ASSESSMENT OF THE COST OF
INACTION OF TACKLING AIR
POLLUTION IN CAMBODIA

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Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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); Chandath
Him (Ministry of Environment of Cambodia); 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 of Environment of Cambodia); Mushtaq Memon (UNEP); Daniel
Mira-Salama (World Bank); Napak Tesprasith (USAID); Ekbordin Winjikul (AIT)
Editing and design:
Jennifer Pangilinan (AIT); Ranjika Perera (AIT); Pitiruedee Angkhananuchat (AIT)3

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

4

Nationally Determined Contribution

Nitrogen DioxideNPS

New Policy Scenario

O3		

Ozone

OC

Organic Carbon

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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

28

Unit Costs

29

Annex 2. Emission scenarios

30

Current Policies

30

Strong Mitigation

31

Individual Measures

32

References

34

5

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 for Cambodia									13Fig. 2.2 PM2.5 concentrations for Cambodia modelled with the GAINS modelfor 2015 (left) and for 2030 under Current Policies (right)					

14Fig. 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
in Cambodia from each ofthe 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 implementationof all measures assessed in this study for Cambodia (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, distinguishing benefits from measures applied in Cambodia,
and in the whole ASEAN region. Note that for forest and peatland fires,the model does not distinguish between the impacts from preventing fires in
Cambodia or in other countries								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 1. Morbidity and mortality attributable to ambient PM2.5 in Cambodia
in 2015 and 2030 under current legislation and strong mitigation,and their costs. For mortality, premature deaths and YLLs are alternative
indicators and are shown for comparison only						6

16Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

EXECUTIVE SUMMARY
This assessment provides a preliminary quantificationof the costs of not taking further action on air pollution
in Cambodia. It quantifies and compares the potential
health costs in two alternative scenarios, one in which
no further measures beyond current policy are taken,
with a future scenario in which 12 solutions (bundlesof measures) are implemented. 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:Cambodia 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 Cambodia. 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 almost 5 thousand
premature deaths due to ambient air pollution
exposure in Cambodia per year by 2030.
Further action could have significant healthbenefits for the population of Cambodia.
Implementing further policies beyond current
legislation could avoid over 3 thousand premature
deaths, 5 thousand hospital admissions and 1.5
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 2.1% of Cambodia’s
Gross Domestic Product (GDP) in 2030. Lack of
immediate further action on air pollution could cost
Cambodia nearly 800 million USD a year in 2030, basedon a selection of mortality and morbidity impacts of
air pollution. This equals about 2.1% of Cambodia’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 Cambodia include a complete switch
to clean cooking technologies, policies supporting
accelerated introduction of electric vehicles andstrengthened emission standards for road transport,
effective waste management strategies, enforcementof bans on agricultural residue burning, and improved
management preventing forest and peatland fires.
These measures alone would capture about 80%of the mitigation potential in terms of exposure andmonetized 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 have benefits
for reducing GHG emissions with benefits for climatechange and could help Cambodia achieve its climate
targets. If implemented, the solutions could alsodeliver multiple other co-benefits, supporting theachievement of other development priorities related
to several Sustainable Development Goals (SDGs).
Therefore, the cost of inaction is likely to be evenhigher 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
Cambodian policymakers and decisions-makers in
the design and implementation of new policies andmeasures and facilitate cross-governmental dialogues
on effective air quality management.

7Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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.
Cambodia is not exempt from this burden; exposure toambient fine particulate matter (PM2.5) was estimatedto cause about 3,500 (1,800-5,700) premature deaths
annually in Cambodia in 2019 (Murray et al., 2020).
Furthermore, exposure to PM2.5 is responsible fora high burden of morbidity from cardiovascular andrespiratory diseases, and air pollution also affects
ecosystems through deposition of 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 Cambodia,
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 (Zhang, 2016; Motokura et al.,2017; Acid Deposition Monitoring Network in East Asia
[EANET], 2020; Ministry of Environment, 2021), andtransport 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 lead to further
worsening of air quality in the region with negative
impacts for health (United Nations Environment
Programme/Climate and Clean Air Coalition [UNEP/CCAC], 2023). To limit the multiple negative impactsof air pollution it is therefore important to consider

what additional actions could be effective and to not
only explore the implications of current policies, butalso 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
and air pollution, while air pollutants and long-lived
GHGs often come from the same source. Consequently,
taking an integrated approach to air pollution andclimate change could result in multiple benefits forhealth and the environment (Haines et al., 2017) andachieve other 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,
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, 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.

