THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SUSTAINABLE FOREST MANAGEMENT: A CASE STUDY OF GOVERNMENT AND EDUCATIONAL SECTORS IN PAKISTAN
Bibliographic record
Abstract
THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SUSTAINABLE FOREST MANAGEMENT: A CASE STUDY OF GOVERNMENT AND EDUCATIONAL SECTORS IN PAKISTAN 1. Dr. Muhammad Irshad Arshad, M. Sc Forestry, Ph. D. Wildlife Management. Director, Punjab Wildlife Department, Faisalabad, Punjab, Pakistan 2. Munir Ahmed Dar, M.Sc. (Forestry), Forestry Specialist Publisher & Chief Editor, CJFR, Canada Ø DOI: 10.5281/zenodo.17576085 Ø ORCID ID: https://orcid.org/0009-0007-1445-4176 Ø Google Scholar: https://scholar.google.ca/citations?user=7qq7WEkAAAAJ&hl=en Ø ResearchGate ID: https://www.researchgate.net/profile/Munir-Dar-3?ev=hdr_xprf Ø Clarivate Web of Science Researcher ID: OHV-2983-2025 Ø Academia.edu Scholar: https://yorku.academia.edu/munirdar Keywords: Artificial Intelligence, Sustainable Forest Management, Pakistan, Remote Sensing, National AI Policy, Deforestation, Climate Change, Digital Governance. Abstract: The escalating threats of deforestation, climate change, and illegal activities necessitate technological innovation in Pakistan's forestry sector. This paper examines the nascent integration of Artificial Intelligence (AI) tools and Machine Learning (ML) techniques within governmental conservation initiatives and academic curricula across the country, driven by the National AI Policy 2025 (Ministry of Information Technology and Telecommunication (MoITT), 2025). Drawing on qualitative reviews of policy documents, pilot projects (e.g., the Smart Forest initiative), and educational frameworks, we analyze current capacity, systemic barriers (including data fragmentation and high infrastructure costs), and prospects for AI adoption. Findings indicate that government-led adoption, primarily for real-time monitoring and disaster detection, is underway. However, this progress is hampered by significant deficiencies in geospatial data infrastructure, a crucial human capital gap in specialized AI-forestry skills, and a lack of sustainable domestic financing. The paper concludes by proposing a multi-pronged roadmap focusing on data standardization, interdisciplinary education, and innovative finance models to ensure AI becomes a scalable, foundational tool for Pakistan's ecological security and sustainable forest management (SFM).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".