# ASEAN Green Jobs Forum 2026 | Day 2 morning policy intelligence
**Date:** 20 October 2026  
**Source:** attendee-provided machine transcript, approximately 08:49–10:42 (Manila/Brunei local clock as recorded).  
**Evidence status:** provisional; prepared for internal interpretation and cross-checking.  
**Scope:** plenary presentations and Q&A covered in the recording. Later morning roundtables and afternoon proceedings are not included.

## Strategic conclusion
Day 1 emphasised demand creation through policy, sector investment and industry transition. The recorded Day 2 morning addressed the corresponding implementation questions: how to identify changing occupational tasks, measure green competency demand, assess training-provider readiness, and move to institution-wide and employer-supported delivery.

## Session 1 — ASEAN demand/supply study, approx. 08:58–09:19
The presenter described complementary evidence from labour force surveys mapped to an occupation/task taxonomy and LinkedIn-based skills and hiring data covering six sufficiently represented ASEAN Member States. The study cited **approximately 22% of employment with green potential** on average. This is an indicative *upper-bound potential* for the greening of existing occupations, not a measure of current green employment or new jobs. The speaker reported **6.7% of hires as green talent in 2024** in the LinkedIn sample; the dataset disproportionately represents formal, urban, highly skilled workers. Recommendations included modular learning and recognition of prior learning; adding green competencies to existing qualifications; linking energy capacity goals to workforce plans; demand creation; and improved labour-market surveys and taxonomies.

## Session 2 — ILO Green Dictionary, approx. 09:20–09:45
The ILO speakers presented complementary green-task/skill and foundational-skills dictionaries, applied to occupation descriptions and online vacancy texts, with NLP-assisted identification of tasks and requirements. Vacancy data can track new demands over time, while occupational and labour-force information helps with comparable employment structure and job quality. The Philippines was discussed as a pilot. Human expert review and employer–worker–government consultation were emphasised; youth representation was explicitly welcomed in the Q&A. Results should not be treated as a final ASEAN classification.

## Session 3 — Green Education Readiness Index (GERI), approx. 09:46–10:16
The presenters described **three blocks, seven pillars and 28 dimensions** in a holistic TVET readiness assessment, scored 0–4 with supporting evidence, validated by trained researchers. The assessment is intended as a developmental diagnostic, **not an institutional ranking**. It examines curriculum, teachers, delivery, campus culture, employers and community engagement. The study was ongoing; the presenter explicitly cautioned that **Brunei results had not been validated**, and a Philippine illustration based on eight institutions was presented as preliminary. Some prior teacher-readiness survey figures were mentioned but require checking against the original slides.

## Session 4 — Philippines / TESDA practice, approx. 10:17–10:42
The speaker outlined five components of a whole-institution greening approach: training regulations/curricula, green research, institutional culture, community engagement and campus practices. The transcript reports **397 occupational standards greened** (speaker-reported, not independently checked). Industry should co-design and co-invest in training capacity. Q&A raised upfront procurement cost, lifecycle value, and differentiated entry points; ILO speakers offered country-focused discussions about whether data readiness supports piloting.

## Brunei policy translation (proposals, not official findings)
1. **Demand evidence:** select a candidate green–blue subsector and validate real employer projects, occupations and timelines before creating new courses.
2. **Classification:** investigate an ILO-style task-based approach applied to authorised Brunei vacancy data; conduct expert review and avoid treating AI text matches as definitive.
3. **Readiness diagnosis:** invite pilot HE/TVET institutions to conduct a non-ranking, evidence-backed capability self-assessment; do not publish unvalidated GERI country or institution results.
4. **Delivery design:** map modular upskilling, prior-learning recognition, trainer and lab capacity, assessment of demonstrated green competencies, and enterprise placements.
5. **Outcomes:** link appropriately governed education/graduation evidence (such as HiED Lens and GES/ESS) to relevant jobs and employer feedback. CERMIN covers *HEI personnel/workforce planning*, not whole-economy occupational demand, and must not be misrepresented as a national labour-market system.
6. **Implementation economics:** evaluate total lifecycle cost and financing; avoid assuming green procurement is always cheaper up front.

## Day 1 → Day 2 analytical convergence
| Day 1 demand-side evidence | Day 2 supply-side evidence | Brunei diagnostic test |
| --- | --- | --- |
| Investment makes green-sector jobs feasible | Labour-market intelligence and task classifications describe required skills | Are planned projects matched to validated occupation and competency needs? |
| Firms report technical, digital and green skill changes | Existing qualifications, modular training, and prior-learning recognition can adapt supply | Which tasks can be added to existing programmes rather than new qualifications? |
| Industry/education cooperation needed | GERI probes evidence of curriculum, trainer, facilities and partnerships | What can public HEIs/TVET providers *actually* deliver and demonstrate? |
| Just transition must include worker voice and protections | Inclusion and employment quality must be measured alongside skills | Are mid-career workers, women and vulnerable groups in the intended pathways? |

## Data-quality warnings
- Machine transcription repeatedly renders **TVET** as unrelated words (such as “Tibet”); treat this as corrected context, not a quotation.
- Names and organisations are imperfectly recognised; match to slides or official programme before publication.
- Do not quote garbled percentage and forecast claims (such as the renewable-energy date) without source checking.
- The 22%, 6.7%, and 397 figures are **speaker-reported** here; the latter particularly awaits the TESDA deck. Neither 22% nor 6.7% represents Brunei.
- Recorded materials end at approximately **10:42**; this is not a transcript of the entire day.