KATHOLIEKE
UNIVERSITEIT
LEUVEN
INTERACTION BETWEEN LOCAL AIR
POLLUTION AND GLOBAL WARMING
AND ITS POLICY IMPLICATIONS FOR BELGIUM
Stef Proost
Denise Van Regemorter
CES KULeuven
Naamsestraat 69, 3000 Leuven, Belgium
e-mail:denise.vanregemorter@econ.kuleuven.ac.be
Abstract
A policy to reduce greenhouse gas emissions has not only an impact at global level but can also bring
benefits locally by reducing other air pollutants linked to energy consumption. Moreover the benefits of
the reduction of local air pollutants will accrue to the current generation contrary to the reduction of
climate change and also mostly to the population undertaking the mitigation actions, though the
transportation of pollutants can be rather extensive. In view of the Kyoto agreement and the target it
implies for Belgium, it seems important to take these benefits into account for a more correct evaluation
of the cost of GHG reduction policies. Moreover Belgium has signed different agreements to reduce the
more local pollutants linked to energy. Therefore, a policy fully exploiting these mutual benefits and
reaching the targets for the environmental problems considered is desirable. This paper explores with the
Belgian MARKAL model what are the implications of the interactions between pollutants for policy
design. MARKAL is a dynamic energy optimisation model, that allows to compute the external cost and
benefits of pollutants linked to energy consumption, such as CO2, NOx, SO2, VOC and PM, inclusive
their abatement cost and to integrate in the policy evaluation the interactions between pollutants and their
abatement. It goes beyond the simple approach to evaluate the secondary benefits/costs of a policy from
the decrease/increase of the emissions of other pollutants. The importance of defining policies integrating
the interactions between pollutants is evaluated for Belgium by comparing a policy addressing local
pollution, a policy addressing global warming and a policy combining both.
Keywords: global warming, ancillary benefits, policy design
This research is financed under the 'Global Change' research program of the Belgian Prime
Minister's Office – Federal Office for Scientific, Technical and Cultural Affairs.
ENERGY, TRANSPORT AND ENVIRONMENT
CENTER FOR ECONOMIC STUDIES
Naamsestraat 69 B-3000 LEUVEN
BELGIUM
Table of Contents
1.
Introduction .................................................................................................................... 1
2.
The modelling framework for MARKAL .................................................................... 1
2.1.
2.2.
Basic modelling framework.............................................................................................. 1
Approach for the implementation in MARKAL .............................................................. 3
3.
Database Extension ........................................................................................................ 4
3.1.
3.2.
3.3.
3.3.1.
3.3.2.
3.4.
Emission coefficients and abatement possibilities ........................................................... 4
Coefficients for the transformation and transport of emissions ........................................ 4
Damage Parameters and their Monetary Valuation .......................................................... 5
Impact on public health .................................................................................................... 6
Impacts on territorial ecosystems and materials ............................................................... 8
Damage from emissions in Belgium ................................................................................ 9
4.
Illustration with Policy Scenarios ................................................................................. 9
4.1.
4.2.
Definition of the policy scenarios ..................................................................................... 9
The scenarios comparison .............................................................................................. 10
5.
Conclusion ..................................................................................................................... 11
1
1. INTRODUCTION
A policy to reduce greenhouse gasses emissions has not only an impact at global level but can also
bring benefits locally by reducing other air pollutants linked to energy consumption. Moreover the
benefits of the reduction of local air pollutants will accrue to the current generation contrary to the
reduction of climate change and also mostly to the population undertaking the mitigation actions,
though the transportation of pollutants can be rather extensive. In view of the Kyoto agreement and the
target it implies for Belgium it seems important to take these benefits into account to get a more
correct evaluation of the cost of GHG reduction policies. Moreover Belgium has signed different
agreements to reduce the more local pollutants linked to energy. Therefore, it might be useful to
induce a choice of policy, which fully exploit these mutual benefits, while obtaining the desired
impact on the environmental problems considered.
