Decarbonization of Indian Banking: Challenges & Pathways Forward
Bibliographic record
Abstract
This study examines the challenges for decarbonizing the Indian banking sector, a critical step in India's commitment to the Paris Agreement. The objective is to assess the current status of decarbonization, identify key challenges, and propose strategic solutions to facilitate sustainable banking practices. In this context, the research employs a qualitative approach, analyzing secondary data from literature, policy documents, and industry reports. The novelty of the research lies in its comprehensive assessment of the multifaceted challenges financial, regulatory, technological, and socio-cultural specific to the Indian context. It provides a nuanced understanding of the sector's progress and the barriers it faces, which is crucial for policymakers and banking institutions. The findings reveal a mixed landscape of decarbonization efforts, with some banks successfully adopted sustainable banking practices, while others struggle with scaling and operational constraints. Key findings indicate that while some Indian banks have made strides in adopting green practics, the sector as a whole is hindered by high costs of green technologies, regulatory uncertainty, outdated technological systems, and a culture resistant to change. The study highlights the need for clear policies, investment in technology upgradation, and a cultural shift towards sustainability within the banking sector.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".