Modeling of municipal greenhouse gas emissions

University of Groningen
CIO, Center for Isotope Research
IVEM, Center for Energy and Environmental Studies
Master Programme Energy and Environmental Sciences
Modeling of municipal greenhouse
gas emissions
Calculation of greenhouse gas emissions and the
reduction possibilities of Dutch municipalities
Willem de Vries
EES 2011-114 M

Master report of Willem de Vries
Supervised by: Prof. dr. H.C. Moll (IVEM)
Dr. A.J.M. Bos (Royal Haskoning)
Drs. I. Hans (Royal Haskoning)
University of Groningen
CIO, Center for Isotope Research
IVEM, Center for Energy and Environmental Studies
Nijenborgh 4
9747 AG Groningen
The Netherlands
http://www.rug.nl/ees/organisatie/CIO
http://www.rug.nl/ees/organisatie/IVEM

Modeling of municipal greenhouse gas
emissions
Calculation of greenhouse gas emissions and the reduction
possibilities of Dutch municipalities
Master thesis
Willem de Vries
University of Groningen
Energy and Environmental studies

Preface
During my training thesis I decided to do my master thesis in the form of an internship at a
company. Curiosity was the driving force behind this choice, to experience the way a company
operates. Royal Haskoning offered me this opportunity. The subject, the design and creation of a
model which calculates the greenhouse gas emissions of municipalities and the reduction
possibilities, was attractive. Looking back I can say the experience was more than valuable.
Beside of the practical skills of programming and documentation, I learned to analyze a problem
from different perspectives, to think from another person’s point of view. I have accomplished the
internship with joy and satisfaction. I write special thanks to my supervisors, Prof. dr. H.C. Moll
and dr. S. Bos. Special thanks also go to my daily mentor, drs. I. Hans. Without the coordination
and support of these persons I would not have been able to bring the model to the level it is now.
Groningen, January 2011

Summary
Municipalities represent an active governmental layer in the Netherlands. They often have
ambitions to reduce greenhouse gas emissions. In this way the municipalities take responsibility
to reduce the threat of global warming. To implement effective measures benchmarks are needed.
Therefore municipalities are first of all keen to know what the emissions within their
geographical boundaries consist of. Secondly, it is important to be able to calculate the perceived
effect of the designed measures. Models are made to perform these tasks. Several are known, but
all are either specified on either the current emissions or on reduction calculations. None of the
existing models is well capable of combining multiple tasks. The challenge was to design a model
that would be able to perform the following three tasks:
- Accurately calculate of the current greenhouse gas emissions.
- Make a trustworthy projection of the emissions into the future
- Calculate the reduction potential of all measures designed by municipalities.
The model designed and build during this research is well capable of exercising all these three
tasks. The resulting model will be named the Royal Haskoning Gemeentelijke Emissies Model
(ROHAGEM—model) in this report from this point on. The model automatically calculates the
greenhouse gas emissions of a municipality with high accuracy for the year 2009 and projects
these until 2020. Also, the ROHAGEM-model offers the user a large number of measures that
could be taken by municipalities to reduce their greenhouse gas emissions. Coupled to this report
are two appendixes. These appendixes are needed for a full understanding of the calculation
methods of the ROHAGEM model.

Contents
Introduction ................................................................................................................................9
1. Background and literature research ....................................................................................11
1.1 Research into existing models ....................................................................................11
1.2 Emission reducing measures ......................................................................................14
2 The ROHAGEM-model.....................................................................................................17
2.1 Delimitation of the ROHAGEM-model and technical manual ....................................17
2.2 Calculation of GHG emissions in the reference year...................................................20
2.3 Future projections ......................................................................................................22
2.4 Implementing emission reducing measures.................................................................27
3 Assessment of research questions and discussion ...............................................................29
3.1 Analysis of the emission reducing measures...............................................................29
3.2 Assessment of the main research question ..................................................................32
3.3 Validation of the ROHAGEM model .........................................................................34
3.4 Discussion .................................................................................................................35
3.5 Conclusion.................................................................................................................36
Reference list ............................................................................................................................37

Abbreviations
CBS Centraal Bureau voor de Statistiek
GHG Greenhouse gas
TSU Trading, services & utilities (sector)
org. Organization
ROHAGEM Royal Haskoning Gemeentelijke Emissies Model
GWP Global warming potential
IPCC International Panel on Climate Change
List of tables and figures
Figure 1-1: Screenshot from the ROHAGEM-model, depicting emission reduction ...................14
Figure 2-1: Future projection of the CO2 emissions (...).............................................................24
Figure 2-2: Future projections relative to the year 2009 .............................................................25
Figure 3-1: GHG emission reducing measures according to their place in the Trias Energetica ..29
Figure 3-2: Number of measures per sector in the ROHAGEM - model.....................................30
Figure 3-3: Measures sorted according to the emission source they aim to reduce. .....................31
Table 1-1: List of measures in the ROHEM model ....................................................................16
Table 2-1: Sectors & categories used in the ROHAGEM-model ................................................18
Table 2-2: Composition of GHG emissions in the Netherlands in (...) ........................................20
Table 2-3: Division of the greenhouse gas emissions by stationary (...) ......................................21
Table 2-4: Division of greenhouse gas emissions of mobile sources (...) ....................................22
Table 3-1: Comparison of calculation methods ..........................................................................33

Introduction
Climate change is one of the biggest challenges the current society has to cope with. The effects
are unpredictable and possibly severe (IPCC, 2007). Concerns in all layers of society about the
possible effects of climate change are a driving force for most national governments to set up
programs to reduce their greenhouse gas (GHG) emissions (UN, 1997; VROM, 2005). These
concerns are addressed not only by most national governments, but also by a great number of
local governments (Gemeente Apeldoorn, 2001; Roos et al., 2007; Hans I., 2008).
Municipalities can play an important role in stimulating and implementing reduction of GHG
emissions on a local level. Estimating the dimension of their GHG emissions is difficult for
municipalities, let alone obtaining more accurate emission figures. These problems have a
negative influence on the ability of municipalities to develop and execute effective GHG
emissions reduction programs. External consulting companies or universities may calculate these
emissions for the municipalities to ensure the effectiveness of reduction measures (Bos & Braber,
2002; Hans & Bos, 2009). However, this is a time-consuming and expensive activity. As
municipalities often do not know in advance which data is needed and which data is in their
possession, the whole process of searching and acquiring data is usually time-consuming.
Converting the available data sets to a useful format is often a second problem (Bos, pers. com.
2010; Hans, pers. com. 2010).
Due to the encountered difficulties in efforts associated with the calculation of GHG emissions
the use of a computer model seems logical. This study aims to discover the demands and potential
of models that are capable of calculating the GHG emissions of municipalities, their future
projection and possible emission reduction measures. Therefore, the main research questions is
defined as following:
“What are the properties of a model that can both calculate the current GHG emissions of
municipalities and project the future developments, including emission reducing measures?”
The main research question was split up in 4 sub questions:
1. Which models already exist in the field and what are their capabilities?
2. What public available data can be used to calculate the current GHG emissions and to which
level of accuracy does this data enable a model?
3. Are there known future projections for GHG emissions in the Netherlands and can they be
implemented in a model?
4. What measures can municipalities take and can these be implemented in a model?
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An additional research question was added to validate the model:
10
5. Does the model meet the expectations of the eventual users?
The research first aimed at investigating currently existing models. It became clear that for the
needs of a municipality a better model could be developed. This report will describe the
properties of the model that resulted (the ROHAGEM-model). Coupled to this report are two
appendixes. These are crucial in understanding the calculation methods of the model.
The existing models work with a bottom up method which demand large amounts of specific and
detailed information. The problems encountered when making specific calculations are also
encountered in the use of these models. The detailed level of information needed is often an
obstacle, as explained above. The ROHAGEM model uses a top down method, as will be
explained later in this report.
Each sub research questions is assessed in a different chapters. An additional chapter will
summarize and discuss the results.

1. Background and literature research
This report starts with assessing the literature research and the measures found to be taken by
municipalities. The latter is described later in this chapter. The literature research aimed at finding
existing models used in the field to calculate GHG emissions from municipalities. The existing
models are described below to shape an clear image of the available models in the field at the
moment. Additionally, the report describes why these models are not meeting the criteria. These
criteria are as following:
The model should be able to
1. Calculate the GHG emissions in a reference year with moderate precision.
2. Make a trustworthy future projection of these emissions.
3. Enable a user to fill in a list of measures taken by the chosen municipality. The effect of
these measures should be calculated by the model and displayed visually to the user.
4. Perform these tasks for a large number of municipalities in the Netherlands.
These criteria are deduced from two principles. The first is the politically driven need of
municipalities to reduce their GHG emissions (as described in the introduction). The second is the
practical reason that Royal Haskoning wants to be able to offer a reliable model commercially to
as many municipalities as possible.
Chapter 2 of this report will be concerned with explaining the calculation methods of the
ROHAGEM-model.
1.1 Research into existing models
The research into the existing models that calculate the greenhouse gas emissions of
municipalities has been primarily conducted on the internet. Other used methods was the search
in scientific papers and also interviews with model makers and experts. The most important
reason for the high use of internet is the novelty of this topic. Another reason is the high
involvement of companies and governmental institutions in this subject. These are less
accustomed to publicize their findings via articles or books, compared to most scientific
organizations. As a result, the scientific channels have been looked at with few convenient
results. The results themselves will be assessed later in this chapter.
There are two categories to be distinguished in the models found during the research. These are
the free versions and the commercial ones. The latter are primarily created by companies which,
naturally, have as goal to gain money with their model. These models are harder to analyze, as the
calculation methods and the database behind the model are never visible. The free models are
often made by or paid for by municipalities or other governments. These are often free for use
and not protected against analysis. These will be assessed first.
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1.1.1 Non-commercial models
Municipalities are often keen to know the GHG emissions they are responsible for. The method to
decide which emissions should be attributed to the municipality often differs. A standard
approach is to sum all the GHG emissions in the geographic area of the municipality.
Another approach is to attribute all the emissions for which the inhabitants living in the
municipality are responsible for to the municipality. Often a combination of these two approaches
is used, depending on the circumstances. For example: point-source GHG emitters as large oilrefining
installations are often considered not to be a responsibility of the municipality. Another
example is the electricity use of citizens. The GHG emissions that are coupled to the production
of the electricity are often attributed to the municipality in which the citizens live. The way these
issues are dealt with differ per country, area and sometimes even between municipalities.
Most open source models are created to deal with the emissions of a specific municipality. The
primary function of these models is to facilitate the calculations, rather than to calculate
emissions on the basis of a database of some kind. These models are built to offer a template
where the user can fill in the quantities of used fossil fuels. With this data input, the model can
calculate the emissions accurately, using constants only. For the more advanced user these
models are often not adequate, as they are able to do these calculations themselves. In these cases
the time gain when using such a linear model is commonly modest, as most of these users are
often well known with calculation programs as Excel and Access.
Numerous internet pages offer the possibility to calculate your personal CO2 emission. These are
often part of “Ecological Footprint” models. If this is not the case, the CO2 emission is commonly
called “Carbon Footprint”. On these internet pages a variety of questionnaires are offered to the
user to serve as a basis for the calculation. These questionnaires are different in setup and method.
Some of these footprint calculators ask very specific information about the behavior of the user
(like electricity use), where other footprint calculators are content with information about the
housing type of the user.
Shortlist of free models:
A few free for download models will be discussed here to give the reader an idea of the structure
and build up of these models:
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• C-FAR v3 (City of Columbus, 2009)
• Emfac 2007 (State of California, 2007)
• EBVU Senternovem (SenterNovem, 2009)
• Quintel model (Quintel intelligence, 2010)
C-FAR v3
The C-FAR v3 model was developed in the United States by the University of Ohio, to serve as a
calculation template for the city of Columbus. The model asks the user to fill in specific