The objective of this paper is to explore with the Belgian Markal model 1 what are the implications of
the interactions between pollutants for policy design. MARKAL is a dynamic energy optimisation
model, that allows to compute the external cost and benefits of pollutants linked to energy
consumption, such as CO2, NOx, SO2, VOC and PM, inclusive their abatement cost. By adding to
MARKAL objective function a damage function, it is possible to integrate in the policy evaluation the
interactions between pollutant and their abatement. The damage function will depend on the
immissions from Belgian origin, i.e. the change in pollutant concentration and in pollutant deposition,
the impact of these changes on health and on the ecosystem and their monetary valuation. The
development of MARKAL in this direction allows to take the external impact of all air pollutants
explicitly into account, when evaluating long term options for air pollution reduction policies. This
goes beyond the simple approach to evaluate the secondary benefits/costs of one policy through the
decrease/increase of the emissions of other pollutants.
In the first section, we formulate the basic structure of a model where the damage from pollution is
internalised and the adaptations to be made to MARKAL. Then, in the second section we give a
description of the extension of the database of MARKAL with three types of data: emission
coefficients for the main air pollutants and their abatement technologies, data for the modelling of the
transport of pollutants and their impact on air quality and finally the damage valuation data. Finally, a
first analysis is done with the adapted model to evaluate the importance of defining policies
integrating the interactions between pollutants. We look at a policy addressing local pollution, a policy
addressing global warming and a policy combining both.
2. THE MODELLING FRAMEWORK FOR MARKAL
MARKAL is a dynamic optimisation model that represents all energy demand and supply activities
and technologies for Belgium with a horizon of up to 40 years, with their associated emissions (CO,
CO2, SO2, NOx, VOC and PM). The environmental problems considered in this study, global
warming and local air pollution, are both linked to energy consumption and their abatement
possibilities are interrelated. This interaction has to be integrated in the modelling framework for a
correct policy evaluation. The basic modelling framework is given in the first section to show what the
interaction implies for policy evaluation. Then the implementation in MARKAL is described.
2.1.
Basic modelling framework
For illustration purposes, we consider the case of two pollutants linked to energy, CO2 and SO2,
where CO2 can only be abated through a reduction in energy consumption but SO2 has specific
abatement technologies2. In a partial equilibrium framework, the problem for the policy maker
deciding on pollution reduction policies, can be represented as a maximisation of the consumer and
1
2
The Markal model is a long term multi-period energy technology optimisation model developed in the IEA framework by an international
users group ETSAP, the Energy Technology Systems Analysis Programme. CES and VITO (Vlaams Instelling voor Technologisch
Onderzoek) are responsible for the Belgian Markal model.
the SO2 abatement technologies are assumed not to increase directly energy consumption and hence CO2 emissions.
2
producer surplus, taking into account the production possibilities, the damage from pollution and the
abatement possibilities:
Max
q , I , E , sr s
C (q)  p I I  p eE  Cs ( srs )  p dses (1  srs ) E  p dcecE
(1)
under the production constraint
F (I , E)  Q
where
( p)
q
demand for an energy service, e.g. heating
C (q)
the consumer/producer surplus (surface under the demand curve)
(2)
p I , pe the price of capital and energy
p
the price of the energy service (shadow price of the constraint)
I, E
the production inputs, annualised capital and energy
Cs ( srs ) cost of SO2 emission abatement for a reduction of sr s
p ds , p dc damage from SO2 and CO2 emissions, assumed constant here3
es , ec
emission coefficients of SO2 and CO2 per energy unit
At the optimum, the first order conditions imply that the marginal utility of the energy service q be
equal the price p, the marginal productivity of capital equal to the price of capital p I and for energy
and emission reduction:

the marginal productivity of energy equal to the cost of energy plus the damage from SO2 and
CO2 emissions
p F  p e  p ds es(1  srs )  p dc ec
E
(3)
or, in terms of ton CO2 abated4, the marginal abatement cost equal to the damage from SO2 and CO2
taking into account the SO2 abatement
p F  p e / ec  p ds es / ec (1  srs )  p dc
ec E
(4)
Because of the interaction between SO2 and CO2 reduction (through energy consumption), the
optimum abatement effort for CO2 takes into account both the damage from CO2 and SO2. It will
therefore be higher than when there is no interaction. It has also implications for the choice of policy
instrument: the policy instrument has to give the incentive to internalise this interaction.

