Sustainable Practices and Environmental Impact Assessment in a Lifelong Learning Center
Bibliographic record
Abstract
Climate change is a major environmental challenge that should be mitigated by all parties concerned.One such party is the KMITL Lifelong Learning Canter (KLLC) which has committed to reducing its environmental externalities, including the impact of its operations on climate change.The idea of a "green KLLC" seeks to reduce adverse effects on the environment and improve indoor environmental quality through the use of natural building materials and biodegradable products, resource conservation (water, energy, paper), responsible waste disposal, and eco-friendly practices (recycling).To reduce these environmental externalities, the environmental performance of the KLLC canter should first be examined to establish measures for improvement.However, accurate evaluations of the environmental impact of Lifelong Learning Centers (LLCs) are uncommon because of the lack of data and the immaturity of appraisal methodologies.With a case study of KLLC, the newest LLC in Thailand, this paper appraises the environmental effects, thus setting benchmarks for subsequent studies.The appraisal demonstrates that the KLLC community can significantly reduce the environmental consequences by using e-certificates, motion sensor light installation, banana leaf packaging, solar cell installation, and carpooling systems.Although e-certificates and banana leaf packaging are the most cost-effective methods of implementation, the adoption of carpooling systems and electric vehicles demonstrates the highest potential for Greenhouse Gas (GHG) emissions reduction.The paper showcases how KLLC can reduce its GHG emissions and wastes, thus turning into a more environmentally sustainable business.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".