NATIONAL ASSESSMENT OF THE COST
OF INACTION OF TACKLING AIR
POLLUTION IN THAILAND

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National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

ACKNOWLEDGEMENTS
Authors:
Gregor Kiesewetter (IIASA); Zbigniew Klimont (IIASA); Muye Ru (Earth Institute); Jessica Slater (IIASA)
Reviewers:
Tom Bannister (UNEP); Nathan Borgford-Parnell (Climate and Clean Air Coalition); Savitri Garivait (King Mongkut’s
University of Technology Thonburi); Alin Halimatussadiah (Universitas Indonesia); Ansa Heyl (IIASA); Rasheed
Hussain (WHO); Maarten Kappelle (UNEP); Nguyen Thi Kim Oanh (AIT); Kaye Patdu (UNEP); Johan Kuylenstierna(Stockholm Environment Institute); Manop Udomkerdmongkol (UNRCO Thailand); Chou Mandarin (Ministry ofEnvironment of Cambodia); Mushtaq Memon (UNEP); Daniel Mira-Salama (World Bank); Siwaporn Rugsiyanon(Ministry of Natural Resources and Environment of Thailand); Napak Tesprasith (USAID); Sirirat Yensong (Ministryof Natural Resources and Environment of Thailand); Ekbordin Winjikul (AIT)
Editing and design:
Jennifer Pangilinan (AIT); Ranjika Perera (AIT); Shayan Naveed (AIT); Pitiruedee Angkhananuchat (AIT)3

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

ACRONYMSASEAN		

Association of Southeast Asian Nations

BC		Black Carbon

CH4 		

Carbon Dioxide

GAINS

Greenhouse Gas – Air Pollution Interactions and Synergies Model

GBD

Global Burden of Disease

GDP

Gross Domestic Product

GHG

Greenhouse Gas

GWP

Global Warming Potential

IEA

International Energy Agency

IIASA

International Institute for Applied Systems Analysis

LPG

Liquefied Petroleum Gas

NH3		

Ammonia

NMVOC

Non-methane Volatile Organic Compound

NOx

Nitrogen Oxides

NPS

New Policy Scenario

O3

Ozone

OC

Organic Carbon

PM

Particulate Matter

SDGs

Sustainable Development Goals

NO2

4

MethaneCO2		

Nitrogen Dioxide

SDS

Sustainable Development Scenario

SLCPs

Short Lived Climate Pollutants

SO2 		

Sulphur Dioxide

UN

United Nations

UNEP

United Nations Environment Programme

UNEP – CCAC

United Nations Environment Programme – Climate and Clean Air Coalition

VOLY

Value of a Statistical Life Year

VSL		Value of a Statistical Life

WHOWorld Health Organization

YLLs

Years of Life Lost

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

TABLE OF CONTENTS
1. Introduction

8

1.1 Background8

1.2 Objective

9

1.3 Approach9

1.4 Methodology

10

1.5 Scenarios

11

2. Results
2.1 Emissions and ambient concentrations

13
13

2.1.1 Current policies scenario

13

2.1.2 Additional policies

14

2.2 Health impacts and cost of inaction

16

2.3 Climate and other co-benefits

20

3. Limitations

22

3.1 Scenarios and solutions

22

3.2 Data availability/reliability

22

3.3 Modeled PM2.5 concentrations

22

3.4 Costs of action

23

3.5 Scope of impacts assessed23

4. Conclusion and recommendations

24

Annexes25

Annex 1. Methodology and data sources

25

Overview

25

Emissions and ambient PM2.5 concentrations

27

Validation of ambient PM2.5 concentrations

28

Concentration-response functions

29

Baseline Morbidity Rates

29

National data

29

Baseline rates of the related diseases: Default estimates

30

Unit Costs

30

National data

30

Default estimates from international data

30

Annex 2. Emission scenarios

32

Current Policies

32

Strong Mitigation

33

Individual Measures

34

References

36

5National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

LIST OF BOXES
Box 1: Clean Air and Climate Solutions for ASEAN						

8Box 2: The GAINS model									10LIST OF FIGURES
Fig. 1.1 Schematic picture of the proposed approach for quantifying
the cost of action vs cost of inaction, relying on the comparisonof Current Policies and Additional Policies							

9Fig. 1.2 12 key solutions to address exposure to fine particulate matter in ASEAN		

12

Fig. 2.1 Trends in emissions of CO2 and air pollutants in the Current Policies
scenario forThailand									13Fig. 2.2 PM2.5 concentrations for Thailand modelled with the GAINS modelfor 2015 (left) andfor 2030 under Current Policies (right)					

14Fig. 2.3 Population exposure to PM2.5 modelled with the GAINS modelfor 2015 and for 2030 under different scenarios						

15Fig. 2.4 Expected improvement in population-weighted mean PM2.5 concentrations inThailand from each of the 12 solutions in 2030, distinguishing already implemented
measures (dark blue), legislation passed after 2015 but not yet fully implemented(light green), and the further potential (yellow)						

15Fig. 2.5 Monetized health burden from PM2.5 exposure in 2030,
comparing the Current Policies scenario, and effective implementationof all measures assessed in this study for Thailand (Strong Mitigation case).
Stacked Bars – Left axis: Burden expressed as million USD per year,
Red dots – Right axis: comparison to GDP							

17Fig. 2.6 Monetized morbidity burden from PM2.5 exposure in 2030,
comparing the Current Policies scenario, and effective implementationof all measures (Strong Mitigation scenario)							18Fig. 2.7 Summary of the incidence of included impacts estimated for 2015
and analyzed scenarios. Deaths, emergency room visits and hospital admissions
are shown on the left axis, restricted activity days (dots) use the right axis			

18

Fig. 2.8 Monetized health benefits from individual measures applied in 2030
on top of current policies									19Fig. 2.9 Co-benefits of individual measures for GHG emissions (CO2 + CH4)
when fully implemented in 2030 (left) and 2050 (right)					

21LIST OF TABLES
Table 2.1 Morbidity and mortality attributable to ambient PM2.5 in Thailand
in 2015 and 2030 under current legislation as well as Strong Mitigation
implemented across all ASEAN, and their associated impact costs.
For mortality, premature deaths and YLLs are alternative
indicators and are shown for comparison only						6

16National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

EXECUTIVE SUMMARY
This assessment provides a preliminary quantificationof the costs of not taking further action on air pollutionin Thailand. It quantifies and compares the potential
health costs in two alternative future scenarios, one
in which no further measures beyond current policy
are taken, with a future scenario in which 12 solutions(bundles of measures) are implemented. In addition
to looking at health impacts, the assessment also
highlights co-benefits for climate change that would
be missed if further measures are not implemented.
The key findings of this initial assessment are:Thailand has introduced effective policies whichhave improved air quality over the last decade
but more needs to be done. Despite recent progress,
air pollution remains a significant problem in Thailand.
Even considering current air quality legislation, thehealth burden from air pollution exposure is projected
to increase, due to economic and population growth aswell as population aging. This assessment calculates
that without any additional action on air pollution,
there will be almost 24 thousand premature deaths
due to ambient air pollution exposure in Thailand peryear by 2030.
Further action could have significant healthbenefits for the population of Thailand.
Implementing further policies beyond current
legislation could avoid 17 thousand premature
deaths, 16 thousand hospital admissions and almost
12 thousand emergency room visits due to poor air
quality every year by 2030.
The human health related costs of not taking
further action on air pollution are estimatedto be equal to about 2.0% of Thailand’s Gross
Domestic Product (GDP) in 2030. Lack of
immediate further action on air pollution could cost
Thailand 12.5 billion USD a year in 2030, based ona selection of mortality and morbidity impacts of air
pollution. This equals about 2.0% of Thailand’s GDP
for 2030. The actual cost of inaction will likely behigher if all other costs and foregone benefits areaccounted for.

There are proven measures to improve air
quality and achieve large health benefits in thenear term. 12 key solutions were identified which
could deliver significant air quality benefits. Of these
12 solutions, the policies which could lead to thelargest benefits in Thailand include a complete switch
to clean cooking technologies, increased renewableelectricity generation capacity, policies supporting
accelerated introduction of electric vehicles andstrengthened emission standards for road transport,
effective waste management strategies, and bans on
agricultural residue burning. These measures alone
would capture about 80% of the mitigation potential
in terms of exposure and monetized benefits.
The proposed 12 solutions also result insignificant reduction of greenhouse gas (GHG)
emissions and could also have multiple otherbenefits. This assessment shows that implementing
the 12 solutions for clean air would also reduce
GHG emissions with benefits for climate change andcould help Thailand achieve its climate targets. If
implemented, the solutions could also deliver multiple
other co-benefits, supporting the achievement of otherdevelopment priorities related to several Sustainable
Development Goals (SDGs). Therefore, the cost ofinaction is likely to be even higher than the figure
estimated in this assessment.
Quantifying the costs of inaction of tackling air
pollution provides useful evidence which can beused to promote action. Through quantifying thehigh costs of not acting on air pollution, the results ofthis assessment can be used to motivate action. The
initial results presented here can be used to support
Thai policymakers and decisions-makers in the design
and implementation of new policies and measures andfacilitate cross-governmental dialogues on effective air
quality management.