the marginal cost of SO2 emission reduction level equal to the damage from the (unabated) SO2
emissions at the optimum
Ccs
 p ds esE
srs
(5)
or, in terms of ton SO2 abated, the marginal abatement cost equal to the damage from SO2 (marginal
damage is assumed constant)
3
4
If a constraint is imposed on CO2 instead of a damage per unit of CO2 emission, the shadow price of this constraint would replace the
damage figure in the computation.
Improving energy efficiency is the option to abate CO2 emissions
3
Ccs
 p ds
ems
(6)
This is the classic result when only one pollutant is considered because neither the SO2 abatement
options neither the SO2 emissions have an impact on CO2 emissions or damage.
This basic framework can easily be extended to more interactions between pollution damage and
pollution abatement, but it already shows clearly the importance for environmental policy design to
take these interactions into account.
2.2.
Approach for the implementation in MARKAL
The objective was to adapt MARKAL to be able to take into account in the analysis of policy options
the benefits/costs coming from the pollution interactions. The local environmental problems
considered are: (i) problems related to the deposition of acidifying emissions and (ii) ambient air
quality linked to acidifying emissions and ozone concentration. We consider the energy-related
emissions of NOx, SO2, VOC and particulates, which are the main source of air pollution. NOx is
almost exclusively generated by combustion process, whereas VOC’s are only partly generated by
energy using activities (refineries, combustion of motor fuels); other important sources of VOC’s are
the use of solvents in the metal industry and in different chemical products.
The approach followed for the evaluation of the benefits from the reduction of local pollutants is based
on the bottom up damage function approach as developed by the ExternE project. This approach can
be illustrated by the following figure (EC, 1995).
EMISSIONS

DISPERSION

IMPACT

DAMAGE VALUATION
It implies an extension of the database towards the different datas needed for the evaluation of the
benefits, described in the next section, and the construction of a damage function.
The damage per pollutant or damage function is modelled as follows:
DAM (env)  EV coef (env) * EM (env) EV ( env)
where EV (env) reflects the relation between the damage cost and the level of emissions; at this
stage, to keep the model linear, the damage per unit of emission or the marginal cost per emission is
kept constant, i.e. EV (env)= 1,
EVcoef(env) is computed through calibration with the marginal cost, i.e. the derivative of the
damage function w.r.t. emission, equal to the value per unit of emission derived from the ExternE
results, as explained in the next section.
The sum of the damage-functions per pollutant is added to the objective function and therefore taken
into account in the optimisation process. When the damage function is used but not added to the
objective function, it allows to compute the environmental damage generated by a policy, without
feedback into the optimisation process.
4
As the computation are based on dose response functions which give the incremental damage from air
pollution, the results should also be interpreted in these terms, i.e. in terms of the change in total
damage compared to a reference year (the base year).
3. DATABASE EXTENSION
The Markal database has been extended into three directions:
a) emission coefficients for pollutants such as NOx, SO2, VOC and PM, and the
emissions abatement technologies,
b) immission coefficients for those pollutants, i.e. coefficient for the translation of
emissions into concentration, inclusive the transportation mechanism,
c) impact of emissions and immissions and their monetary valuation.
3.1.
Emission coefficients and abatement possibilities
Emission coefficients of NOx, SO2, VOC and PM associated with the energy using technologies have
been added to the Belgian Markal database. This is a rather extensive work because, contrary to CO2
emissions coefficients, these coefficients are technology linked. Also the technological options to
abate those emissions were added to the database.
3.2.
Coefficients for the transformation and transport of emissions
This step establishes the link between a change in emissions and the resulting change in concentration
levels of primary and secondary pollutants. The transboundary nature of pollutants leads to the
necessity to account for the transport of SO2, NOx, VOC and particulates emissions between countries.
In the case of tropospheric ozone (a secondary pollutant), besides the transboundary aspect, the
relation between VOC and NOx emissions, the two ozone precursors, and the level of ozone
concentration has also to be considered.
