Analysing research patterns on low carbon development, climate change mitigation and renewable energy through text analytics: An artificial intelligence approach

The study examines research on low carbon development, climate change mitigation, and renewable energy in the last decade. It reveals that research has primarily focused on China, policy, sustainable practices, and urban factors. However, there is limited research on carbon capture and storage, per capita household emissions, urban emissions, land use, long-term energy scenarios, green energy technology innovation, agriculture, tourism, and sukuk. Artificial intelligence (AI) has been used to identify broad themes such as energy usage, renewable energy, climate change, carbon emissions, urban growth, national and international policies, green energy development, power production, and tourism. A sample of 323 most relevant research papers were retrieved from ProQuest database for analysis. The study used text analytics approach. Wordcloud, TF-IDF, word correlation graphs, topic modelling were employed to conduct scientometric research. This study can help in understanding the connections between low carbon development, climate change mitigation and renewable energy and other relevant keywords which then can be used by policymakers and other stakeholders for formatting appropriate policies and researchers can use it to find research gaps.