This thesis examines whether Sustainable Development Goals (SDG) labels, especially SDG 7 (Affordable and Clean Energy), function as meaningful informational signals in private capital markets where information asymmetry is common. Contextualized within the climate crisis and the energy transition, the study frames the financing gap as a core bottleneck. Because the energy transition cannot be financed by public resources alone, the mobilization of private capital is essential. Impact investing has thus emerged as a promising mechanism to bridge this gap, wherein labels and narratives shape how deals are evaluated. The thesis therefore focuses on impact investing in the European energy sector, asking whether SDG7-aligned deals are systematically associated with larger investment rounds. Building on signaling theory, the analysis moves beyond a simple SDG7 present/absent indicator and decomposes SDG7 into (i) label composition (SDG7 alone vs. bundled with other SDGs), (ii) label breadth (sdg_count), and (iii) SDG7 sub-target content (7.1–7.b), while also testing heterogeneity across investor types and funding stages. Empirically, the thesis uses an enriched Crunchbase-based dataset covering 406 European energy impact-investment rounds (244 firms) from 2015–2024, totaling approximately USD 9.52 billion in disclosed volume. The empirical analysis estimates OLS regressions in which the dependent variable is deal size (ln(amount_usd)), controlling for year and deal-context fixed effects and using firm-clustered robust standard errors; sub-target p-values are adjusted using the Benjamini–Hochberg FDR procedure. Results show that SDG7 presence alone does not yield a robust market-wide premium, whereas stronger associations emerge when SDG7 is bundled with other SDGs and when labeling breadth increases. At the content level, sub-target 7.2 is consistently linked to larger deal sizes, and the SDG7 relationship varies across investor and stage regimes. Practically, these findings imply that SDG labels are most informative when they are specific, measurable, and verifiable. Findings suggest companies to articulate SDG alignment more precisely, investors to evaluate deals based on label composition and content rather than binary labels, and policymakers to strengthen standards that enhance comparability and reduce labeling noise.
Questa tesi esamina se le etichette degli Obiettivi di Sviluppo Sostenibile (SDG), in particolare l’SDG 7 (Energia pulita e accessibile), funzionino come segnali informativi significativi nei mercati privati dei capitali, caratterizzati da elevata l’asimmetria informativa. Nel contesto della crisi climatica, il divario di finanziamento rappresenta un ostacolo importante: poiché la transizione energetica richiede più delle sole risorse pubbliche, mobilitare capitale privato diventa essenziale. In questo scenario, gli investimenti a impatto emergono come strumenti per colmare tale divario, con etichette e narrazioni che influenzano le valutazioni degli investitori. La tesi indaga quindi se, nel settore energetico europeo, le operazioni allineate all’SDG7 siano associate a round di investimento di maggiore entità. Sulla base della teoria del segnalamento, l’analisi va oltre un approccio binario e scompone l’SDG7 in tre dimensioni: (i) composizione (SDG7 da solo vs. in combinazione con altri SDG), (ii) ampiezza (sdg_count, ovvero numero di diversi SDG associati) e (iii) contenuto dei sotto- obiettivi (7.1–7.b), testando l'eterogeneità per investitore e fase. Empiricamente, si utilizza un dataset Crunchbase di 406 round europei (244 imprese, periodo dal 2015 al 2024) per circa 9,52 miliardi di USD. Le regressioni OLS stimano la dimensione dell’operazione (ln(amount_usd)) con effetti fissi di anno/contesto ed errori standard clusterizzati per impresa; i p-value sono corretti tramite Benjamini–Hochberg FDR. I risultati mostrano che la sola presenza dell’SDG7 non genera un premio robusto a livello di mercato. Associazioni più forti emergono combinando l’SDG7 con altri SDG e aumentandone l’ampiezza. In particolare, il sotto-obiettivo 7.2 “entro il 2030, aumentare notevolmente la quota di energie rinnovabili nel mix energetico globale” è associato a operazioni di maggiore entità, e la relazione varia per investitore e fase. In pratica, le etichette risultano più informative se specifiche, misurabili e verificabili: si suggerisce alle imprese di articolare con precisione l’allineamento, agli investitori di valutarne il contenuto oltre l'etichetta binaria, e ai decisori di rafforzare gli standard al fine di ridurre il rumore.
SDG labels as signals: an empirical test of the signaling value of SDG7 in european energy impact investments
Yagci, Mehmet
2024/2025
Abstract
This thesis examines whether Sustainable Development Goals (SDG) labels, especially SDG 7 (Affordable and Clean Energy), function as meaningful informational signals in private capital markets where information asymmetry is common. Contextualized within the climate crisis and the energy transition, the study frames the financing gap as a core bottleneck. Because the energy transition cannot be financed by public resources alone, the mobilization of private capital is essential. Impact investing has thus emerged as a promising mechanism to bridge this gap, wherein labels and narratives shape how deals are evaluated. The thesis therefore focuses on impact investing in the European energy sector, asking whether SDG7-aligned deals are systematically associated with larger investment rounds. Building on signaling theory, the analysis moves beyond a simple SDG7 present/absent indicator and decomposes SDG7 into (i) label composition (SDG7 alone vs. bundled with other SDGs), (ii) label breadth (sdg_count), and (iii) SDG7 sub-target content (7.1–7.b), while also testing heterogeneity across investor types and funding stages. Empirically, the thesis uses an enriched Crunchbase-based dataset covering 406 European energy impact-investment rounds (244 firms) from 2015–2024, totaling approximately USD 9.52 billion in disclosed volume. The empirical analysis estimates OLS regressions in which the dependent variable is deal size (ln(amount_usd)), controlling for year and deal-context fixed effects and using firm-clustered robust standard errors; sub-target p-values are adjusted using the Benjamini–Hochberg FDR procedure. Results show that SDG7 presence alone does not yield a robust market-wide premium, whereas stronger associations emerge when SDG7 is bundled with other SDGs and when labeling breadth increases. At the content level, sub-target 7.2 is consistently linked to larger deal sizes, and the SDG7 relationship varies across investor and stage regimes. Practically, these findings imply that SDG labels are most informative when they are specific, measurable, and verifiable. Findings suggest companies to articulate SDG alignment more precisely, investors to evaluate deals based on label composition and content rather than binary labels, and policymakers to strengthen standards that enhance comparability and reduce labeling noise.| File | Dimensione | Formato | |
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2026_03_Yagci.pdf
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https://hdl.handle.net/10589/252597