A model proposal for analysis of energy performance of world countries

Achieving sustainability goals requires making energy use worldwide more efficient and environmentally friendly, as the energy sector plays a central role in both economic development and environmental protection. In this context, accurately analysing energy performances is critical for the efficient use of resources and the development of sustainable policies. In this study, the Data Envelopment Analysis (DEA) method is preferred to analyse the energy performances of the world countries within the scope of 2030 sustainability goals. The study differs from other studies in the literature in that it evaluates the energy performance of 100 countries using an extended input and output set. Within the scope of the model, various inputs such as renewable energy consumption, labour force, gross capital formation, and diversity of electricity generation sources are used, while carbon dioxide (CO₂), methane (CH₄), and diazot monoxide (N₂O) emissions are considered as outputs. Although most of the data is available until 2020, the Random Forest algorithm, one of the machine learning methods, was utilised to complete the missing years, and the forecasts were extended until 2022. This innovative approach addresses data gaps and allows for a more precise and reliable assessment of energy performances. The results of the study shed light on the development of energy policies and sustainability strategies. Especially for low-performing countries, important information is provided for determining targeted improvement strategies. Moreover, the findings emphasise the importance of energy efficiency in diversifying renewable energy sources and reducing carbon emissions. In this respect, the study aims to be a source of guidance in the development of energy policies.