Theoretically, the concentration/deposition (IM) at time t of a pollutant ip in a grid g is a function of
the total antropogenic emissions before time t, some background concentration5 (BIM) in every
country, and other parameters such as meteorological conditions, as derived in models of atmospheric
dispersion and of chemical reactions of pollutants:
IMip,g (t)  imip,g ( EM p,c (t   t), BIMip,g (t),..  p, c) ,
For the model, the equations are made static and the problem is linearised through transfer coefficients
TPC which reflect the effect the emitted pollutants in the different countries have on the
deposition/concentration of a pollutant ip in a specific grid, such as to measure the incremental
deposition/concentration, compared to a reference situation:
IMip,g =  TPC p,ip [g,c]  EM p,c
p
,
c
where TPC[g,c] is an element of the transport matrix TPC with dimension GxC. In the model here the
grid considered is a country and deposition/concentration levels are national averages.
The transport/deposition coefficients for SO2 and NOx emissions are derived from EMEP budgets for
airborne acidifying components which represents the total deposition at a receptor due to a specific
source. Basically, the EMEP model is based on a receptor orientated one layer trajectory (Lagrangian)
model of acid deposition at 150 km resolution. Characteristics of the various pollutants and their
transport across countries, as well as atmospheric conditions are taken into account. For particulates,
Mike Holland (ETSU, 1997) has estimated country to country transfers of primary particulates. His
computations are based on a simple model which accounts for the dispersion of a chemically stable
5
Resulting from natural emissions and emissions from geographic parts that are not included in the country set.
5
pollutant around a source, including deposition by wet and dry processes. To convert deposition into
air concentration, use was made of linear relations estimated by Mike Holland (1997).
Tropospheric ozone is a secondary pollutant formed in the atmosphere through photochemical reaction
of two primary pollutants, NOx and VOC. The source-receptor relationship is not as straightforward as
for acid deposition. However, it is recognised (EMEP, 1996) that there is a relatively strong linearity
between change in ozone concentration and change in its precursors emissions (both VOC and NO x),
allowing an approximation through linear source-receptor relationships.
It would be useful to include the distinction in the source of emission, for instance between emissions
from mobile sources and/or low height stationary sources as opposed to high stack sources as it is
expected that the deposition of pollutants per unit emitted will be different in each case. However,
there is no information available at this moment that allows making such distinction.
3.3.
Damage Parameters and their Monetary Valuation
The damage parameters and their monetary valuation are taken from the ExternE project of the
European Commission, in which CES and VITO were responsible for its Belgian application.
Therefore the approach followed here is entirely based on the framework derived in the project,
though at a much more aggregated level. The damage occurs when primary (e.g. SO2) or secondary
(e.g. SO4--) pollutants are deposited on a receptor (e.g. in the lungs, on a building) and ideally, one
should relate this deposition per receptor to a physical damage per receptor. In practice,
dose/exposure-response functions are related to (i) ambient concentration to which a receptor is
submitted, (ii) wet or dry deposition on a receptor or (iii) ‘after deposition’ parameters (e.g. the PH of
lake due to acid rain). Following the ‘damage or dose-response function approach’, the incremental
physical damage DAM per country is given as a function of the change in deposition/concentration
(acidifying components or ozone concentration in the model),
DAM cACID,d (t)  damcACID,d (  IMip,c (t),..  ip) ,
The damages categories considered in the model are
1. damage to public health (acute morbidity and mortality, chronic morbidity, but no occupational
health effect)
2. damage to the territorial ecosystem (agriculture and forests) and to materials, this last category
being treated in a very aggregated way at this stage.
The impact on biodiversity, noise or water is not considered, either because there are no data available
that could be applied in this study or because air pollution is only a minor source of damage for that
category.
For the monetary valuation of the physical damage, a valuation function VAL for the physical damage
is used:
VALco (t)  val co (  DAM co,d (t),.. d) .
The economic valuation of the damage should be based on the willingness-to-pay or willingness to
accept concept. For market-goods, the valuation can be performed using the market price. When
impacts occur in non-market goods, three broad approaches have been developed to value the damages.
