The Use of Sustainable Financial Instruments in Relation to the Social Impact Investment: ESG Policies, Capital Markets’ Approach and Investors’ Protection: An Innovative Perspective for a Global Surveillance Authority
Bibliographic record
Abstract
Social Impact Investment (SII) refers to the provision of finance to organizations addressing social needs. It is intended to achieve both a social impact as well as garner financial returns. Thus, SII may be understood as a business case for what was previously considered mostly philanthropic activities. In the case of SRI (Sustainable and Responsible Investment), investors decide in which company to invest on the basis of Environmental, Social and Governance (ESG) criteria. They consider a company's performance with respect to its effects on nature, its relationships with employees, clients and society, and the transparency of its governance. Where possible, companies should provide quantitative, comparable and forward-looking performance metrics to facilitate ESG integration. It is recommended that companies should use credible indicators which are included in internationally recognised reporting frameworks like GRI, SASB, IIRC, TCFD, CDP or UNGC. Companies should disclose their methodology and provide explanations to support quantitative indicators (ESG metrics), establishing the link between ESG and financial performance. The investor perspective is in important issue, which should be taken under consideration by European and local supervising authorities, under the provisions of the Market Abuse Directive and the MiFID, especially after the introduction and use of ESG and sustainable financial instruments, in both, Banking and Capital Markets. Therefore, SII can be a very useful tool for achieving EU Social Policy objectives. EU efforts (DG EMPL/FISMA) can be targeted at four specific aims that concern impact measurement: 1) promotion of the standardisation of impact measurement through the further development of the taxonomy regulations to include social objectives or the creation of standardised guidelines or the further development of ESG metrics; 2) capacity building in the area of impact measurement; 3) increasing the availability of the data needed; and 4) the promotion of additional and more specialized sustainable financial instruments linked to the Banking Sector and Capital Markets, which in turn will leverage more the total investment amount derived from the InvestEU tool and offer to all stakeholders more flexibility. Therefore, the global liquidity will be increased, and a strong economic turmoil could be avoided under the surveillance of a theoretically established Global Surveillance Authority. The use of sustainable financial instruments by a P.E.S. (social enterprise) under a public-private partnership (PPPs) is also feasible and socially rewarding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".