NATIONAL ASSESSMENT OF THE
COSTS OF INACTION OF TACKLING AIR
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National Assessment of the Cost of Inaction of Tackling Air Pollution in Indonesia

ACKNOWLEDGEMENTS
Authors:
Gregor Kiesewetter (IIASA); Zbigniew Klimont (IIASA); Muye Ru (Earth Institute, Columbia University); 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); Puji Lestari (Bandung Institute ofTechnology); Kaye Patdu (UNEP); Didin Agustian Permadi (Bandung Institute of Technology); Johan Kuylenstierna(Stockholm Environment Institute); Manop Udomkerdmongkol (UNRCO Thailand); Chou Mandarin (Ministry ofEnvironment of Cambodia); Mushtaq Memon (UNEP); Daniel Mira-Salama (World Bank); Napak Tesprasith (USAID);
Donald Simanjuntak (Ministry of Health of Indonesia); 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 Indonesia

ACRONYMS
AHRQ		

Agency for Healthcare Research and Quality

ASEAN		

Association of Southeast Asian Nations

BC		Black Carbon

CH4 		

MethaneCRFs		

Concentration-response Functions

EANET

Acid Deposition Monitoring Network in East Asia

GAINSGreenhouse Gas – Air Pollution Interactions and Synergies Model

GBD		

Global Burden of Disease

GDP

Gross Domestic Product

GHG

Greenhouse Gas

GWP

Global Warming Potential

HFC

Hydrofluorocarbon

IEA		

International Energy Agency

IIASA		

International Institute for Applied Systems Analysis

CO2		

Carbon Dioxide

LPG                   Liquefied Petroleum Gas
NDC		
NH3		

Ammonia

NMVOC

Non-methane Volatile Organic Compound

NOx

Nitrogen Oxides

NO2

Nitrogen DioxideNPS

New Policy Scenario

O3		

Ozone

OC

4

Nationally Determined Contribution

Organic Carbon

OECD

Organization for Economic Co-operation and Development

PM

Particulate Matter

SCR

Selective Catalytic Reduction

SDGs

Sustainable Development Goals

SDS

Sustainable Development Scenario

SLCPs

Short Lived Climate Pollutants

SO2

Sulfur Dioxide

UN

United Nations

UNEP

United Nations Environment Programme

UNEP/CCAC

United Nations Environment Programme/Climate and Clean Air Coalition

VOLY

Value of a 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 Indonesia

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

19

3. Limitations

21

3.1 Scenarios and solutions

21

3.2 Data availability/reliability

21

3.3 Modeled PM2.5 concentrations

21

3.4 Costs of action

21

3.5 Scope of impacts assessed

22

4. Conclusion and recommendations

23

Annexes24

Annex 1. Methodology and data sources

24

Overview

24

Emissions and ambient PM2.5 concentrations

26

Validation of ambient PM2.5 concentrations

27

Concentration-response functions (CRFs)

28

Baseline Morbidity Rates

28

National data

28

Baseline rates of the related diseases: Default estimates

29

Unit Costs

29

National Data

29

Default estimates from international data

30

Annex 2. Emission scenarios

31

Current Policies

31

Strong Mitigation

32

Individual Measures

33

References

34

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

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 vscost of inaction, relying on the comparison of 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 for Indonesia 13
Fig. 2.2 Population exposure to PM2.5 modelled with the GAINS model for 2015 andfor 2030 under different scenarios

14

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

15

Fig. 2.4 Expected improvement in population-weighted mean PM2.5 concentrations inIndonesia from each of the 12 solutions in 2030, distinguishing already implemented
measures (dark blue), legislation passed after 2015 but not yet fully implemented (green),
and the further potential (orange)									15Fig. 2.5 Monetized health burden from PM2.5 exposure in 2030, comparing the Current Policies
scenario, and effective implementation of all measures assessed in this study for Indonesia(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)								17Fig. 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							

18Fig. 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)						

20LIST OF TABLES
Table 1. Morbidity and mortality attributable to ambient PM2.5 in Indonesia in 2015 and 2030
under current legislation, and their costs. For mortality, premature deaths and YLLs arealternative indicators and are shown for comparison only						6

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

EXECUTIVE SUMMARY
This assessment provides a preliminary quantificationof the costs of not taking further action on air pollution
in Indonesia. It quantifies and compares the potential
health costs that come under a scenario in which nofurther measures beyond current policy are taken,
with future scenario in which 12 solutions (bundlesof measures) are introduced. In addition to looking
at health impacts, the assessment also highlights cobenefits for climate change that would also be missed
if further measures are not implemented. The key
findings of this initial assessment are:Indonesia has introduced effective policies
which have improved air quality over the last
decade but more needs to be done. Despite
recent progress, air pollution remains a significant
problem in Indonesia. Even considering current air
quality legislation, the health burden from air pollution
exposure is projected to increase, due to economic
and population growth as well as population aging.
This assessment calculates that without any additional
action on air pollution, there will be over 216 thousand
premature deaths due to ambient air pollution
exposure in Indonesia per year by 2030.
Further action could have significant healthbenefits for the population of Indonesia.
Implementing further policies beyond current
legislation could avoid over 132 thousand premature
deaths, 32 thousand hospital admissions and 18
thousand emergency room visits due to poor air
quality every year by 2030 in Indonesia.
The human health related costs of not taking
further action on air pollution are estimatedto be equal to about 1.6% of Indonesia’s
Gross Domestic Product (GDP) in 2030. Lackof immediate further action on air pollution could
cost Indonesia 27 billion USD a year in 2030, basedon a selection of mortality and morbidity impacts of
air pollution. This equals about 1.6% of Indonesia’s
GDP for 2030. The actual cost of inaction will likely
be higher 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 Indonesia include policies relating
to accelerated introduction of electric vehicles andstrengthened emission standards for road transport,
increased renewable electricity generation capacity,enhanced emission standards for industry andimproved. These measures alone would capture about
75% of the mitigation potential in terms of exposureand monetized benefits.
The proposed 12 solutions also result insignificant reduction of GHG emissions andcould also have multiple other benefits. This
assessment shows that implementing the 12 solutions
for clean air would also have benefits for reducing
GHG emissions with benefits for climate change andcould help Indonesia 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. Therefore, the cost of inaction is
likely to be even higher than the figure estimated inthis 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
Indonesian policymakers and decisions-makers in
the design and implementation of new policies andmeasures and facilitate cross-governmental dialogues
on effective air quality management.

