A Sustainability-Oriented Spatial Multi-Criteria Decision Analysis Framework for Optimizing Recreational Ecological Park Development
Bibliographic record
Abstract
Recreational Ecological Parks (REPs) play a pivotal role in advancing sustainable development by promoting ecotourism, conserving biodiversity, and providing inclusive recreational opportunities with minimal environmental impact. Assessing the potential for REP development is crucial to optimize the sustainable use of natural resources and enhance ecological and socio-economic benefits. This study introduces a sustainability-oriented Spatial Multi-Criteria Decision Analysis (SMCDA) framework to evaluate and optimize REP development in Mazandaran Province, Iran. The framework incorporates 33 criteria across five categories: 13 for network connectivity and accessibility; 10 for facilities, services, and tourism potential; 4 for landform and land use; 3 for natural hazards; and 3 for climate conditions. Criteria were standardized using the minimum–maximum method and weighted based on expert input via the Best–Worst Method. A weighted linear combination approach was then applied to generate REP suitability maps. Existing recreational and tourism (R&T) sites were assessed against these maps, and a non-parametric bootstrapping method quantified uncertainties in suitability classifications. The results revealed that approximately 8%, 17%, 26%, 30%, and 19% of the study area fell into very low, low, moderate, high, and very high suitability classes, respectively. Among 151 R&T sites, 33% and 34% were situated in areas classified as very high and high suitability. Prediction rates were most accurate in the very high suitability category, while uncertainty analysis indicated a mean of 13% and a standard deviation of 1.7%, with uncertainties predominantly concentrated in lower suitability classes. The findings underscore the SMCDA framework’s efficacy in guiding sustainable REP development by identifying optimal sites and managing uncertainties. This study contributes to sustainability by integrating ecological, economic, and social dimensions into decision-making processes, thereby fostering resilience and long-term environmental stewardship in tourism planning.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".