7

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

1. Introduction
1.1. Background
Air pollution poses a substantial threat to the healthand well-being of the 660 million people living in the
Association of Southeast Asian Nations (ASEAN) region.
Thailand is not exempt from this burden; exposure tofine particulate matter (PM2.5) was estimated to cause
about 32,000 premature deaths annually in Thailand
in 2019 (Murray et al., 2020). Furthermore, exposure
to PM2.5 is responsible for a high burden of morbidityfrom cardiovascular and respiratory diseases, and air
pollution also affects ecosystems through depositionof nitrogen and sulfur, leading to acidification,
eutrophication, and loss of biodiversity.
There are well known measures which, if effectively
implemented, could successfully reduce air pollution
and its associated impacts, and increasing efforts
have been taken in recent years to counteract the air
pollution problem in ASEAN countries. In Thailand,
multiple strategies and action plans have beendeveloped which either directly or indirectly target
emissions of air pollutants across multiple sectors.
These include sector specific strategies relating toenergy efficiency improvements, increased renewableenergy capacity, reducing agricultural burning, moresustainable transport, improved waste management
as well as legislation defining air pollutants’ emission
limit values for industrial stationary combustion andproduction facilities ((Nikam, Archer and Nopsert,
2021), and transport sources (He et al., 2021).
However, even if these current policies and legislation
are effectively implemented, it is likely that continued
population growth, urbanization and economic growth
will largely offset the achieved reductions and leadto further worsening of air quality in the region withnegative impacts for health (UNEP/CCAC, 2023). To
limit the multiple negative impacts of air pollution itis therefore important to consider what additional
actions could be effective and to not only explore theimplications of current policies, but also opportunities
for further mitigation.

Tackling air pollution can also have largely positive
benefits for mitigating climate change. Somepollutants, known as short-lived climate pollutants(SLCPs), contribute directly to both climate change andair pollution, while air pollutants and long-lived GHGs
often come from the same source. Consequently, taking
an integrated approach to air pollution and climatechange could result in multiple benefits for healthand the environment (Haines et al., 2017), as wellas having other benefits such as achieving the SDGs.
The United Nations Environment Programme – Climate
and Clean Air Coalition (UNEP-CCAC) Assessment on
Air Pollution in Asia and the Pacific (United Nations
Environment Programme [UNEP], 2019) and the Clean
Air and Climate Solutions for ASEAN Report (UNEP/CCAC, 2023) took this perspective and identified a
portfolio of solutions that could be implemented with
benefits for air quality and climate as well as otherdevelopment priorities. This assessment builds on theprevious analysis and utilizes some specific solutions
highlighted in the report (see Box 1).
Mitigation measures to tackle air pollution are often
associated with an economic burden that would not
only be imposed on polluting industries but also impact
a wide range of stakeholders from individual citizens
to businesses and local government. Hence, costs are
sometimes mentioned as arguments against stricter
legislation. However, the impacts of air pollution also
impose costs to society resulting in economic losses,
for example costs to the health care system due toincreased levels of attributable disease or economic
losses due to a reduced work force through deathand illness. Therefore, not acting on air pollution is
also costly, and quantifying these costs of inaction,
as a counterbalance to the costs of action (i.e., ofimplementing new mitigation measures), can be an
important argument in support of more stringent andambitious control policies.

Box 1: Clean Air and Climate Solutions for ASEAN
In 2023, The United Nations Environment
Programme (UNEP), Association for Southeast
Asian Nations (ASEAN), and Climate and Clean Air
Coalition (CCAC) released a report entitled: ‘Clean
Air and Climate Solutions for ASEAN’. The report
identifies 15 solutions—12 of which overlap with thesolutions in this cost of inaction assessment—that
were selected based on their potential to deliver
the maximum reduction in ASEAN population's
exposure to PM2.5. In fact, full implementation ofthese solutions can reduce population weighted

8PM2.5 average concentrations across the ASEAN
region by 50 to 70 percent by 2030. Further, the 15
solutions would also deliver important climate cobenefits from the reduction of short-lived climate
pollutants (SLCPs). The solutions involve actions inmany sectors, ranging from conventional industrial
process controls to shifts in diets and agricultural
practices. They would also gain momentum andthereby reduce the costs of inaction from efforts
to strengthen governance, increase finance, andenhance regional cooperation.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

1.2. Objective
Through quantifying the costs of not acting on air
pollution, this assessment aims to increase the evidencebase to support policymakers and decision makers in
Thailand to take further action and prioritize ambitious
policies and cost-effective measures to improve air
quality. It provides an initial quantification of someof the costs of inaction from tackling air pollution in
Thailand, through quantifying and costing the healthbenefits which could be achieved from implementing
12 specific mitigation solutions. It also highlights someother benefits, such as for climate change, which
could be achieved if these solutions are implemented.
Through comparing the quantified health impacts
from air pollution exposure in the baseline scenario,
representing current policies, with a ‘strong mitigation’scenario in which 12 additional ambitious solutions

are implemented, some of the costs of not acting on
air pollution are directly quantified. This assessment
therefore gives an initial indication of the future costs
which Thailand will experience if no further action
is taken and highlights specific solutions which if
implemented could significantly reduce these costs inthe future. This approach takes a different perspective
in comparison to a typical air pollution mitigationassessment, which normally focuses on the benefitsof action rather than the costs of not acting. The aimof this assessment is therefore to provide a strong
motivation and justification for further action and allow
for the development, prioritization and implementationof cost-effective, progressive and integrated policy
measures to tackle air pollution with benefits for healthand climate.

1.3. Approach
The ‘cost of inaction’ is defined here as the total damage
cost that will remain without policy intervention, orconversely, as the damage cost that can be avoided
by taking action. The cost is related to the key impacts
that are associated with air pollution and their total
damage costs. The assessed and monetized impacts
can include direct health costs, lost labour days,
mortality costs, declining crop yields, ecosystem
impacts, material damage, impact on tourism, noise,
visibility, traffic accidents and congestion.

A simplified, conceptual representation of the approach
used in this assessment is shown in Fig. 1.1. The key
principle is the comparison of two different scenarios
for a given target year: a Current Policies scenario,
representing the implications of current legislation(assuming no further policy action), is compared toan alternative Additional Policies scenario in which abundle of new measures to curb pollution, reduce GHG
emissions, and address SDGs are implemented.

Additional Policies

Cost of Inaction

Cost of Action

Current Policies

Pollution Level

Emission Control Costs

Impact Costs

Fig. 1.1 Schematic picture of the proposed approach for quantifying the cost of action vs cost of inaction, relying
on the comparison of Current Policies and Additional Policies

9National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

The Current Policies case is associated with a certain
level of ambient air pollution (symbolized by the blue
bar in Fig. 1.1), costs for implementation of existing
pollution legislation (the yellow bar), and a certain
level of related costs (the green bar) from air pollution,
which is here expressed in monetary terms. In the
Additional Policies case, the emission control costs arehigher while pollution levels and associated impact

costs are lower. The cost of action is then defined as
the difference between the emission control costs in
the Current Policies and Additional Policies scenarios,
whereas the cost of inaction is the difference between
the impact costs or, in other words, the forgone or ‘lost’monetized benefits if no action is taken.

1.4. Methodology
The analysis in this assessment employs the GHG – Air
pollution Interactions and Synergies (GAINS)1 model(Amann et al., 2011; UNEP, 2019) developed at theInternational Institute for Applied Systems Analysis(IIASA) (see Box 2 below and Fig. 1 in the Annex).
GAINS is an integrated assessment model quantifying
emissions of various air pollutants at a granular sectorallevel, ambient concentrations of PM2.5 and the associatedmortality. For the purpose of this assessment, the model
framework has been further developed and extended toinclude additional health endpoints related to morbidity(cardiovascular and respiratory hospital admissions,asthma related emergency room visits, and restrictedactivity days) and respective costs.

costs are quantified as the monetary value per yearsof life lost (YLLs) (as estimated from willingness-topay studies) and the health system costs of morbidity.
Concentration-response functions for several morbidity
outcomes were generated from a dedicated metaanalysis from international studies; mortality calculations
follow the Global Burden of Disease (GBD) methodology.
Input data for the calculations (such as baseline
incidence rates, cost parameters) were assembled fromlocal sources where available and supplemented withdata from international sources in other cases. Detailsof the methodology and data sources are explained in
the Annex.

The assessment quantifies health impacts in terms ofmortality and morbidity from ambient PM2.5. Impact

Box 2: The GAINS model
The GAINS model explores cost-effective multipollutant emission control strategies that meetenvironmental objectives on air quality impacts (onhuman health and ecosystems) and GHGs. GAINS,
brings together data on economic development,
the structure, control potential and costs ofemission sources, the formation and dispersion ofpollutants in the atmosphere and an assessmentof environmental impacts of pollution1.
Emissions are estimated using the GAINS emission
factor database that has been peer-reviewed andcompiles both national and international data on
source- and technology-specific measurements;
more than 1000 technologies to control emissions
are represented. The mitigation options include
impact on emissions of all key air pollutants (SO2,NOx, PM (including black carbon (BC) and organiccarbon (OC)), NMVOC, NH3) and GHGs.

1

10

The model computes the atmospheric dispersion
and formation of secondary pollutants for defined
scenarios. This allows the quantification of PM2.5
concentrations and their changes from applicationof each measure/solution at a resolution of
0.1°×0.1° or roughly 10x10km. Overlaid withpopulation at the same resolution, the exposure
distribution to ambient PM2.5 in the population
is calculated. Applying concentration-response
functions from the international literature, GAINS
calculates premature mortality from long-term
exposure to PM2.5, and the associated YLLs. For thisanalysis, the model has been extended to include
other health related impacts, i.e., morbidity,
work time loss, their costs, etc. (see Fig. 1 in the
Annex). Details of the methodology are explainedin the Annex.

