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Hierarchical Mixed-Effects Modeling of Socioeconomic Factors Influencing Renewable Energy Adoption: A Case Study in the Western Balkans

Author

Listed:
  • Raco, Endri

    (Department of Mathematical Engineering, Polytechnic University of Tirane, Tirana, Albania)

  • Zaçaj, Oriana

    (Department of Mathematical Engineering, Polytechnic University of Tirane, Tirana, Albania)

  • Haxhi, Kleida

    (Department of Mathematical Engineering, Polytechnic University of Tirane, Tirana, Albania)

  • Beliu (Llagami), Etleva

    (Department of Mathematical Engineering, Polytechnic University of Tirane, Tirana, Albania)

Abstract

The Western Balkans encounter significant challenges in their pursuit of EU accession, necessitating accelerated renewable energy deployment and enhancement of energy security to achieve the objectives of the European Green Deal. Three-tier models evaluate renewable energy capacity data from IRENA covering the period from 2000 to 2024, with World Bank socioeconomic indexes and EU accession criteria. Spatial correlation structures encapsulate technological diffusion impacts among proximate nations, employing LASSO regularization for variable selection and leave- one-country out cross-validation for model validation. The advancement of EU candidacy is strongly associated with renewable growth (2010-2024). Serbia (3,413 MW), Albania (2,827 MW), and North Macedonia (1, 633 MW) have the highest absolute capacity levels, driving regional development. The amount of renewable energy that can be used in the region grew by 98% from 5, 709 MW in 2000 to 11,305 MW in 2024. Hierarchical mixed- effects models can explain 79.6% of the differences in deployment patterns, estimating a controlled annual growth rate of 5.3% expansion in each region every year (P < 0.001). The progression of economic and institutional structures via EU accession promotes adoption, while spatial dynamics suggest that coordinated policies may improve efficiency, thus informing strategies for post-transition economies.

Suggested Citation

  • Raco, Endri & Zaçaj, Oriana & Haxhi, Kleida & Beliu (Llagami), Etleva, 2025. "Hierarchical Mixed-Effects Modeling of Socioeconomic Factors Influencing Renewable Energy Adoption: A Case Study in the Western Balkans," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 15(6), pages 460-467, October.
    • Handle: RePEc:eco:journ2:v:15:y:2025:i:6:id:21086
    DOI: 10.32479/ijeep.21086
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    References listed on IDEAS

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    1. Zafar, Muhammad Wasif & Shahbaz, Muhammad & Sinha, Avik & Sengupta, Tuhin & Qin, Quande, 2020. "How Renewable Energy Consumption Contribute to Environmental Quality? The Role of Education in OECD Countries," MPRA Paper 100259, University Library of Munich, Germany, revised 08 May 2020.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Aguirre, Mariana & Ibikunle, Gbenga, 2014. "Determinants of renewable energy growth: A global sample analysis," Energy Policy, Elsevier, vol. 69(C), pages 374-384.
    4. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    5. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Li, Na & Liu, Feng, 2022. "Spatiotemporal characteristics and influencing factors of renewable energy production in China: A spatial econometric analysis," Energy Economics, Elsevier, vol. 116(C).
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