The first one, the contingent valuation method, involves asking people open- or closed-ended questions
for their willingness-to-pay in response to hypothetical scenarios. The second one, the hedonic price
method, is an indirect approach, which seeks to uncover values for the non-marketed goods by
examining market or other types of behaviour that are related to the environment as substitutes or
complements. The last one, the travel cost method, particularly useful for valuing recreational impacts,
determine the WTP through the expenditure on e.g. the recreational impacts.
It is clear that measuring environmental costs at the global level as in this model, raises different
problems, which are extensively discussed in ExternE: transferability of the results from specific
6
studies, time and space limits, uncertainty, the choice of the discounting factor, the use of average
estimates instead of marginal estimates and aggregation. However, despite all these uncertainties, it is
possible, according to ExternE, to give an informative quantified assessment of the environmental
costs.
3.3.1.
Impact on public health
The ExternE project retains, as principal source of health damages from air pollution, particulates6
resulting from direct emission of particulates or due to the formation of sulphates (from SO 2) and of
nitrates (from NOx), and ozone. They retain also a direct effect of SO2 but no direct impact of NOx
because it is likely to be small. Direct damages from HC are not yet considered here, because the
ExternE figures are still at a preliminary stage. The assessment of health impacts is based on a
selection of exposure-response functions from epidemiological studies on the health effects of ambient
air pollution (both for Europe and the US). They are reported in the ExternE report (1997) and
summarised hereafter. For sulphates, the dose-response functions associated with PM2.5 are taken into
account, whereas for nitrates the dose-response functions associated with PM10 are used. ExternE
recommends also using the E-R functions related to PM2.5 for the particulates with as primary source
transport. When chronic mortality impacts are explicitly accounted for, one must exclude the acute
mortality impacts because they are already considered in the former (Hurley et al., 1997).
Table 1: Health impact of pollutants from ExternE (in cases/(yr-1000people-µg/m3)
Pollutant
PM10
ug/m3
PM2.5
ug/m3
6
Effect
Acute mortality
Respiratory hospital admissions
Congestive heart failure
Cerebrovascular hospital admission
RADs
Bronchodilator usage by children for asthma
Bronchodilator usage by adults for asthma
Cough in asthmatic children
Cough in asthmatic adults
Wheeze in asthmatic children
Wheeze in asthmatic adults
Chronic mortality
Chronic bronchitis in adults
Change in prevalence of children with bronchitis
Change in prevalence of children with chronic cough
Acute mortality
Respiratory hospital admissions
Congestive heart failure
Cerebro-vascular hospital admission
RADs
Bronchodilator usage by children for asthma
Bronchodilator usage by adults for asthma
Cough in asthmatic children
Cough in asthmatic adults
Wheeze in asthmatic children
Wheeze in asthmatic adults
Chronic mortality
Chronic bronchitis in adults
Change in prevalence of children with bronchitis
Rate
0.00399
0.00207
0.00259
0.00504
20.00000
0.54250
4.56376
0.93100
4.69284
0.72030
1.69681
0.03860
0.03920
0.32200
0.41400
0.00677
0.00346
0.00433
0.00842
33.60000
0.90440
7.60206
1.56100
7.82046
1.20050
2.82802
0.06430
0.06240
0.53800
PM10, i.e. particulates of less than 10 µg/m3 aerodynamic diameter, is taken as the relevant index of ambient particulate concentrations.
7
O3
6hr ppb
SO2
ug/m3
Change in prevalence of children with chronic cough
Acute mortality
Respiratory hospital admissions
Minor RADs
Change in asthma attacks (days)
Symptom days
Acute mortality
Respiratory hospital admissions
0.69200
0.01168
0.00709
15.61600
0.30030
66.00000
0.00719
0.00204
For the valuation of the different health impacts, ExternE makes a distinction between morbidity and
mortality impacts. The valuation of morbidity is based on estimates of WTP to avoid health related
symptoms, measured in terms of respiratory hospital admissions, emergency room visit, restricted
activity days, symptom days, etc. They are based on an extensive study of the literature on the costs of
morbidity, mainly US based. In general the WTP for an illness is composed of three parts: the value of
the time lost because of the illness, the value of the lost utility because of the pain and suffering and
the expenditure on averting and/or mitigating the effects of the illness. The costs of illness (COI) is
measured directly: the actual expenditure associated with the different illnesses plus the cost of lost
time (working and leisure time). The other cost components, which are more difficult to evaluate, are
measured by CVM methods (for the value of pain and suffering7) and models of averting behaviour.