For more details about the GAINS model, please visit: https://gains.iiasa.ac.at/models.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

1.5. Scenarios
To calculate the cost of inaction from air pollution, as
illustrated in above Fig. 1.1, this assessment directly
quantifies the differential impacts and costs due tothe health impacts of air pollution exposure for twoalternative future scenarios. The first, or ‘Current
Policies’ scenario, assumes that only current legislationand policies related to air pollution are implemented,
following analysis by Nikam, Archer and Nopsert (2021).
While, the additional policies or ‘Strong Mitigation’scenario, directly assumes the implementation of 12
additional ambitious clean air solutions, which havebeen identified and developed within the Clean Air
and Climate Solutions for ASEAN Report (UNEP/CCAC,
2023), and which draw on concepts applied in the
UNEP-CCAC Assessment for Air Pollution in Asia andthe Pacific (Amann et al., 2019; UNEP, 2019). A broad
description of the underlying scenarios for the ASEAN
region is provided in the Annex.
Emissions into the future for both scenarios are
dependent on changes in activity which themselves area product of key sociodemographic and macroeconomic
drivers. Both scenarios assume that Thailand will
experience significant economic development in the
future with its GDP growing by nearly 70% to 2030
from 401 billion USD in 2015, following the projections
made in the International Energy Agency’s World
Energy Outlook 2018 (International Energy Agency[IEA], 2018). Following the United Nations (UN) World
Population Prospects 2017 (United Nations [UN], 2017),
Medium Scenario, the population of Thailand is also
assumed to change, growing from 68.6 million in 2015,
to a maximum of 69.7 million in 2025, before decreasing
slowly to 65.4 million in 2050, while the average age ofthe population is also projected to increase.
The Current Policies scenario that is used as a baselinein this assessment is a rather conservative benchmark
for future development. It considers only policies
which have already been implemented or agreed in
Thailand by mid-2020. The information about current
policies, emission limit values and standards is takenfrom Motokura et al. (2017), Dieselnet (n.d.), He et al.
(2021) , and a recent review of air quality relevant policy
landscape in Thailand (Nikam, Archer and Nopsert,

2021). The energy trends used in the Current Policies
scenario are consistent with the IEA ‘New Policy Scenario(NPS)’, and air pollution controls are implemented to theextent foreseen under current legislation, but no further
action is taken. Some of the above studies also provide
assessment of progress in implementation of policies,
which is important to create a more realistic outlookof impacts from introduction of existing legislation andassessment of future mitigation potential; reported
experience is reflected in the modelling approach usedin this assessment.
In contrast to this, the Strong Mitigation scenario(additional policy scenario in above Fig. 1.1), assumes
that the additional 12 key solutions2 developed under
the Clean Air and Climate Solutions for ASEAN Report(UNEP/CCAC, 2023) are effectively implemented tothe maximum extent possible. These solutions are
not individual actions but rather a bundle of actions
relating to similar technologies or sectors (e.g., forroad transport, strengthened emission standards
and increased population of electric vehicles is one
solution) (Fig. 1.23) and have been selected based on
their potential to deliver the maximum reduction in the
population’s exposure to PM2.5. The selected solutions
combine application of technological solutions to reduce
emissions as well as exploit potential for the energy
efficiency, fuel switching, renewables and electrificationof vehicle fleet as identified in the IEA Sustainable
Development Scenario (SDS). Furthermore, diets, andhence agricultural production, are in line with the Lancet
EAT Planetary Diet (Willett et al., 2019). Further details
on the solutions included in this assessment can befound in the Annex and in UNEP/CCAC (2023) report.
As well as calculating the total combined impact of
implementing all 12 solutions (Fig. 1.2), each individual
solution is also independently quantified in terms of
their impacts on PM2.5 exposure, this can help to identify
those solutions which could have the largest impact on
improving air quality. As shown later in this assessment,
several of these measures also bring strong co-benefits
including reduction of GHG emissions and contribute to
the achievement of several SDGs.

The UNEP/CCAC Clean Air and Climate Solutions for ASEAN Report (UNEP/CCAC, 2023) investigates 15 solutions, some ofwhich however do not directly influence air pollution levels. This study includes the 12 solutions relevant for PM2.5 concentrations.
2

3

More detailed information about the 12 solutions is provided in Table 1 in the Annex.

11National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

12 SOLUTIONS

ExpandAccess to
Clean Cooking

Dietary
Changes

Improved Waste
Management

Livestock andN Fertilizer
Application

Ban Agriculture
Residue Burning

Prevention ofForest andPeatland Fires

Stricter Vehicle
EmissionsStandards *VehicleInspection
and Maintenance

TighterStandards
for International
Shipping

Improved
Industrial
Process
Standards

Renewable
Energy andPost Combustion
Controls

Fossil Fuels *** The future potential shown includes the potential scope for what accelerated electrification of vehicle fleet can achieve, i.e.,most likely less than half of that could be achieved by electrification by 2030.

* The future potential shown includes the potential scope for what accelerated electriﬁcation of vehicle fleet can achieve, i.e., most likely less than half of that could be achieved by electriﬁcation by 2030**Improvements
Improvements
to coal, oil and gas production and distribution, including through reducing leaks and utilizing captured gas.
to coal, oil and gas production and distribution, including through reducing leaks and utilizing captured gas.
**
Fig. 1.2 12 key solutions to address exposure to fine particulate matter in ASEAN

12

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

2. Results
2.1. Emissions and ambient concentrations
This section presents results from the assessment
itself. Section 2.1 quantifies the emissions and ambient
PM2.5 concentrations in the Current Policies scenario
and the Strong Mitigation scenario. Section 2.2 thenquantifies health impacts and costs of inaction for
individual measures contained in the Strong Mitigation
scenario, and Section 2.3 analyzes co-benefits ofmeasures for climate change mitigation.

2.1.1 Current policies scenario
2.5

CO2
SO2

2

NOx
PM2.5
NH3

1.5

1

0.5

0

2015

2020

2025

2030

2035

2040

2045

2050

Fig. 2.1 Trends in emissions of CO2 and air pollutants in the Current Policies scenario for Thailand

In the Current Policies scenario, the assumed
implementation of existing and recently introduced
legislation already shows some effect at slowing the
growth of emissions of PM2.5 and key PM precursor
pollutants (sulphur dioxide (SO2), nitrogen oxides(NOx), Ammonia (NH3), non-methane volatile organic
compounds (NMVOCs) (Fig. 2.1). However, current
policies are not sufficient to offset the increase infuel use and production activities, which combinedwith assumed strong economic growth in Thailand
drives the significant increases in carbon dioxide (CO2)emissions (Fig. 2.1). The fact that the emissions of
air pollutants are growing slower than those of CO2,
suggesting gradual decoupling of economic growthfrom air pollutant emissions. Thailand has beenpromoting clean cooking and has achieved significant
reduction of emissions of PM precursors throughproviding access to clean fuels both in urban and rural
areas. However, significant further reductions were
not identified in existing policies and legislation andtherefore by 2030 in the Current Policies scenario about
a quarter of primary PM2.5 emissions still originate fromthis source.

Annual mean PM2.5 concentrations in Thailand
estimated by GAINS are between 5 and 40 µg/m3 in2015 (Fig. 2.2, left panel), with only a very small shareof the population exposed to the highest concentrationlevels. While this means that the PM2.5 pollution
problem in Thailand is smaller than in many other
Asian countries, concentrations are still considerably
above the World Health Organization (WHO) guideline
values of 5 µg/m3 (World Health Organization [WHO],2021), particularly in cities. It should also be notedthat the modelled concentrations quantified here
are less than the measured concentrations at Thaimonitoring sites (which may be influenced by local
sources below the model resolution). This means thatthe conclusions drawn here are likely to be a lower
estimate, and the actual exposure levels in certain
areas could be higher than those quantified here. A
validation of concentration levels estimated by GAINS
against monitoring data is shown in the Annex.

13National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Fig. 2.2 PM2.5 concentrations for Thailand modelled with the GAINS model for 2015 (left) and for 2030 under
Current Policies (right)

Even assuming the successful implementationof existing policies and legislation, there is little
improvement in PM2.5 concentrations by 2030 in the
Current Policies scenario (Fig. 2.2 right panel). This isalso illustrated in Fig. 2.3, which shows the distributionof population exposure to PM2.5. In 2015, 94% of the
population were exposed to PM2.5 levels above thecurrent WHO air quality guideline of 5 µg/m3 and over
80% were exposed to levels above the 2005 WHO
guideline of 10 µg/m3 (redefined as Interim Target 4 inthe 2021 WHO Guidelines), while 28% were exposed
to levels between interim targets 2 and 3 (15-25 µg/m3), and 1% at concentrations higher than 25 µg/
m3. In the Current Policies scenario, the situation is
not expected to improve by 2030. Even with assumed
effective implementation of current legislation, morethan 80% of the population could still be experiencing
concentrations above the 2005 WHO guideline of 10µg/m3 (now Interim Target 4) and the number ofpeople exposed to concentrations exceeding 15 µg/m3
would also grow from 19 to more than 23 million due
to increasing concentrations in some regions (Fig. 2.2)
and continued urbanization.