7

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

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.
Indonesia is not exempt from this burden; exposure tofine particulate matter (PM2.5) was estimated to cause
about 107,000 (77,000-138,000) premature deaths
annually in Indonesia in 2019 (Murray et al., 2020).
Furthermore, exposure to PM2.5 is responsible for a highburden of morbidity from cardiovascular and respiratory
diseases, while air pollution also affects ecosystems
through deposition of nitrogen and sulfur, leading toacidification, eutrophication, and loss of biodiversity.
Indonesia, the world’s fourth most populous country,
has an estimated population of over 276 million people,
57% of whom live in urban areas. Within its long-term
national development strategy, Indonesia has set out aroute to be a fully developed economy by 2045, aiming
for at least a 5% increase in GDP per year. Without
significant action, this economic growth, combinedwith expected increases in population and urbanisation
could lead to negative environmental impacts including
worsening air quality. There are well known measures
which, if effectively implemented, could successfully
reduce air pollution and its associated impacts, andincreasing efforts have been taken in recent years tocounteract the air pollution problem in ASEAN countries.
Indonesia has recognised the need to take decisive
action on mitigating air pollution and in 2020 publishedits first strategic plan for air pollution control (20202024), while there are several other sector specificinitiatives such as implementing emission standards
for vehicles and emission standards and regulations
for industry which may have had some success in thesmall reduction in population weighted average PM2.5
exposure over the past 20 years (State of Global Air,2020). However, even if 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 (United Nations Environment
Programme/ Climate and Clean Air Coalition [UNEP/CCAC], 2023). To limit the multiple negative impacts of
air pollution it is therefore important to consider what
additional actions could be effective and to not only
explore the implications of current policies, but also
opportunities for further mitigation.
Tackling air pollution can also have largely positive benefits
for mitigating climate change. Some pollutants, known
as short lived climate pollutants (SLCPs), contributedirectly to both climate change and air pollution, while
air pollutants and long-lived greenhouse gases (GHGs)
often come from the same source. Consequently, taking
an integrated approach to air pollution and climatechange could result in multiple benefits for health andthe environment (Haines et al., 2017) and achieve otherSustainable Development Goals (SDGs). The 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, forexample costs to the health care system due to increased
levels of attributable disease or economic losses due
to a reduced work force through death and illness.
Therefore, not acting on air pollution is also costly, andquantifying these costs of inaction, as a counterbalance
to the costs of action (i.e., of implementing new
mitigation measures), can be an important argument insupport of more stringent and ambitious control policies.

Box 1: Clean Air and Climate Solutions for ASEAN
In 2023, UNEP, ASEAN, and CCAC released areport entitled: ‘Clean Air and Climate Solutions
for ASEAN’. The report identifies 15 solutions—12of which overlap with the solutions in this cost ofinaction 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 of these solutions can reduce
population weighted PM2.5 average concentrations
across the ASEAN region by 50 to 70 percent by

82030. Further, the 15 solutions would also deliver
important climate co-benefits from the reductionof SLCPs. The solutions involve actions in many
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 Indonesia

1.2. Objective
Through quantifying the costs of not acting on air
pollution, this assessment aims to increase the evidencebase to support policymakers and decisionmakers inIndonesia 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 inIndonesia, 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 Indonesia 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 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.

Additional Policies

Cost of Inaction

Cost of Action

Current Policies

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: Current Policies scenario,
representing the implications of current legislation(assuming no further policy action), is compared toan alternative Additional Policies scenario in whicha bundle of new measures to curb pollution, reduce
GHGs, and address SDGs are implemented.

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 Indonesia

The Current Policies case is associated with a certain
level of ambient air pollution (symbolized by theblack bar in Fig. 1.1), costs for implementation ofexisting pollution legislation (the orange bar), and acertain level of related costs (the blue bar) from air
pollution, which is here expressed in monetary terms.
In the Additional Policies case, the emission control
costs are higher while pollution levels and associatedimpact costs are lower. The costs of action are thendefined as the difference between the emission
control costs in the Current Policies and Additional
Policies scenarios, whereas the costs of inaction are
the difference between the impact costs or, in otherwords, the forgone or ‘lost’ monetized benefits if noaction is taken.

