Corporation financialization and its impact on green project investments: A systematic literature review
Bibliographic record
Abstract
Sustainable Development Goals (SDGs) 8, 9, 12, 13, and 16 highlight the importance of industrial innovation to align the firms’ financial goals with sustainable green investments. There is a vast literature investigating the trade-off between Corporation Financialization (CF) and green project investments. This study reviews such literature by following a systematic review approach of PRISMA and selected 90 research papers from the Scopus database published during 2011-2025. The findings suggest that CF has a deep impact on firms’ innovation and sustainability behavior, which is largely influenced by governance structures, managerial incentives, and strategic priorities. The literature informs that excessive CF usually discourages investments in innovation and green projects. Firms’ CF activities prioritize shareholder value over green project investments. However, environmental regulations, green finance initiatives, and carbon market mechanisms change the CF behavior of firms in highly polluting industries with strong governance. Policy uncertainty reduces the incentive for green project investments. Moreover, firms’ financial constraints increase CF and reduce investments in green projects. However, digitalization and technological change help to increase green investments. Moreover, state-owned firms are more active in green project investments compared to private firms. In addition, the government's stringent environmental regulations and governance reforms help to mitigate CF’s crowding-out effect on green project investments. The literature provides policy implications to integrate sustainability into core financial strategies by aligning investment decisions with long-term environmental goals, which can be achieved by strengthening governance, adopting green finance frameworks, and influencing firms’ strategic financial management in favor of green projects.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".