When no WTP estimates is available, the COI approach was followed and a ratio of 2 for WTP/COI
for adverse health effects other than cancer and 1.5 for non fatal cancer was assumed.
For the valuation of the mortality effect, ExternE has changed the approach followed compared to
their first report: instead of the ‘value of a statistical life’ approach (VSL) used in the first report it
uses the ‘value of life years lost’ approach (VLYL), because the E-R functions used are closer to this
concept for most health impacts (see Markandya, 1997)8. The valuation figures used in ExternE are
summarised in Table 29.
Table 2 : Valuation of mortality and morbidity impacts from ExternE (ECU 1990)
Mortality
Statistical life
Lost life year
Acute Morbidity
Hospital admission for respiratory or cardiovascular symptoms
Emergency room visit or hospital visit for childhood croup
Restricted activity days (RAD)
Symptoms of chronic bronchitis or cough
Asthma attacks or minor symptoms
Chronic Morbidity
Chronic bronchitis/asthma in adults
Non fatal cancer/malginant neoplasm
Changes in prevalence of cough/bronchitis in children
2600000
81000
6500
185
62
6
31
87000
372000
186
Putting the impact and valuation data together, an estimation of the health damage figure per
incremental pollution can be computed for PM10 en PM2.5 (direct and indirect), for SO 2 (direct) and
ozone (cf. Table 3).
Table 3: Damage from an increase in air pollution (106 ECU90 per 1000 persons)
7
8
9
The altruistic cost, i.e. pain and suffering to other people is not included in the ExternE figures
The VSL estimates are based on studies of individuals with normal life expectancies whereas the pollution impacts for some kinds of
mortality were on individuals with much shorter life expectancies.
The latest ExternE figures (1997) are expressed in ECU 1995. They were transformed in ECU 1990 assuming a price increase of 20.8%
between 1990 and 1995.
8
From an increase of one µg/m3 of PM10 and nitrite concentration
From an increase of one µg/m3 of sulphite concentration
From an increase of one µg/m3 of PM 2.5 concentration and Diesel particulates
From an increase of one µg/m3 of SO2 concentration
From increase of one ppb of ozone concentration
3.3.2.
0.019602
0.032397
0.033281
0.000540
0.001538
Impacts on territorial ecosystems and materials
a) Impact on terrestrial ecosystems (agriculture and forest)
Damage on agriculture and forest is done by foliar uptake or due to acid deposition on the soil. It is
often not possible to accredit damage to a specific stress agent and multiple stress hypotheses have
been proposed (climate, pests, pathogens). Damage related to chronic exposure is usually different
from the acute injury. In some areas plants have been found to grow better in the presence of low levels
of fossil fuel related pollution than without any pollution because sulphur and nitrogen are essential
nutrients for living organisms (fertilisation). At higher concentrations however pollutants interfere with
plant functions and cause damage. A given dose of a pollutant will produce a variable response
depending on a wide range of factors: species affected, age of the organism, other pollutants, time of
day or season, temperature, water status, light conditions, soil and plant nutrient status, heavy metals in
the soil, etc. Moreover, especially in agriculture land, actions are taken to counteract the effect of
pollution. These different elements make it very difficult to assess the impact of incremental air
pollution.
The ExternE gives the following table for the importance of pollutants for different damage categories.
Table 4 : Importance of pollutants vs damage categories
SO2
NOx
NH3
O3
Total acid
Total N
forests
crops
fisheries
Natural flora
XX
XX
0
XX
X
X
0
X
X
X
0
X
XXX
XXX
0
XXX
XXX
X
XXX
XXX
XXX
0
XX
XXX
0: not ecologically significant
X: indirect effects
XX: significant effects in some areas
XXX: direct and significant effects in large areas of Europe
Source: ExternE
b) Impacts on materials
Discoloration, material loss and structural failure are the main impact categories for most materials,
which result from interactions with acidifying substances like SO2 and NOx, particulates and ozone.