1.4. Methodology
The analysis in this assessment employs the Greenhousegas – Air pollution Interactions and Synergies (GAINS)
model (Amann et al., 2011; UNEP, 2019) developed at
the International Institute for Applied Systems Analysis(IIASA) (see Box 2 and Fig. 1 in the Annex). GAINS is
an integrated assessment model quantifying emissionsof various air pollutants at a granular sectoral level,
ambient concentrations of PM2.5 and the associatedmortality. For the purpose of this assessment, the model
framework has been further developed and extended
to include additional health endpoints, impacts on
workforce, and respective costs.
The assessment quantifies health impacts in terms ofmortality and morbidity from ambient PM2.5. Impact

costs are quantified as the monetary value per year oflife lost (as estimated from willingness-to-pay studies)and the health system costs of morbidity. Concentrationresponse functions (CRFs) for several morbidity outcomes
were generated from a dedicated meta-analysis frominternational 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 from local
sources where available and supplemented with datafrom international sources in other cases. Details of
the methodology and data sources are explained in the
Annex.

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 assessment ofenvironmental impacts of pollution (https://gains.
iiasa.ac.at/models).
Emissions are estimated using the GAINS emission
factor database that has been peer-reviewed andcompiles both national and international dataon source- and technology-specific measures;
more than 1000 measures to control emissions
are represented. The mitigation options include
impact on emissions of all key air pollutants (sulfur
dioxide (SO2), nitrogen oxides (NOx), PM (including
black carbon (BC) and organic carbon (OC)), non-10

methane volatile organic compound (NMVOC),
ammonia (NH3) and GHGs.
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 application ofeach measure/solution at a resolution of 0.1°×0.1°
or roughly 10x10km. Overlaid with population
at the same resolution, the exposure distribution
to ambient PM2.5 in the population is calculated.
Applying CRFs from the international literature,
GAINS calculates premature mortality from longterm exposure to PM2.5, and the associated life years
lost (YLLs). For this analysis, the model has been
extended to include other health related impacts,
i.e., morbidity, work time loss, their costs, etc. (seeFig. 1 in the Annex). Details of the methodology are
explained in the Annex.

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

1.5. Scenarios
To calculate the cost of inaction from air pollution,
as illustrated in 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.
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 study (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 Indonesia will
experience significant economic development in the
future with its GDP growing by about 95% to 2030
from 861 billion US$ in 2015, following the projections
made in the International Energy Agency’s World
Energy Outlook 2018 (IEA, 2018). The population ofIndonesia is also assumed to change into the future,
growing from 258 million in 2015 to 296 million in 2030
and 322 million in 2050, following the United Nations(UN) World Population Prospects 2017 (UN, 2017),
Medium Scenario. At the same time, the Indonesian
population is projected to age significantly, which has
a considerable effect on the health impact calculations
undertaken in this assessment.
The Current Policies scenario that is used as abaseline in this assessment is a rather conservative
benchmark for future development. It considers only
policies which have already been implemented oragreed in Indonesia by mid-2020. The information
about current policies, emission limit values andstandards is taken from Zhang (2016), Motokura etal. (2017), TransportPolicy.net1 (n.d.), Organization
for Economic Co-operation and Development (OECD)(2019), Acid Deposition Monitoring Network in East

1

Asia [EANET] (2020), He et al. (2021), and Lestari
et al. (2022). The energy trends used in the Current
Policies scenario are consistent with the IEA ‘New
Policies’ scenario (NPS), and air pollution controls are
implemented to the extent foreseen under current
legislation, but no further action is taken. Some of theabove studies also provide assessment of progress in
implementation of policies, which is important to createa more realistic outlook of impacts from introduction ofexisting legislation and assessment of future mitigation
potential; reported experience is reflected in themodelling approach used in 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 study(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 andincreased population of electric vehicles is one solution)(Fig. 1.2/Infographics3) and have been selected basedon their potential to deliver the maximum reductionin the population’s exposure to PM2.5. The selected
solutions combine application of technological solutions
to reduce emissions as well as exploit potential forthe energy efficiency, fuel switching, renewables
and electrification of vehicle fleet as identified in
the IEA Sustainable Development Scenario (SDS).
Furthermore, diets, and hence agricultural production,
are in line with the Lancet EAT Planetary Diet (Willett
et al., 2019). Further details on the solutions includedin this assessment can be found in the Annex andin 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 ofthese measures also bring strong co-benefits including
reduction of GHGs emissions and contribute to theachievement of several SDGs.

For more details, refer to: https://www.transportpolicy.net/standard/indonesia-fuels-diesel-and-gasoline/.

The UNEP/CCAC Assessment on Clean Air and Climate in the ASEAN investigates 15 solutions, some of which however do notdirectly 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 Indonesia

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 Indonesia

2. Results
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. Emissions and ambient concentrations
2.1.1 Current policies scenario
3

CO2

2.5

SO2

2

PM2.5

NOx
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 Indonesia

In the Current Policies scenario, the assumed
implementation of existing and recently introduced
legislation in the power, industry and transport sectors
already shows some effect at slowing the growth ofemissions of PM2.5 and key PM precursor pollutants SO2,
NOx, (Fig. 2.1). In fact, these precursors are growing
slower than carbon dioxide (CO2), suggesting gradualdecoupling of economic growth from air pollutant
emissions. The main contributions and growth of CO2
emissions in Indonesia are from power and industry
sectors. However, current policies are not sufficient tooffset the increase in fuel use and production activities,
which combined with assumed strong economic
growth in Indonesia drives the significant increases
in CO2 emissions (Fig. 2.1). Another notable trend in
the baseline involves the residential sector. For primaryPM2.5 emissions, cooking and lighting contributed
about 30% of PM2.5 in 2015, according to GAINS model
estimates, successful policy virtually eliminating use ofkerosene for lighting and a trend towards clean fuels
for cooking (providing access in both urban and rural
areas) is expected to result in decline of emissions fromthis sector reducing its share to less than 20% and