electricity, gas and other fossil fuel use. On the basis of this input data and some conversion
factors the model then calculates the emissions. The user can do this for a number of successive
years. The model uses these different years to create a linear trend and extrapolates this into the
future. In this way a projection of future emissions is created.
Emfac 2007 Model
The Emfac model is also developed in the United States. It is developed by the state of California
to give insight in the traffic emissions of the state, areas and even estimates on county-level. The
user can assume the model knows the data and can ask the model to calculate the emissions.
Bringing in own information about the area of choice is also possible. For this, the model
demands specific information, like for example the quantity of cars and in which year they were
assembled.
EBVU Senternovem
The EBVU model has as goal to provide companies insight in measures that reduce the primary
energy use in their buildings. The user can fill in what the current state of the building is and fill
in which measures he is planning to take (for example: insulate the walls) to lower the primary
energy use. The model calculates the current energy use and the reduction in energy use live.
Quintel Model
This model is build to provide insight in the effectiveness of GHG emission reduction measures.
This municipal model is comparable to the Quintel model (Energietransitie - model) that
simulates the energy use and GHG emissions on a national level. The model is extensive in the
options it offers. These lie in the fields of energy supply, efficiency, isolation etc. Downside of
this model is its lack to generate the exact quantity of the emissions, only the relative changes are
shown. Other downsides are the apparent high complexity for the user, as well as the fact that the
model can only be used for a very limited amount of municipalities. In 2010 this model was
launched for 2 municipalities in the Netherlands. In the future this model will be available for the
10 biggest municipalities in the Netherlands (Pers. com. Quintel).
1.1.2 Commercial models
As said above, the commercial models are more difficult to analyze. This has multiple reasons.
First of all is the fact that these models are all protected, which means that a user can never see
how the results are calculated. The second reason is the apparent higher complexity of these
models. The last reason is the fact that these models are commercial, which makes analysis
difficult in any sense.
COWI model (COWI, 2009)
The COWI model is specifically interesting, as it uses a comparable calculation method for the
GHG emissions in the reference year as the ROHAGEM-model. It was developed by the COWI
Company in Denmark. This model uses three methods (Tier 1, 2 & 3) to assess the emissions of
an arbitrary municipality. The first method (Tier 1) divides the national emissions over all
municipalities according to the amount of inhabitants. The other methods demand detailed
information about the municipality. This model can only be applied to Danish municipalities.
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DHV model (DHV, 2009)
This model is developed by a consultancy company called DHV. It enables municipalities in the
Netherlands to enter their emission reduction measures and calculate the effects. A downside to
this model is the lack of a precise calculation of the current day emissions. Another downside is
the low amount of measures the model offers to municipalities (Pers. com. Bos S. & Hans I.),
therefore restricting their ability to describe their emission reduction programs.
1.1.3 Summary
As described on the pages above, no models have been found that fulfill all the named criteria at
the same time. To fulfill the wishes of the municipalities a new model should be developed. A
large part of the research was dedicated to the development of this new model.
1.2 Emission reducing measures
The most important reason to calculate the GHG emissions in a reference year and the future
projection is to use them as a benchmark. These can be used to place emission reducing measures
into perspective. The absolute reduction resulting from a measure is obviously important, but if
the goal is to reduce the total emission substantially, the actual proportion of the measure to the
total emissions is required. For policy making institutions percentages and graphs are more
striking than actual emission reduction figures. For this reason, the ROHAGEM-model has been
designed to show the user what the actual emission reduction is of a taken measure. More
important, the difference in the future projections between a scenario with the measures and a
scenario without the measures can be visualized, as showed in Figure 1-1.
Figure 1-1: Screenshot from the ROHAGEM-model, depicting emission reduction
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The blue line expresses the expected greenhouse gas emissions in a scenario with no measures.
The red line indicates the emissions expected in a scenario where the measures are taken into
account.
1.2.1 Method of selection of the measures.
Measures that are incorporated in the ROHAGEM-model need to comply with the requirement
that the measure needs to be in the zone of influence of a municipality. For example, closing
down factories which are licensed by higher governments is not in the zone of influence of a
municipality. The ROHAGEM-model considers municipalities in 6 sectors. Table 1-1 on the
following page shows the measures present in the model. Measures can be implemented in
multiple sectors. The establishment of this sectors will be treated in chapter 2.1.
The list of measures presented Table 1-1 is compiled with use of the following methods:
- List of measures actually taken by municipalities (pers. com. - Bos S., Hans I. & van Zon X.)
- Deducing measures from the “Trias Energetica” (Novem, 1996)
- Straightforward diminishment of emission sources
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Table 1-1: List of measures in the ROHEM model
Measure category
Lower the energy need
Production or purchase of sustainable
energy:
Efficient use of
fossil fuels
16
Non-energy
related
Measures Sector of implementation
Separate measures
Improving insulation of buildings X X X
Improving energy label of buildings X X X
Smart thermostats X X* X
Smart electricity & gas meters X X* X
Lowering room temperature in buildings X X*
Purchase and use of CFL and LED light sources X X*
Improving infrastructure for vehicles X
Offer driving workshops/training X X
Improving public transport infrastructure X
More use of public transport X
Improving bicycle infrastructure X
Stricter demands on greenhouse reconstruction X
Better the energy efficiency existing greenhouses X
Lower the acreage of greenhouses X
Use of rest heat originating from industrial processes X
Lower energy use of sector with fixed percentage per year X X
Less use of- or higher efficiency street lights X
PV systems X X X X X X
Solar heaters X X X X X
(mini) wind turbines X X X X X X
Subsoil thermal storage X X X X X X
Geothermal installations X X X X X
Digesters for biomass X X
Other ways of producing sustainable energy X X X X X X
Purchase of green electricity X X X
Purchase of green gas X X X
Sustainable fuels for vehicles X X
Higher efficiency conventional vehicles X
Use of vehicles which use CNG as fuel X X
Use of electric vehicles X X
Improving efficiency heating installations of buildings X X X
Demand higher efficiency public transport X
Lower the amount of animals in the municipality X
Lower the acreage where manure is spread X
Capture of methane emissions of animals, manure or waste X X
* These measures are summarized in one measure called " Energy awareness"
Municipal org.
Households
TSU
Traffic
Agriculture
Industry
General measures

2 The ROHAGEM-model
This chapter is concerned with the calculation methods of the newly created ROHAGEM-model.
The chapter is build up in the same order as the calculation order used in the ROHAGEM-model.
This order consists of:
1. Calculation of GHG emissions in the reference year
2. Future projections
3. Implementation of GHG emission reducing measures.
Before the calculation methods are treated this chapter starts with delimitation of the
ROHAGEM-model and a small technical manual.
2.1 Delimitation of the ROHAGEM-model and technical manual
2.1.1 Greenhouse gasses
According to the IPCC (IPCC, 1997) there are three main greenhouse gasses:
1. Carbon dioxide CO2
2. Methane CH4
3. Di-nitrogen monoxide N2O
So far, only these three greenhouse gasses are taken into account by the ROHAGEM-model. The
remaining group of greenhouse gasses consists primarily of CFCs and HFCs, and consists of a
large number of different compounds. Taking all these into account would severely complicate
the model. The named group of gasses make up 13% of the radiative forcing exerted by long
lived GHG (IPCC, 2007), of this group the radiative forcing of the CFCs is major. The use of the
strongest CFC's in the sense of radiative forcing have been banned by the Montreal protocol.
Although these gasses still exert radiative forcing, the current emission of these gasses is minor.
2.1.2 Attribution of indirect emissions
Aim is to attribute GHG emissions to the eventual user of energy or products. This would for
example implicate that the owner of a BMW-automobile would be considered accountable for the
GHG emitted during the production of the car. To execute such a division of GHG emissions the
model needs data on the use of all products in all municipalities. This data does either not exist or
is not publicly available. Therefore the ROHAGEM-model attributes all emissions to the emitters,
with electricity as exception. This because the use of electricity can be estimated per municipality
with considerable accuracy. In essence this means that all emissions emitted in the production of
goods, services etc. are attributed to the location where they are emitted. As said before,
electricity is an exception to this rule. The first reason for this is the broad use of electricity in
society, (as well as by citizens at home as by industry). The second reason is that the use of
electricity is well documented, and can thus be easily attributed to the user (CBS Statline, 2010)
2.1.3 Sectors used in the ROHAGEM-model
The structure of the model is made as such that it is understandable for municipalities. As a
consequence the structure will not necessarily be the structure that fits best to the data used to
17

calculate the GHG emissions. The model divides the emissions over six sectors, as shown in
Table 2-1. Sectors are sometimes divided in a few categories, depending on the availability and
the nature of the data.
Table 2-1: Sectors & categories used in the ROHAGEM-model
2.1.4 Technical manual
In this report different units are used. The ROHAGEM-model calculates the emissions in actual
kilograms of emitted greenhouse gas. Complicating factor is that different greenhouse gasses
have different global warming potentials (GWP). The results though are also displayed in CO2
equivalents. This report will display greenhouse gas emissions only in CO2 equivalents. This
stands for the amount of kilograms of CO2 needed to achieve the same global warming as 1
kilogram of a specific greenhouse gas. This is the same as GWP.
18
Sector Categories Description
Households - All buildings used as homes by citizens.
Traffic Personal mobility Cars, mopeds, etc…
Freight transport Trucks, cargo-boats, etc.
Public Transport Busses, trains, etc.
Trade, Services & Utilities Environmental services Waste collection, waste processing etc.
Remaining Trade, Services &
Utilities
Companies, ngo's, (other) governmental buildings
etc.
Municipal organization - All buildings and vehicles belonging to the
municipal organization.
Industry Food and stimulants industry
Construction materials industry
Chemical industry
Metal industry
Remaining industry
All companies that produce final or intermediate
products out of raw materials are considered
industry.
Agriculture Greenhouse agriculture All horti- & floriculture practiced in greenhouses.
Remaining agriculture Stock & arable farming, open horticulture, etc.

