Forecasting Green Technology Diffusion in OECD Economies Through Machine Learning Analysis

Abstract

An accelerating global shift towards sustainable development has made the diffusion of green technologies a critical area of focus, particularly within OECD economies. This study aims to use a machine-learning approach to explore the future diffusion of green technology across OECD countries. It provides detailed forecasts from 2023 to 2037, highlighting the varying rates of green technology diffusion (GTD) among different nations. To achieve this, the Autoregressive Integrated Moving Average (ARIMA) model is employed to offer new evidence on how the progress of green technology can be predicted. Based on empirical data, the study categorizes countries into high, moderate, and low GTD growth. The findings suggest that Japan, Germany, and the USA will experience significant growth in GTD, while countries like Australia, Canada, and Mexico will see moderate increases. Conversely, some nations, including Ireland and Iceland, face challenges with low or negative GTD values. The study concludes that applying this machine-learning model provides valuable insights and future predictions for policymakers aiming to enhance green technology adoption in their respective countries.

Keywords

• sürdürülebilir kalkınma
• yeşil teknoloji yayılımı
• çevresel sürdürülebilirlik
• makine öğrenimi analizi
• arima modeli
• sustainable development
• green technology diffusion
• environmental sustainability
• machine learning analysis
• arima model