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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
Cambodia 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
Cambodia, 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 Cambodia 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 which abundle of new measures to curb pollution, reduce GHG
emissions, 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Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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., 2011a; 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 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;that should read source- and technology-specificmeasures; more than 1000 measures 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

10

organic carbon (OC)), Non-methane Volatile
Organic Compound (NMVOC), ammonia (NH3) andGHGs.
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 thesame resolution, the exposure distribution to ambient
PM2.5 in the population is calculated. Applying
concentration-response (CRF) functions from theinternational literature, GAINS calculates premature
mortality from long-term exposure to PM2.5, and theassociated years of life lost (YLLs). For this analysis,
the model has been extended to include other healthrelated impacts, i.e., morbidity, work time loss, theircosts, etc. (see Fig. 1 in the Annex). Details of themethodology are explained in the Annex.

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 Cambodia will
experience significant economic development in the
future with its GDP more than doubling by 2030 from18 billion US$ in 2015, following the projections madein the International Energy Agency (IEA)’s World
Energy Outlook 2018 (IEA, 2018). The populationof Cambodia is also assumed to change into the
future, growing from 15.4 million in 2015 to 18.6
million in 2030 and 21.7 million in 2050, following
the UN World Population Prospects 2017 (UN, 2017),
Medium Scenario. At the same time, the Cambodian
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 Cambodia by mid-2020. The information
about current policies, emission limit values andstandards is taken from Zhang (2016), Motokura etal. (2017), EANET (2020), He et al. (2021), and Clean
Air Plan of Cambodia (Ministry of Environment, 2021).
The energy trends used in the Current Policies scenario
are consistent with the IEA ‘New Policies’ (NPS)
scenario, and air pollution controls are implemented tothe extent foreseen under current legislation, but nofurther action is taken.
In contrast to this, the Strong Mitigation scenario(additional policy scenario in above Fig. 1.1), assumes
that the additional 12 key solutions1 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/Infographics2) 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 GHG emissions and contribute to theachievement 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Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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
2

CO2
SO2
NOx

1.5

PM2.5
NH3

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 Cambodia

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(sulphur dioxide (SO2), nitrogen oxides (NOx), (Fig.
2.1). In fact, these precursors are growing slower thancarbon dioxide (CO2), suggesting gradual decouplingof economic growth from air pollutant emissions.
The main contributions and growth of CO2 emissions
in Cambodia are from transport, 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 Cambodia 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 contributed over 50% of
PM2.5 in 2015, according to GAINS model estimates,

a trend towards clean fuels for cooking (providing
access in both urban and rural areas) is expected toresult in decline of emissions from this sector reducing
its share to about 40% and 30% by 2030 and 2050,
respectively, and contributing near stabilization of
overall PM2.5 emissions (Fig. 2.1).
Annual mean PM2.5 concentrations in Cambodia
estimated by GAINS for 2015 are between 5 and 30µg/m3 (Fig. 2.2, left panel). The highest modelledconcentrations of PM2.5>20 µg/m3 are seen in Phnom
Penh, in agreement with the observations from recent
years (19-21 µg/m3). While this means that the PM2.5
pollution problem in Cambodia is smaller than inmany other Asian countries, concentrations are still
considerably above the World Health Organization(WHO) guideline value of 5 µg/m3, particularly incities.