GWP of Carbon dioxide (CO2) = 1 (By definition)
GWP of Methane (CH4) = 21 (IPCC, 1997)
GWP of Di-nitrogen monoxide (N2O) = 310 (IPCC, 1997)
These are figures used in national emission inventories, according to the Kyoto protocol (United
Nations, 1997).
Units used in this report:
Kiloton = 10 6 kg (One million kilograms)
Megaton = 10 9 kg (One billion kilograms)
Gg = 10 9 g or 10 6 kg (One million kilograms)
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2.2 Calculation of GHG emissions in the reference year
In the ROHAGEM model the total Dutch emissions are divided over the Dutch municipalities.
This top-down approach is used is used instead of using detailed data on fossil fuel use to
calculate the GHG emissions (Bottom-up approach). There are two main reasons for this choice.
First of all, the Dutch national GHG emissions are well known and documented (CBS Statline,
2010). The second reason is the high availability of information about the municipalities to
facilitate the division of the emissions. Together with other national data division of the national
emissions is possible. This can be seen as a top-down approach.
2.2.1 National emissions
The total emissions of the Netherlands in 2009 were 214 Megatons of CO2 equivalents (CBS
Statline, 2010). The CBS divides the emission sources into mobile and stationary sources. The
actual emissions per GHG was as follows:
CO2 = 185,000 Gg
CH4 = 804 Gg
N2O = 38 Gg
The emissions by aviation, refineries, sea shipping and fishery are not taken into account in the
ROHAGEM-model. These sectors account for large amounts of greenhouse gasses emitted and
are extremely localized. A municipality would therefore be attributed all the emissions of one of
these point like sources. As the municipalities have absolutely no influence over these emissions
they are not attributed to them. Substracting these sources, the remaining emissions of 2009 are
(CBS Statline, 2010):
CO2 = 168,843 Gg
CH4 = 803 Gg
N2O = 38 Gg
Table 2-2 depicts the division over stationary and mobile sources.
Table 2-2: Composition of GHG emissions in the Netherlands in 2009 (Source: CBS Statline 2010)
2.2.2
20
Emissions by stationary sources:
CO2 = 78.7%
CH4 = 99.6%
N2O = 96.4%
Total Dutch Emissions (100%)
Emissions by mobile sources:
CO2 = 21.3%
CH4 = 0.4%
N2O = 3.6%

Division of the emissions over the sectors in the ROHAGEM-model
In the following schemes the division of the emissions is shown. The figures showed are as
percentage of the total CO2 emissions. Figures on the distribution of CH4 and N2O emissions can
be found in Appendix A, section 1. First the division of the emissions of the stationary sources is
depicted in Table 2-3.
Table 2-3: Division of the greenhouse gas emissions by stationary sources in the Netherlands in 2009
Categories according to CBS and as percentage
of all stationary sources
Industry
Stationary agricultural
sources
Households
Trade, service &
government
Environmental services
Remaining stationary
sources
Electricity producing
sector
23.2%
6.2%
14.4%
8.7%
5.9%
0.5%
41.2%
Categories in the ROHAGEM model
Industry
Agriculture
Households
Trade, services &
utilities
The electricity producing sector is not accounted for in the ROHAGEM-model. This sector
provides electricity to all categories in the ROHAGEM model. In the ROHAGEM model the
sectors are responsible for their share of the electricity (called indirect emissions). The exact
division of these "indirect emissions" can be found in Appendix A.
Table 2-4 shows how the emissions by mobile sources are divided over sectors in the
ROHAGEM-model.
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Table 2-4: Division of greenhouse gas emissions of mobile sources in the Netherlands in 2009
Categories according to CBS and their
percentage of all mobile sources
The public transport seems to be a small factor in the total GHG emissions. This is not entirely
true, as public transport in the Netherlands consist for a large part of electrified trains, trams and
subways. These are not accounted for here, only the direct emissions of busses and diesel trains
are shown. In appendix A a detailed scheme and explanation of how the ROHAGEM model
divides the Dutch national emissions over the municipalities can be found.
Natural gas & direct emissions
Practically all direct emissions are result from the combustion of natural gas. This accounts
completely for the Households and the TSU. In the sector traffic the direct emissions are
generally petrol and diesel. A small percentage of the direct emissions of the municipal
organization consist of petrol and diesel too. In the agriculture, large proportions of the emissions
originate from animal and manure emissions. In the industry natural gas combustion is
responsible for more or less 50% of the direct emissions, the other 50% consists of oil, coal and
other fossil fuels (CBS Statline, 2010). As natural gas makes up the bulk of the fossil fuels
combusted in stationary sources, this report will appoint all stationary fossil fuel emission sources
as natural gas, keeping in mind that this is only the largest proportion of the stationary sources.
2.3 Future projections
ECN (“Energy research Centre of the Netherlands”) is one of the leading institutions on in this
field of research in the Netherlands. This institution is the only institution that makes
substantiated emission projections of the future for the Netherlands. Lower level (Provincial or
22
Mobile agricultural
sources 3.5%
Mobile sources in the
construction &
remaining mobile
sources
Personal traffic
Public transport
Goods transportation
3.2%
55.5%
2.0%
35.8%
Categories in the ROHAGEM model
Agriculture
Trade, services &
utilities
Traffic

municipal) projections are not available. For this reason the ECN future projections of the Dutch
GHG emissions is used.
2.3.1 Projection used by the model
The emission projections into the future used in the ROHAGEM model are acquired from the
“Referentieraming energie en emissies 2010-2020” (Daniels et al., 2010), a future emission
assessment by ECN. The named report is successor of several previous reports in which the GHG
emissions of the Netherlands are projected into the future. This report uses the emissions of the
year 2008 as the base line. From this year on the emissions are projected until 2020. For the
calculations and the assumptions made concerning the emission projections into the future the
reader is requested to consult the ECN report. Later in this report the choice for this specific
future projection will be assessed.
Policy versions
The ECN report distinguishes between three policy versions. The difference between these types
lies in European and national policy. All three named above are used in the ROHAGEM model.
These are:
- RR2010-0: This version does not incorporate policy implemented after 2007 and serves as a
basic version or base-line.
- RR2010-V: This version incorporates all the policy concerning GHG emissions implemented
until the date the assessment was released.
- RR2010-VV: Additional to the former future projection, this version does not only
incorporate implemented policy but also the proposed policy. Important here is that this is
the proposed policy of the government in seat at the start of 2010. As elections took place in
2010 the political landscape changed. This change might have an influence on the proposed
policies.
23

Assumptions made
The fractional change in emissions between 2008 and 2010 calculated by the ECN assessment is
evenly spread between the years 2008 and 2009 by the ROHAGEM-model. The assessment only
states the expected economic growth in these years, but does not elaborate on their influence in
the change of emissions until 2010. The model used by the ECN calculates in periods of 5 years,
and is as such unable to discriminate between separate years.
2.3.2 Calculation method
Due to the reasons mentioned earlier in this chapter the absolute figures delivered by the ECN
assessment have to be transformed. From the absolute figures a fractional change between years
(Or groups of 5 years) is calculated. In this assessment the model projects the emissions from
2005 to 2010, 2010 to 2015 and from 2015 to 2020. Combining the results of this projection with
historical data Figure 2-1 arises:
Figure 2-1: Future projection of the CO2 emissions (Source: ECN ReferentieRaming energie en emissies 2010-2020)
Figure 2-1 depicts the emissions relative to the emissions in 2008. However, the ROHAGEM
model uses the emission figures of 2009 as the base year. For this reason the fractional change
between the GHG emissions of 2008 to those in 2010 are divided by two. This calculated figure is
then used as the fractional change between 2009 and 2010. When taking the year 2009 as the base
year and calculating the fractional change with respect to 2009 Figure 2-2 arises.
24

Figure 2-2: Future projections relative to the year 2009
The difference between the two charts is minimal. Nevertheless, the important change is the
shifted base year from 2008 to 2009. The choice to divide the fractional change between 2008
and 2010 equally between the two years is justified by the fact that the emission calculation
model used by the ECN to produce these figures does not distinguish between separate years.
Future projections of the specific sectors
In the “ReferentieRaming Energie en Emissies 2010” the Dutch CO2 emissions are divided over
the same sectors as the ones used in the ROHAGEM-model, i.e.:
- Households
- Traffic
- Trade, services and utilities (TSU)
- Agriculture
- Industry
As the reader may notice, the ECN made no future projection for the ROHAGEM-model sector
“Municipal organization”, for obvious reasons. The future development of the emissions of this
ROHAGEM-model sector is assumed to be similar to the TSU emissions by the researcher.
Another reason is the fact that the emissions of the municipal organization are calculated using
the size of the TSU. Therefore the future projection of the TSU emissions will be used for the
municipal organization.
25

The fact that for each sector a CO2 emission projection into the future exists means that the
ROHAGEM-model can make future projections of the CO2 emissions for each sector separately.
This is not the same for CH4 and N2O. For this two greenhouse gasses the ECN provides general
projections which are representative for all sectors, with exceptions for the sector Agriculture and
two categories within other sectors. For these cases tailored projections are made.
In the case of CH4 these sectors are:
- Agricultural sector
- Waste management category (Part of the TSU)
For N2O the exceptions are:
- Agricultural sector
- Chemical industry category (Part of the sector Industry)
These sectors/categories have their proper future projections with respect to the greenhouse
gasses named. The CH4 emissions of the waste management category are equivalent to the CH4
emissions of the ROHAGEM-model category “Millieudienst”, or environmental services. The
chemical industry is a category of the industry sector, also used in the ROHAGEM-model.
Influence of population growth projections
The Dutch centre for Statistics (CBS Statline, 2010) provides a projection of the population per
municipality untill 2025. The projections provided by the ECN do not distinguish between
municipalities, ensuring a common future projection for all municipalities. To be able to
differentiate between the municipalities the ROHAGEM-model uses population projections. Not
all sectors are influenced by population changes. Agriculture and industry for example are
minimally influenced by the growth or decimation of the population. The influence also differs
between municipalities. For the sector Trade, Services and Utilities (TSU) the effects are again
local. In cities for example, the growth of the amount of shops is probably in correlation with the
population growth. For rural communities on the contrary, the nearest shopping mall may be in
another municipality. For this reason the influence of population growth is not incorporated in the
sectors industry, agriculture, TSU and the municipal organization. The latter is due to the
coupling of the future projection of the municipal organization to the future projection of the
TSU.
The sectors households and traffic are coupled to the population growth. This is due to the
manner in which the traffic emissions and household emissions are calculated. In these
calculations the emissions of these sectors are linearly dependent on the population. This
incorporates the assumption that the amount of inhabitants per household in a specific
municipality changes with the same proportions as in the rest of the Netherlands (As this figure is
incorporated in the ECN future projections). The national emissions of the two last named sectors
also change due to population growth. This has also been incorporated in the ECN future
26

