Are regional public budgets associated with renewable energy development? Evidence from Ukraine

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Type of the article: Research Article

Abstract
The growing importance of renewable energy in ensuring energy security and sustainable development has increased attention to the role of public finance, particularly at the regional level. This study aims to assess whether different categories of regional public expenditure are associated with renewable energy development in Ukraine, distinguishing between installed capacity and electricity generation. The analysis is based on a balanced panel dataset for 25 Ukrainian regions over 2018–2021 and applies two-way fixed effects models with lagged specifications and Driscoll–Kraay standard errors. The results show that expenditures on electric transport exhibit the strongest positive association with installed renewable energy capacity (β ≈ 0.078, p < 0.001), followed by SME support (β ≈ 0.025, p < 0.001), other environmental activities (β ≈ 0.017, p < 0.001), and natural resource management (β ≈ 0.013, p < 0.001). In contrast, most general economic expenditures are not statistically significant, suggesting that these expenditure categories are not statistically associated with higher renewable energy development within the analyzed period. For renewable electricity production, contributions to the statutory capital of enterprises are positively associated (β ≈ 0.006, p < 0.05), while co-financing of investment projects is negatively associated (β ≈ −0.027, p < 0.001), reflecting implementation lags. Additionally, capital investments in environmental protection are negatively associated with renewable electricity production (β ≈ −0.072, p < 0.001), suggesting that installed capacity expansion differs from renewable electricity production.

Acknowledgment
The authors acknowledge funding from the Swiss National Science Foundation (SNSF) [Grant No. IZURZ1_224119]. The authors bear sole responsibility for the conclusions and results of the research.

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    • Figure 1. Leave-one-region-out sensitivity of the lagged electric-transport coefficient in the installed-capacity model
    • Table 1. Two-way fixed effects regression results (non-lagged models)
    • Table 2. Two-way fixed effects estimates (lagged specification, (t – 1)) with Driscoll–Kraay standard errors
    • Table 3. Robustness analysis: Lagged two-way fixed effects models with region-clustered standard errors (Arellano HC1)
    • Table 4. Robustness analysis: Reduced lagged two-way fixed effects models
    • Table A1. Descriptive statistics of variables used in the empirical analysis
    • Conceptualization
      Serhiy Lyeonov, Nadiya Kostyuchenko, Denys Smolennikov, Inna Tiutiunyk, Oleksandr Telizhenko
    • Data curation
      Serhiy Lyeonov, Inna Tiutiunyk
    • Formal Analysis
      Serhiy Lyeonov, Inna Tiutiunyk
    • Investigation
      Serhiy Lyeonov
    • Methodology
      Serhiy Lyeonov
    • Project administration
      Serhiy Lyeonov
    • Software
      Serhiy Lyeonov, Oleksandr Telizhenko
    • Supervision
      Serhiy Lyeonov
    • Validation
      Serhiy Lyeonov, Denys Smolennikov
    • Visualization
      Serhiy Lyeonov, Nadiya Kostyuchenko
    • Writing – original draft
      Serhiy Lyeonov, Nadiya Kostyuchenko, Denys Smolennikov, Inna Tiutiunyk, Oleksandr Telizhenko
    • Writing – review & editing
      Serhiy Lyeonov, Nadiya Kostyuchenko, Denys Smolennikov, Inna Tiutiunyk, Oleksandr Telizhenko
    • Funding acquisition
      Nadiya Kostyuchenko, Denys Smolennikov, Inna Tiutiunyk, Oleksandr Telizhenko
    • Resources
      Nadiya Kostyuchenko, Denys Smolennikov, Inna Tiutiunyk, Oleksandr Telizhenko