2.1.2 Additional policies
The full extent of conceivable action against increasing
air pollution levels is explored in the Strong Mitigation
scenario. This is an extremely ambitious scenario,
which would require ambitious goal setting and
immediate strong policy action to take advantage
of all 12 mitigation solutions illustrated in Fig. 2 andlisted in Table 1 (Annex). As illustrated in Fig. 2.3,
implementing all 12 of these solutions in the Strong
Mitigation scenario, would mean that by 2030, morethan 8 million people would enjoy PM concentrations
below the current WHO guidelines and only 2% would
be exposed to levels above 10 µg/m3 compared to

14

more than 80% in the Current Policies scenario. The
Current Policies and the Strong Mitigation scenarios
constitute the extreme ends of a spectrum of possible
scenarios between business as usual and extremely
ambitious mitigation. Within this range, a more
realistic policy scenario would apply policies to somedegree to exploit much of the potential while keeping
policy costs limited. To identify which of the 12
solutions have the largest impact, within this analysis
we quantify the individual potential of each individual
solution to improving air quality.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

StrongMitigation
2030
Current
Policies 2030
2015
0

10

20

30

40

50

60

70

80

Million people exposed to PM2.5<WHO guideline 5 µg/m35-10 µg/m310-15 µg/m315-25 µg/m325-35 µg/m3>35 µg/m3

Fig. 2.3 Population exposure to PM2.5 modelled with the GAINS model for 2015 and for 2030 under different
scenarios

Fig. 2.4 shows the contribution of each of the
12 solutions to improved air quality in terms of
population-weighted mean PM2.5 concentrations inThailand in 2030, assuming the full implementationof each solution both in Thailand but also acrossthe whole ASEAN region. This figure also shows theimpact on exposure from measures which had alreadybeen implemented by 2015 (dark blue), and havealready contributed to improved air quality, with thosethat have been included in recent legislation passed
after 2015 but may not yet fully implemented (lightgreen), potentially contributing to future improved
air quality if successfully implemented. The further
potential (yellow) for each solution would therefore
come from implementing the highest level of ambition.
The aim of this analysis is to show in which areas or
sectors progress has already been taken and where
there remains the most potential for further ambition
through implementation of the solutions.

to have already resulted in 5 µg/m3 reduction in
population weighted PM2.5 concentrations compared
to if no policies or controls were implemented inthese areas, while implementation of stricter emission
standards for vehicles has also effectively reduced air
pollution concentrations by around 2 µg/m3 (dark blue
and light green parts of the bars). Despite the successin some areas, Fig. 2.4 shows that there is a large
potential for further improvements (yellow) which isthe focus of the analysis presented in the remaining
part of this report. The largest areas where there isadditional potential for improving air quality are inclean cooking (eliminating solid fuels in the residential
sector), waste management (elimination of open
waste burning), and further measures controlling
large point sources such as power and industry, as
well as further measures in the transport sector. The
latter also include the electrification of road transport,
while the measures in power and industry sectors
include a transition to renewable energy and efficiency
improvement.

Controls on PM and SO2 emissions from electricity
generation and industry have already contributed tolarge improvements in air quality in Thailand, estimatedClean cooking
Renewables, post-combustion controls in power and industry
Industrial processes standards, incl. energy efficiency
Emission standards/electrification - transport
Vehicle inspection and maintenanceInternational shipping
Livestock and N-fertilizer application
Dietary changes
Open burning of agricultural residues
Waste management
Coal, oil and gas production
Prevention of forest and peatland fires
0

2

4

6

8

Already implemented
Recent legislation
Further potential

Population exposure to PM2.5 [µg/m3]

Fig. 2.4 Expected improvement in population-weighted mean PM2.5 concentrations in Thailand from each of the
12 solutions in 2030, distinguishing already implemented measures (dark blue), legislation passed after 2015
but not yet fully implemented (light green), and the further potential (yellow)

15

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

2.2. Health impacts and cost of inaction
Exposure to PM2.5 leads to considerable health impactsin Thailand. The GAINS model estimates that in 2015,
more than 16,000 premature deaths were attributable
to ambient PM2.5, corresponding to 313,000 YLLs.
These estimates are lower than the numbers publishedby the GBD and likely constitute a lower range. As
mentioned in Section 2.1, PM2.5 concentrations
estimated by the GAINS model are low biased against
monitoring stations, which may at least partly explain
this difference. In the Current Policies scenario, due toincreases in PM2.5 concentrations, as well as population
aging, the mortality burden is projected to increase toalmost 24,000 premature deaths and 413,000 YLLs in
2030.
At the same time, PM2.5 also contributes to a significant
burden of morbidity. Numbers of morbidity andmortality attributable to PM2.5 exposure estimated in2015 and under the Current Policies scenario in 2030
are given in Table 2.1 below, along with their monetizedvalues and the unit costs used for the calculation. For
mortality, either the number of premature deaths canbe used in conjunction with the value of a statisticallife (VSL), or the years of life lost (YLL) in combination
with the value of a statistical life year (VOLY). As isusually the case, we find that the approach monetizing
deaths × VSL gives a higher cost than YLLs × VOLY,
reflecting some of the difficulties with attaching amonetary value to human life. For all further analysis
shown in this report, we use the YLLs monetization
approach for valuating loss of human life.
Unit costs, VSL and VOLY shown in below Table 2.1
combine local information with internationally available
data sets adapted to Thailand’s per-capita GDP.

Table 2.1 Morbidity and mortality attributable to ambient PM2.5 in Thailand in 2015 and 2030 under current
legislation as well as Strong Mitigation implemented across all ASEAN, and their associated impact costs. For
mortality, premature deaths and YLLs are alternative indicators and are shown for comparison only
Health Impacts in Physical Units[cases/year]
Indicator

Historical

Current
Policies

StrongMitigation

2015

2030

2030

Unit cost
[USD
2015]

Impact Costs [Thousand USD
2015/year]
Historical

Current
Policies

StrongMitigation

2015

2030

2030

Mortality
Premature deaths

16,45323,9986,530

1,094,019

17,999,895

26,254,268

7,143,944

YLLs

315,588

413,094

114,036

41,37013,055,996

17,089,853

4,717,719

Asthma (Emergency Room
Visits), all age

20,332

21,603

9,867

15

310

330

151

Cardiovascular hospital
admissions, below 65
years of age

2,815

2,705

1,154

920

2,589

2,487

1,061

Cardiovascular hospital
admissions, post 65 yearsof age5,167

10,074

4,645

920

4,751

9,263

4,271

Respiratory hospital
admissions, all age

16,236

17,257

7,956

920

14,93015,8697,316

31,838,063

36,508,057

13,469,368

33

1,064,200

1,220,297

450,219

Morbidity

Respiratory restrictedactivity days, working age

16National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Fig. 2.5 shows the total costs due to morbidity andmortality impacts from air pollution exposure in 2030
in the two alternative future scenarios considered inthis assessment. In the Current Policies scenario, by
2030, the health damage costs alone in Thailand are
estimated to reach approximately 18 billion USD per
year, a value equal to 2.7% of Thailand’s GDP4 due
to little improvement in air quality. In contrast, in theStrong Mitigation scenario, due to the success of the
12 clean air solutions in reducing PM2.5 concentrations,this cost is estimated to decrease to approximately
5.8 billion USD per year, equivalent to about 0.8%
of GDP in 2030. Therefore, this means that by not
implementing the 12 clean air solutions and acting
on air pollution, the cost of inaction in Thailand isestimated to be 12.5 billion USD per year, equivalent
to around 2.0% of GDP in 2030. These costs, relating
only to the health burden of air pollution exposure are
an indication of the large costs which could be avoided
if action on air pollution is taken.

18000

Cost of inaction

Million $US/year

16000
14000
12000
10000
8000

2.5%
2.0%
1.5%
1.0%

6000
4000

0.5%

2000
0

Current Policies Strong Mitigation

Morbidity

Mortality

0.0%

Monetary burden as share of GDP (%/year)

3.0%

20000

Share of GDP

Fig. 2.5 Monetized health burden from PM2.5 exposure in 2030, comparing the Current Policies scenario, andeffective implementation of all measures assessed in this study for Thailand (Strong Mitigation case). Stacked
Bars – Left axis: Burden expressed as million USD per year, Pink dots – Right axis: comparison to GDP
Most of the economic burden from the costs of inaction– or the costs of not implementing all measures-is
associated with mortality (shown here is the monetized
estimate for YLLs) representing consistently about

90% of costs. The remaining 10% are dominated(98%) by costs associated with respiratory restrictedactivity days, which are monetized here as working
day losses (Fig. 2.6).

GDP values are projected for the respective year, expressed in terms of 2015 USD like all other costs. The comparison to GDP
is given as a reference only; damage costs do not directly represent economic losses since they contain a large share of nonmarket costs from monetized loss of life expectancy.
417National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

1400
Respiratory restricted activity days, work age

1200

Respiratory hospital admissions, all age
Cardiovascular hospital admissions, post 65
years of age

800

Cardiovascular hospital admissions, below 65
years of age

Million $US/year

1000

Asthma (Emergency Room Visits), all age

600
400
200
0

Current Policies

Strong Mitigation

Fig. 2.6 Monetized morbidity burden from PM2.5 exposure in 2030, comparing the Current Policies scenario, andeffective implementation of all measures (Strong Mitigation scenario)

Fig. 2.7 provides a summary of premature death,hospital admissions, and restricted activity days due
to ambient PM2.5 as estimated for 2015 and expecteddevelopment towards 2030 in the Current Policies
scenarios as well in the Strong Mitigation scenario
where all the proposed measures are taken. It wasestimated that in 2015, around 20,000 asthma related
emergency room visits, 4,000 hospital admissions for
cardiovascular reasons, 16,000 respiratory related
hospital admissions, and almost 32 million days ofrestricted activity for respiratory reasons among
the working age population are attributed to PM2.5
exposure. In the Current Policies scenario, these
health impacts are projected to increase, while
implementing all 12 solutions in the ambitious Strong
Mitigation scenario could effectively reduce total
hospital admissions by around 16 thousand as well as
reducing the number of emergency room visits fromasthma by 12 thousand and avoiding 17 thousand
premature deaths (Fig. 2.7).
40

30000

Cases, admissions/year

30

20000

25

15000

20
15

10000

10

5000
0

Million labour days lost/year

35

25000

5
2015

Current Policies

Strong Policies0

Premature deaths

Asthma (Emergency Room Visits), all age

Cardiovascular hospital admissions, below 65 years of age

Cardiovascular hospital admissions, post 65 years of age

Respiratory hospital admissions, all age

Respiratory restricted activity days, work age

Fig. 2.7 Summary of the incidence of included impacts estimated for 2015 and analyzed scenarios. Deaths,
emergency room visits and hospital admissions are shown on the left axis, restricted activity days (dots) use theright axis

18National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Beyond the analysis of impacts for policy introducing
12 solutions as a whole (Fig. 2.5), we also quantify
the specific health benefits in monetary terms from
implementing each of the individual solutions includedin the Strong Mitigation scenario. These healthbenefits can be also seen as the cost of inaction foreach solution if they are not implemented. This can be
useful for understanding which measures or solutions
will have the largest health benefits and avoided
health related costs, and could also be comparedagainst the costs for implementing each solution ifsuch data becomes available. Fig. 2.8 shows that thelargest benefits are expected from the switch to clean
cooking for which monetized benefits are estimatedat about 2.5 billion USD5, while increasing renewables
and applying post combustion controls in the power
and industrial sectors as well as improving industrial
standards could bring benefits of nearly 4 billion USD.
While combined, the three agricultural solutions of
reducing agricultural residue burning, dietary shifts
and more efficient fertilizer application and livestock
management practices could also avoid healthdamages worth about 3 billion USD. Finally, solutions

which address emissions from transport, improve
waste management, and improve forest management
could result in monetary benefits in the order of 1.5
billion USD per sector.
Thailand is also impacted by transboundary air
pollution from neighbouring countries which also
contributes to health impacts in Thailand; consequently,
Thailand would also benefit from mitigation efforts in
those countries. The initial assessment shows that
implementation of all 12 solutions in the whole ASEAN
region would bring another 0.6 billion USD per yearof avoided damage costs in Thailand, in addition to
those avoided by action in Thailand itself. Fig. 2.8
directly shows the additional benefits gained from
implementation of each of the solutions in Thailand(blue), and in the other ASEAN countries (yellow).
It is therefore clear, especially in some specific areasthat a large benefit can be reaped from collaboration
on air quality action. While it is also likely that other
ASEAN countries would benefit from implementation of
ambitious air quality actions in Thailand.