projections. Therefore the effect of municipal population growth has to be corrected for the
national growth. This is done with use of the following equation:
δ
=
M
M
endyear
baseyear
⋅ N
⋅ N
baseyear
endyear
In this equation:
δ stands for the eventual emission change caused by population change
Mendyear stands for the population of the municipality in the desired coming year.
Mbaseyear stands for the population of the municipality in the year 2009.
Nendyear stands for the national population in the desired coming year.
Nbaseyear stands for the national population in the year 2009.
In the sectors traffic and households this factor is multiplied with the calculated future emissions
for a chosen municipality.
2.4 Implementing emission reducing measures
In essence, the implementation of measures that reduce GHG emissions is straightforward. If
there is a measure, it can be quantified in the sense that one can bestow it a certain reduction
potential, the reduction potential can be subtracted from the calculated total GHG emission. The
result is a reduced total emission. Unfortunately, this is not the case for a substantial amount of
measures. Two effects are the main reason for this. The first is the fact that emission reduction
measures often have their effect on the same emission source. When one measure is implemented,
it reduces the potential of the other and vice versa (Example: Different types of insulation). The
latter effect is that measures sometimes even influence each other without having exactly the
same effect (Example: Solar panels and the consumption of green electricity).
We distinguish a set of challenges that arise in the implementation of emission reducing
measures:
1. The first challenge is to find the right amount of measures to ensure the effectiveness
of the model when used by municipalities. When the amount of measures is too low
and does not reflect the diversity of measures that municipality’s use, the model will
become useless. On the other hand, including too many measures could create an
unworkable environment in the model.
2. Secondly, the challenge of quantifying the emission reduction potential. For a
number of measures solid references can be found. However, this is not the case for
all. Sometimes the quantification of measures is constrained by the level of detail of
the emissions calculated previously by the model.
27

28
3. The third challenge is quantifying the influence measures have on each other. The
order in which calculations are executed is often the most important aspect in this
case.
The second challenge named above influences the amount of measures that can be implemented
in the model. Numerous measures taken by municipalities cannot be quantified and can thus not
be implemented in the model. This does not mean that these measures do not have any influence.
Measures that aim to enlarge the awareness among citizens and companies concerning GHG
emission problems for example can have effects on the greenhouse gas emissions. Unfortunately
these effects cannot be quantified with a minimally required amount of certainty.
In Appendix B (Section 1) the reader can find the list of measures implemented in the
ROHAGEM-model. Each measure can have a number of sectors where it finds its
implementation. In Section 2 of Appendix B the reader can find the quantifications of the
measures.

3 Assessment of research questions and discussion
In this chapter the GHG emission reducing measures are analyzed. Secondly, the research
question is evaluated. Last is the discussion and conclusion.
3.1 Analysis of the emission reducing measures
Emission reducing measures can be categorized in different ways. One way of categorizing is the
way how the measures avoid emissions (i.e. the location in the Trias Energetica.). The Trias
Energetica (Novem, 1996) is a method to reach a more sustainable use of energy. It states that the
first and most important step to a more sustainable energy use is to lower the total energy
demand. The second step is the use of sustainable energy and the last resort is the more efficient
use of fossil fuels. Figure 3-1 shows the division of measures according to the Trias Energetica.
Figure 3-1: GHG emission reducing measures according to their place in the Trias Energetica
Figure 3-1 nicely shows that the ROHAGEM-model follows the concept of the Trias Energetica.
Most of the measures aim to reduce the energy demand while only a small group aims at the more
efficient use of fossil fuels.
Another way of categorizing the measures is defining the sectors in which the measures find their
application. This method of categorization also shows the number of ways a municipality can
influence the distinct sectors. Figure 3-2 displays the number of measures per sector.
29

Figure 3-2: Number of measures per sector in the ROHAGEM - model
Figure 3-2 shows that the measures are most abundant in the municipal organization itself. This
is, on itself, not very surprising, as the municipality has full control on this share of the municipal
GHG emissions. Surprising though is the large amount of measures available in the agricultural
sector. This is partly explained by a number of measures in the agricultural sector that were born
out of pure "reduction" of emission sources. These measures offer to reduce the number of dairy
cattle for example. This type of measures is not possible in other sectors. One cannot propose to
lower the amount of inhabitants for example. The measures in the agricultural sectors which
lower the size of the emission source (like in the case of the dairy cattle) are not expected to be
part of an emission reduction program. These measures are meant as a correction for future
development of the municipality (Development of nature, new neighborhoods etc.).
Another interesting method of analysis is the emission source which the measures aim to reduce.
These can be electricity, natural gas, car fuels and "animal & manure". The first three are the
most common energy carriers in the Netherlands. The latter causes emissions in the form of CH4
and N2O. Figure 3-3 displays the division of emission sources which the measures aim to reduce.
30

Figure 3-3: Measures sorted according to the emission source they aim to reduce.
The division of the measures over the emission sources is strikingly comparable to the share of
the emissions sources in the national GHG emissions. The emissions due to use of natural gas and
other fossil fuels in the industry make up 45% of the GHG emissions in the Netherlands (CBS
Statline, 2011). This is comparable to the share of measures aiming at lowering the emissions of
the use of natural gas (44%). Electricity use is responsible for 25% of the Dutch GHG emissions.
The measures aiming at reducing the electricity use make up 26% of all measures. The measures
that aim to reduce the car fuel emissions are slightly bigger than the share of the car fuels in the
national GHG emissions (23% versus 16% respectively).
Most of the measures listed in the model can be categorized as being implemented on buildings
used by people. Insulation and solar panels are two examples of measures that can be
implemented on all buildings used by people, whether this concerns living or working. These find
their application in different sectors (households, TSU and the municipal organization). The
remaining measures are generally sector specific. Examples are usage of rest heat of industrial
processes, the building of energy neutral greenhouses and the offering of drivers training.
31

3.2 Assessment of the main research question
3.2.1 Main research question
As mentioned before, the research questions is:
“What are the properties of a model that can both calculate the current GHG emissions of
municipalities and project the future developments, including emission reducing measures?”
It can be concluded that the phrase of "future developments" is extremely important. The most
important aspect of this ROHAGEM-model was to become the emissions reducing measures.
Although the national developments are important, municipalities are extremely interested in the
effect of their emission reducing measures. The completeness of the measures set and the ability
to calculate the reduction had the highest priority. The aim of emission calculations in general is
not to know the exact emission of that moment, but what the possibilities are to reduce that
figure.
3.2.2 Comparison ROHAGEM-model results with other calculations
The ROHAGEM-model uses a top-down method to calculate the emissions of any Dutch
municipality. The resulting figures are a result of calculation rules and statistical figures that are
not related to actual emissions. It is self-evident that there is a need to check the outcome of these
calculations to a number of bottom-up calculations. For this case the municipalities Haren and
Groningen are chosen. For both of these municipalities Royal Haskoning (Hans & Bos (2009);
pers. com. Hans) has calculated the GHG emissions, partly using a bottom-up procedure. The
emissions for the city of Groningen can also be compared to data available from a master thesis
of Ingmar Hans (Hans I., 2008). Table 3-1 on the following page displays the separate calculated
GHG emissions of the two municipalities.
The first detail that strikes is the overall higher total GHG emissions resulting from the
ROHAGEM-model calculation. The reason for this is not clearly understood. One explanation
could be the fact that the ROHAGEM-model also incorporates the CH4 and N2O emissions
explicitly. The Royal Haskoning calculation concerning the municipality of Haren does this too,
but only in the case of agriculture.
32

Table 3-1: Comparison of calculation methods
Calculation done by:
ROHAGEM-
Model Royal Haskoning Ingmar Hans
Emissions are displayed in kiloton CO2 equivalents
Municipality of Groningen
Year of calculation: 2009 2008 2000
Sectors
Households 449 419 483
Traffic 356 365 297
Trade, services & utilities
(TSU) 522 689 378
Municipal organization 22 38 -
Agriculture 20 - 21
Industry 307 - 245
Total 1677 1511 1424
Municipality of Haren
Year of calculation: 2009 2009
Sectors
Households 41.7 54
Traffic 40.9 37
Trade, services & utilities
(TSU) 52.8 52
Municipal organization 2.2 1.5
Agriculture 23.0 -
Industry 0.4 -
Total 161.2 144.5
Municipality of Haren
In the case of the Royal Haskoning emission calculation for Haren the agriculture is incorporated
in the TSU sector and makes up 20% of the TSU sector GHG emissions. In the calculation for
Haren done by Royal Haskoning an earlier version of the ROHAGEM-model calculation method
was used. This method was still in development and is expected to have reached a more mature
state in the ROHAGEM-model. Subtracting the agriculture from the TSU in the Royal Haskoning
calculation we end up with 42 kilotons of CO2 equivalents, lower than the figure generated by the
ROHAGEM-model. The household sector is higher in the case of Royal Haskoning. Both sectors
were calculated by Royal Haskoning using a bottom-up procedure. An explanation in the case of
households can be the high amount of large detached dwellings in the municipality of Haren. This
33

is a detail that is unknown to the ROHAGEM-model. Detached dwellings consume larger
amounts of natural gas for heating. This enlarges the GHG emissions of the households in total. A
possible explanation in the case of the TSU is the fact that the municipality of Haren is very close
to Groningen. Groningen is a larger city with a great number of facilities. This could point at a
lower availability of facilities in Haren (too close to compete). Furthermore there are no
significant business or trade areas in Haren. Haren primarily is a municipality where people live
that work elsewhere.
Municipality of Groningen
The city of Groningen more or less makes up the municipality of Groningen. There are large
differences between the calculation methods. Not only in outcome, but also in classification
method. The figures presented by Ingmar Hans and the ROHAGEM-model are the most
comparable. Between 2000 and 2009 the national emissions in the sector Households have
lowered slightly (6.4%, CBS Statline 2010), whereas traffic emissions have risen with a few
percent (5.6%, CBS Statline 2010). Both effects can be seen in Table 3-1. The Industry sector is
showing an opposite trend compared to the national. Table 3-1 shows an increase, though
nationally the industrial emissions have lowered. This can be attributed to the diversity in the
sector Industry and the point like character of the emissions of the Industry sector. The large
difference between the figures of Ingmar Hans and the ROHAGEM-model in case of the TSU
sector hints towards a difference in definition. This is also expected to be the reason for the large
differences between the Royal Haskoning and ROHAGEM-model calculations. The Royal
Haskoning method sees the industry as part of the TSU. This makes the comparison difficult.
Summary
The calculation methods used by Ingmar Hans and Royal Haskoning in the case of Groningen
were both not bottom-up. In the case of Haren (calculation by Royal Haskoning) this is partly the
case and the effects are clear. Circumstantial facts concerning the municipalities have their effects
on the outcome of a bottom-up procedure. It is presumed that the discrepancy between the
ROHAGEM-model and more accurate bottom-up calculations found an extreme in the case of
Haren. The municipality of Haren is inhabited by relatively high-income families that work
outside the municipality of Haren. This leads to a large difference between the amount of people
living in the municipality (Important for the Households) and people working in the municipality
(Important for the TSU). The total emissions in both the case of Haren as in the case of
Groningen were in a 10% margin of the other calculations (Taking the national emission growth
into account).
3.3 Validation of the ROHAGEM model
The model was validated by two user-groups. The first group consists of advisors employed by
Royal Haskoning. The second group consist of official working for municipalities. Both groups
consist of people generally employed in the field of energy and environmental related issues.
34

