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    PublicationOpen 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.
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    PublicationOpen Access
    Bidirectional LSTM-CRF for Named Entity Recognition
    (32nd Pacific Asia Conference on Language, Information and Computation, 2018-12-01) Panchendrarajan, R; Amaresan, A
    Named Entity Recognition (NER) is a challenging sequence labeling task which requires a deep understanding of the orthographic and distributional representation of words. In this paper, we propose a novel neural architecture that benefits from word and character level information and dependencies across adjacent labels. This model includes bidirectional LSTM (BI-LSTM) with a bidirectional Conditional Random Field (BI-CRF) layer. Our work is the first to experiment BI-CRF in neural architectures for sequence labeling task. We show that CRF can be extended to capture the dependencies between labels in both right and left directions of the sequence. This variation of CRF is referred to as BI-CRF and our results show that BI-CRF improves the performance of the NER model compare to an unidirectional CRF and backward CRF is capable of capturing most difficult entities compare to the forward CRF. Our system is competitive on the CoNLL-2003 dataset for English and outperforms most of the existing approaches which do not use any external labeled data.