The impact is highly dependent on the material in question: buildings, textile, paper, etc. ExternE
considers that discoloration and structural failure resulting from pollutant exposure are likely to be
small, though there are no specific studies estimating these possible damages. Therefore their analysis
has been limited to the effect of acidic deposition on corrosion, the direct effect of SO2 and the effects
of acidity resulting from SO2 and NOx.
c) Aggregate figure for damage to territorial ecosystems and materials
Because of the great uncertainty around dose response functions and the valuation of the damages, it
was impossible to derive a damage impact coefficient with a valuation term associated to it for each
category of damage. Moreover first results from ExternE showed that they were relatively less
important than public health impact: in the first ExternE evaluation they represented approximately
9
25% of total damage from particulates (direct and indirect). Therefore Mike Holland (ExternE, ETSU)
computed an average damage cost per person from the ExternE detailed computations to be used as an
indicative value.
Table 5: Damage from an increase in air pollution (106 ECU90 per 1000 persons)
From an increase of one µg/m3 of sulphite concentration
From an increase of one µg/m3 of nitrite concentration
3.4.
0.0028
0.0018
Damage from emissions in Belgium
Combining the figures for the transportation and transformation of pollutants and the figures for the
damages, one can compute a figure representing the damage per unit of emission of a primary
pollutant. The distinction can be made between the damage within the country and the damage across
the border, generated by the emission of a pollutant in one country, though at the country aggregation
level it remains approximate, because the geographic location of the source can be important. The
estimations for Belgium are given in the table below.
Table 6: Damage from emissions in Belgium (106 BF90 per kton emission of pollutant)
Damage in Belgium
Nox
SO2
VOC
PM
PM transp (PM2.5)
18.4
54.5
0.6
190.8
315.4
Total damage (in
Belgium and abroad)
194.4
183.3
9.9
609.2
1006.8
4. ILLUSTRATION WITH POLICY SCENARIOS
4.1.
Definition of the policy scenarios
We consider three policy scenarios to simulate with MARKAL, addressing local air pollution and
global warming. The first one focuses on local air pollution, the second one on GHG emission
reductions and the third combines both types of policies. They are compared to a reference scenario in
which no environmental policy is imposed, neither for local air pollution neither for GHG emission
reductions.
For the local air pollution policy (LAP scenario), we impose an environmental tax on SO2, NOx, VOC
and particulates emissions. The level of the tax is put equal to the total damage (in Belgium and
abroad) generated by the pollutant emitted in Belgium, as given in Table 6. In a further stage, it might
be interesting to relate this scenario to the different agreement Belgium has signed on local air
pollutant. A more geographically disaggregated model, both at the level of the generation of emissions
and at the level of the transformation and transportation of emissions10, would clearly enhance the
analysis because the damages from air pollution are ‘location’ dependent.
For the global warming policy (GW scenario), the EU Kyoto target, translated into a target for
Belgium through the burden sharing agreement within the EU, is imposed on the greenhouse gas
emissions in Belgium in 2010. This target consists in reducing the emissions of greenhouse gasses in
2008-2012 by 7.5% compared to the level of 1990. For after 2010, we have assumed that the GHG
emissions must continue to decrease at the same rate: in 2030, they must be 15% below their 1990
level. We also assume that this target has to be met in Belgium and that no tradable permits or other
flexible mechanisms can be used to achieve the required reduction in Belgium. The links between the
10
in this exercise the country is taken as ‘one’ grid.
10
reduction of certain pollutants and global warming, e.g. the cooling effect of sulphur emissions, should
be taken into account in this type of analysis, but this not done yet.
The third scenario, addressing both local pollution and global warming (LAPGW scenario), is a
combination of the two scenarios. The focus of the comparison of scenarios lies, at this stage, on the
mutual impact of the policies and not on the definition of optimal environmental policies or the choice
of policy instruments.
4.2.
The scenarios comparison
The comparison between scenarios focuses, at this stage, on the cost differences and not on the
technological options to reach the environmental targets neither on the distribution of the cost between
sectors. The main results are given in Table 7 (for the entire horizon) and in Table 8 (per period).
