Northeast Bank of Brazil's Role in Promoting Entrepreneurial Innovation in Family Farming through Renewable Energy Financing
Bibliographic record
Abstract
Objective: This study aims to analyze the influence of Banco do Nordeste (Northeast Bank of Brazil) in promoting innovative entrepreneurship in family farming through its renewable energy financing policies. Method: A quantitative research methodology was employed, involving a survey of 164 family farmers located in the rural areas of Rio Grande do Norte. The collected data were subjected to confirmatory factor analysis (CFA) and multiple linear regression (MLR) to assess the relationships between entrepreneurial innovation, renewable energy adoption, and competitive advantage in family farming. Results: The findings demonstrate that innovative entrepreneurship facilitated by Banco do Nordeste's financing policies significantly contributes to 59.5% of the development in family farming. Furthermore, renewable energy adoption is shown to have a considerable influence, accounting for 39.3% of the entrepreneurial innovation by lowering operational costs and minimizing environmental impacts. These results underscore the critical role of Banco do Nordeste in supporting regional development and encouraging sustainable practices among family farmers. Conclusions: The study concludes that Banco do Nordeste’s renewable energy financing policies are instrumental in fostering entrepreneurial innovation and promoting the sustainable development of family farming. This emphasizes the need for increased awareness and dissemination of such financing programs to enhance their effectiveness. Additionally, the development of a framework for analyzing entrepreneurial innovation in family farming offers a valuable resource for future research and policy development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".