INFORMATION

2026-08-11 17:51:00

 

  • July 23 2026 – First Meeting of the Anti-Corruption Coordination Committee (KTT):
The government moved forward with measures to combat bribery and corruption, promoted the “One Department, One Transparency” policy, established subcommittees to drive anti-corruption efforts, and strengthened cooperation between the public and private sectors.
  • August 5 2026 – Closed-door Dialogue:
The Zero Corruption: JSCCIB and Friends working group, together with the Thailand Institute of Justice (TIJ), held a closed-door dialogue to discuss structural corruption issues and exchange practical solutions among government, private sector, academia, and other relevant stakeholders.

Link : https://www.jsccib.org/news/detail/public-private-civil-society-dialogue-anti-corruption-data-transparency-oecd

 

JSCCIB: Joint Standing Committee on Commerce, Industry and Banking


2026-08-10 12:40:00
HS-TECH ENGINEERING Contributes Article to the Japanese Monthly Chemical Management Journal

HS-TECH ENGINEERING has contributed an article to the July 2026 issue of Chemical Substances Management, a monthly journal published by Johokiko Co., Ltd., Tokyo, titled "Thailand's Path Toward OECD Accession: An Analysis of the Sweeping Reform of Its Chemical Regulatory Framework." The issue is scheduled for release on July 8.

 

Contents

1. Introduction

2. Thailand's Efforts Toward OECD Accession

3. The Five Pillars of Chemical Regulatory Reform (Background, Current Status, and Direction of Reform)

3-1 Full Alignment with GHS (Complete Transition to the Latest Revision)
3-2 Establishment of a National Chemical Inventory and Integration of the Underlying Data Infrastructure
3-3 Modernization of Chemical Storage Standards (Harmonization with International Norms)
3-4 Introduction of a PRTR System
3-5 Rigorous Implementation of Process Safety Management (PSM)

4. Sector-by-Sector Qualitative Impact Analysis and the Positioning of the Chemical and Materials Industries

5. Precedents from Prior OECD Accessions

6. Thailand's Anticipated OECD Accession Process

7. Conclusion

 

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2026-08-05 19:02:00
บริการจัดทำเอกสารข้อมูลความปลอดภัยสารเคมี อย่างมืออาชีพ โดย HS-TECH Engineering



 

 

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2026-08-05 18:51:00
Professional Thai Safety Data Sheet (SDS) Preparation Services by HS-TECH Engineering Co., Ltd.



  • A template of Thai SDS

pdf SDS_TH_HS-TECH_ 05082026_1.pdf (0.15MB)


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CONTACT: https://hstecheng.com/contact

 


2026-07-15 15:27:00

Thailand Province-Level Analysis

Author
Affiliation

Sasiton Treeprak, Sciences Manager

HS-TECH Engineering

 

This report summarizes two province-level scatter analyses relating Gross Provincial Product (GPP) per capita to two health outcomes: life expectancy at birth (2020) and suicide rate (2019). Both analyses cover all 77 Thai provinces.

 

1 GPP per Capita vs. Life Expectancy (2020)

Overall correlation: Pearson r ≈ -0.36 (n = 77 provinces) — a weak-to-moderate negative relationship. Higher GPP per capita is associated with slightly lower life expectancy across provinces.

 

GPP


Figure 1: GPP per capita (2020) vs. life expectancy (2020), clustered by region-group

1.1 By 8 Regions

Table 1: Mean GPP and Life Expectancy
Region group Mean GPP/capita (Baht) Life expectancy (yrs)
Bangkok 585,689 74.7
East 353,730 75
Central 214,621 75.1
North 105,101 75.7
Perimeter 277,842 76.1
Northeast 80,127 76.5
West 155,443 77
South 136,596 77.8

1.2 Key observations

  • The richest provinces — Rayong, Bangkok, Prachin Buri (all industrial / Eastern-seaboard) — sit at the lowest life-expectancy tier.

  • The South and Northeast — lower-GPP regions — have the highest average life expectancy.

1.3 Note

  • Important data limitation: life expectancy is only published at the regional level (8 groups) by NESDB/NSO for 2020, not by individual province. Every province in the same region-group shares an identical assigned value, so this correlation reflects a regional pattern rather than true province-specific variation.

 

2 GPP per Capita vs. Suicide Rate (2019)

Overall correlation: Pearson r ≈ -0.20 (log GPP, n = 77 provinces) — a weak negative relationship. Higher GPP per capita is associated with slightly lower suicide rate.

gpp-suicide.png
Figure 2: GPP per capita (2019, log scale) vs. suicide rate (2019)

 

2.1 By 6 regions

Table 2: Mean GPP and Suicide
Region Mean GPP/capita (Baht) Suicide rate (per 100,000)
Bangkok & Vicinities 352,508 4.11 (lowest)
Southern 162,237 6.1
Western 191,117 6.15
Eastern 395,695 6.9
Northeastern 79,897 6.94
Northern 109,503 9.33 (highest)

 

2.2 Key observations

  • Bangkok & Vicinities (highest GPP) has the lowest suicide rate, consistent with the overall negative trend.

  • The Northern region is a clear outlier: it has only middling GPP but the highest suicide rates — Nan, Phrae, and Lamphun all exceed 11 per 100,000.

  • The Northeast, despite being the poorest region by GPP, is mid-pack for suicide rate, not the worst — breaking a simple “poorer means higher risk” story.

  • Southern provinces — Narathiwat, Pattani, Yala — have the lowest suicide rates in the country (under 2.5), despite modest GPP, likely reflecting cultural/religious factors in this majority-Muslim region rather than economic conditions.

  • Suicide rate 2019 was computed from raw counts: (male deaths + female deaths) / (male population + female population) × 100,000, combining both sexes rather than averaging published sex-specific rates.

 

3 Combining the Two Analyses

  • Both relationships point in the same direction — provinces with higher GPP per capita trend toward a slightly lower suicide rate, but slightly lower (not higher) life expectancy. However, neither relationship is strong: r² is approximately 13% for life expectancy and 4% for suicide rate, meaning GPP per capita explains only a small share of the variation in either outcome.

  • The life-expectancy result is likely confounded by industrial and environmental exposure in high-GPP Eastern-seaboard provinces. The suicide-rate pattern is dominated by a distinct Northern-region effect that overwhelms any simple wealth-based explanation. GPP per capita should be treated as a weak, non-causal predictor for either outcome on its own — other factors (healthcare access, environmental exposure, culture, religion, urbanization, and reporting practices) likely play a larger role.

Note: Note: This is a descriptive, exploratory summary intended to surface patterns for further investigation — not a causal or policy analysis.


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