Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach
Given the severity of global environmental degradation, particularly in countries like Ethiopia, it is urgent to rethink its drivers and measurements for actionable policy development. The relationships among these predictors are complex, often nonlinear, non-additive, and include reverse causality,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Environmental and Sustainability Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972724002228 |
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| author | Tesfaye Etensa Tekie Alemu Mengesha Yayo |
| author_facet | Tesfaye Etensa Tekie Alemu Mengesha Yayo |
| author_sort | Tesfaye Etensa |
| collection | DOAJ |
| description | Given the severity of global environmental degradation, particularly in countries like Ethiopia, it is urgent to rethink its drivers and measurements for actionable policy development. The relationships among these predictors are complex, often nonlinear, non-additive, and include reverse causality, making it difficult for traditional econometric models to capture them. Conventional CO₂ metrics also tend to overlook unique emission sources in developing countries, where emissions are closely linked to energy production, unsustainable agriculture, deforestation, and land use rather than industry. To address these gaps, this study applies a kernel-based machine learning model and develops context-specific CO₂ metrics to analyze environmental degradation predictors and forecast their long-term impacts in Ethiopia using quarterly data from 2000Q1 to 2020Q4. The findings indicate that economic growth, industrialization, energy poverty, urbanization, ICT, and resource rent are significant predictors, exhibiting complex, nonlinear relationships. Long-term prediction analysis shows that energy poverty, economic growth, ICT, and urbanization initially worsen degradation but lead to stabilization over time. In contrast, industrialization and resource rent predominantly exacerbate environmental issues before leveling off. The study recommends policies to enhance energy access and efficiency through renewable energy subsidies and financial incentives, integrate green infrastructure into urban planning, incentivize clean industrial technologies, promote environmentally inclusive growth, regulate eco-friendly ICT, such as energy-efficient data centers and e-waste management, implement a resource rent tax, and use adaptive policies with real-time analytics to address degradation thresholds, balancing economic growth with resilience and sustainability. |
| format | Article |
| id | doaj-art-633eb97e0f584b2bbcdbc95108783453 |
| institution | Kabale University |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-633eb97e0f584b2bbcdbc951087834532025-01-29T05:01:47ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-02-0125100554Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approachTesfaye Etensa0Tekie Alemu1Mengesha Yayo2Corresponding author.; Department of Economics, Addis Ababa University, Addis Ababa, EthiopiaDepartment of Economics, Addis Ababa University, Addis Ababa, EthiopiaDepartment of Economics, Addis Ababa University, Addis Ababa, EthiopiaGiven the severity of global environmental degradation, particularly in countries like Ethiopia, it is urgent to rethink its drivers and measurements for actionable policy development. The relationships among these predictors are complex, often nonlinear, non-additive, and include reverse causality, making it difficult for traditional econometric models to capture them. Conventional CO₂ metrics also tend to overlook unique emission sources in developing countries, where emissions are closely linked to energy production, unsustainable agriculture, deforestation, and land use rather than industry. To address these gaps, this study applies a kernel-based machine learning model and develops context-specific CO₂ metrics to analyze environmental degradation predictors and forecast their long-term impacts in Ethiopia using quarterly data from 2000Q1 to 2020Q4. The findings indicate that economic growth, industrialization, energy poverty, urbanization, ICT, and resource rent are significant predictors, exhibiting complex, nonlinear relationships. Long-term prediction analysis shows that energy poverty, economic growth, ICT, and urbanization initially worsen degradation but lead to stabilization over time. In contrast, industrialization and resource rent predominantly exacerbate environmental issues before leveling off. The study recommends policies to enhance energy access and efficiency through renewable energy subsidies and financial incentives, integrate green infrastructure into urban planning, incentivize clean industrial technologies, promote environmentally inclusive growth, regulate eco-friendly ICT, such as energy-efficient data centers and e-waste management, implement a resource rent tax, and use adaptive policies with real-time analytics to address degradation thresholds, balancing economic growth with resilience and sustainability.http://www.sciencedirect.com/science/article/pii/S2665972724002228Environmental degradationPredictorsPredictionLong-termKernel-based machine learningEthiopia |
| spellingShingle | Tesfaye Etensa Tekie Alemu Mengesha Yayo Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach Environmental and Sustainability Indicators Environmental degradation Predictors Prediction Long-term Kernel-based machine learning Ethiopia |
| title | Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach |
| title_full | Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach |
| title_fullStr | Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach |
| title_full_unstemmed | Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach |
| title_short | Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach |
| title_sort | rethinking the measurements and predictors of environmental degradation in ethiopia predicting long term impacts using a kernel based machine learning approach |
| topic | Environmental degradation Predictors Prediction Long-term Kernel-based machine learning Ethiopia |
| url | http://www.sciencedirect.com/science/article/pii/S2665972724002228 |
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