Economic Valuation of Sustainable Lake Tourism at Lake Maninjau, Indonesia: A Combined CVM and TCM Analysis
Bibliographic record
Abstract
Lake-based tourism provides significant recreational and ecological services but faces environmental pressures that threaten long-term sustainability.Lake Maninjau in West Sumatra exemplifies this tension between tourism development and environmental degradation from intensive aquaculture.This study estimated the economic value of sustainable lake tourism at Lake Maninjau using integrated valuation methods to inform sustainable development strategies.A mixed-method approach combined Contingent Valuation Method (CVM) and Travel Cost Method (TCM), with 397 visitors surveyed between July and August 2025.Logistic regression analyzed willingness to pay determinants, while Poisson regression estimated recreational demand.71.3% of visitors expressed willingness to pay for conservation, with mean WTP of IDR 23,500 per visitor (CVM) and consumer surplus of IDR 55,000 per visit (TCM), yielding annual recreational values of IDR 1.37-3.21billion.Income, education, environmental perceptions, and visit frequency significantly influenced WTP.The dual-method approach revealed substantial economic potential for sustainable tourism, with revealed preferences (TCM) exceeding stated preferences (CVM).Tourism can provide viable economic alternatives to environmentally harmful practices, but requires transparent governance and community participation to ensure sustainable development outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".