2024

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Author: search.filters.author.Dharmasena, T

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    PublicationOpen Access
    Towards a greener future: examining carbon emission dynamics in Asia amid gross domestic product, energy consumption, and trade openness
    (Springer Nature, 2024-02-10) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Jayathilaka, R; Dharmasena, T; Wickramaarachchi, C; Rathnayake, N
    The purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20 years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specifc contributions of each country to the study variables and their impact on carbon emissions. The fndings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly signifcant impact (p<0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development,and international cooperation within Asia to reach the goal of being carbon neutral by 2050.
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    PublicationOpen Access
    Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach
    (PLoS ONE, 2024-03-08) Karasinghe, N; Peiris, S; Jayathilaka, R; Dharmasena, T
    Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.