Browsing by Author "Gunawardana, H"

Now showing 1 - 4 of 4

Open Access
Carbon emissions and global R&D patterns: a wavelet coherence perspective
(Springer, 2025-03-23) Senevirathna, D; Gunawardana, H; Ranthilake, T; Caldera, Y; Jayathilaka, R; Rathnayake, N; Peter, S
This study examines the causality between Research and Development (R&D) and Carbon dioxide (CO2) emissions at the global level, utilising data gathered from 2000 to 2020 across various countries categorised as developed, developing, economies in transition, and least-developed. The data collected for the study are analysed using the Wavelet coherence methodology. The findings reveal both bidirectional and unidirectional causality between the variables, which have evolved over time. Globally, a bidirectional relationship is present in the short-term, no causality in the medium-term and unidirectional causality in the long-term. Developed countries exhibit a two-way causality in the short-term, while no causality exists in the medium-term and long-term. Developing countries show a bidirectional relationship across all time frequencies. In economies in transition, a bidirectional relationship appears towards the end of the period over the short, medium, and long-term. The least developed countries show no causality in the short and long-term, but a one-way causality in the medium-term. Governments and the policymakers can implement environmental policies to mitigate carbon emissions through R&D. The findings suggest targeted and strategic strategies to enhance the impact of R&D on emissions reduction. Policymakers can use this analysis to prioritize funding for clean energy innovations, establish incentives for low-tech technologies, and promote international cooperation in green technology research. Additionally, focusing on these carbon mechanisms and aligning R&D efforts to support development goals can increase the effectiveness of climate policies, ensuring a balance between economic growth and environmental sustainability.
Embargo
Renewable realities: Charting a greener course for the world's high-emitting nations through information technology insights
(Wiley, 2024-11-14) Ranthilake, T; Caldera, Y; Senevirathna, D; Gunawardana, H; Jayathilaka, R; Peter, S
Carbon dioxide (CO₂) is the most abundant gas among all greenhouse gas emissions,severely impacting global warming. This study examines the impact of Informationand Communication Technology (ICT), population dynamics, Per Capita GrossDomestic Product (PGDP), and Renewable Energy Consumption (REC) on CO₂ on aglobal scale, representing 38 countries selected using the Pareto principle. Resultsfrom the panel regression model indicate a significantly positive relationship betweenICT, PGDP, and population on CO₂ emissions. In contrast, REC exhibits a negativerelationship. The Multiple Linear Regression model shows that an increase in PGDPleads to higher CO₂ emissions, except in Uzbekistan. ICT increases emissions in theUnited States, Argentina, Australia, Canada, and Egypt. Population growth raisesemissions, except in the United States, France, Germany, and Russia. REC reducesCO₂ emissions in most countries. Policymakers in individual countries can gain a pre-cise understanding of how these variables impact CO₂ emissions, enabling them tomitigate the risks associated with global warming
Open Access
Renewable realities: Charting a greener course for the world's high-emitting nations through information technology insights
(John Wiley, 2025-04) Ranthilake, T; Caldera, Y; Senevirathna, D; Gunawardana, H; Jayathilaka, R; Peter, S
Carbon dioxide (CO₂) is the most abundant gas among all greenhouse gas emissions, severely impacting global warming. This study examines the impact of Information and Communication Technology (ICT), population dynamics, Per Capita Gross Domestic Product (PGDP), and Renewable Energy Consumption (REC) on CO₂ on a global scale, representing 38 countries selected using the Pareto principle. Results from the panel regression model indicate a significantly positive relationship between ICT, PGDP, and population on CO₂ emissions. In contrast, REC exhibits a negative relationship. The Multiple Linear Regression model shows that an increase in PGDP leads to higher CO₂ emissions, except in Uzbekistan. ICT increases emissions in the United States, Argentina, Australia, Canada, and Egypt. Population growth raises emissions, except in the United States, France, Germany, and Russia. REC reduces CO₂ emissions in most countries. Policymakers in individual countries can gain a precise understanding of how these variables impact CO₂ emissions, enabling them to mitigate the risks associated with global warming
Open Access
Understanding the interplay of GDP, renewable, and non-renewable energy on carbon emissions: Global wavelet coherence and Granger causality analysis
(PLoS ONE, 2024-09-19) Caldera, Y; Ranthilake, T; Gunawardana, H; Senevirathna, D; Jayathilaka, R; Rathnayake, N; Peter, S
This study examines the causality of Per Capita Gross Domestic Production (PGDP), Renewable Energy Consumption (REC), and Non-Renewable Energy Consumption (NREC) on Carbon dioxide (CO2) emissions at the global level utilising data gathered from 1995 to 2020 across various countries categorised based on income levels as High, Low, Upper Middle and Lower Middle and analysed through wavelet coherence. The findings reveal both bidirectional and unidirectional causality between the variables which have evolved. Globally, a bi-directional relationship is observed with a positive correlation between PGDP and NREC and in contrast, a negative correlation with REC. Furthermore, the analysis highlights varying causalities between CO2 emissions and PGDP, except for high-income and lower-middle-income country categories, all other shows one-way causality in different periods in the short term. Moreover, CO2 and REC, show unidirectional causality throughout the short-term, exceptionally medium & long term have both unidirectional and bidirectional causalities across all country categories with a positive correlation. In contrast, CO2 and NREC depict similar causalities to REC, however, with a negative correlation. A cross-country analysis was performed between CO2 and PGDP, CO2 and REC, and CO2 and NREC using Granger causality which shows mixed relationships. The findings hold significant implications for policymakers, providing valuable insights into the trade-offs between economic growth, energy consumption, and carbon emissions.