10% by 2030 and 2050, respectively, and contributing
near stabilization of overall PM2.5 emissions (Fig. 2.1).
Annual mean PM2.5 concentrations in Indonesia
estimated by GAINS for 2015 are between 5 and 80µg/m3 (Fig 2.2, top panel). The pollution problem isvery unequally distributed across the country, withthe densely populated island of Java suffering thehighest concentrations. The capital region around
Jakarta in the West of Java is most severely polluted,
with annual mean concentrations estimated higher
than 80 µg/m3 in individual locations, followed by
the area around Surabaya. Outside Java, the highest
concentrations are seen in large cities such as Medan,
Makassar, Palembang, Bandar Lampung, Pontianak
or Banjarmasin. Outside large cities, only Sumatra
faces elevated concentrations, while much of theouter islands enjoy clean air even below the 2021
World Health Organization (WHO) Guideline level of
5 µg/m3, consistent with the low population density
there. A validation against available monitoring data isprovided in the Annex .

13

PM2.5 [μg/m3]

PM2.5 [μg/m3]

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

Fig. 2.2 PM2.5 concentrations for Indonesia modelled with the GAINS model for 2015 (top) and for 2030 under
Current Policies (bottom)

Even assuming the successful implementation ofexisting policies and legislation, the situation in
populated areas, is expected to get worse by 2030 in
the Current Policies scenario (Fig. 2.2, bottom panel).
This is also illustrated in Fig. 2.3, which shows the
distribution of population exposure to PM2.5. In 2015,
86% of the population was exposed to PM2.5 levels
above the current WHO air quality guideline of 5 µg/
m3 and 67% was exposed to levels above the 2005
WHO guideline of 10 µg/m3 (now Interim Target 4),
30% between interim targets 1 and 3 (15-35 µg/m3), and 25% at concentrations higher than 35 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 87% of population could be experiencing stillconcentrations above the 2005 WHO guideline andthe number exposed to concentrations exceeding 15µg/m3 would grow from 131 to more than 180 million
owing to increasing concentrations in some regions(Fig. 2.2.) as well as continuing 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. 1.2
and listed in Table 1 (Annex). As illustrated in Fig.
2.3, implementing all 12 of these solutions in theStrong Mitigation scenario, would mean that by
2030, more than 100 million people would enjoy PM
concentrations below the current WHO guidelines and14

only 37% would be exposed to levels above 10 µg/
m3. The Current Policies and the Strong Mitigation
scenarios constitute the extreme ends of a spectrumof possible scenarios between business as usual andextremely ambitious mitigation. Within this range, amore realistic policy scenario would apply policies tosome degree to exploit much of the potential while
keeping policy costs limited. To identify which of
the 12 solutions have the largest impact, within thisanalysis we quantify the individual potential of eachindividual solution to improving air quality.

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

All measures
2030
Current
Policies 2030
2015
0

50

100
150
200
Million people exposed to PM2.5

250

300

<WHO guideline 5 µg/m35-10 µg/m310-15 µg/m315-25 µg/m325-35 µg/m3>35 µg/m3

350

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 populationweighted mean PM2.5 concentrations in Indonesia in
2030, assuming the full implementation of each solution
both in Indonesia but also across the whole ASEAN
region. This figure also shows the impact on exposure
from measures which had already been implemented
by 2015 (blue), and have already contributed to
improved air quality, with those that have been included
in recent legislation passed after 2015 but may not yet
fully implemented (green), potentially contributing tofuture improved air quality if successfully implemented.
The further potential (yellow) for each solution would
therefore come from implementing the highest level ofambition. The aim of this analysis is to show in whichareas or sectors progress has already been taken andwhere there remains the most potential for further
ambition through implementation of the solutions.

estimated to have already resulted in 5 µg/m3 reductionin 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 almost 9 µg/m3 (darkblue and 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 in
the transport sector (including electrification of roadtransport, strict standards on remaining internal
combustion vehicles and their enforcement throughregular inspection and maintenance), further measurescontrolling large point sources such as power andindustry (including a transition to renewable energy andefficiency improvement), improved waste management(elimination of open waste burning), as well as better
agricultural practices which minimize nitrogen losses.

Controls on PM2.5 and SO2 emissions from electricity
generation and industry have already contributed
to large improvements in air quality in Indonesia,

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
Prevention of forest and peatland fires
0

5

10

15

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 Indonesia from each of
the 12 solutions in 2030, distinguishing already implemented measures (dark blue), legislation passed after
2015 but not yet fully implemented (green), and the further potential (orange)

15

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

2.2. Health impacts and cost of inaction
Exposure to PM2.5 leads to considerable health impacts
in Indonesia. The GAINS model estimates that in 2015,
more than 127,000 premature deaths were attributable
to ambient PM2.5, corresponding to 2.2 million YLLs.
In the Current Policies scenario, due to increases in
PM2.5 concentrations, as well as population aging, themortality burden is projected to increase to 217,000
premature deaths and 3.5 million 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 estimatedin 2015 and under the Current Policies scenario in
2030 are given in Table 2, 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 number of YLL in combination withthe value of a life year (VOLY). As is usually the case,
we find that the approach via deaths × VSL gives ahigher cost than YLLs × VOLY, reflecting some of the
difficulties with attaching a monetary value to human
life. For all further analysis shown in this report, we
use the YLL monetization approach for valuating lossof human life.
Unit costs, VSL and VOLY shown in Table 2 are
combining national data and internationally available
data sets adapted to Indonesia’s per-capita GDP;details are described in Annex 1.

