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When does it pay to be green for startups? Sustainability signaling and venture funding

Markus Koenigsmarck et al.

Journal of International Financial Markets, Institutions and Money2026https://doi.org/10.1016/j.intfin.2025.102284article
AJG 3ABDC A
Weight
0.50

Abstract

This study examines the relationship between sustainability and financial performance for startups. Startups must decide how to integrate sustainability considerations into their business model early on. Thereby, they suffer from scarcer resources compared to large firms and liability of newness, while familiar sustainability data are unavailable. Based on stakeholder and signaling theory, we test this relationship on a dataset of over 27,000 startups, combining Crunchbase data with sustainability signaling data – generated via natural language processing – as a sustainability indicator. We find a robust ∪-shaped connection between sustainability signaling and venture funding, with the most and least sustainable startups attracting more funding than their peers. This pattern is persistent for just-green and just-brown subsamples, suggesting investors discriminate for strategic sustainability differentiation in general and within subgroups. Our findings confirm the crucial role of strategic differentiation for startups, extending it to sustainability.

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https://doi.org/https://doi.org/10.1016/j.intfin.2025.102284

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@article{markus2026,
  title        = {{When does it pay to be green for startups? Sustainability signaling and venture funding}},
  author       = {Markus Koenigsmarck et al.},
  journal      = {Journal of International Financial Markets, Institutions and Money},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.intfin.2025.102284},
}

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When does it pay to be green for startups? Sustainability signaling and venture funding

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.