Validation by advisors of Royal Haskoning
This validation was performed during the development of the model to be able to implement the
comments and suggestions. These suggestions were generally aiming at improve the workability
of the model. Advisors are keen to show their work and tools as clear as possible to their clients.
Practically all of these suggestions were implemented in the model. General impression was very
positive. The model was seen as useful in facilitating calculations and in depicting the actual
effect of measures.
Validation by officials of municipalities
This validation was performed in the uttermost last stage of the research. The reason for this
timing was to ensure that the model was as complete as it could get during the research before it
was shown to the eventual clients. Most notable was that most suggestions suggested by the other
validation group were not noted by this group. There are two possible explanations: the first is the
fact that user-friendliness in general is only noted when it is absent, not when it is present. The
second reason is that this group pays less attention to notifications and other text displayed. Most
important lesson from the last said is that further development of this model should focus on
using as less text as possible. Crucial notifications should be displayed with persistence to ensure
that the user reads it.
The most important suggestion of this validation group was to provide users with a manual. This
manual should explain the user how to use the model and explain thoroughly what calculations
are performed by the model and what data is used. In accordance with this suggestions a small
manual was made to explain the use of the model. This manual is short and it is advised to extend
it. To support the calculations and used data this report will suffice.
3.4 Discussion
In this subchapter the three separate tasks of the ROHAGEM-model will be discussed.
Calculating the GHG emissions in the reference year
Calculating the exact GHG emissions in a geographical area is not possible with a top down
method, as used in this model. Top-down methods by definitions do not possess all the required
data for this calculation. Only when the exact amount of combusted (fossil) fuels in a
geographical area is known, the exact GHG emissions can be calculated. The huge advantage of a
well designed top-down method is the absence of the need for an extensive amount of data. A top
down method can be asked to determine the emissions in a split second with moderate to high
precision. The researcher believes that this is presumably the highest precision that can be
obtained using publicly available figures.
Projection into the future
The ECN is the only institute that makes substantiated emission projections concerning
specifically the Netherlands. To have some differentiation between municipalities, the
ROHAGEM-model uses the CBS Statline (2010) population prediction. With help of these
predictions the emissions of the Household and Traffic sector are changed. Although this will not
35

e the correct projection for all the sectors in all municipalities, it is believed that this is as close
as one get come with public available data.
Implementation of GHG emission reducing measures
A substantial amount of measures have been implemented in the ROHAGEM-model. This list
does not contain all the measures implemented by municipalities. This often due to the inability
of measures to be quantified. If a measure cannot be quantified it does not mean that it does not
have any effect. It means that in there are various variables in the calculation which are hard to
estimate or have large error margins. Quantification of the other measures is done as precise as
possible. Problem here is often the lack of scientific research into the effects of the separate
measures. Often the effect is published by companies or organizations with no scientific proof or
background.
Further research
Comparison between top-down and bottom-up methods for the calculation of the current
greenhouse gas emissions. In this report this was done for two municipalities. To be able to make
conclusions with a minimal amount of certainty ‘the amount of comparisons needs to be higher (n
> 2). This could lead to a better insight into the question which methods of the ROHAGEMmodel
need adjustment.
3.5 Conclusion
The ROHAGEM-model has proven to be able to calculate GHG emissions of all Dutch
municipalities with considerably high accuracy. The eventual model enables a user to fill in most
of a municipalities emission reduction measures and calculate the expected effect. Especially the
latter proved to be very important. The emission reduction is the primary objective. To be able to
perform that task an accurate emission calculation and an substantiated future projection are
crucial.
Improvements are primarily achievable in the calculation of the GHG emissions in the reference
year and in the quantification of the measures. For the first: the comparison between the emission
calculations for the municipality of Haren shows that more general data could improve the
eventual outcome. In this specific case extra information about the type of dwellings present
would improve the accuracy of the outcome of the calculation for example. In the quantification
of the measures the model often relies on weak sources. For example, some of the sources are the
manufacturers of devices that can help reduce GHG emissions. This type of sources have the
appearances against them. Due to the novelty of a number of these measures, user-experience
and users-data are not available yet. When available, adding this data would strengthen the
credibility of this component of the model.
36

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Development: C. Duijvestein.
37

38
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Hague.
39

Appendix A: Dutch national emissions divided over Dutch
municipalities.
This appendix explains how the Dutch national emissions are divided over the categories used in
the ROGEM model and later distributed over the Dutch municipalities. The first chapter will
make an inventory of the total Dutch emissions. The second chapter will explain how these
emissions are divided over the Dutch municipalities.
Section A1: Inventory of national emissions
This part of the Appendix is a continuation of chapter 2 ("Calculation of the GHG emissions in
the reference year"). The reader is urged to read that chapter before reading this part of the
appendix.
The Dutch institution for statistics (CBS) has the following data on the Dutch emissions (CBS
Statline, 2010). All the figures refer to the year 2009, unless specified else. Table 1-1 shows the
division in emissions emitted by stationary sources and those by mobile sources.
Table 1-1: Greenhouse gas emissions in the Netherlands in 2009 (Source: CBS Statline 2010)
Emission in Pg
Source CO2 CH4 N2O
Total emissions 185000 803,84 37,98
Stationary sources 143600 800,85 36,5
Mobile sources 41400 2,99 1,48
Stationary sources
Table 1-2 shows the division of emissions by stationary sources in the Netherlands in 2009.
Table 1-2: Stationary sources and their emissions in the Netherlands in 2009 (Source: CBS Statline 2010)
Emission in Pg
Source CO2 CH4 N2O
Stationary sources, total 143600 800,85 36,5
Stationary sources in the agriculture 8200 488,2 30,3
Refineries 10700 0,86 0,03
Industry 30800 14,13 3,44
Households 19200 16,38 0,24
Energy sector 54800 40,55 0,48
Trade, services & governmental buildings 11600 5,56 0,11
Environmental services 7800 233,43 1,91
Remaining stationary sources 600 1,74 0
41

The industry is a well monitored part of the stationary sources; therefore their direct emissions are
better known compared to other stationary sources. The direct emissions of the industry are
divided into the following categories.
Table 1-3: Emissions from stationary sources in the industry in the Netherlands in 2009 (Source: CBS Statline
2010)
Emission in Pg
Source CO2 CH4 N2O
Total industry 30800 14,13 3,44
Food and stimulants producing industry 3400 0,71 0,01
Construction material industry 2200 0,33 0,01
Chemical industry 15400 11,52 3,37
Metal industry 6000 0,96 0
Remaining industry 3800 0,61 0,05
Mobile sources
The mobile sources are defined as following:
Table 1-4: Mobile sources and their emissions in the Netherlands in 2009 (Source: CBS Statline 2009)
Emission in Pg
Sources CO2 CH4 N2O
Total mobile sources 41377 2,99 1,478
Road traffic 31109 2,567 1,284
National shipping, persons 114 0,008 0,001
National shipping, goods transportation 1499 0,041 0,038
Recreational shipping 186 0,012 0,001
Fishery 551 0,037 0,004
Person transportation trains 24 0,002 0
Goods transportation trains 42 0,003 0
Aviation 655 0,013 0,02
Sea shipping, not on the sea 1072 0,028 0,027
Sea shipping on the Dutch national waters 3169 0,09 0,08
Mobile sources in the agriculture 1241 0,057 0,01
Mobile sources in the construction 1137 0,034 0,009
Remaining mobile sources 579 0,098 0,003
42

The road traffic is the biggest source among the mobile sources. It is also the best monitored.
Table 1-5 depicts the division inside the road traffic.
Table 1-5: Road traffic emissions in the Netherlands in 2009 (Source: CBS Statline 2010)
Emission in Pg
Sources CO2 CH4 N2O
Total road traffic 31109 1,28 2,57
Person cars 19375 1,04 1,92
Motorbike 321 0 0,2
Motorized bicycle 60 0 0,3
Delivery cars 4379 0,18 0,05
Lorries 2233 0,02 0,03
Truck-combinations 3586 0,03 0,04
Auto busses 580 0 0,01
Special vehicles 575 0,01 0,02
Emissions not accounted for
The emissions emitted by the refineries, aviation, sea shipping and fishery are not taken into
account in the ROGEM model. This is because they are geographically extremely localized.
Municipalities cannot be expected to geographically share in their emissions.
Energy sector
The setup of the ROGEM model is such that the use of electricity is seen as having share in the
emissions needed to produce that electricity. As almost everyone in society uses electricity, the
model divides the emissions of the “Energy sector” between the electricity users. Point of interest
is that the energy sector, besides of producing electricity, has a small contribution in the heating
of neighborhoods and some companies. The efficiency of this heating is unknown, which makes
the attribution of some of the emissions to the heating function very complex. A second reason is
that it is not known where this heating takes place. As those companies will get a share of
electricity and a share of natural gas use it is assumed that this will not be cause of any large
faults. Table 1-6 shows the net use of electricity in the Netherlands in 2009.
Table 1-6: Electricity use in the Netherlands in 2009 (Source: CBS Statline 2010)
User of the electricity Quantities in GWh As a percentage of the total use
Industry 31239 35,8%
Transport 1664 1,9%
Households 24154 27,7%
Remaining users 30204 34,6%
Note: These numbers are the net-use of the different sectors.
43

The category “Transport” consists of the electrified trains the Netherlands and other public
transport using electricity. The category "Remaining users" is made up by the municipal
organization, TSU and agriculture. This will be assessed later in this appendix. As the electricity
use of the industry is better categorized then their direct emission as show in Table 1-7.
Table1-7: Spread of electricity use among industry types (Source: CBS Statline 2010)
Sub-category of the industry Use in % of Belonging to direct emission
Food and stimulants producing industry 14,9% Food and stimulants producing
Textile and clothing industry 1,0% Remaining industry
Paper etc. industry 6,3% Remaining industry
Fertilizers etc. industry 1,4% Chemical industry
Petrochemical products etc. industry 9,2% Chemical industry
Standard chemical industry 11,7% Chemical industry
Inorganic chemical industry 8,2% Chemical industry
Chemical end products industry 3,1% Chemical industry
Ferro-metal industry 7,1% Metal industry
Non-Ferro-metal industry 12,3% Metal industry
Metal-industry 12,2% Metal industry
Glassworks, cement etc industry 4,5% Construction material industry
Wood, plastics etc industry 8,2% Construction material industry
An extra column was added to indicate to which direct emission sub-category each specific
electricity sub-category is assigned. The need for this extra indication will become clear later in
this appendix.
44