Table 1. Morbidity and mortality attributable to ambient PM2.5 in Indonesia in 2015 and 2030 under current
legislation, and their costs. For mortality, premature deaths and YLLs are alternative indicators and are shown forcomparison only

Current Policies
Indicator

2015

StrongMitigation

2030

2030

Cases - YLLs/year

Unit
Cost
$US
2015

Costs Current Policies
2015

2030

Costs –
StrongMitigation
2030

Thousand $US 2015/year

Mortality
Premature deaths

127,178

216,032

83,295249,284

31,703,495

53,853,414

20,764,147

2,196,731

3,500,409

1,377,596

9,427

20,707,914

32,997,285

12,986,176

Asthma (Emergency
Room Visits), all age

17,77827,847

9,337

11

189

295

99

Cardiovascular hospital
admissions, below 65
years of age

8,656

13,301

4,174

1,152

9,976

15,3294,810

Cardiovascular hospital
admissions, post 65 yearsof age

4,980

12,716

4,225

1,152

5,739

14,655

4,869

Respiratory hospital
admissions, all age

13,99822,017

7,314

561

7,846

12,341

4,100

259,084,841

438,353,537

137,800,276

23

6,020,742

10,186,676

3,202,271

YLLs

Morbidity

Respiratory restrictedactivity days, working age

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

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 Indonesia are
estimated to reach over 43 billion USD per year, a value
equal to 2.6% of Indonesia’s GDP. 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

16.2 billion USD per year, equivalent to about 1%
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 Indonesia isestimated to be 27 billion USD per year, equivalent toaround 1.6% 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.

45000
2.5%

Cost of inaction

Million $US/year

40000
35000
30000
25000
20000
15000
10000

2.0%

1.5%

1.0%

0.5%

5000
0

Current Policies Strong Mitigation

Morbidity

Mortality

0.0%

Monetary burden as share of GDP (%/year)

3.0%

50000

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 Indonesia (Strong Mitigation case). Stacked
Bars – Left axis: Burden expressed as million USD per year, Red dots – Right axis: comparison to GDP
The majority of the economic burden is associatedwith mortality (shown here is monetized estimate forYLL) representing about 80% of costs. The remaining

Respiratory restricted activity days, work age

10000

Million $US/year

20% are dominated (99%) by costs associated withrespiratory restricted activity days, i.e., working day
losses (Fig. 2.6).

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

8000

Cardiovascular hospital admissions, below 65 years of age
Asthma (Emergency Room Visits), all age

6000
4000
2000
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)

17National Assessment of the Cost of Inaction of Tackling Air Pollution in Indonesia

Fig 2.7 provides a summary of premature death, hospital
admissions, and working days lost due to ambient
PM2.5 as estimated for 2015 and expected development
towards 2030 in the Current Policies scenarios aswell in the Strong Mitigation scenario where all the
proposed measures are taken. It was estimated thatin 2015, around 18,000 asthma related emergency
room visits, over 13 thousand hospital admissions
for cardiovascular reasons, 14,000 respiratory related
hospital admissions, and 259 million days of restrictedactivity 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 byaround 32 thousand as well as reducing the numberof emergency room visits from asthma by 18 thousand
and avoiding 132 thousand premature deaths (Fig.
2.7).
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
costs, and could also be compared against the costs
for implementing each solution if such data becomes
available. Fig. 2.8 shows that the largest benefits are
expected from electrification and emission standards
in road transport for which monetized benefits are
estimated at about 14.7 billion USD, with stricter
vehicle inspection and maintenance adding further
3.5 billion USD. Increasing renewables and applying
post combustion controls in the power and industrial
sectors as well as improving industrial production
standards could bring benefits of 18.2 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 bring benefits worth
about 10.3 billion USD. Solutions which improvemunicipal waste management could result in benefitsof 5.9 billion USD per year, a complete transition toclean cooking could result in benefits of 4.8 billion
USD per year from ambient PM2.5 only4, and improved
enforcement of forest fire prevention could result in
health benefits in order of 0.5 billion USD, on top of
benefits from avoided ecosystem damage.

Cases, admissions/year

7000

10

6000
8

5000
4000

6

3000

4

2000
2

1000
0

2015

Current Policies

Strong Policies

Million labour days lost/year

12

8000

0

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

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.
4

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

Fig. 2.8 distinguishes the benefits gained from
implementation of measures in Indonesia (blue), and in
the other ASEAN countries (yellow). Due to Indonesia’s
location and size, the influence of transboundary inflowof pollution as well as the benefits which can be reaped
through other countries’ implementing measures arelimited. It is likely, however, that Indonesia’s influenceon neighbouring ASEAN countries is larger than theother way, and therefore neighbouring countries would
benefit from implementation of ambitious air quality
actions in Indonesia.

