Assessing the efficiency of renewable energy policies: A DEA, machine learning, and panel data approach

Maksym W. Sitnicki, Serhiy Lyeonov, Dmytro Kurinskyi, Oleksii Havrylenko

Open source

DOI: 10.21511/ee.17(2).2026.17
Published: 2026-06-24
Container: Environmental Economics
Publisher: LLC CPC Business Perspectives
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.21511/ee.17-2-.2026.17,
  title = {Assessing the efficiency of renewable energy policies: A DEA, machine learning, and panel data approach},
  author = {Maksym W. Sitnicki and Serhiy Lyeonov and Dmytro Kurinskyi and Oleksii Havrylenko},
  year = {2026},
  journal = {Environmental Economics},
  doi = {10.21511/ee.17(2).2026.17},
  url = {https://doi.org/10.21511/ee.17(2).2026.17}
}

RIS

TY  - JOUR
TI  - Assessing the efficiency of renewable energy policies: A DEA, machine learning, and panel data approach
AU  - Maksym W. Sitnicki
AU  - Serhiy Lyeonov
AU  - Dmytro Kurinskyi
AU  - Oleksii Havrylenko
PY  - 2026
JO  - Environmental Economics
DO  - 10.21511/ee.17(2).2026.17
UR  - https://doi.org/10.21511/ee.17(2).2026.17
ER  -

APA

Sitnicki, M. W., Lyeonov, S., Kurinskyi, D., & Havrylenko, O. (2026). Assessing the efficiency of renewable energy policies: A DEA, machine learning, and panel data approach. Environmental Economics. https://doi.org/10.21511/ee.17(2).2026.17

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crossref · retrieved 2026-09-27T04:49:37.129Z