METHODOLOGICAL FOUNDATIONS OF COUNTRIES' GREEN ECONOMY POLICY IMPLEMENTATION BASED ON CLUSTER ANALYSIS

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🔬研究者:The SOM-based clustering framework provides a replicable methodology for cross-country decarbonization benchmarking and dynamic policy analysis.

🏢実務担当者:Corporate sustainability teams can use the clustering results to identify peer countries for benchmarking energy transition strategies.

🏛政策担当者:The paper offers a data-driven method for designing differentiated national decarbonization pathways based on country profiles.

Background. In the context of global climate policy, it is necessary to systematize the countries by CO₂ emissions, energy consumption structure and economic development to identify typical patterns of trajectories of transition to a low-carbon economy. For this purpose, the authors perform country clustering by decarbonization potential using Kohonen self-organizing maps (SOMs). To achieve this goal, the study focuses on the following tasks: generating a dataset of economic, energy, and environmental indicators to study decarbonization processes across countries over time; processing data (filling gaps, normalization), and eliminating multicollinearity; algorithmizing the SOM construction, taking into account the stages of training and hyperparameter tuning; determining a method for optimizing the number and validating the composition of clusters based on quantitative metrics, taking into account their semantic consistency and economic content; identifying country clusters based on decarbonization profiles and key factors for achieving renewable energy development targets and emission reductions; and analyzing the dynamic trajectories of countries on the map over time to formulate practical recommendations for decarbonization policy. Methods. Self-organizing maps were used to cluster countries by decarbonization potential. This tool allows modeling nonlinear relationships and taking into account high data dimensionality, providing flexible and accurate segmentation and a more effective means for analytical research compared to other clustering methods. The article included hyperparameter tuning, preparation and normalization of the input dataset (which consists of 14 indicators of a country's socio-economic, energy, and environmental sectors, which collectively contribute to its decarbonization potential, covering the period 2013–2022 for 40 countries), and cluster validation using series of quantitative metrics (the Silhouette coefficient, the Davies-Bouldin and Calinski-Harabasz indices). Results. The six clusters of countries with different decarbonization profiles (leaders-decarbonization hubs, followers, resource-intensive economies, producing countries, hydrocarbon-oriented countries, and large industrial countries) have been identified in the study. Based on the clustering results, the authors assessed the dynamic trajectories of countries over time and identified key indicators that influence the achievement of renewable energy targets and emission reductions. In addition, the original approach based on using Kohonen maps in scenario modelling, which shows how simple clustering turns into practical scenario analysis and reveals which structural changes are most critical for achieving a country's target profile has been proposed. Within the framework, the analysis indicates that for Ukraine, improving global positioning by increasing the share of renewable energy sources in the energy sector can ensure a transition to follower or leader clusters. Conclusions. The use of SOM provides an effective tool for formulating strategic recommendations for the development of a low-carbon economy and the optimization of national energy policy.

• openalex https://doi.org/10.17721/1728-2667.2026/229-2/8first seen 2026-07-24 05:30:28