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Start date: 2020Subject: search.filters.subject.Greenhouse gas emissions

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    Understanding green house gases emission dynamics from forest fires in Thailand using predictive models
    (2026-02-01)
    Shahzad, Fahad
    ;
    Mehmood, Kaleem
    ;
    Anees, Shoaib Ahmad
    ;
    Adnan, Muhammad
    ;
    Hussain, Khadim
    Forest fires are a major driver of carbon emissions, particularly in tropical regions where climate variability and land use practices intensify their frequency and impact. This study investigates the spatiotemporal trends and emission dynamics of forest fires across Thailand's three dominant vegetation types- Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Grassland over three climatic seasons (Dry, Hot, and Wet) in the period 2001–2023. Using the Mann-Kendall trend test and Sen's Slope estimator, we observed significant declines in burnt area during the Dry season in EBF and Grasslands, with no consistent trend in DBF. Fire–vegetation interactions revealed seasonally specific effects: positive correlations between fire count and Net Primary Productivity (NPP) were detected in the Wet and the hot seasons in the case of DBF and Grasslands, respectively. Emission analysis showed that CO₂ was the dominant greenhouse gas released, with the Dry season contributing to most emissions, although Hot season emissions have increased over time. Machine learning models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) explained over 78 % of the variance in CO₂ emissions on test data (R<sup>2</sup> = 0.79 for RF, 0.78 for XGBoost), despite higher Root Mean Square Error (RMSE) values (∼550) on unseen data. The Shapley Additive Explanations (SHAP) analysis identified wind components and solar radiation as key predictive variables. Central, Northeastern, and Northern Thailand emerged as emission hotspots. These findings improve our understanding of emission dynamics from tropical fires and underscore the need for region-specific mitigation strategies to inform carbon inventories and climate policy.
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    Sustainable Practices and Environmental Impact Assessment in a Lifelong Learning Center
    (2024-01-01)
    Koiwanit, Jarotwan
    ;
    Areerob, Yonrapach
    ;
    Saepoo, Sakkarin
    ;
    Filimonau, Viachaslau
    Climate change is a major environmental challenge that should be mitigated by all parties concerned. One such party is the KMITL Lifelong Learning Canter (KLLC) which has committed to reducing its environmental externalities, including the impact of its operations on climate change. The idea of a “green KLLC” seeks to reduce adverse effects on the environment and improve indoor environmental quality through the use of natural building materials and biodegradable products, resource conservation (water, energy, paper), responsible waste disposal, and eco-friendly practices (recycling). To reduce these environmental externalities, the environmental performance of the KLLC canter should first be examined to establish measures for improvement. However, accurate evaluations of the environmental impact of Lifelong Learning Centers (LLCs) are uncommon because of the lack of data and the immaturity of appraisal methodologies. With a case study of KLLC, the newest LLC in Thailand, this paper appraises the environmental effects, thus setting benchmarks for subsequent studies. The appraisal demonstrates that the KLLC community can significantly reduce the environmental consequences by using e-certificates, motion sensor light installation, banana leaf packaging, solar cell installation, and carpooling systems. Although e-certificates and banana leaf packaging are the most cost-effective methods of implementation, the adoption of carpooling systems and electric vehicles demonstrates the highest potential for Greenhouse Gas (GHG) emissions reduction. The paper showcases how KLLC can reduce its GHG emissions and wastes, thus turning into a more environmentally sustainable business.