Public R&D support, financial instruments, and energy start-up ecosystems in Europe: Evidence on non-linear and delayed effects
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DOIhttp://dx.doi.org/10.21511/imfi.23(2).2026.31
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Article InfoVolume 23 2026, Issue #2, pp. 424-441
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Type of the article: Research Article
Abstract
The transition to climate neutrality in Europe increasingly depends on the capacity of public policy to stimulate innovation, entrepreneurship, and investment in the energy sector. This study aims to examine whether, and under what conditions, public R&D support and finance-related policy instruments are associated with energy-related start-up activities and financing across European and neighboring economies, while accounting for delayed, non-linear, and country-specific effects. The analysis is based on panel data for 37 countries over the period 2018–2023. The study employs fixed-effects, PPML, lagged, and quadratic specifications. The findings suggest weak short-term associations between policy support and energy start-up outcomes, while several delayed and non-linear patterns emerge. Two-year lagged estimations reveal significant delayed associations: government support is negatively associated with green start-ups (–0.0072) and digital start-ups (–0.0114), which may reflect crowding-out mechanisms or reactive policy behaviour, although these explanations are not directly tested. In contrast, finance-related support is positively associated with digital energy start-ups after two years (0.0225). Funding models show weaker transmission effects, although government support is negatively associated with early-stage green funding (–0.0605). Quadratic specifications reveal meaningful thresholds for government support at 51.33, 93.71 and 107.39 in baseline models and between 80.80 and 158.28 points in lagged models. The results suggest that policy–start-up relationships vary by timing, intensity, support type, and start-up segment. This highlights the scientific value of analyzing public support as a delayed, non-linear and context-dependent mechanism rather than as a simple direct stimulus.
Acknowledgment
This article was prepared based on the results of the project 101127491-EnergyS4UA-ERASMUS-JMO2023-HEI-TCH-RSCH. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or European Education and Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
- Keywords
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JEL Classification (Paper profile tab)O38, Q42, Q55, G28, C23
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References44
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Tables11
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Figures0
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- Table 1. Poisson fixed-effects estimation results for energy start-ups
- Table 2. Lagged Poisson fixed-effects estimation results for energy start-ups
- Table 3. Log-linear fixed-effects estimation results for funding variables
- Table 4. Quadratic fixed-effects estimation results
- Table 5. Turning point analysis of quadratic models
- Table 6. Lagged quadratic fixed-effects estimation results (t − 1)
- Table 7. Lagged quadratic fixed-effects estimation results (t − 2)
- Table 8. Turning point analysis for lagged quadratic models (t − 1)
- Table 9. Turning point analysis for lagged quadratic models (t − 2)
- Table A1. Descriptive statistics of variables
- Table B1. Lagged log-linear fixed-effects estimation results for funding variables
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