The drivers of GHG emissions: A novel approach to estimate emissions using nonparametric analysis. Gondwana Research, 127, 4-21. | |
| dc.identifier.doi | https://doi.org/10.1016/j.gr.2023.10.004 | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/1487 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.title | The drivers of GHG emissions: A novel approach to estimate emissions using nonparametric analysis | |
| dc.type | Article |