Clean cooking
Renewables, post-combustion controls in power and industry
Industrial processes standards, incl. energy efficiency
Emission standards/electrification - transport
Vehicle inspection and maintenanceInternational shipping
Livestock and N-fertilizer application
Dietary changes
Open burning of agricultural residues
Waste management
Coal, oil and gas production

From Thailand
From other countries

Prevention of forest and peatland fires

0

1000

2000

3000

Total health benefits [Million USD]

Fig. 2.8 Monetized health benefits from individual measures applied in 2030 on top of current policies

Note that this estimate includes only the monetization of health impacts from ambient PM2.5. Additional benefits would be
expected from indoor air pollution reductions in the measure Clean Cooking.
5

19

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

2.3. Climate and other co-benefits
This assessment has estimated the costs that Thailand
will experience in the future if no further action istaken due to the increasing health burden of air
pollution. It has also shown the large benefit for air
quality and health in Thailand which could be realized
through the implementation of ambitious clean air
solutions. Implementing the 12 clean air solutions arelikely to also have multiple other additional benefits
beyond reducing emissions and concentrations of air
pollutants, these include benefits for climate change
through reducing GHG emissions. Fig. 2.9 shows thepotential for each of the individual clean air solutions to
reduce GHG emissions and specifically compares PM2.5
concentration reductions with emission reductions forCO2 and methane (CH4) (converted to CO2-equivalentemissions using Global Warming Potential (GWP)-100)
from implementing each solution.
In 2030, the largest potential for reducing GHG
emissions are for the solutions related to power andindustry sectors: introducing renewables in the power
sector, combined with strict post-combustion emission
controls, saves 60 Mt of CO2 equivalent emissions
annually, while also decreasing population average
exposure to PM2.5 by more than 1 µg/m3. Strengthening
industrial process standards, in conjunction with theenergy efficiency improvements associated with them,also have sizeable benefits for reducing GHG emissions,while the effects of electrification of the car fleet are
only marginal in 2030 due to slow vehicle turnoverrates. Although not considered in this analysis, it islikely that for road transport, comparable benefits
could be achieved by demand side policies, including
improved public transport, low emissions (car free)zones, congestion charging schemes, developing
new bike lanes and incentivize active mobility, etc.
Such policies could at the same time have multiple
other co-benefits such as for reducing congestion andtime spent in traffic jams, reduced number of roadaccidents, as well as additional health co-benefits from
active travel.

20

The measure with the highest potential for PM2.5
reduction in 2030, clean cooking, comes with asmall negative impact on CO2 emissions because of
the switch from biofuel (which is CO2 neutral in our
accounting under the assumption that the biofuel issustainably produced) to LPG (liquefied petroleumgas). Given the high benefits of clean cooking for othersustainability goals this should not be an argument
against this transition. For example, it would also
reduce the health burden from indoor air pollution andhelp achieving several SDGs, such as improved genderequality (SDG 5) since women are more exposed toindoor air pollution and spend more time cooking withinefficient stoves or collecting firewood. Furthermore,
CO2 emission increases during transition to clean
cooking can be avoided by switching directly to electriccooking, instead of LPG, provided renewable energy isused to produce electricity.
The analysis presented in this report focuses primarily
on benefits that can be achieved in the near term,
typical air quality policy time horizon. Climate
policies set longer-term targets and respective
transformational measures require often longer time
to be fully implemented. Fig. 2.9 shows both the air
quality and climate co-benefits achievable in the nearterm (by 2030) as well as by 2050 to highlight cobenefits of transformative changes in the longer term.
A longer time perspective helps to appreciate betterthe co-benefits of some measures like electrificationof the transport sector, which have a longer inertia
due to fleet turnover, and large-scale deployment of
renewables in power and industry. Conversely, inactionin these sectors would also forgo the substantial GHG
reductions and thus have a ‘cost of inaction’ in termsof emissions, which can also be associated with amonetary cost if a carbon price is introduced.
Climate policies on their own bring also co-benefits
for air pollution, although they might not explore
full potential existing via combined air quality anddevelopment policies.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

GHG emissions [Mt CO2 eq]

2030

2050

200

200

Clean cooking

150

150

Renewables, post-combustion controls in power andindustry

100

100

50

50

0

0

0

0.5

1

1.5

PM2.5 [µg m-3]

2

Industrial processes standards, incl. energy efficiency
Emission standards/electrification - transport
Vehicle inspection and maintenanceInternational shipping
Livestock and N-fertilizer application
Dietary changes
Open burning of agricultural residues

0

0.5

1

1.5

2

PM2.5 [µg m-3]

Waste management
Prevention of forest and peatland fires

Fig. 2.9 Co-benefits of individual measures for GHG emissions (CO2 + CH4) when fully implemented in 2030 (left)
and 2050 (right)

21

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

3. Limitations
3.1. Scenarios and solutions
The analysis presented in this report relies on existing
scenarios developed for the Clean Air and Climate
Solutions for ASEAN Report (UNEP/CCAC, 2023);
development of completely new scenarios fit foranalysis with the GAINS model was not feasible due toavailable resources. However, the available scenarios
reflect a broad range of interventions with respect toimpacts and are expected to cover the potential scopeof local policies. Indeed, a review of the recent policy
mapping providing a regulatory context and outlook
for Thailand (Nikam, Archer and Nopsert, 2021) showsthat the assumptions about policies considered in the
Current Policies scenario are largely complete andconsistent with the existing legislative framework in
Thailand addressing air quality and climate change.
Even though, some of the most recently decided
policies considered for implementation in the coming
years are not included in the current policy case,their reduction potential is captured in the further

mitigation potential estimated in the assessment. The
same applies for the planned policies which are well
represented in the Strong Mitigation scenario.
It was not possible to compare results of thisassessment with few existing studies for Thailand
where health impacts and benefits were estimated(Chavanaves et al., 2021; Chulabhorn ResearchInstitute, 2018) as they were either performed only
for specific regions (cities, provinces) of Thailand,
methodologies were different or fully transparent
enabling fair comparison.
The default temporal model resolution is five years
and although the analysis can be done for single orindividual years, the available set of scenarios does
not have finer resolution. The respective assessments
are aiming at demonstration of benefits (assessmentof cost of inaction) in a longer-term perspective, whichis consistent with typical responses to policy actions.

3.2. Data availability/reliability
There are several limitations due to available data,
tools as well as available resources. The quality andrepresentativeness of the results and assessment
critically depends on the underlying data on baseline
rates of mortality, morbidity, and costs. While thedefault data set generated from international sourcesis scientifically robust, it is not necessarily fully

representative of the local circumstances, and local
data are clearly preferable. The assessment is currently
based on a combination of local and international data.
In particular, for hospitalization costs and working day
costs, no local data were available.

3.3. Modeled PM2.5 concentrations
Although GAINS reproduces the variability of measured
ambient PM2.5 concentrations throughout the country
well, absolute concentrations are underestimated. This22

likely leads to an underestimate in health impacts anddamage costs. The difference to the GBD estimate ofPM2.5 attributable deaths is almost a factor of 2.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

3.4. Costs of action
While the assessment focuses on costs of inaction,an estimate of the cost of action (including bothcurrent policies as well as further mitigation measures’implementation) has not been undertaken in thisassessment and would require further discussion withnational experts to validate and extend valuation of
measures.
The GAINS model routinely quantifies annualized
costs for implementation of technical (‘end-of-pipe’)
air pollution control measures. A holistic assessment
should also include costs for structural transformations
such as, for example, decarbonization of the energy
sector, which is quantified in the energy system models,
typically used as source of the energy use scenariosin the GAINS model. Further costs associated withtransformation to low pollution economy might include
costs of providing access to clean energy for cooking,improvement of waste management, transformationof agricultural production system, and costs ofenforcement of considered legislation. A preliminary
estimate in GAINS, considering only technicalmitigation options and assuming international costs,
indicates annualized costs for additional measures(Stringent vs Current policy cases) in order of 4-5
billion USD, which represents about a third of theestimated cost of inaction. However, as noted earlier,this estimate does not include costs of transformation
towards higher share of renewables, alternative diets,
etc.
In summary, in terms of cost of action estimation,
there are number of limitations in the models anddata:
i. GAINS relies on international defaults for costs
and does not include local cost factors whereappropriate (although, most air pollution control
technology is traded internationally),ii. GAINS covers only costs for technical air pollution
control measures and not the system costs forenergy transition, dietary changes, institutionalcosts, etc., andiii. it assumes full implementation even of very costly
technologies which would be left out in a costeffective scenario.