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 Cambodia
From other countries

Prevention of forest and peatland fires

0

50000

10000

15000

Total health benefits [Million USD]

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

2.3. Climate and other co-benefits
This assessment has estimated the costs thatIndonesia will experience in the future if no further
action is taken due to the increasing health burden of
air pollution. It has also shown the large benefit for air
quality and health in Indonesia 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,transport and industry sectors. The measure which
has the largest potential to reduce GHG emissions
is introducing renewables in the power sector. This,
combined with strict post-combustion emission
controls, is estimated to reduce GHG emissions
by more than 100 Mt (CO2-eq.) by 2030 and morethan 400 Mt (CO2-eq) by 2050, while simultaneously

reducing population weighted PM2.5 exposure by 4 µg/
m3 by 2030 and 6 µg/m3 by 2050. In addition, themeasure which has the largest potential for reducing
air pollution also has significant climate co-benefits:
introducing more stringent emission standards andaccelerating electrification in the road transport sector
could simultaneously reduce GHG emissions by 35 Mt(CO2-eq.) by 2030 and 150 Mt by 2040, while also
decreasing population average exposure to PM2.5 bymore than 5 µg/m3 by 2030 and 13 µg/m3 by 2050. It isalso likely 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. Suchpolicies could at the same time have multiple other cobenefits such as reducing congestion and time spentin traffic jams, reduced number of road accidents, aswell as additional health co-benefits from active travel.
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 near

19

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

term (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.

2045. Both climate change and air pollution haveeconomic costs, and policies such as those highlighted
above, could therefore have substantial benefits forboth the health and wellbeing of the population but
also in aiding economic development and achieving
the SDGs. At the same time, some policies relating
to climate or sustainable development such as
those detailed in Indonesia’s Nationally Determined
Contribution (NDC) or Long Term Development
Strategy will also have co-benefits for air pollution,
quantifying these additional health and economic
benefits could enhance the evidence base and provide
further motivation for action.

Indonesia has set ambitious targets to reduce GHG
emissions in the country by at least 31% in 2030
compared to a baseline scenario, while simultaneously
aiming to becoming a fully developed economy by

GHG emissions [Mt CO2 eq]

2030

2050

400

400

300

300

200

200

Vehicle inspection and maintenance

100

100

Livestock and N-fertilizer application

00Open burning of agricultural residues

Clean cooking
Renewables, post-combustion controls in power and industry
Industrial processes standards, incl. energy efficiency
Emission standards/electrification - transport

0

5

10

PM2.5 [µg/m3]

International shipping
Dietary changes
Waste management

0

5

10

Prevention of forest and peatland fires

PM2.5 [µg/m3]

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

20

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

3. Limitations
3.1. Scenarios and solutions
The analysis presented in this report relies on existing
scenarios; development of completely new scenarios fit
for analysis with the GAINS model was not feasible due
to available resources. However, the available scenarios
reflect a broad range of interventions with respect
to impacts and are expected to cover the potential
scope of local policies. Reviews and assessments ofstate of policies and progress in their implementation(UNEP, 2015; OECD, 2019; EANET, 2020) show thatthe assumptions about policies considered in the
Current Policies scenario are largely complete andconsistent with the existing legislative framework inIndonesia 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 (e.g.,transport fuel standards5,6), their reduction potential iscaptured in the further mitigation potential estimatedin the assessment. The same applies to proposedor evaluated potential and impacts of mitigation of
air quality (e.g., Greenstone and Fan (2019)) as therespective mitigation potentials are well represented
in the Strong Mitigation scenario.
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. Whilethe default data set generated from internationalsources is scientifically robust, it is not necessarily
fully representative of the local circumstances, andlocal data are clearly preferable. The assessment
is currently based on a combination of local andinternational data.

3.3. Modeled PM2.5 concentrations
GAINS
reproduces
measuredambient
PM2.5
concentrations in the less polluted regions of thecountry well, but seems to overestimate concentrationsin the Western Java region around Jakarta. This maylead to an overestimate in health impacts and damage

costs since this is one of the most populated areas ofIndonesia. Attributable deaths estimated for current
levels of PM2.5 are slightly higher than the estimate of
the GBD by 11%.

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,5

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 8-10
billion USD, which represents about a third of theestimated cost of inaction. However, as noted earlier,

For more details, refer to: https://www.transportpolicy.net/standard/indonesia-fuels-diesel-and-gasoline/.

For more details refer to: https://www.reuters.com/business/energy/indonesia-bar-sale-gasoline-below-90-octane-2023regulator-2022-10-26/.
621

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

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 and data:i. GAINS relies on international defaults for costs anddoes not include local cost factors where appropriate(although, most air pollution control technology istraded 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:༪Crop losses associated with elevated ozone,༪

Ecosystem impacts from air pollution (Sulfur andNitrogen deposition),༪

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 outcomesare considered, so the total impact via morbidity is likely
underestimated. Exposure to other air pollutants likenitrogen dioxide (NO2) and O3 is associated with healthand ecosystems impacts and crop loss due to elevatedO3 concentrations. O3 formation is influenced by CH4emissions and therefore introduction of measures

22

analyzed in this assessment can bring additional cobenefits. However, these are orders of magnitude
smaller than the PM 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 theassessment would bring reductions of SO2, NOx, andNH3 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 costs 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.

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

4. Conclusion and recommendations
Indonesia 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 27 billion USD in 2030
with the majority costs associated with premature
mortality. Introducing policies stimulating the rapid
introduction of identified further mitigation measureswould results in significant benefits at potentiallymuch lower costs, although the latter was not fully
estimated. A preliminary estimate of implementation
costs of technical measures indicates that these would
represent in 2030 less than a third of the cost ofinaction.

༪Electrification of the vehicle fleet, stringent emission
controls on the remaining combustion engines,
including regular inspection and maintenance toeliminate high-emitting vehicles,༪More stringent emission standards for industry,༪Improved agricultural practices, including reducing
emissions from livestock management and fromfertilizer application as well as effective enforcementof ban on open burning of agricultural residues,༪A complete transition to clean cooking fuels, ideallydirectly to electricity,༪Efficient waste management, prevention of open
burning of waste

Further actions driven by health concerns will bringalso important climate co-benefits (reduction of GHG
emissions), which will be larger in the long-termtranslating 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.