13

PM2.5 [μg/m3]

PM2.5 [μg/m3]

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

In 2015, the entire population of Cambodia areexposed to PM2.5 levels above the current WHO
air quality guideline of 5 µg/m3 and over 80% areexposed to levels above the 2005 WHO guideline of 10µg/m3. In the Current Policies scenario, the situation is
not expected to improve by 2030, even with assumed
effective implementation of current legislation

85% of the population will still be exposed to PM2.5
concentrations above the 2005 WHO guidelines andthe population exposed to concentrations exceeding
15 µg/m3 would more than double from about 2 million
to over 4 million owing to increasing concentrations
in some regions (Fig 2.2 and Fig 2.3) as well as
population growth and 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 ofall 12 mitigation solutions illustrated in Fig. 1.2 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 2 million people would enjoy PM concentrations
below the current WHO guidelines and less than 10%

14

would be exposed to levels above 10 µg/m3. 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.

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

All measures
2030
Current
Policies 2030
2015
0

2

4

6
8
10
12
Million people exposed to PM2.5

14

16

18

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

20

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 in Cambodia in 2030,
assuming the full implementation of each solution bothin Cambodia but also across the whole ASEAN region.
This figure also shows the impact on exposure frommeasures which had already been implemented by2015 (blue), and have already contributed to improved
air quality, with those that have been included inrecent legislation passed after 2015 but may not yet
fully implemented (green), potentially contributing tofuture improved air quality if successfully implemented.

For example, if implemented, recent policies relating
to the transition towards clean cooking will havesome success in reducing air pollution (green bar) but
there is also potential for further reductions throughthe implementation of more ambitious policies in this
sector (yellow bar). 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 mostpotential for further ambition through implementationof the solutions.

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

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 Cambodia 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Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

2.2. Health impacts and cost of inaction
Exposure to PM2.5 leads to considerable health impacts
in Cambodia. The GAINS model estimates that in 2015,
more than 3,200 premature deaths were attributable
to ambient PM2.5, corresponding to almost 70,000
YLLs. In the Current Policies scenario, due to increasesin PM2.5 concentrations, as well as population aging,the mortality burden is projected to increase to 4,700
premature deaths and 83,000 YLLs in 2030 undercurrent legislation.
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 takenfrom internationally available data sets and adapted
to Cambodia’s per-capita GDP (see Appendix 1 fordetails).

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

StrongMitigation

Current Policies
Indicator

2015

2030

2030

Cases - YLLs/year

Costs –
StrongMitigation

Costs Current Policies

Unit
Cost
$US
2015

2015

2030

2030

Thousand $US 2015/year

Mortality
Premature deaths

3,259

4,692

1,480

333,422

1,086,622

1,564,416

493,465

YLLs

69,53782,64826,123

12,608876,750

1,042,053

329,364

2,205

2,827

1,349

5

12

15

7

Cardiovascular hospital
admissions, below 65
years of age

88

110

49

263

23

29

13

Cardiovascular hospital
admissions, post 65 yearsof age

106

222

107

263

28

58

28

5,703

7,302

3,520

263

1,501

1,922

926

7,211,875

10,206,569

4,036,485

12

84,761

119,957

47,441Morbidity
Asthma (Emergency Room
Visits), all age

Respiratory hospital
admissions, all age
Respiratory restrictedactivity days, working age

16Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

1200

3.0%

1000

2.5%

800
600

2.0%
1.5%

400

1.0%

200

0.5%

0

0.0%

Current Policies Strong Mitigation

Morbidity

Mortality

Monetary burden as share of GDP (%/year)

approximately 380 million USD per year, equivalent
to about 1% of GDP in 2030. Therefore, this meansthat by not implementing the 12 clean air solutions
and acting on air pollution, the cost of inaction in
Cambodia is estimated to be almost 800 million USD
per year, equivalent to around 2.1% 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 istaken.

Cost of inaction

Million $US/year

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 Cambodia
are estimated to reach almost 1.2 billion USD per
year, a value equal to 3.1% of Cambodia’s GDP. In
contrast, in the Strong Mitigation scenario, due to thesuccess of the 12 clean air solutions in reducing PM2.5
concentrations, this cost is estimated to decrease toShare 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 Cambodia (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 consistently about 90% of costs.

