The role of stakeholders and AI-based city forest management strategies in increasing public awareness of city forest ecosystem services and its implications for the success of the ecotourism program
Bibliographic record
Abstract
Urban forest management has a strategic role in maintaining ecosystem balance and supporting the quality of life of urban communities. Urban forests provide various ecosystem services, such as providing clean air, temperature regulation, carbon storage, and being a habitat for biodiversity. In the midst of increasing urbanization and climate change, the existence of urban forests is increasingly important to support environmental sustainability. However, public awareness of the importance of urban forests and the ecosystem benefits they provide is often inadequate. This is a challenge in efforts to conserve and develop urban forests as an integral part of the urban environment. Palangkaraya City, Central Kalimantan, the Himba Kabui ecotourism program has been launched as an initiative to increase public awareness of urban forests and the ecosystem benefits, they provide. This research aims to analyze the role of stakeholders and artificial intelligence (AI)-based urban forest management strategies in improving public awareness of urban forest ecosystem services and their impact on the success of the Himba Kabui ecotourism program in Palangkaraya City, Central Kalimantan. Stakeholders involved include government, local communities, the private sector, and academia, each of which has an important role in the implementation and development of this program. The use of AI in urban forest management offers various innovations in monitoring, decision making, and conveying information to the public. This study found that the integration of AI technology was able to accelerate increasing public awareness of the importance of urban forests as providers of ecosystem services, such as providing clean air, climate regulation and biodiversity habitat. Additionally, this increased awareness contributes significantly to the success of the Himba Kabui ecotourism program, which aims to promote sustainable tourism and environmental conservation in the region. This research provides the implication that synergy between stakeholders and AI technology can become an effective and sustainable urban forest management model, which not only has a positive impact on the environment but also on the local economy through increasing tourism. It is hoped that these findings can become a reference for urban forest managers and policy makers in developing innovative and participatory environmental management strategies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".