Rethinking Green versus Conventional Investment Flows in BRIC Countries: Review of Emerging Trends and a Model for Future Research
Bibliographic record
Abstract
The article explores the emerging trends and future potential for diverting capital flows from conventional to green activities in Brazil, Russia, India, China, Mexico, and South Africa (BRIC countries). At present, Chinese and Indian investors fund both environmentally unfriendly and green projects at a speedy pace, given these two countries’ high rates of gross fixed capital formation and general independence from external financial markets. By contrast, in Mexico, in South Africa, and especially in Brazil and Russia, environmentally sensitive projects to a considerable extent raise funds in the form of foreign loans. Meanwhile, in all BRIC countries except Russia, the bulk of green investment comes from domestic sources of funding. While recognizing the accomplishments of the previous research on the subject, the article identifies deficiencies in the available data. The author uses generalizations of evidence from case studies to propose a model for future econometric testing. It is hypothesized that 1) the longer the time horizon of the investment institution is, the sounder the environmental profile of its investments; 2) the more stringent and predictable the environmental regulations in host economies are, the longer the investor’s time horizon is; 3) financial institutions with open and publicly accountable ownership structure have a longer-term orientation than those with closed and opaque ownership; 4) investors’ interest and expertise in diversification beyond environmentally unfriendly industries extend their time horizon.
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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 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".