Respiratory restricted activity days, work age

120

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

100
Million $US/year

The remaining 10% are dominated (98%) by costs
associated with respiratory restricted activity days
(Fig. 2.6).

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

80
60
40
20
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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

Fig 2.7 provides a summary of premature death,hospital admissions, and working days lost due toambient 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 2,200 asthma related
emergency room visits, 200 hospital admissions for
cardiovascular reasons, 5,700 respiratory related
hospital admissions, and 7.2 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
by almost 4,000 as well as reducing the number of
emergency room visits from asthma by 1,500 andavoiding 3,200 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 a switch to clean cooking for whichmonetized benefits from introducing the measure in
Cambodia alone are estimated at about 190 million
USD per year from ambient PM2.5 only. Prevention offorest fires within Cambodia and surrounding countries
could bring health benefits of 150 million USD per
year, on top of benefits from avoided ecosystemdamage. Electrification of road transport and strict
emission standards for remaining combustion vehicles
in Cambodia could avoid health damages worth 100
million USD per year, with vehicle inspection andmaintenance adding another 20 million USD. Solutions
which improve municipal waste management could
result in benefits of almost 90 million USD per year.
Increasing renewables and applying efficient post
combustion controls in the power and industrial sectors
as well as improving industrial production standards
could bring benefits of 65 million USD. Measures in
the agriculture sector are estimated to have a smaller
potential, except the prevention of agricultural wasteburning.

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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

Fig. 2.8 distinguishes the benefits gained from theimplementation of measures in Cambodia (blue),and in the other ASEAN countries (yellow). It istherefore clear, especially in some specific areas thata large benefit can be reaped from collaboration on
air quality action. While it is also likely that other
ASEAN countries would benefit from implementationof ambitious air quality actions in Cambodia. While thenumbers mentioned in the previous paragraph referto the implementation of measures in Cambodia, theinitial assessment shows that implementation of all
12 solutions in the whole ASEAN region would bringanother 0.2 billion USD per year of avoided damage
costs in Cambodia, in addition to those avoided byaction in Cambodia itself.

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

50

100

150

200

250

Total health benefits [Million USD]

Fig. 2.8 Monetized health benefits from individual measures applied in 2030 on top of current policies,
distinguishing benefits from measures applied in Cambodia, and in the whole ASEAN region. Note that for forestand peatland fires, the model does not distinguish between the impacts from preventing fires in Cambodia or inother countries

2.3. Climate and other co-benefits
This assessment has estimated the costs that
Cambodia 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 Cambodia 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 Protocol (GWP)-100)
from implementing each solution.
In 2030, two of the most important measures for
air quality also have significant co-benefits for GHG
reductions: The largest potential for reducing GHG
emissions are for the solutions related to electrificationin the road transport sector as well as the transition toclean cooking, each of which could reduce Cambodia’s
GHG emissions by 1 Mt CO2-eq annually in 2030
while simultaneously reducing population weighted
PM2.5 exposure by 2 µg/m3 (cooking) and 1.3 µg/m3(transport), respectively. It is also likely that for roadtransport, 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 andincentivize active mobility, etc. Such policies couldat the same time have multiple other co-benefits
such as reducing congestion and time spent in trafficjams, reduced number of road accidents, as well as
additional health co-benefits from active travel.
GHG reductions from clean cooking are due to large
efficiency gains despite a transition from biofuelto LGP; they could be even larger if the transition
is made directly to electric stoves, to the extent
possible, provided that the electricity is generated
from renewables. The switch to renewables in power
and industry itself has significant co-benefits whencombined with efficient end-of-pipe air pollution

19

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

emission abatement although the contribution fromthese sectors to pollution in Cambodia is smaller.
Also, several other measures have a potential forco-benefits, for example improved municipal waste
management or agricultural waste burning, throughavoided CH4 emissions.