Key policies delivering major benefits include:༪Power 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,

23National Assessment of the Cost of Inaction of Tackling Air Pollution in Indonesia

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,
CRFs for the selected disease endpoints, and baseline
rates for these disease endpoints. For the assessmentof impact costs, unit costs for the same are needed.
A chart of the information flows and calculation
steps is shown in Fig. 1. The GAINS model (Amannet al., 2011; online at http://gains.iiasa.ac.at) is thecentral tool used in the analysis. GAINS, developedby the IIASA, brings together data on economic
development, the structure, control potential and costsof emission sources, the formation and dispersion ofpollutants in the atmosphere and an assessment ofenvironmental impacts of pollution (http://gains.
iiasa.ac.at). It is used here to estimate emissions of
PM2.5 and its 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.

24

All outcomes are listed in Box 1. GAINS routinely
quantifies health impacts in terms of premature deaths
ad YLLs from six diseases in line with the methodology
developed within the GBD assessments (Murray et al.,2020). Within this assessment, impact calculations
have been extended to cover morbidity related to
cardiovascular and pulmonary diseases. For thispurpose, new concentration-response relationships forseveral morbidity endpoints have been developed andare currently under review in the scientific literature.
A full list of endpoints for mortality and morbidity
covered by the assessment is given in Box 1 and a
description of the CRFs 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.

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

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

25

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

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 GBD results for 2019 (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 thatincidence 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:༪Chronic obstructive pulmonary disease༪Asthma-related emergency room visits༪Ischemic heart disease༪༪

StrokeCardiovascular hospital admissions (pre/post 65
years)

༪

Lung cancer

༪Respiratory hospital admissions

༪

Acute lower respiratory infections

༪Respiratory restricted activity days (working age)༪Type 2 diabetes

From premature deaths, YLLs are calculated bymultiplying the number of attributable deaths with theremaining life expectancy at the age of death. We notethat this approach is conservative in the sense thatit relies on the actual life table for the country itself.
Other approaches, such as the GBD assessments, useremaining life expectancy from countries with thehighest observed life expectancies for this purpose,such as Japan, and therefore arrive at higher estimatesof 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 VSL or the
VOLY can be used. VSL needs to be combined with thenumber of attributable deaths, while VOLY is used inconjunction with the number of YLLs. Cost data usedin the assessment rely on a combination of national
inputs and default estimates from 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, the riskfunctions 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 health outcome
based on the concentration and health impact levels
attained under the Current Policies scenario and theStrong 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,
NMVOC) and GHGs. Each technology is associatedwith an emission factor for each pollutant, and cost

26

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:
4

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

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 sub-national
units in most parts of Asia. Cambodia is represented as
one source region at national level.

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 data forrecent year provided by the Ministry of Environment
and the US Embassy stations in Jakarta. Only
stations with temporal coverage >75% are used in
the comparison. GAINS reproduces the variation ofconcentrations well but overestimates total PM2.5 in the
more polluted regions in Western Java like Bandung;
also Jakarta seems somewhat overestimated. Thismay be due to problems in the geographic distributionof emission sources in the GAINS model.

8070

PM2.5 modelled [µg/m3]

60
50

Jakarta

40
30
20
Mean bias: 8.71 μg/m3
RMSE: 18.23μg/m3
R2: 0.24

10
0
0

20

40

60

80

PM2.5 observed [µg/m3]
Fig 2. 2 Comparison of PM2.5 concentrations modelled with GAINS for 2015 against observations from 2016 to 2021

27

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

Concentration-response functions (CRFs)
CRFs used in this project have been derived from ameta-analysis of recent epidemiological studies going
beyond the evidence available to earlier assessments.
Endpoints were selected based on the CarbonH tool
(Spadaro et al., 2018).

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.

༪

Asthma-related emergency room visits༪Cardiovascular hospital admissions (pre/post 65
years)

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.

༪Respiratory hospital admissions

In the log-linear CRFs, relative risk is expressed as,༪Respiratory restricted activity days (working age)

The full documentation is currently under review (Ruet al., in review). Outcomes are:

In addition to these endpoints, CRFs for asthma related
With the coefficient β derived from the regression analysis as
Asthma-related emergency room visits

0.0034/µg/m3

Cardiovascular hospital admissions (pre 65 years)

0.0009/µg/m3

Cardiovascular hospital admissions (post 65 years)

0.0013/µg/m3

Respiratory hospital admissions

0.0013/µg/m3

Respiratory restricted activity days (working age)

0.0102/µg/m3

Although the CRFs are for outcomes which typicallyrelate to impacts of short-term exposure to PM2.5, they
are applied here to the annual mean concentrations.
Ru et al. compared the effects of using annual means
versus the time series in the United States and founda small overestimation of 0.1% to 1.3% dependingon the morbidity outcome, which is not taken into
account in this analysis in view of many other – likely
much larger – uncertainties.

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 GBD
Results Tool (https://vizhub.healthdata.org/gbdresults/) 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 not available, default rates
have been estimated from international datasets as
described in Section 1.5.2.

National data
Data on hospital admissions by illness in 2019-2021
have been made available to this assessment by the
Ministry of Health. These have been used to calibrate
the baseline morbidity levels in the assessment for theoutcomes related to cardiovascular and respiratory
hospital admissions. We used only 2019 data in order
to avoid possible temporary effects during the Covid-19
pandemic.

