Modelo estratégico para avaliação do impacto das práticas esg no valuation de startups deep tech

Abstract

Despite the growing prominence of ESG practices in recent years, a relevant gap remains in the scientific literature and in business practice regarding their translation into measurable financial effects, particularly in the context of startups. This limitation is even more evident in Deep Tech startups, whose characteristics of high technological intensity, elevated risk, and long development cycles challenge traditional valuation models. In this context, this thesis aims to develop a strategic model to assess the impact of ESG practices on the valuation of Deep Tech startups. The study was organized into five phases, including: (i) a systematic literature review to identify the mechanisms through which ESG practices generate competitive advantages and how these mechanisms translate into financial levers; (ii) a normative analysis of ABNT PR 2030, the GRI standards, and ISO IWA 48, aimed at consolidating ESG practices adapted to the Deep Tech sector; (iii) the prioritization and estimation of the impact of these practices through a questionnaire answered by 92 experts using the Fuzzy Delphi Method; (iv) the integration of the resulting coefficients into a Discounted Cash Flow (DCF) model; and (v) the validation of the model in a real Deep Tech startup, accompanied by the development of the “ESG Valuation” web application, which operationalizes all stages. The results indicate that ESG practices exert a direct influence on growth, risk, operational efficiency, and the financial performance of startups. In the analyzed case study, their incorporation resulted in a 68.35% increase in the company’s valuation compared to the scenario without ESG, confirming the economic impact of these practices in the Deep Tech context. It is concluded that the proposed model offers an applicable method to support managers and investors in the structured incorporation of ESG practices into the strategic decision-making processes of Deep Tech startups.