to double its contribution to ambient PM2.5 if themeasure is not taken. With the expected increase inmeat consumption under the Current Policies scenario,
a switch to healthier diet has a significant potential for
GHG abatement in 2050.
Cambodia has set ambitious targets to reduce GHG
emissions in the country by 42% in 2030 compared
to a baseline scenario, both climate change and air
pollution have economic costs, and policies suchas those highlighted above, could therefore havesubstantial benefits for both the health and wellbeingof the population but also in aiding economic
development and achieving the SDGs. At the sametime, some policies relating to climate or sustainable
development such as those detailed in Cambodia’s
Nationally Determined Contribution (NDC), thePhnom Penh Sustainable City Plan, or the National
Environmental Strategy will also have co-benefits for
air pollution, quantifying these additional health andeconomic benefits could enhance the evidence baseand provide further motivation for action.

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. Also, the co-benefits of better
waste management avoiding both open burning ofwaste and CH4 emissions from landfills are much more
pronounced in 2050; by then, this sector is expected

GHG emissions [Mt CO2 eq]

2030

2050

4

4

3

3

2

2

Vehicle inspection and maintenance1

1

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

1

2

PM2.5 [µg/m ]3

3

International shipping
Dietary changes
Waste management

0

1

2

3

Prevention of forest and peatland fires

PM2.5 [µg/m ]3

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

20

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 toimpacts and are expected to cover the potential scopeof local policies. Reviews and assessments of state ofpolicies and progress in their implementation (EANET,
2020; Ministry of Environment, 2021) show that theassumptions about policies considered in the Current
Policies scenario are largely complete and consistent
with the existing legislative framework in Cambodia
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 (Ministryof Environment, 2021), their reduction potential iscaptured in the further mitigation potential estimatedin the assessment. The same applies to proposed or
evaluated potential and impacts of mitigation of air
quality, as the respective 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. 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 basedon a combination of local and international data (fordetails, see Annex 1).

3.3. Modeled PM2.5 concentrations
There was only very limited information available tovalidate the ambient PM2.5 concentrations calculated
in GAINS against observations. Annual mean PM2.5
concentration levels in downtown Phnom Penhmodelled by GAINS in 2015 (26 µg/m3 are only
slightly higher than those measured by the Ministryof Environment with their monitoring station in 2018-2021 (19-21 µg/m3) but it is impossible to assess
the model performance for the whole country from asingle monitoring point. Attributable deaths estimatedfor current levels of PM2.5 are very close to the values
reported by the GBD study (in 2015 the GAINS
estimate is slightly higher by 7%).

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 0.30.4 billion USD, which represents nearly half 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 relative terms, the initial cost estimates
represent a larger share of the cost of inaction than21

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

in other ASEAN countries, primarily because thecurrent legislation addressing transport and industry(as reflected in the Current Policies scenario) is lessstringent than in some of the other countries in theregion.
In summary, in terms of cost of action estimation, there
are number of limitations in the models and data: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.

i. GAINS relies on international defaults for costs anddoes not include local cost factors where appropriate(although, most air pollution control technology istraded internationally),

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
outcomes are considered, so the total impact via
morbidity is likely underestimated. Exposure to otherair pollutants like nitrogen dioxide (NO2) and (O3)is associated with health and ecosystems impacts
and crop loss due to elevated O3 concentrations. O3
formation is influenced by CH4 emissions and therefore

22

introduction of measures analyzed in this assessment
can bring additional co-benefits. However, these areorders 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
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 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.