3.5. Scope of impacts assessed
The range of possible costs on the impacts side,
used for quantifying the ‘costs of inaction’, is large.
Beyond monetized mortality costs and market costs
for morbidity, they can include other macroeconomicand environmental costs such as:‧ Crop losses associated with elevated ozone,‧ Ecosystem impacts from air pollution (Sulfur andNitrogen deposition),‧ Macroeconomic impacts of loss of labour force.
The analysis in this assessment is limited to costsof mortality and morbidity, and to only ambientconcentrations of PM2.5. Only some morbidity
outcomes are considered, so the total impact via
morbidity is likely underestimated. Exposure to otherair pollutants like nitrogen dioxide (NO2) and ozone(O3) is associated with health and ecosystems impacts
and crop loss due to elevated ozone concentrations. O3
formation is influenced by CH4 emissions and therefore
introduction of measures analyzed in this assessment

can bring additional co-benefits. However, these areorders of magnitude smaller than the particulatematter reduction related benefits and are not reported
here.
Impact from deposition of sulfur and nitrogen, e.g.,
acidification and eutrophication of ecosystems,
could be significant and the measures analyzed in
the assessment would bring reductions of SO2, NOx,
NH3 emissions contributing to reduced depositionand consequently leading to at least partial recoveryof ecosystems in the long-term. However, lack ofconsistent datasets on ecosystem sensitivity acrossthe region as well as cost assessments for associatedimpacts does not yet allow for such analysis.
Finally, the assessment of macroeconomic impacts andbenefits using macroeconomic models was beyond theresources available for this study and would require
involvement of respective economic modelling teamequipped with appropriate tools and data.

23

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

4. Conclusion and recommendations
Thailand bears a high health burden from exposure
to ambient PM2.5 from both mortality as well as
morbidity. Impact costs associated with this burden
are substantial. The efficient implementation of
already passed legislation will be important to slow
down or halt the increasing trends in emissions and air
pollution impacts. However, if no further policies are
introduced, decreases are not expected.
Building upon the assessment for clean air in the ASEAN
region, this study identified significant mitigation
potential consisting exclusively of proven technical andnon-technical actions that, if fully implemented, would
deliver significant reductions of air pollution and in thelonger-term important climate co-benefits.
Lack of further action translates into ‘cost of inaction’which has been estimated here considering mortality
and morbidity due to air pollution. Such cost has been
estimated at about 12.5 billion USD in 2030 with mostcosts associated with premature mortality. Introducing
policies stimulating the rapid introduction of identified
further mitigation measures would result in significant
benefits at potentially much lower costs, although thelatter was not fully estimated. A preliminary estimate of
implementation costs of technical measures indicatesthat these would represent in 2030 less than a third of
the cost of inaction.
The assessment supports implementation andenforcement of already existing policies in Thailandand shows that the potential scope for mitigation
developed in this study is consistent with the mostrecent plans, although goes beyond and is more
ambitious both in terms of pace and efficiency of
implementation. This work also aligns with otherassessments which are being developed by the Pollution
Control Department in Thailand to simultaneously
tackle the issues of climate change, air quality andsustainable development. For example, an Integrated
Assessment of Air Pollution and Climate Change for
Thailand has recently been developed under the
Supporting National Action Planning (SNAP) initiative

24

by the Climate and Clean Air Coalition (CCAC). This
assessment also directly quantifies the potential forvarious policies to reduce emissions of air pollutants,
short lived climate pollutants and greenhouse gases
at the national and regional level in Thailand. Aligningwith the assessment presented here, the SNAP
assessment identifies key policies which could largely
reduce air pollution with benefits for human health aswell as reducing emissions of GHGs and SLCPs with
benefits for mitigating climate change (CCAC, 2023).
Key policies delivering major benefits from bothassessments include those related to:‧ Clean Cooking: A complete transition to clean
cooking fuels, ideally directly to electricity,‧ Power and industry: Accelerated transition to
renewable electricity generation in conjunction
with end-of-pipe air pollution emission controls,
electrification in industry, optimized energy efficient
production processes,‧ Road Transport: Electrification of the vehicle fleet,stringent emission controls on the remaining
combustion engines,‧ Waste Management: More efficient waste
management and prevention of open burning ofwaste,‧ Agricultural Burning: Strict enforcement of the ban
on agriculture stubble burning; increased preventionof forest fires and wildfires.
Further actions driven by health concerns will also
bring important climate co-benefits (reduction of
GHG emissions), which will be larger in the longterm translating by 2050 into significant reductions of
CO2 and CH4. Among the most important measuresdelivering these longer-term benefits are promotion ofstronger employment of renewables in power sector,
electrification of transport, and waste management
policies.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Annexes
Annex 1. Methodology and data
sources
Overview
The calculation of the attributable mortality andmorbidity burden in the assessment follows acomparative risk assessment framework, which meansthat out of a total burden of disease (in terms ofdeath, or cases of morbidity events), a certain shareis attributed to exposure to PM2.5. The necessary
ingredients for this calculation are the ambient PM2.5
concentrations, population at the same grid resolution,
concentration-response functions (CRFs) for theselected disease endpoints, and baseline rates forthese disease endpoints. Further, particular unit costs
are needed for the assessment of impact costs.
A chart of the information flows and calculation steps
is shown in Fig 1. The GAINS1 (Greenhouse gas-Air
Pollution Interactions and Synergies) model (Amannet al., 2011) is the central tool used in the analysis.
GAINS, developed by the International Institute forApplied Systems Analysis (IIASA), brings together
data on economic development, the structure, control
potential and costs of emission sources, the formationand dispersion of pollutants in the atmosphere andan assessment of environmental impacts of pollution.
It is used here to estimate emissions of PM2.5 andits precursor pollutants, to calculate ambient PM2.5
concentrations arising from the emissions, as
described further in Section 1.2, and to evaluate thehealth impacts as detailed below.
From ambient PM2.5 concentrations, health impacts
in terms of mortality and morbidity are calculated.
All outcomes are listed in Box 1. GAINS routinely

1

quantifies health impacts in terms of premature
deaths ad YLLs from six diseases in line with themethodology developed within the Global Burden of
Disease (GBD) assessments (Murray et al., 2020).
Within this assessment, impact calculations have been
extended to cover morbidity related to cardiovascularand pulmonary diseases. For this purpose, newconcentration-response relationships for several
morbidity endpoints have been developed and are
currently under review in the scientific literature. A fulllist of endpoints for mortality and morbidity coveredby the assessment is given in Box 1 and a descriptionof the concentration-response functions is given in
Section 1.4.
For each health outcome d (disease-specific mortality
or morbidity), we calculate a population attributablefraction of the total burden based on the population
exposure distribution to PM2.5,
1where RRnda is the relative risk for annual mean
PM2.5 concentration level n, to which popn people areexposed. RR and thus also PAF can be specific to age
a; for disease endpoints where the CRF are not age
specific, the index a can be dropped from Eq. 1.

For more details, refer to: http://gains.iiasa.ac.at.

25

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

LegendScenario Analysis with GAINSInputsAvailable

SCENARIO ANALYSIS WITH GAINSGAINSParameters
Activity Levels(Fuel Use, Livestock, etc.)

Application of
Emission Control Technologies

Technology Speciﬁc
Emission Factors

EnergySystem Costs

GHG
Emissions

Input
Needed(Intermediate)
Outputs

Technology Speciﬁc
Unit Costs

Air Pollutant
Emissions

Air Pollution
Control Costs

Atmospheric
Dispersion

Concentrations of PM2.5

Concenrationresponse
FunctionsConcenrationresponse
FunctionsBaseline
Mortality Levels

Baseline
Mobidity Levels
Morbidity
Due to PM2.5

Morbidity
Due to PM2.5

Value of a Life Year
(VOLY)/ Value ofa Statistical Life (VSL)

Unit Costs
Monetized
Mortality

Monetized
Costs

Health Related Costs

Fig 1. Flow of information in the GAINS model to assess policy costs and impact related costs for one scenario(Current Policies or Mitigation cases). Orange fields highlight data inputs needed from local partners

26

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Attributable cases c of death, hospitalization,
emergency room visits or restricted activity days arecalculated as2
where cBL,da are the baseline number of cases for the
specific outcome d. The calculation is age specificfor mortality. Baseline mortality rates in five year
age groups were taken from the GBD Results Tool,
representing the Global Burden of Disease results for2019 (Murray et al., 2020).). Baseline morbidity rates
have been derived from internationally available data
sets and national inputs, as described in Section 1.5.
We assume that incidence rates stay constant in the
future. Population projections follow the UN World
Population Prospects 2017 (UN, 2017).

Box 1: Endpoints considered in the assessment.
Causes of death considered in the assessment:

Morbidity indicators considered in the assessment:‧ Chronic obstructive pulmonary disease‧ Asthma-related emergency room visits‧ Ischemic heart disease‧ Cardiovascular hospital admissions (pre/post 65
years)‧ Stroke‧ Respiratory hospital admissions‧ Lung cancer‧ Respiratory restricted activity days (working age)

‧ Acute lower respiratory infections‧ Type 2 diabetes

From premature deaths, years of life lost (YLLs) arecalculated by multiplying the number of attributable
deaths with the remaining life expectancy at the ageof death. We note that this approach is conservative inthe sense that it relies on the actual life table for thecountry itself. Other approaches, such as the Global
Burden of Disease assessments, use remaining life
expectancy from countries with the highest observed
life expectancies for this purpose, such as Japan, andtherefore arrive at higher estimates of YLLs.
The calculation of impact related costs relies on unit
costs ucd for each outcome d which are multiplied with
the PM2.5 attributable number cd for each outcome:3
For the valuation of mortality, either the value of astatistical life (VSL) or the value of a life year (VOLY)
can be used. VSL needs to be combined with thenumber of attributable deaths, while VOLY is usedin conjunction with the number of life years lost
(YLLs). Cost data used in the assessment rely on a
combination of national inputs and default estimatesfrom international data sets, as described in Section
1.6.
The quantification of health impacts and impact costsby measure relies on the definition of mitigation
measures as described in the main report, Table 1.
While the GAINS model uses linear relationships
between emissions and PM2.5 concentrations, therisk functions for mortality are non-linear, which would
imply that the size of the effect of a measure depends
on the sequence of the measures taken. In order toavoid this complication, we linearize the relationshipbetween PM2.5 concentrations and each healthoutcome based on the concentration and health impact
levels attained under the Current Policies scenario and
the Strong Mitigation case.

