Browsing by Author "Vithanage, N"

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    PublicationOpen Access
    Drivers of carbon emissions in G7 economies: Evidence on energy use, globalisation, urbanisation, industrialisation and innovation
    (Elsevier Ltd, 2026-08-26) Weerasinghe, L; Vithanage, N; Rupasinghe, D; Keesha, C; Jayathilaka, R
    Rising CO2 emissions remain a major sustainability challenge, particularly in advanced economies that account for a considerable share of historical emissions. This study examines the key determinants of CO₂ emissions in G7 countries by jointly considering globalisation, energy consumption, urbanisation, industrialisation, and technological innovation. Using a balanced panel dataset for Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States over the period 1997–2023, the analysis applies panel regression techniques and country-specific multiple linear regressions to capture both common effects and heterogeneity across economies. The results indicate that energy consumption is a robust driver of CO₂ emissions across the G7. Technological innovation contributes to emission reduction at the panel level; however, its effect becomes statistically insignificant in country-specific estimations, reflecting cross-country differences in innovation structures and policy environments. Globalisation significantly increases emissions in Canada, while its influence is negligible elsewhere. Urbanisation shows a mitigating effect only in the United States, and industrialisation increases emissions in Canada and Italy but reduces emissions in Japan. Overall, the findings highlight that decarbonisation strategies in advanced economies should prioritise the transition to clean energy while strengthening innovation-oriented climate policies tailored to country-specific contexts.
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    Predictive Models for Urban Air Quality Management Using AI
    (Institute of Electrical and Electronics Engineers Inc., 2026-03-19) Liyanage, D; Vithanage, N; Wijewardane, I; Fernando, N; Wijendra, D; Dassanayake, T
    Air pollution threatens public health in datascarce urban areas like Sri Lanka, where sparse monitoring hinders proactive management. We propose an integrated AI framework: hybrid SARIMAX-Temporal Fusion Transformer for multi-pollutant forecasting, ensemble spatial estimation for gap-filling, CEEMDAN-Seq2Seq for 24-hour AQI risk alerting, GRU for anomaly detection, and XAI for transparency. Validated on Central Environmental Authority data (20192024), the model achieves an 81.6% decrease in the value of the RMSE metric for ozone forecasting, as well as an R2 value of 0.9077 for high-risk AQI prediction, outperforming the baseline methods by 15-81%. The framework is modular in nature, thereby providing policymakers with the ability to use real-time dashboards, thus making Sri Lanka move from reactive to proactive management.