28

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.

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

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 genders from the GBD Results Tool (https://
vizhub.healthdata.org/gbd-results/).

cases and applied to the country. By doing this, weunderstand that our results are based on a strong
assumption that the relationship between morbidity
outcomes and the related diseases causing theoutcomes are the same universal and same with therelationship derived in the US. We realize that othercountries may have different availabilities for hospital
admissions and emergency rooms, especially in rural
areas.

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 (Agency for Healthcare Research andQuality [AHRQ], 2022) (https://hcupnet.ahrq.gov/)
as benchmark data. We then calculated a scaling
factor between the related diseases and the morbidity

We obtained the baseline prevalence rate of therelated diseases from the GBD 2019 (Global Burdenof Disease Collaborative Network, 2021) with thefollowing 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
Unit costs for hospital admissions for cardiovascular andrespiratory diseases were provided by the Indonesian
Ministry of Health, alongside with a preferred estimate
for the value of a statistical life in Indonesia relating
to (Robinson et al., 2019). The value of a life year

used in the analysis was adjusted by the ratio of the
VSL in this estimate over the VSL in our default data
set. All costs provided for years other than 2015 were
inflation-adjusted to 2015.

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 werein Europe and Central Asia. We also obtained the unit
cost data from HCUPNet (AHRQ, 2022). As such, we
had a unit cost dataset from 55 countries. We thenobtained the GDP per capita data from the World Data
Bank (World Bank, 2021). We ran regressions for theunit cost of each endpoint using the logarithm of GDP
per capita:

29

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

where i indicates different morbidity endpoints, and j indicates different countries. The coefficients we obtained
from the regressions were as below:
Endpoint

Intercept

SlopeChildren asthma symptom day

6.06

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.

30

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

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 GHGs as is illustrated in thefollowing 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 are frompower, industry, and transport sectors (Fig. 3). The
scenario draws from the World Energy Outlooks New
Policy Scenario and includes NDCs reported up to 2018
(IEA, 2018).

of emissions of key PM precursors. In fact, emissionsof these precursors are growing slower than CO2,
suggesting gradual decoupling of economic growthfrom air pollutant emissions. At the same time,
however, the existing legislation is not sufficiently
strong to offset the increase in fuel use and production
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.

4.0
3.5

1.5
1.0
0.5

3.0
2.5
2.0
1.5
1.0
0.5

0.0
2015 2020 2025 2030

0.02015 2020 2025 2030

NH3

2.5
2.0
1.5
1.0

Million tons NMVOC

Million tons NH3

3.0

0.5
0.0
2015 2020 2025 2030

5.0
4.0
3.0
2.0

PM2.5

2.5

8.0
7.0

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

3.0

0.0
2015 2020 2025 2030

8.0

4.5

NOx

9.0

NMVOC

5.0

3.5

10.0

30.0

Agriculture

25.0

Waste management

Million tons CH4

2.0

4.0

SO2

Million tons NOx (as NO2)

CO2

Million tons SO2

Billion tons CO2

2.5

Million tons PM2.5

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.com (n.d.); TransportPolicy.net (n.d.)) are
implemented effectively, and have slowed the growth

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

31

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

Strong Mitigationin line with the Lancet EAT Planetary Diet (Willett etal., 2019).

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
SO2

1.5
1.0
0.5

4.0

10.0

3.5

9.0

3.0
2.5
2.0
1.5
1.0
0.5

Baseline

Mitigation

0.0

NH3

3.5
3.0
2.5
2.0
1.5
1.0

Baseline

Mitigation

6.0
5.0

1.5

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

5.0

0.0

Mitigation

1.0

0.5
Baseline

7.0
6.0

Million tons CH4

Million tons NMVOC

Million tons NH3

4.0

2.5

1.0

7.0

4.5

PM2.5
3.0

8.0

NMVOC

5.0

0.0

NOx
Million tons NOx (as NO2)

2.0

Million tons SO2

Billion tons CO2

2.5

0.0

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).

Million tons PM2.5

CO2

The potential for energy efficiency, fuel switching,electric vehicles originate from the assessment andcomparison of the IEA, NPS and the SDS where thelatter is designed to achieve CO2 reduction consistent
with the Paris Agreement targets (IEA, 2018).

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 estimated to
decline by from 55% (NMVOC) to 84% (SO2), andemissions of CH4 decline by 40%. Achieving such
reductions would require additional policy action
stimulating introduction of further measures which wereThat report identified 15 solutions which included measures targeting reduction of CH4 and hydrofluorocarbons (HFCs), i.e., ricepaddies, wastewater treatment, and HFC-refrigerant replacement.
732developed 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)7 andalso UNEP (2019) reports.

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

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 bundlesof policies rather than individual actions and havebeen selected based on their potential to deliver the
maximum reduction in population’s exposure to PM2.5.

As shown in the national assessment report, several ofthese measures also bring strong co-benefits including
reduction of GHGs emissions and contribute to theachievement of several SDGs.

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 Liquefied Petroleum Gas(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 desulfurization, 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 sulfur 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 SO2 as when using low
sulfur fuel.

Livestock and N fertilizer
application

Control of NH3 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 CH4, there are some reductions of PM
precursors (including BC) when routine flaring is reduced or banned as well
as reducing tailing fugitive dust emissions from mining industry following
reduced demand for coal in SDSs.

33

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

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