Section A2: Coupling of national emissions to categories used in the
ROGEM model
In the ROGEM model the following six sectors are distinguished:
- Agriculture
- Traffic
- Households
- Industry
- Trade, services & utilities (TSU)
- Municipal organization
All these sectors will be assigned a proper direct emission. Secondly, all of the sectors will be
assigned their proper part of the emissions of the energy sector, according to their electricity use.
Four of these sectors are easily acquainted for. These are Traffic, Households, Municipal
organization and Industry. The other two are partly coupled.
Table 2-1: Attribution of emission among the sectors
Industry
Households
Traffic
Sector Type of emission
Direct emissions “Industry”
Emission of CBS categories attributed
to this sector
Indirect emissions 35.8% of energy sector
Direct emissions “Households”
Indirect emissions 27.7% of energy sector
Direct emissions
Total road traffic
All train emissions
All shipping emissions
Remaining mobile sources
Indirect emissions 1.9% of energy sector
Municipal organization 3% of total TSU 3% of total TSU
45

Municipal organization
The municipal organization is naturally not listed by the CBS, but is assumed to be part of
ROGEM model sector TSU. On the basis of former emission calculations (XX, XX) the
Municipal organization is attributed 2.7% of the eventual TSU sector direct emission and 4.5% of
its indirect emissions (due to electricity use).
Coupling direct & indirect emissions of Agriculture and TSU
The agriculture is a net producer of electricity in the Netherlands. Agriculture and TSU are listed
together in the “Remaining electricity users” in the electricity statistics shown in the first part of
this appendix. This means that the TSU sector also uses produces by the agriculture sector, and
thus should receive some of its direct emissions.
In the sector agriculture the greenhouse agriculture and remaining agriculture are distinguished.
This is because the production of electricity takes place in the greenhouses. The electricity is
produced in cogeneration plants. Data about the net production of electricity in the greenhouses is
available for the years 2000 till 2008. The net use of electricity in the rest of the agriculture is
only available for the years 2000 till 2007 though. For this reason it is important that known
trends are made visible.
Table 2-2: Use of natural gas and electricity in the agriculture (Source: CBS Statline 2010)
Greenhouses Remaining agriculture
Year Gas use in Electricity use in Gas use in Electricity use in
2000 3709 1213 330 2131
2001 3611 1230 320 2194
2002 3442 1437 297 2192
2003 3488 1535 256 2086
2004 3610 1712 236 2010
2005 3593 1328 202 2064
2006 3283 -435 195 2179
2007 3548 -2034 188 2161
2008 3992 -4824 - -
The table (XX) clearly shows that the use of electricity in the remaining agriculture does not
change much over time. Because of this it is assumed that the figures of 2007 are representative
for 2008 and 2009. Although the production of electricity in the greenhouses seems to grow over
time we cannot extrapolate this trend, as it is only visible in the last four years. The figures for the
greenhouses in 2008 are used for 2009. One can state that effectively in each case the least
outdated figures are used.
46

For the time being we assume there are three sectors instead of two. These sectors are:
- Trade, services & utilities (TSU)
- Greenhouses
- Remaining agriculture (RA)
The direct emissions of the actual sector Agriculture are divided among “greenhouses” and
“remaining agriculture”. This is done according to their use of natural gas in 2007. This means
that 5% of the direct CO2 emissions of the actual sector Agriculture are attributed to RA and 95%
to the greenhouses. The CH4 and N2O emissions of the actual sector Agriculture are relatively
large and are coupled to animals and manure. The (extremely small) proportion that originates
from the combustion of natural gas is hard to estimate and is seen as negligible. Therefore only
the CO2 emissions are contributed to the greenhouses for this moment. N2O emissions are later
also attributed to the greenhouses.
The direct emissions of the greenhouses should partly be attributed to the TSU and RA sectors.
As the electricity is produced in cogeneration it becomes very difficult to define what part of the
direct emissions of greenhouses should be attributed to the indirect emissions of TSU and RA.
The choice made here is to attribute the same emissions to the electricity originating from the
greenhouses as is attributed to the electricity originating from the energy sector. The energy
sector delivers a total of 87261 GWh in 2009, with an emission of 54800 Pg. This is 0.638 Pg per
GWh. This emission is also attributed to the 4824 GWh produced by the greenhouses, resulting in
3029.5 Pg of the direct emissions of greenhouses attributed to the indirect emissions of TSU and
RA.
RA and TSU together are responsible for 34.6% of the energy sector emissions (Remaining
electricity users, 30204 million kWh) and the emissions coupled to the electricity production in
the greenhouses. In total the electricity use is thus 35028 million kWh. The emission coupled to
this amount of electricity is 21990.3 Pg CO2 (0.346*Emission energy sector + 3029.5).
The “remaining agriculture” used 2161 million kWh in 2009 (This is 6.17% of the 35028). The
rest is used up by the TSU (This is 93.83% of the 35028). The same percentage of the emission
stated above is attributed to the remaining agriculture sector and the trade, services and utilities
sector. The reader should keep in mind that there are also small CH4 and N2O emissions coupled
to the electricity originating from the energy sector, these are also divided among the RA and the
TSU. This is done with the same percentages.
47

Emissions Agriculture
The emissions attributed to the actual sector agriculture:
48
- 5170.5 Pg CO2 of the direct emissions of the CBS category agriculture (Of which 410 to
remaining agriculture and the rest to the greenhouses).
- All the CH4 and N2O emissions of the CBS category agriculture.
- All emissions of “mobile sources in the agriculture”
- Indirect emissions of the remaining agriculture due to electricity use
Emissions Trade, services & utilities
The emissions attributed to the sector trade, services & utilities:
- All emissions from the CBS category “trade, services & governmental buildings”.
- All emissions from the CBS category “environmental services”.
- Indirect emissions coupled to electricity use

Section A3: Division of emissions among municipalities
Households
The emissions coupled to the Households are:
- Direct emissions:
o CO2 19200 Pg
o CH4 16.38 Pg
o N2O 0.24 Pg
- Indirect emissions (via electricity use):
o CO2 15180 Pg
o CH4 11.23 Pg
o N2O 0.13 Pg
The amount of homes per Dutch municipality is known (CBS, 2010). The CBS distinguishes
between the following types of homes:
- Normal homes (NH)
- Living-units (LU)
- Recreational homes (RH)
- Special living buildings (SL)
- Empty homes (EH)
Living-units are mostly rented rooms. These occur primarily in cities with a large student
population. Special living buildings are the room/apartments in nursing homes. These two types
are seen in this perspective as normal homes. The recreational homes are not used all year and
should therefore not be weighed as normal homes. The occupation of recreational homes differs
slightly, but 40% is a safe average (Haas & Huig, 2009; Haas & Huig, 2010). To take the
recreational homes as 40% of normal homes is seen as a safe upper level as these homes are
normally used in summer, when the energy demand is lower. The empty homes are obviously not
taken into account.
49

The emissions of a municipality with concern to households can be described as following:
Municipal _ household _ emissions = Dutch _ household _ emissions
The fraction δ can be described as following:
δ
=
The upper line with the m in the underscore stands for the amount of the different types of homes
present in a specific municipality. The lower line with the N in the underscore stands for the
amount of the different types of houses present in the Netherlands.
Discussion
The indirect emissions are emissions resulting from electricity use. Electricity use is typically
more depending on the amount of persons living in a municipality compared to gas use, which is
more depending on the amount of homes. The indirect emissions are therefore attributed to the
municipality according to the percentage of the Dutch population living in that municipality.
There is a certain error margin in this calculation. This is because all the homes are treated as if
they are the same. Unattached houses consume more natural gas for heating than attached houses
or apartments. Because these figures are not public, there is no method to compensate for this
error. A second effect that creates an error margin is the density of people per house, as more
people in one home lowers the combined electricity use. Also in this case the information is not
freely available per municipality and can thus not be corrected.
Industry
The industry makes up a large percentage of the national emissions. These sources are often not
equally spread over the municipalities. An exact allocation of these emissions does not exist.
Emissieregistratie (EmissieRegistratie, 2010) though has an approximation of the industry
emissions per municipality. There are two downsides to the figures used by Emissieregistratie:
- The data delivered by Emissieregistratie describes emissions in the year 2008
-There are discrepancies between the total industrial emissions in 2008 according to the two
institutions (CBS Statline and Emissieregistratie).
Both downsides are solved with the same operation. Emissieregistratie uses the same industry
categories as the CBS Statline uses. The model assumes all industries in all municipalities grew
or reduced in the same pace between 2008 and 2009. As this is obviously not true for all
municipalities it is expected that only in cases of closure of factories or construction of new
factories this assumption will be proven wrong. With this assumption the model can calculated
with fraction of each industry type can be allocated to the chosen municipality. This fraction is
subsequently multiplied with the emissions in 2009 of that same industry type according to the
50
NH
NH
m
N
+ LU
+ LU
m
N
+ RH
+ RH
m
N
* 0.
4
* 0.
4
+ SL
+ SL
m
N
* δ

CBS. With this method the industrial emissions in all municipalities are corrected for the national
decline in industrial emissions between 2008 and 2009 (CBS Statline, 2010). The discrepancies
between CBS Statline and Emissieregistratie are caused by the accountability of electricity
production. In several factories heat and electricity is cogenerated, often leading to a surplus of
electricity. This is fed into the main electricity grid. CBS Statline accounts the resulting emissions
to the "Energy sector" while Emissieregistratie accounts the emissions to the Industry. Another
assumption has to be made here: A homogeneous geographical spreading of the cogeneration.
Discussion:
This method is seen as the most exact method of allocating industrial emissions automatically to
municipalities. Downside is the difference in datasets of the two organizations. Due to this
difference two important (and possibly influencing) assumptions had to be made.
Traffic
When calculating traffic emissions in a certain municipality one encounters several difficult
issues. First of all is the choice of the method itself. For two methods there is a certain amount of
data available. These methods are:
- Population based, in combination with mobility
- Roads based
Method choice
The first method looks at the amount of people living in the municipality and what their behaviors
are, based on circumstantial statistics. The second method looks at the length of roads present in
the municipality and whether these are highways or not. Although both have their downsides, the
second is thought to have the most. Most important downside of the second method is that all the
roads in the Netherlands are taken as the same. This means that a normal road in the countryside
gets the same amount of emissions attributed as the same road in a city. The second downside is
that some municipalities which have a number of important roads or highways crossing their
surface are attributed big amounts of emission, for which they do not always feel responsible for.
The first method is seen as balanced, as it incorporates the fact that city streets are intensely used
due to the high population density. In combination with the mobility, which shows that city
dwellers use the car les then people on the countryside, this gives a balanced view. The error
margin is the largest in the municipalities on the countryside. This is because in these regions the
distance from a person to facilities can have large differences. Some municipalities have few
facilities where some have a lot. Although people on the countryside use their car more then city
dwellers, this will have an effect on the distances they travel. This is not incorporated in the
model due to the large amount of time needed to implement it.
51