Emissions and ambient PM2.5 concentrations
GAINS uses activity projections from external sources(for example, macroeconomic projections and energy
production and use from IEA world energy model(IEA, 2019), projections of livestock and fertilizer use
from the UN Food and Agriculture Organization (e.g.,
Alexandratos and Bruinsma (2012)) as drivers andcombines them with information on application rates
of a large portfolio of emission control measures tocalculate emissions of air pollutants (all key precursorsof PM2.5, including primary PM2.5, SO2, NOx, NH3, and
NMVOC) and greenhouse gases. Each technology is
associated with an emission factor for each pollutant,and cost characteristics used to quantify costs of air
pollution abatement measures.
Emissions are calculated at a detailed sectoral levelbased on activity data, uncontrolled emission factors,
the removal efficiency of emission control technologies
and the extent to which such technologies (measures)are applied:
427

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

where:

Source region, activity type, abatement measure, pollutant, respectively
Emissions of pollutant p (for SO2, NOx, NMVOC, NH3, PM2.5) in source region i. Emissions of
GHGs (CO2, CH4, N2O) are also calculated.
Activity level of type k (e.g., coal consumption in power plants) in source region i
Emission factor of pollutant p for activity k in region i after application of control measure m
Share of total activity of type k in region i to which a control measure m for pollutant p is applied.

In terms of source regions, GAINS has global coveragewith source regions which are countries or subnational units in most parts of Asia. In Thailand, GAINS
distinguishes five regions: Bangkok, Central Valley,
Southern Peninsular, Northern Highlands, Northeast
Plateau.

Transport Model (Simpson et al., 2012) as describedin the Supplementary Information to Amann et al.
(2020). PM2.5 concentrations are calculated on a 0.1°
grid (roughly 10x10km) and thus correspond to urban
background levels, not to pollution hotspots.

To calculate ambient PM2.5 concentrations, GAINS
uses a linear approximation of the EMEP Chemistry

Validation of ambient PM2.5 concentrations
Fig 2 shows a comparison of modelled PM2.5
concentrations for 2015 against monitoring datafor 2015 provided by the Thailand Pollution Control
Department, and observations for 2019 as containedin the WHO Ambient Air Quality Database. Only
stations with temporal coverage >75% are used in
the comparison. GAINS reproduces the variation ofconcentrations well but underestimates total PM2.5 byapproximately 40% on average, which can be due tounderestimated emissions and missing sources, or
lacking model resolution. Stations can be influenced
by local sources such as busy roads and may reflect
local conditions which GAINS cannot represent with its
resolution of ~10km.

50
45

PM2.5 modelled [µg m-3]

40
35
30
25
20
15
10
5
0
0

10

20

30

40

50

PM2.5 observed [µg m-3]
Fig 2. Comparison of PM2.5 concentrations modelled with GAINS for 2015 against observations in 2015 and 201928

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Concentration-response functions
Concentration-response functions (CRFs) used in thisproject have been derived from a systematic review andmeta-analysis of recent epidemiological studies going
beyond the evidence available to earlier assessments.
Most existing CRFs for short-term exposures to PM2.5
assume a fixed effect size in a log-linear function overan extrapolated exposure range, based on evidence
primarily from Europe and North America, while thesystematic review used for this assessment includes
all available evidence globally. Endpoints were selectedbased on the CarbonH tool (Spadaro et al., 2018).
The full documentation is currently under review (Ruet al., in review). Outcomes are:‧ Asthma-related emergency room visits

In addition to these endpoints, CRFs for asthma related
hospital admissions, bronchitis incidence in children,and incidence of dementia have been developed by Ru
et al. (in review) but these are not used in the current
assessment.
Ru et al. (in review) developed two versions of the
CRF using either a log-linear or a nonlinear regression
model to fit the available studies. In this assessment,
we use the log-linear version. Since the quantificationof benefits from individual measures requires alinearization step, the log-linear CRFs seem better
suited.
In the log-linear CRFs, relative risk is expressed as‧ Cardiovascular hospital admissions (pre/post 65
years)‧ Respiratory hospital admissions‧ Respiratory restricted activity days (working age)With the coefficient β derived from the regression analysis as
Asthma-related emergency room visits

0.0034/µg m-3

Cardiovascular hospital admissions (pre 65 years)

0.0009/µg m-3

Cardiovascular hospital admissions (post 65 years)

0.0013/µg m-3

Respiratory hospital admissions

0.0013/µg m-3

Respiratory restricted activity days (working age)

0.0102/µg m-3

Baseline Morbidity Rates
The robustness of the results of the assessment
crucially depends on baseline rates for mortality andmorbidity incidences. While cause specific mortality
estimates by age group are available from the Global
Burden of Disease Results Tool2 consistent with the
GBD 2019 assessment (Vos et al., 2020), baseline rates

for the morbidity endpoints considered here were only
partially available from national data (Section 1.5.1).
For those endpoints where national data were notavailable, default rates have been estimated frominternational datasets as described in Section 1.5.2.

National data
Data on hospital admissions by illness in 2019 havebeen made available to this assessment by the Health
Data Center (HDC), Ministry of Public Health. These
have been used to calibrate the baseline morbidity
levels in the assessment for the outcomes related to
cardiovascular and respiratory hospital admissions.
Only the primary diagnosis for hospital admission was
taken into account.

2

Asthma related emergency room visits were estimatedbased on the asthma hospital admission rate, scaledwith the ratio of asthma related ERVs to hospital
admissions in the default data set.

For more details, refer to: https://vizhub. healthdata.org/gbd-results.

29

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Baseline rates of the related diseases: Default estimates
Data on baseline morbidity rates are often difficult toobtain, which is why a default data set was estimatedfrom global sources. The morbidity endpoints
considered here, such as hospital admissions andemergency room visits, are the combined outcomesof the prevalence of the diseases and some otherfactors. These factors influence whether people withthe disease get admitted to a hospital, or whether they
go to the emergency room. As such, we derive thecountry-specific baseline morbidity data based on thebaseline prevalence rates of the respective diseasesof the country and then adjust with the benchmark
baseline rates reported in available sources.

Morbidity endpoints

Related diseases in GBD 2019

Asthma emergency room visit (ERV) for all population

Prevalence rate of asthma (>=20)

Asthma hospital admission for post-20

Prevalence rate of asthma (>=20)

Asthma hospital admission for below-20

Prevalence rate of asthma (<20)

Cardiovascular hospital admission for below-65

Prevalence rate of cardiovascular diseases (<70)

Cardiovascular hospital admission for post-65

Prevalence rate of cardiovascular diseases (>=70)

Respiratory hospital admission for all-age population

Prevalence rate of chronic respiratory diseases, all age

Country-specific data was obtained for the year 2015
for both males and females from the GBD Results Tool3
Direct statistics of the respective morbidity endpoints
were obtained for the United States from the Agency
for Healthcare Research and Quality’s HCUPnet Online
Database (AHRQ, 2022) as benchmark data. We thencalculated a scaling factor between the related diseases
and the morbidity cases and applied to the country. By
doing this, we understand that our results are basedon a strong assumption that the relationship between

morbidity outcomes and the related diseases causing
the outcomes are the same universal and same withthe relationship derived in the US. We realize that
other countries may have different availabilities for
hospital admissions and emergency rooms, especiallyin rural areas.

We obtained the baseline prevalence rate of therelated diseases from the Global Burden of Diseases
2019 (Global Burden of Disease Collaborative Network,2021) with the following mapping:

Unit Costs
As with the baseline morbidity data, the unit costs
used in this study (see main report, Table 2) are
informed in parts by national data (Section 1.6.1) andcomplemented by default estimates from international
data for all outcomes where national data were notavailable (Section 1.6.2).

National data
The Report on Environmental Benefits Mapping andAnalysis Program (BenMap) input data for Thailand(Pham, 2022) lists VSL estimated from studies
conducted in Thailand. We use this value for VSL andestimated the Value of a Life Year (VOLY) by scaling
the Thai VSL with the ratio of VOLY to VSL in thedefault data set.

Default estimates from international data
Unit costs for each morbidity outcome were estimatedfrom international data sets. Specifically, we obtained
unit cost data for 54 countries from the CaRBonH
tool (Spadaro et al., 2018). Most of these countries
were in Europe and Central Asia. We also obtained
the unit cost data from the Healthcare Cost andUtilization Project (HCUPNet). As such, we had aunit cost dataset from 55 countries. We then obtained
the Gross Domestic Product (GDP) per capita data

330

from the World Data Bank (World Bank, 2021). We ran
regressions for the unit cost of each endpoint using
the logarithm of GDP per capita:
where i indicates different morbidity endpoints, andj indicates different countries. The coefficients weobtained from the regressions were as below:

For more details, refer to: https://vizhub.healthdata.org/gbd-results.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Endpoint

Intercept

SlopeChildren bronchitis symptom day

8.80

0.66

Children asthma symptom day

6.06

0.65

Adult bronchitis day

13.46

0.67

Work loss day

7.19

0.65

Restricted activity day

6.84

0.65

Hospital admission

10.81

0.78

VSL

17.09

0.65

VOLY

13.82

0.65

We used the regression coefficients to estimate unit costs for other countries for each endpoint. The estimatedcost is per case of the morbidity.