Table 7: Welfare and Environmental Benefits over the entire horizon (1990-2030)
(differences with reference scenario)
Discounted welfare, excluding environmental benefit(106BF)
LAP
-41 869
GW
-183 859
LAPGW
-203 623
Discounted local environmental benefits (106BF)
+90 978
+60 596
+118 962
-487
-1 759
-1 761
GHG emissions (Mton)
Imposing a tax on local pollutants equal to the damage generated by these pollutant, reduces both local
air pollution and GHG emissions. This reduction occurs through investment in abatement technologies
and a decrease of the demand of energy services because of the increase in price. Investment in
abatement technologies has an impact on the local pollutant, whereas the decrease in demand reduces
both the local pollution and the GHG emissions. The abatement investment and the decrease in energy
services demand reduce the welfare, but the total welfare change (including the environmental
benefits) remains positive.
The cost of addressing local air pollution remains limited especially considering the reduction in
damage such a policy induces. Those results are clearly dependant on the damage figures used and the
abatement possibilities modelled in Markal. Moreover how specific policies regarding the local
pollutants are modelled in the reference scenario is also important; for instance the progressive
introduction of more stringent standards for cars and trucks in Europe which are partly taken into
account in our database reduces the potential environmental benefits.
When imposing a GHG constraint, the GHG emission are reduced up to the target but the local
pollutants are also reduced. GHG reduction are obtained through energy efficiency improvement and
through a decrease of the demand for energy services. However in this scenario no incentive is given
to take into account the damage from local pollutant in the decision process.
When combining the local pollution scenario and the global warming, the interactions between
pollutant is taken into account. The tax on local pollution creates an incentive for integrating the
damage from these pollutant in the decision for GHG emissions11. For a same level of GHG emissions,
the benefit from reducing local pollution is much higher than in the GW scenario and also higher
compared to the LAP scenario. The loss in non environmental welfare is higher than in the GW
scenario but lower than the sum of the losses in LAP and GW, but the total cost (non environmental
welfare loss minus the local environmental benefit) is lower.
When looking at the results per period (Table 8), the same conclusions can be drawn. However the
gain in terms of cost decreases when higher GHG emission target are imposed because the gains from
local pollution abatement are becoming marginal compared to the GHG abatement cost.
11
When a constraint is binding, the shadow price of the constraint is equivalent to a GHG tax. If a constraint is not binding, there is no
incentive to take the damage from the remaining pollutant into account when deciding on other pollutant.
11
Table 8: Welfare and Environmental Benefit per period
(undiscounted, differences with reference scenario)
2010
2020
2030
Welfare, excluding environmental benefit(106BF)
LAP
GW
LAPGW
-10 922
-30 260
-35 780
-16 829
-112 468
-115 905
-15 119
-326 795
-329 237
LAP
GW
LAPGW
+24 592
+12 752
+31 979
+30 177
+32 764
+46 704
+26 264
+54 678
+55 062
LAP
GW
LAPGW
-8.94
-27.32
-27.32
-25.76
-63.77
-63.77
-14.55
-104.1
-104.1
Local environmental benefit (106BF)
GHG emissions (Mton)
This is also observed in the marginal cost of GHG reduction, the shadow price of the GHG constraint
(Table 9).
Table 9: Marginal cost of GHG reduction (BF90/ton)
GW
LAPGW
2010
-2160
-1423
2020
-3888
-3290
2030
-12624
-12666
5. CONCLUSION
This exercise has shown the importance of examining jointly interrelated environmental problems for
policy design. A policy to reduce GHG will simultaneously reduce local pollution and thus the
benefits from the reduction of local pollutants should be included for a correct evaluation of the cost of
the GHG policy. A policy aiming at reducing local pollution reduces also the GHG emissions and
might therefore reduce the cost of reaching a GHG reduction target. Our results have shown that
combining both policies would allow to get the same overall benefits at a lower cost. This is only a
first step in the analysis and our research will continue in two directions. The definition of the local air
pollution policy should be related to the different agreements Belgium has signed for this type of
pollution. The implications for the choice of policy instrument must be further examined as it is a
crucial element for a full exploitation of the interactions between pollutants.
A-1
APPENDICES
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