The Economic Impact of Forest Hydrological Services on Local Communities: A Case Study from the Western Ghats of India
Bibliographic record
Abstract
The conventional wisdom that 'more forest is always better' has dominated policy making in the management of forested watersheds. In the context of the supposed hydrological regulation service provided by forest ecosystems, however, hydrologists have debated this assumption for more than two decades. Unfortunately, detailed studies of the relationship between forest cover, hydrology and the economic use of water have been relatively scarce, especially in the tropical forests of South Asia. Building upon a larger research project at four sites in the Western Ghats of peninsular India, this study examines the link between stream flow, agricultural water use an economic returns to agriculture. The study attempts to simulate the likely impacts of regeneration of a degraded forest catchment on stream flow and the consequent impact on irrigation tank based agriculture in a downstream village. The authors find that regeneration of forests would reduce the ratio of runoff to rainfall in the forested catchment thereby significantly reducing the probability of filling the well-used irrigation tank. This in turn reduces the probability of the command area farmers being able to cultivate an irrigated paddy crop, particularly in the summer season, thereby reducing expected farm income as well as wage income for landless and marginal landowning households. The study results seem counter intuitive to conventional wisdom. This result is, however, not because the hydrological relationships in this region are peculiar, but because the community immediately downstream of the forest is using water in a particular manner, viz., through irrigation tanks for growing water-intensive crops. The main implication is that policymakers must move away from simplistic notions of forests being good for everything and under all circumstances, and facilitate context-specific, ecologically and economically informed forest governance.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".