31

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Annex 2. Emission scenarios
Both developed scenarios (Current Policies and Strong
Mitigation) draw on the socio-economic assumptionsin the World Energy Outlook 2018 scenarios (IEA,
2018). The macroeconomic outlook for the ASEAN
region in that study forecasted a robust growth of therespective economies. The scenarios differ, i.a., withrespect to considered energy and agriculture sector

efficiency improvements and assumptions about theimplementation of air pollution control technologies/
policies, which has implications on emissions of
air pollutants as well as greenhouse gases as is
illustrated in the following sections and in the national
assessment.

Current Policies
As a result of the continued strong economic growth,
the modelling anticipates significant increase of CO2
emissions for the 2030 in the Current Policies scenario,
which represents a baseline in this assessment: nearly
60% increase from 2015. The main contributions andgrowth of CO2 emissions in the ASEAN region arefrom power, industry, and transport sectors (Fig. 3).
The scenario draws from the World Energy Outlooks
New Policy Scenario (NPS) and includes Nationally
Determined Contributions (NDCs) reported up to 2018
(IEA, 2018).
The baseline (Current Policies) also assumes thatexisting and recently introduced legislation in the power,
industry and transport sectors (e.g., Zhang, 2016;
Motokura et al., 2017; He et al., 2021; Dieselnet, n.d.;TransportPolicy.net, n.d.) are implemented effectively,

32

and have slowed the growth of emissions of key PM
precursors. In fact, emissions of these precursors are
growing slower than CO2, suggesting gradual decouplingof economic growth from air pollutant emissions. At
the same time, however, the existing legislation is not
sufficiently strong to offset the increase in fuel use andproduction activities, which explains the relatively faster
growth in CO2 (Fig. 3).
Another notable trend involves residential cooking, asector that contributes a significant share of primaryPM2.5. For primary PM2.5 emissions, a trend towards
clean fuels for cooking is clearly seen through declining
emission in this subsector (Fig. 3); affecting also trend
for NMVOC. This is the result of the long-standing
policies to provide access to clean energy both forrural and urban residents in the region.

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

SO2

1.0
0.5

10.0

3.5

9.0

3.0
2.5
2.0
1.5
1.0
0.5

0.0
2015 2020 2025 2030

0.02015 2020 2025 2030

NH3

3.5
3.0
2.5
2.0
1.5
1.0

Million tons NMVOC

4.0

7.0

0.5
0.0
2015 2020 2025 2030

5.0
4.0
3.0
2.0

2.0

6.0

1.5

5.0
4.0

1.0

3.0
2.0

0.5

1.0

0.0
2015 2020 2025 2030

CH4

7.0
6.0

2.5

0.0
2015 2020 2025 2030

8.0

4.5

3.0

8.0

NMVOC

5.0

Million tons NH3Million tons NOx (as NO2)

1.5

4.0

PM2.5

30.0

Agriculture

25.0

Waste management

Million tons CH4

2.0

Million tons SO2

Billion tons CO2

2.5

NOx

Million tons PM2.5

CO2

1.0
0.0
2015 2020 2025 2030

20.0
15.0

Transport
Solvents
Fuel extraction

10.0

Industry

5.0

Residential

0.02015 2020 2025 2030

Power plants

Fig 3. Sectoral emissions of CO2, primary PM2.5, key precursors of ambient PM2.5, and CH4 for the ASEAN region
in the Current Policies scenario

Strong Mitigation
This scenario identifies further reduction potential by2030 (beyond Current Policies) considering applicationof technologies with lowest emissions included in
the GAINS model database, assuming their full andeffective application while considering the limits oftechnical feasibility, and impact of selected nontechnical measures. The ‘non-technical measures’refer to measures that explore the potential for: further
improvements in energy efficiency in different sectors,
increasing the share of electric vehicles, accelerating
access to clean energy for cooking, achieving
significant improvements in nitrogen use efficiency
in agriculture, and dietary changes (e.g., lower meat
protein consumption) assuming that calorific intake is
in line with the Lancet EAT Planetary Diet (Willett etal., 2019).

The potential for energy efficiency, fuel switching,electric vehicles originate from the assessment andcomparison of the International Energy Agency (IEA)
NPS scenario and the Sustainable Development
Scenario (SDS) where the latter is designed to achieve
CO2 reduction consistent with the Paris Agreementtargets (IEA, 2018).
While the policies implemented in the Current Policies
contribute to the slower growth in air pollution (Fig.
3), comparing Strong Mitigation scenario with theCurrent Policies shows significant opportunities toreduce emissions further (Fig. 4).

33

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

SO2

1.5
1.0
0.5

10.0

3.5

9.0

3.0
2.5
2.0
1.5
1.0
0.5

Baseline

Mitigation

0.0

NH3

Million tons NMVOC

Million tons NH3

4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.0

Baseline

Baseline

7.0

Mitigation

6.0
5.0

1.5

5.0
4.0

1.0

3.0
2.0

0.5
Baseline

Mitigation

2.0

Baseline

Mitigation

Baseline

Mitigation

30.0

Agriculture

25.0

Waste management
Transport
Solvents

15.0

3.0

0.0

CH4

20.0

4.0

0.0

2.0

6.0

0.0

Mitigation

1.0

0.5

2.5

1.0

7.0

4.5

3.0

8.0

NMVOC

5.0

PM2.5

Million tons CH4

0.0

4.0

Million tons NOx (as NO2)

2.0

Million tons SO2

Billion tons CO2

2.5

NOx

Million tons PM2.5

CO2

Fuel extraction

10.0

Industry

5.0

Residential

0.0Power plants
Baseline

Mitigation

Fig 4. Sectoral emissions of CO2, primary PM2.5, key precursors of ambient PM2.5, and CH4 for the ASEAN region
in the Current Policies (Baseline) and Strong Mitigation scenario estimated for 2030

While CO2 emissions decline by nearly 25% considering
efforts to increase energy efficiency and fuel switchingpotential, the air pollutant emissions are estimatedto decline by from 55% (NMVOC) to 84% (SO2), andemissions of methane decline by 40%. Achieving
such reductions would require additional policy action
stimulating introduction of further measures which weredeveloped for the Strong Mitigation case. They include
12 measure packages; each package includes several
technologies applicable to the particular sector(s).
More detailed discussion of how the measures were
derived are included in the UNEP/CCAC (2023)4 andalso UNEP (2019) reports.

Individual Measures
Table 1 provides description of the 12 measure
packages that bring significant reduction of air
pollutant emissions in the ASEAN region. The
measures represent bundles of policies rather than
individual actions and have been selected based on
their potential to deliver the maximum reduction

That report identified 15 solutions which included measures targeting reduction of CH4 and HFCs, i.e., rice paddies, wastewater
treatment, and HFC-refrigerant replacement.
434

in population’s exposure to PM2.5. As shown in thenational assessment report, several of these measures
also bring strong co-benefits including reduction ofgreenhouse gases emissions and contribute to theachievement of several Sustainable Development
Goals (SDGs).

National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

Table 1. Description of key mitigation options associated with the identified 12 solutions
12 solutions

Brief description

Clean cooking

Clean alternatives for traditional cooking including LPG stoves and higher
efficiency solid fuel stoves incl. fan assisted stoves.

Renewables, post-combustion
controls in power and industry

Consideration of potential for fuel switch and renewable energy andapplication of high efficiency flue gas cleaning technology in power plants
and industrial boilers, including flue gas desulphurization, high efficiency
dust removal.

Industrial Process standards,
incl. energy efficiency

Improvements in process technology, more efficient capture and removal ofprocess and fugitive emissions from industrial production.

Emission standards/
electrification - transport

Introduction of more stringent emission limit values and energy efficiency
standards for vehicles. Further potential is estimated assuming the immediate
introduction of legislation requiring for new vehicles (road and non-road) theEuro VI/6 equivalent emission standards and/or accelerated electrificationof fleet.

Vehicle inspection andmaintenanceIntroduction of stricter legislation requiring more frequent and enforced
vehicle inspection and maintenance that will enable early recognition andelimination/repair of high emitting vehicles.

International shipping

Low sulphur fuel, i.e., 0.5%S in heavy fuel oil with further reduction to
0.1%S, introduction of particulate filters and NOx Reduction Selective Catalyst
Reduction (deNOX SCR) installations. Alternatively, flue gas desulfurization
can be installed to achieve the same reduction of sulfur dioxide (SO2) as
when using low sulfur fuel.

Livestock and N fertilizer
application

Control of ammonia emissions from livestock production and mineral nitrogen
fertilizers application. Livestock measures include construction of new low
emission housing, covered stores for manures, and efficient application ofmanures on land. For mineral fertilizers, emissions from urea application are
addressed either by replacing urea with, for example, ammonium nitrate,
improving urea application (proper timing and doses), and promotion of new
formulations and urease inhibitors.

Dietary changes

Shift to less meat protein in diets resulting in lower numbers of livestock andlower mineral fertilizer use as well as improved nitrogen use efficiency.

Agriculture residue burning

Efficient enforcement or banning the open burning of agricultural residues.

Waste management

Primarily addressing solid municipal waste management by reducing trashburning and introducing efficient waste collection and recycling schemes.

Prevention of forest, peatland
fires

Improved forest, land and water management and fire prevention strategies.
Enhance collaboration through ASEAN Agreement on Transboundary Haze
Pollution.

Coal, oil and gas production anddistribution

While most of the measures in fossil fuel extraction, processing, anddistribution would reduce emissions of methane, there are some reductionsof PM precursors (including BC) when routine flaring is reduced or banned aswell as reducing tailing fugitive dust emissions from mining industry following
reduced demand for coal in sustainable development scenarios.

35National Assessment of the Cost of Inaction of Tackling Air Pollution in Thailand

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