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<p>Understanding methane emissions is crucial for climate change mitigation, given its potency as a greenhouse gas. This study offers a detailed spatiotemporal analysis of methane emissions across 54 African countries, alongside an aggregated continental baseline, from 1990 to 2023, with projections extending to 2030. Eight time-series models, including ARIMA, ETS, and neural network-based approaches (NNAR, ANN/MLP), were developed and assessed for each country to predict future emissions. The model with the lowest Symmetric Mean Absolute Percentage Error (sMAPE) for each nation was selected to generate forecasts. Historical trend analysis using the Mann–Kendall test and Theil-Sen slope indicated a statistically significant upward trend in methane emissions for the continent overall, with notable increases in Ethiopia and Chad, and significant decreases in Nigeria and Libya. Spatial analysis of historical data identified Nigeria as the highest emitter. Forecasts suggest these trends are likely to persist. To explore the spatial dynamics of future emissions, spatial autocorrelation analyses (Local Moran’s I and Getis-Ord Gi*) were conducted for each forecasted year from 2024 to 2030. The results consistently revealed statistically significant hot spots (clusters of high emissions) in North-Eastern Africa and the Horn of Africa, and cold spots (clusters of low emissions) in Southern Africa. These findings emphasize ongoing regional disparities and highlight the need for tailored geographically informed strategies for methane mitigation across the African continent.</p>

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Spatiotemporal forecasting of methane emissions in Africa using time-series models (1990–2030)

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Mohamed Abdi Abdullahi,
  • Abdisalam Hassan Muse,
  • Yahye Hassan Muse,
  • Mukhtar Abdi Hassan,
  • Saralees Nadarajah

摘要

Understanding methane emissions is crucial for climate change mitigation, given its potency as a greenhouse gas. This study offers a detailed spatiotemporal analysis of methane emissions across 54 African countries, alongside an aggregated continental baseline, from 1990 to 2023, with projections extending to 2030. Eight time-series models, including ARIMA, ETS, and neural network-based approaches (NNAR, ANN/MLP), were developed and assessed for each country to predict future emissions. The model with the lowest Symmetric Mean Absolute Percentage Error (sMAPE) for each nation was selected to generate forecasts. Historical trend analysis using the Mann–Kendall test and Theil-Sen slope indicated a statistically significant upward trend in methane emissions for the continent overall, with notable increases in Ethiopia and Chad, and significant decreases in Nigeria and Libya. Spatial analysis of historical data identified Nigeria as the highest emitter. Forecasts suggest these trends are likely to persist. To explore the spatial dynamics of future emissions, spatial autocorrelation analyses (Local Moran’s I and Getis-Ord Gi*) were conducted for each forecasted year from 2024 to 2030. The results consistently revealed statistically significant hot spots (clusters of high emissions) in North-Eastern Africa and the Horn of Africa, and cold spots (clusters of low emissions) in Southern Africa. These findings emphasize ongoing regional disparities and highlight the need for tailored geographically informed strategies for methane mitigation across the African continent.