Makine Öğrenimi Analizi Yoluyla OECD Ekonomilerinde Yeşil Teknoloji Yayılımının Tahmini

Year 2024, Volume: 9 Issue: 3, 484 - 502, 30.09.2024

Büşra Ağan

https://doi.org/10.30784/epfad.1512266

Abstract

Sürdürülebilir kalkınmaya doğru hızlanan küresel değişim, yeşil teknolojilerin yaygınlaşmasını, özellikle OECD ekonomileri içinde, kritik bir odak alanı haline getirmiştir. Bu çalışma, OECD ülkeleri genelinde yeşil teknolojinin gelecekteki yaygınlaşmasını keşfetmek için bir makine öğrenmesi yaklaşımı kullanmayı amaçlamaktadır. Farklı uluslar arasında değişen yeşil teknoloji yaygınlaşma (GTD) oranlarını vurgulayarak 2023'ten 2037'ye kadar ayrıntılı tahminler sunmaktadır. Bunu başarmak için, yeşil teknolojinin ilerlemesinin nasıl tahmin edilebileceğine dair yeni kanıtlar sunmak için Otoregresif Entegre Hareketli Ortalama (ARIMA) modeli kullanılmaktadır. Çalışma, ampirik verilere dayanarak ülkeleri yüksek, orta ve düşük GTD büyümesi olarak kategorize etmektedir. Bulgular, Japonya, Almanya ve ABD'nin GTD üzerinde önemli bir büyüme yaşayacağını, Avustralya, Kanada ve Meksika gibi ülkelerin ise orta düzeyde artışlar göreceğini göstermektedir. Tersine, İrlanda ve İzlanda dahil olmak üzere bazı uluslar, düşük veya negatif GTD değerleriyle zorluklarla karşı karşıyadır. Çalışma, bu makine öğrenmesi modelinin uygulanmasının, kendi ülkelerinde yeşil teknoloji benimsenmesini artırmayı amaçlayan politika yapıcılar için değerli içgörüler ve gelecek tahminleri sağladığı sonucuna varmıştır.

Keywords

Sürdürülebilir Kalkınma, Yeşil Teknoloji Yayılımı, Çevresel Sürdürülebilirlik, Makine Öğrenimi Analizi, ARIMA Modeli

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

Sustainable Development, Green Technology Diffusion, Environmental Sustainability, Machine Learning Analysis, ARIMA Model

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