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

4. Conclusion and recommendations
Cambodia 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 alreadypassed legislation will be important to slow down
the increasing trends in emissions and air pollution
impacts. Building upon the assessment for clean air in
the ASEAN region and aligning with Cambodia’s Clean
Air Plan, this study identified significant mitigation
potential consisting exclusively of proven technicaland non-technical actions that, if fully implemented,
would deliver significant reductions of air pollution andin the longer-term important climate co-benefits. This
study also highlights the importance and benefits ofcollaboration within the ASEAN region since pollution
knows no boundaries and internationally coordinatedefforts to reduce emissions will benefit most countries
within the region.
Not implementing such actions or lack of further
action will have direct economic costs. An initial ‘costof inaction’ has been estimated here for Cambodia asreaching around 800 million USD in 2030, considering
the economic costs of mortality and morbidity due
to air pollution exposure only. While including otherimpacts or foregone benefits in the assessment will
likely increase this cost further. Introducing policies
stimulating the rapid introduction of identified further
mitigation measures would result in significant benefits
at potentially much lower costs, although the latter
was not fully estimated. A preliminary estimate of
implementation costs of technical measures indicatesthat these would represent in 2030 nearly half of thecost of inaction.

༪Improved waste management, including reducing
the amount of waste which is openly burned,

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, most prominently
from effectively banning open burning of agriculturalresidues,

༪Finally, internationally coordinated efforts to reduce
emissions, including exchange of experience insuccessful development and application of policies
and their enforcement will have benefits going
beyond the national borders.

Implementing all of the clean air actions will also haveimportant co-benefits including for climate change
and sustainable development. This assessment foundthat several of the measures to reduce air pollution
would simultaneously reduce emissions of GHGs, withlarger reductions in the long-term translating by 2050
into significant reductions of CO2 and CH4. Among themost important measures delivering these longerterm benefits for simultaneously reducing air pollution
and emissions of GHGs in the long term include theelectrification of the vehicle fleet for transport, more
effective waste management policies and the transition
to clean cooking technologies.

Key policies delivering major benefits from bothassessments include those related to:༪

A complete transition to clean cooking fuels,༪Prevention of forest and peatland fires within
Cambodia and in the surrounding region,

23

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 (Amann etal., 2011; online at https://gains.iiasa.ac.at) is thecentral tool used in the analysis. GAINS, developedby the International Institute for Applied Systems
Analysis (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.
All outcomes are listed in Box 1. GAINS routinely

24

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 concentration-response functions isgiven 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.

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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: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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

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.
Only one station was available, located in Phnom Penh.

Concentrations measured there are reproduced well in
GAINS even if there seems to be some overestimate.
However, assessing the quality of the modelling for thewhole country from one single station is not possible.

50
45PM2.5 modelled [µg/m3]

40
35
30
25
20
15
10

Mean bias: 6.76 μg/m3
RMSE: 6.84 μg/m3
R2: NaN

5
0
0

10

20

30

40

50

PM2.5 observed [µg/m3]
Fig 2. Comparison of PM2.5 concentrations modelled with GAINS for 2015 against observations from 2018 to 2021

27

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

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.

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: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. (in review) compared the effects of using
annual means versus the time series in the UnitedStates and found a small overestimation of 0.1% to1.3% depending on the morbidity outcome, which is
not taken into account in this analysis in view of manyother – 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 for cardiovascularreasons 2018-2021 have been made available to thisassessment by the Ministry of Health. We used only

2019 data in order to avoid possible temporary effects
during the Covid-19 pandemic.

Baseline rates of the related diseases: Default estimates

28

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.
We obtained the baseline prevalence rate of therelated diseases from the GBDs 2019 (Global Burdenof Disease Collaborative Network, 2021) with thefollowing mapping:Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

Unit Costs
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:

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

where i indicates different morbidity endpoints, andj indicates different countries. The coefficients weobtained from the regressions were as below:

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

29

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 (NPS) 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

30

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

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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

31

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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 CH4 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 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 GHG emissions and contribute to theachievement of several SDGs.

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

That report identified 15 solutions which included measures targeting reduction of CH4 and Hydrofluorocarbons (HFCs), i.e.,rice paddies, wastewater treatment, and HFC-refrigerant replacement.
432

12 solutions

Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

12 solutions

Brief description

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.

33Assessment of the Cost of Inaction of Tackling Air Pollution in Cambodia

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35

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