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Subject: search.filters.subject.climate change

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Now showing 1 - 7 of 7
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    PublicationEmbargo
    Impact of economic growth, energy consumption, and trade openness on carbon emissions: evidence from the top 20 emitting nations
    (Taylor and Francis Ltd., 2025) Methmini, D; Dharmapriya, N; Gunawardena, V; Edirisinghe, S; Jayathilaka, R; Wickramaarachchi, C
    he study focuses on the top 20 carbon emission-increasing nations across continents from 2000 to 2021 and the effects of gross domestic product, energy consumption, and trade openness on carbon emissions. The study uses a panel dataset and multiple linear regression analysis to pinpoint the significant factors influencing each nation's carbon emissions. The findings indicate that China, Kazakhstan, Saudi Arabia, and South Korea in Asia; Algeria, Egypt, Morocco, and the Seychelles in Africa; Antigua and Barbuda, Bolivia, Chile, and Panama in America; Albania, Belarus, Lithuania, and Russia in Europe; and Fiji, Samoa, Tonga, and Vanuatu in Oceania have a highly significant impact on carbon emissions in their respective regions. Energy consumption significantly increases carbon emissions in all countries except Panama and Kazakhstan, where it only significantly impacts GDP-related carbon emissions. These insights lay the groundwork for policymakers to prioritise sustainable development, reduce carbon emissions in their decision-making processes, and establish comprehensive strategies that reconcile ecological concerns with socioeconomic goals by understanding the intricate dynamics between gross domestic product, energy use, trade openness, and carbon emissions.
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    PublicationEmbargo
    Data-Driven Bioclimatic Zoning in Sri Lanka: PCA and Clustering Analysis
    (SLIIT, Faculty of Engineering, 2024-10) Nadarajah, P.D; Singh, M.K; Mahapatra, S
    Driven by evolving lifestyles and the escalating demand for thermal comfort, Sri Lanka faces a critical absence of climate zone classifications necessary for constructing energy-efficient and climateresponsive buildings. This study addresses this gap by implementing bioclimatic zoning using a comprehensive 31-year weather dataset of 25 locations across the country. By applying Principal Component Analysis and Hierarchical Clustering to the 31-year weather data, Sri Lanka was classified into three distinct bioclimatic zones: Z1 (Hot and humid), Z2 (Cool and humid), and Z3 (Warm and humid). Bioclimatic potential analysis for each zone reveals natural ventilation as the most effective passive design strategy, demonstrating potential percentages of 64 ± 13%, 63 ± 10%, and 83 ± 4% in a year for Z1, Z2, and Z3, respectively. These findings underscore the crucial role of bioclimatic zoning in guiding the design of energy-efficient buildings in Sri Lanka. The approach contributes significantly to achieving national energy efficiency goals by leveraging climate-specific passive design strategies and reducing reliance on energy-intensive cooling systems. Moreover, the study not only classifies Sri Lanka into three bioclimatic zones but also emphasises the broader impact of implementing such strategies on sustainable construction practices. This research, therefore, stands at the intersection of bioclimatic zoning, sustainable building practices, and the evolving energy landscape.
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    PublicationOpen Access
    Impact of economic growth, energy consumption, and trade openness on carbon emissions: evidence from the top 20 emitting nations
    (Taylor and Francis, 2024-07-08) Methmini, D; Dharmapriya, N; Gunawardena, V; Edirisinghe, S; Jayathilaka, R; Wickramaarachchi, C
    The study focuses on the top 20 carbon emission-increasing nations across continents from 2000 to 2021 and the effects of gross domestic product, energy consumption, and trade openness on carbon emissions. The study uses a panel dataset and multiple linear regression analysis to pinpoint the significant factors influencing each nation’s carbon emissions. The findings indicate that China, Kazakhstan, Saudi Arabia, and South Korea in Asia; Algeria, Egypt, Morocco, and the Seychelles in Africa; Antigua and Barbuda, Bolivia, Chile, and Panama in America; Albania, Belarus, Lithuania, and Russia in Europe; and Fiji, Samoa, Tonga, and Vanuatu in Oceania have a highly significant impact on carbon emissions in their respective regions. Energy consumption significantly increases carbon emissions in all countries except Panama and Kazakhstan, where it only significantly impacts GDPrelated carbon emissions. These insights lay the groundwork for policymakers to prioritise sustainable development, reduce carbon emissions in their decision-making processes, and establish comprehensive strategies that reconcile ecological concerns with socioeconomic goals by understanding the intricate dynamics between gross domestic product, energy use, trade openness, and carbon emissions.
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    PublicationOpen Access
    Renewable energy as a solution to climate change: Insights from a comprehensive study across nations
    (PLoS ONE, 2024-06-20) Attanayake, K; Wickramage, I; Samarasinghe, U; Ranmini, Y; Ehalapitiya, S; Jayathilaka, R; Yapa, S
    Without fundamentally altering how humans generate and utilise energy, there is no effective strategy to safeguard the environment. The motivation behind this study was to analyse the effectiveness of renewable energy in addressing climate change, as it is one of the most pressing global issues. This study involved the analysis of panel data covering 138 nations over a 27 year period, from 1995 to 2021, making it the latest addition to the existing literature. We examined the extent of the impact of renewable energy on carbon dioxide over time using panel, linear, and non-linear regression approaches. The results of our analysis, revealed that the majority of countries with the exception of Canada, exhibited a downward trend, underscoring the potential of increasing renewable energy consumption as an effective method to reduce carbon dioxide emissions and combat climate change. Furthermore, to reduce emissions and combat climate change, it is advisable for nations with the highest carbon dioxide emissions to adopt and successfully transition to renewable energy sources.
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    PublicationOpen Access
    Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?
    (MDPI, 2023-05) Jayathilake, T; Gunathilake, M. B; Wimalasiri, E.M; Rathnayake, U
    Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction. © 2023 by the authors.
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    PublicationOpen Access
    The Assessment of Climate Change Impacts and Land-use Changes on Flood Characteristics: The Case Study of the Kelani River Basin, Sri Lanka
    (MDPI, 2022-10-09) Samarasinghe, J. T; Makumbura, R. K; Wickramarachchi, C; Sirisena, J; Gunathilake, M.B; Muttil, N; Yenn Teo, F; Rathnayake, U
    Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors.
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    PublicationOpen Access
    Evaluation of Future Streamflow in the Upper Part of the Nilwala River Basin (Sri Lanka) under Climate Change
    (MDPI, 2022-03-16) Chathuranika, I. M; Gunathilake, M. B; Azamathulla, H. M; Rathnayake, U
    Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate.