Sustainability assessment of start-ups with the ESG Starter and the GHG & Impact Estimator
Bibliographic record
Abstract
The background paper explains the methodology of the developed tools ESG Starter and GHG & IMPACT Estimator, which can be used to assess the sustainability of start-ups. Background: Sustainability assessment of start-ups Assessing the ecological and social impact of start-ups is associated with a high degree of uncertainty and considerable effort. As a result, sustainability-oriented aspects are often not given sufficient consideration in investment decisions. As a result, promising start-ups with high sustainability potential and social benefits are often not ecognized enough and do not receive sufficient financial support (Fichter et al., 2024), while capital may flow into less sustainable or less impactful companies or not into young innovative companies at all. This misallocation of capital means that start-ups with high sustainability potential often face greater challenges in further developing and scaling their ideas and thus realizing their impact potential due to insufficient appreciation of their positive externalities. A directionally sound sustainability assessment makes it possible to better channel capital towards social goals and innovation policy „missions“ (BMBF, 2023) and to increase the chances that innovative solutions for overcoming ecological and social challenges will successfully establish themselves on the market. Special considerations when evaluating start-ups Start-ups are in the initial phases of company and business development. Their products, services and business models are usually still in their infancy and will change considerably in the future due to their innovative nature and the need to find the right „market fit“. As a result, the impact on sustainability can often only be estimated on the basis of assumptions and plausible scenarios. Especially in the early phases, start-ups lack established value chains and historical data that can prove their performance and effects (outcomes and impacts). Compared to large companies or established SMEs, startups also have significantly fewer resources and capacities to deal intensively with sustainability issues and their evaluation. Against this backdrop, there are three key peculiarities when assessing the sustainability impact of start-ups: Firstly, the focus of the assessment cannot usually be on the impacts of a start-up that have already occurred and are measurable, but rather on the sustainabilitypotential - i.e. the future contributions to ecological, social and economic sustainability. Secondly, it is only practicable to integrate and evaluate sustainability aspects at an early stage if this is possible for both the start-up itself and external takeholders (e.g. investors, start-up funding programs) with reasonable effort and provides information relevant to decision-making. Thirdly, an approach is suitable for evaluating a startup if it can be applied flexibly in different phases, sectors and situations (DIN SPEC 90051-1 consortium, 2020).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".