Emissions of Traffic
These emissions are primary direct emissions, in the CBS category “mobile sources”. A small
contribution is done by the electric trains and other electric public transport. These indirect
emissions are for 84% the effect of trains (XX). The remaining 16% are trolley-busses, trams and
subways. As a reminder, these are the direct emissions attributed to the sector “Traffic”.
Table 3-1: Spread of emission among mobile sources in the Netherlands in 2009 (Source: CBS Statline 2010)
Emission in Pg
Sources CO2 CH4 N2O
Road traffic 31109 2,567 1,284
National shipping, persons 114 0,008 0,001
National shipping, goods transportation 1499 0,041 0,038
Recreational shipping 186 0,012 0,001
Fishery 551 0,037 0,004
Person transportation trains 24 0,002 0
Goods transportation trains 42 0,003 0
Aviation 655 0,013 0,02
Remaining mobile sources 579 0,098 0,003
Table 3-2: Spread of emissions among road traffic in the Netherlands in 2009 (Source: CBS Statline 2010)
Emission in Pg
Sources CO2 CH4 N2O
Total road traffic 31109 2,57 1,28
Person cars 19375 1,92 1,04
Motorbike 321 0,2 0
Motorized bicycle 60 0,3 0
Delivery cars 4379 0,05 0,18
Lorries 2233 0,03 0,02
Truck-combinations 3586 0,04 0,03
Auto busses 580 0,01 0
Special vehicles 575 0,02 0,01
Calculation
Two calculation methods will be used in the case of traffic. This is because person mobility has
no influence on the transportation of goods. For all the direct emissions that cannot be coupled to
mobility the calculation is straightforward: The percentage of the Dutch inhabitants living in the
municipality multiplied with the total Dutch emission of the specific direct emission. For the
figures that can be coupled to mobility another method is used.
52

The Dutch institute for public health and environment (RIVM) released a report in 2008 (RIVM,
2008) called the “Mobility research of the Netherlands”. This report is the result of an inquiry on
mobility among the Dutch inhabitants. One of the resulting statistics is the “Mobility per
urbanity-class”. CBS and the RIVM distinguish five different types of urbanity. The CBS
statistics (CBS, 2010) give the amount of people per municipality living in a certain urbanity
class. The “Mobility research of the Netherlands” (RIVM, 2008) gives the use of the following
movement possibilities, depending on the urbanity class:
Table 3-3: Transport modes coupling
Mobility research transportation mode Coupled CBS emission class
Car (driving) Person cars
Car (passenger) None
Train Train (84% of indirect emissions)
Other Public Transport (OPT) Busses & 16% of indirect emissions
Motorized bicycle Motorized bicycle
Walking None
Bicycle None
From these the Car (driving), train, OPT, motorized bicycle and other transport are taken as
indicators of emission. Car (passenger) is seen as a passive form and will not be seen as an
indicator of emission. For the four named indicators one can find a coupled CBS emission
statistic of the year 2009. The emission of a transportation mode can be calculated in the
following manner:
∑ = U 5
U = 1
E = χ * E
m
U
N
* P
U −%
Em stands for the emission of a transportation mode in the municipality.
EN stands for the emission of a transportation mode in the Netherlands.
PS-% stands for the percentage of Dutch living in a specific urbanity class and in the chosen
municipality. The summation is over the 5 urban areas.
xU stands for the urbanity factor: It is calculated per urbanity class and is unique for each
transportation mode.
χ
∑ =
U = M U * U 5
U = 1
P
( P * M )
U
N
U
In this equation Mu stands for the kilometers moved per day by a person with a specific
transportation mode in that urbanity class, PN stands for the total Dutch population and PU stands
for the Dutch population living in that urbanity class. As can be seen, the factor xU is normalized
and carries no unit. The factor xU can be seen as a factor that indicates how much a person from a
53

certain urbanity class uses the car or train or another transportation mode relatively to the rest of
the Dutch.
The 5 urbanity classes (u) are:
54
• 1. Highly urban area = 2500 or more address per km2
• 2. Urban area = 1500 till 2500 addresses per km2
• 3. Moderately urban area = 1000 till 1500 addresses per km2
• 4. Marginally urban area = 500 till 1000 addresses per km2
• 5. Non urban area = 500 or less addresses per km2
To give an impression what the differences is between these classes the following graph has been
made. The urbanity classes are listed as stated here above.
1,80
1,60
1,40
1,20
1,00
Xu
0,80
0,60
0,40
0,20
0,00
Xu for the different urbanity classes
1 2 3 4 5
Urbanity class (u)
Figure 3-1: Xu of two transport modes in the different urbanity classes
Car use
Public Transport

Trade, services & utilities (TSU)
The emissions coupled to the sector TSU are:
Table 3-4: Emissions in the Trade, Services & Utilities sector in the Netherlands in 2009 (Source: CBS Statline
2010)
Emission in Pg
Source CO2 CH4 N2O
Mobile sources in the construction 1137 0.03 0.01
Trade. services & governmental buildings 11600 5.56 0.11
Environmental services 7800 233.43 1.91
Remaining stationary sources 600 1.74 0.00
Indirect emissions via electricity use 20633 13.14 0.19
Calculation
All the named emissions above are divided among the municipalities according to the percentage
of the Dutch civilians living in that municipality.
Discussion
The TSU sector is compared to for example industry spread over the nation. This sector (think of
shops & offices) are ought to be close to their customers and employees. Nonetheless bigger
cities have bigger concentrations of shops and offices compared to countryside municipalities for
example, and will therefore be attributed an emission share that is presumably smaller then their
real emission. The countryside municipalities will often be attributed a bigger share than what is
actually emitted in their region. The extend of this effect may vary between municipalities. To
correct this effect more time is needed in investigating the connection between urban density and
TSU density.
Agriculture
The emissions coupled to the sector agriculture are:
Table 3-5: Emissions of different agricultural subsections in the Netherlands in 2009 (Source: CBS Statline
2010)
Emission in Pg
Source CO2 CH4 N2O
Direct emissions mobile sources agriculture 1241 0.06 0.01
Direct emissions remaining agriculture 427 0.04 0.00
Direct emissions greenhouses 4758 0.00 0.00
CH4 & N2O emissions animals & manure 0 488.20 30.30
Indirect emissions remaining agriculture 1357 0.86 0.01
55

Greenhouses
The model distinguishes between “greenhouses” and “remaining industry”. Mobile sources in the
agriculture are not used in greenhouses. Neither are there animals in the greenhouses that emit
methane. That means that only the direct emissions of greenhouses and the N2O emissions
(partly) are attributed to the greenhouses. The direct emissions of greenhouses are divided over
the area which is used by the greenhouses in the Netherlands. This is multiplied with the acreage
present in the municipality.
The emissions of N2O originating from animal manure are divided over all the Dutch soils that
are in use by the agriculture, including the greenhouses. The total agriculture, greenhouse and
other agriculture areal is known per municipality (CBS, 2010). N2O emissions from manure are
mainly emitted when the manure is spread out on the fields. As all farmland uses manure, it is
chosen to divide these emissions over all the agriculturally used land.
Remaining agriculture
The calculations of the emissions of the remaining agriculture are straightforward for all
emissions named above except for the methane emissions by animals. The N2O emissions by
animals are divided as explained above.
The direct emissions from mobile sources, indirect emissions from electricity use and the direct
emissions in the remaining agriculture are spread equally over all the area used by the remaining
agriculture. This is multiplied with the area used by the remaining agriculture in a municipality.
What remains are the CH4 emissions. These are heavily dependent on the presence of animals,
with emphasis on the presence of cattle. The CH4 emissions per animal are according to the
National Inventory Report 2010 (Maas et. al. 2010) as following:
Table 3-6: Methane emissions per animal (Source: Maas et. al. 2010)
Emissions in kg per year
Animal Direct emissions Manure emissions
Dairy cattle 128 37.50
Young cattle 34 6.86
Other cattle 73 3.45
Horses en pony's 18 1.56
Sheep’s 8 0.19
Goats 8 0.13
Pigs (direct emissions accounting to all) 1.5 0.00
Meat pigs 0 6.32
Breading pigs 0 13.38
Chickens 0 0.02
56

Multiplying these values with all the animals present of each type in the Netherlands in 2009 one
reaches a value of 463 Pg, slightly less than the 488.2 which are the total. A possible explanation
could be that these figures were determined on different dates. To compensate for the established
difference all the values are multiplied at the end of the calculation with 488.2/463 = 1.054. The
CBS offers data on all the animals present in a municipality. By multiplying the factors above
with number of animals in a municipality (and also with the correction factor) one finds the CH4
emissions of animals in a municipality.
Discussion
In the case of the straightforward calculations it is believed that the result is a close
approximation of reality. Practically all the farms and greenhouses in the Netherlands are heavily
automated and mechanized, leading to little difference between them. In the case of methane
division the uncertainty lies in the numbers used. The NIR 2010 report emphasizes that these
numbers are an approximation and have large uncertainties. This uncertainty is influence by
temperature on the location, feed given to the animals, ventilation etc. This cannot be corrected in
any way though by this research, seen the complexity of the subject. The division of the N2O
emissions is coarse and can perhaps be corrected. This correction should be done by research into
the use of the type of manure used on different soil types and different soil purposes. This would
take a large amount of time, for which there is no time in this research.
57

Appendix B: Emission reducing measures in the
ROGEM-model.
This appendix consists of a number of sections. The first section displays the measures offered to
the user of the YYY-model. The second will be concerned with the calculation of each separate
measure.
Section B1: List of measures displayed in the YYY-model
In this section the measures displayed in the YYY-model are listed. Table 1-1 (on the next page)
displays all the measures that are incorporated in the YYY-model. The table shows the measures
and displays an X in the column of each sector where the measure finds it implementation.
The bulk of the measures are energy related. These measures are categorized according to the
concept of the Trias Energetica (XX). The Trias Energetica consists of three steps to reach an as
sustainable possible energy use.
Figure 1-1 shows the three steps of the Trias Energetica. To reach an energy use that is the most
sustainable possible the steps should be followed in the order they are showed in the picture.
Figure 1-1: Trias Energetica. (Source: isover.com)
59

