Tracks > Track 16: AI and Green Finance Accelerating Energy Transition

Track 16: AI and green finance accelerating energy transition

The global transition towards sustainable energy stresses the transformative potential of artificial intelligence (AI) in the energy sector. This study investigates the impact of AI, represented by industrial robots, on the energy transition in the top 10 robotics-adopting countries. It employs both panel-level and country-specific analyses to capture the varied effects of AI on energy transition, considering the role of green finance. Additionally, the research explores how green finance moderates and amplifies the relationship between robotics adoption and energy transition. A novel three-tier methodology is introduced, combining panel estimations and cross-country analysis using a Multivariate Quantile-on-Quantile Regression (Multivariate-QQR) model—an extension of Sim & Zhou's Bivariate-QQR model—and machine learning-based Kernel Regularized Least Squares (KRLS). By analyzing monthly data from Q1 2010 to Q4 2019, the study assesses how AI adoption and green finance influence the progress of ten industries towards achieving Sustainable Development Goals (SDGs) and COP26 commitments. The findings are expected to demonstrate that industrial robots accelerate the energy transition, with green finance acting as a catalyst to enhance AI's positive effects on energy efficiency. These insights are crucial for policymakers seeking to promote sustainable energy transitions by integrating AI and green finance strategies.

Keywords: Artificial intelligence; Energy transition; Green finance; industrialized nations

Organizers: Kishwar Ali kishwarali@ujs.edu.cn  

Co-organizer: Sami Ullah