Measure category
Lower the energy need
Production or purchase of
sustainable energy:
Efficient use of
fossil fuels
Nonenergy
related
Table 1-1: Measures taken by municipalities that are showed in the YYY-model.
60
Measures Sector of implementation
Separate measures
Improving insulation of buildings X X X
Improving energy label of buildings X X X
Smart thermostats X X* X
Smart electricity & gas meters X X* X
Lowering room temperature in buildings X X*
Purchase and use of CFL and LED light sources X X*
Improving infrastructure for vehicles X
Offer driving workshops/training X X
Improving public transport infrastructure X
More use of public transport X
Improving bicycle infrastructure X
Stricter demands on greenhouse reconstruction X
Better the energy efficiency existing greenhouses X
Lower the acreage of greenhouses X
Use of rest heat originating from industrial processes X
Lower energy use of sector with fixed percentage per year X X
Less use of- or higher efficiency street lights X
PV systems X X X X X X
Solar heaters X X X X X
(mini) wind turbines X X X X X X
Subsoil thermal storage X X X X X X
Geothermal installations X X X X X
Digesters for biomass X X
Other ways of producing sustainable energy X X X X X X
Purchase of green electricity X X X
Purchase of green gas X X X
Sustainable fuels for vehicles X X
Higher efficiency conventional vehicles X
Use of vehicles which use CNG as fuel X X
Use of electric vehicles X X
Improving efficiency heating installations of buildings X X X
Demand higher efficiency public transport X
Lower the amount of animals in the municipality X
Lower the acreage where manure is spread X
Capture of methane emissions of animals, manure or waste X X
* These measures are summarized in one measure called " Energy awareness"
Municipal organization
Households
TSU
Traffic
Agriculture
Industry
General measures

Section B2: Quantification of the measures
Technical information
General information
In this section all the measures are quantified. This means that a calculation method is coupled
tot the measure that enables the program to calculate an emissions reduction for each measure.
The order of the measures is the same as in Table 1-1 in section B1.
The YYY-model does not distinguish between the different greenhouse gasses anymore in this
stage. Taking the separate gasses into account would complicate the model for the user. For this
reason all emissions are now considered in CO2-equivalents.
The measures sometimes differ per sector in how specific or detailed the calculation is. This is
primarily dependent on the level of detail in which the current emissions are known to the model.
Whenever the model has insight in the internal structure of emissions it offers the user to point
out on which part of the emissions the measure is applied. In the case of the municipal
organization the model does not know any internal structure of the emissions. The municipal
organization is a small proportion of the total emissions and has the largest diversity of buildings
compared to the other sectors. For this reason the model, in the case of the municipal
organization, assumes that the measure applies to the whole organization.
Not all measures need input. Whenever a given number in a calculation is used as input this will
be indicated. For all measures the user states the period in which the measure that is chose will be
implemented. Some measures have a fixed reduction, which lasts from the moment of
implementation onwards. Some measures on the other hand have a growing emission reduction
potential. In this case a t (time) dependent formula will be displayed in this appendix.
Some measures in this model have comparable counterparts (Replacement of street lights can be
done with different types of energy efficient lights). The purpose of the model was to enable the
user to fill in all the measures taken by a municipality. However, when the number of options
increases, the workability of the model is at stake. Therefore the model will only display the most
used option.
Calculation order
Some measures are efficiency related measures, some are absolute reduction measures that lower
the needed amount of energy by replacing or removing a process. In the calculations of the
emissions, the latter is always performed first.
For example: A car owner (Emission of his car = 100 kg per month) decides to drive more
efficient (25% less fuel use) and to use the bike to go to his work (He calculated this measure to
reduce his emissions with 30 kg per month, with his previous driving style). Applying first the
latter measure and then the first (the correct method) yields a reduction of 47.5 kg per month.
Doing the calculation the other way around yields a reduction of 55 kg. Important to realize in
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this is that the efficient driving has nothing to do with his trips to his work anymore, but only of
the remainder (100 - 30) of the emissions.
Constants and abbreviations used in this appendix
The reader should be aware of the fact that the emissions (EX) are also functions of time.
Generally this is not emphasized as most of the measures have a static or fixed effect.
All calculated emission reductions are denoted in kg!
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Often the reduction is calculated with use of the following constants:
EG = Emission due to use of natural gas in that sector
EE = Emission due to use of electricity in that sector
EV = Emission due to use of vehicle fuels in that sector
EA = Emission due to animals, manure and waste in that sector (This can be CH4 or N2O)
Some characters are used for quick denotation of the quantification:
RX = Reduction potential
FX = Fraction of sector
PX = (Input) Percentage
CX = Capacity of an application
Y = (Input) Year
A = Time in hours that an installation is actually effectively working
V = Volume in cubic meters
t = Time in years
The following constants are used in some calculations (Agentschap NL, 2010)
56.1 = kg CO2 emission per GJ of heating (Assuming natural gas as fuel)
68.9 = kg CO2 emission per GJ of used electricity (Final energy use)
0.6086 = kg CO2 emission per kWh of used electricity
0.408 = Efficiency (energetically) of the Dutch electricity production. (Averaged)
0.0036 = Conversion factor from kWh to GJ
1.78 = kg CO2 emissions emitted during the combustion of 1 cubic meter of natural gas

Measures that lower the energy demand
Improving insulation of buildings
The model perceives insulation as a lower energy loss and thus a lower energy demand. The
model subtracts a fixed percentage of the energy demand for each type of insulation. These are
the available types of insulation and their reduction percentage (Source: EBVU model[2009]):
• Wall insulation (RW = 15% reduction)
• Roof insulation (RR = 35% reduction)
• Floor insulation (RF = 1% reduction)
• Replacing single glass for double (RG = 10% reduction)
These numbers are tentative of nature. The effect of insulation differs strongly between buildings.
Important to note is that these reduction figures assume a building where the insulation was
practically absent. Input here is the types of insulation that are to be applied.
Calculation:
ReductionPotential --> RT = RW + RR + RF + RG
Fraction TSU --> FT = Percentage (Input) of buildings in this sector where the insulation is placed
Fraction Households --> FH = Amount of households applied (Input) / Total amount of
households in the municipality.
Emission reduction in the case of:
Municipal organization = EG * RT
Households = EG * RT * FH
TSU = EG * RT * FT
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Improving energy label buildings
Energy labels are calculated by certified companies in the Netherlands. The energy labels
represent a range in the Energy Index (For example, B => 1,05 < EI < 1,3). The calculation used
is not publicly available. The guideline in this case is a brochure "Voorbeeldwoningen Bestaande
Bouw" (SenterNovem 2007). Plotting the energy use from all building types and energy labels we
obtain the following figure:
Figure 2-1: Energy index as function of energy per person per m2. (Source: SenterNovem 2007)
In the legend the reader sees 4 different types of dwelling houses. Figure 2-1} indicates a linear
connection between the energy index and the energy demand per person per square meter. As the
energy labels cover a range of 0,3 of the energy index on average, the input for the model is set to
be amount of label-steps the label changes of a certain building. The effect of this label change is
set on 100% divided by 7 (R=14,4% reduction per label step) possible label steps (Energy labels
vary from A to G).
Fraction TSU --> FT = Percentage of buildings in this sector of which label changes
Fraction Households --> FH = Amount of households where the label changes / Total amount of
households in the municipality.
Emission reduction in the case of:
Municipal organization = EG * R
Households = EG * R * FH
TSU = EG * R * FT
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Smart thermostats
Smart thermostats are appliances that detect the presence of people in a building or a room. The
thermostat turns the heating off when no activity is detected and turns it back on when people
enter the building or the room. This is known to reduce about 15% (R = 15%) of the gas use in a
dwelling (Essent, 2010).
Fraction TSU --> FT = Percentage of buildings in this sector of which label changes
Emission reduction in the case of:
Municipal organization = EG * R
TSU = EG * R * FT
Smart gas and electricity meters
Smart meters are meters that measure the energy use and send the data for analysis to the energyuser.
Using this data, the user can point out activities or appliances that use extreme amounts of
energy. These can be replaced or used less. Smart meters are known to save energy: 5% less gas
use and 5% less electricity use (Energieraad 2005). Important for this measures is that this
measure is only implemented when the owner has as goal to reduce energy.
Fraction TSU --> FT = Percentage of buildings in this sector of which label changes
Emission reduction in the case of:
Municipal organization = EG * R
TSU = EG * R * FT
Lowering room temperature in buildings
Heat loss is linear with the temperature difference. Lowering the room temperature will thus
lower the heat loss linearly. The model assumes that if a building is kept on 3.3 °C, average
yearly temperature in the Netherlands (KNMI, 2010), the building will not need energy for
cooling or heating. The relative change towards 3.3 °C from the old room temperature (input) to
the new room temperature (input) is seen as relative reduction on the gas use.
Emission reduction (Only in the municipal organization) = EG * R
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Purchase and use CFL and LED light sources
Replacing conventional light bulbs for CFL or LED light sources reduce the amount of used
electricity. Leefmilieu Brussel (2010) states that 40% of the electricity use of the tertiary sector is
used for lighting. Assuming that 50% of the electricity use in municipalities is used in buildings
(Royal Haskoning, 2010), we find that 20% of the electricity demand of a municipality is used for
lighting . The reduction in energy demand when replacing conventional light bulbs with CFL is
75% (RC) , replacing with LED is 85% (RL) (Milieucentraal, 2010). Input here is the percentage
(PC & PL) of the light bulbs being replaced by more CFL and which percentage by LED.
Emission reduction (Only in the municipal organization) = EG * (PC * RC + PL * RL) * 20%
Energy awareness of civilians
Several projects of municipalities are in the setting of "energy awareness". These projects try to
inform the citizens about the global climate problems and the need to reduce energy demand. As
the reduction of the energy demand can be done in several ways (As shown in the last specific
measures). As the municipality does not control whether civilians take energy demand reducing
measures, let alone which measures, these have to be confined in a general constant. The model
asks the user to define the percentage of people (input) living in the municipality that actually
start to reduce their energy demand. The model then multiplies this with a certain average
reduction per household (R = 10% of total energy demand). The average reduction per household
is a figure deduced from the average reduction in the last 4 measures. There are no generic figures
in this case.
Emission reduction (Only in traffic) = EG * Input * R + EE * Input * R
Improve infrastructure for vehicles
Measures that have their influence on the traffic emissions are extremely hard to asses. One of the
reasons is that infrastructure differs a lot between municipalities. There is no universal approach
to estimate how much emission reduction is reached with a certain measure. For this reason we
ask the user to tell the program how much car kilometers are avoided (input) by a certain measure
(Reducing traveling time due to extra road, declaring car-free zones etc.). Each automobile
kilometer represents 180 grams of CO2 - equivalent emissions (den Boer, Brouwer & van Essen,
2008).
Emission reduction (Only in traffic) = Input * 180 grams CO2 per km
Offer driving workshops/training
"Het nieuwe rijden" (The new way of driving) is a national project in the Netherlands to
persuade car drivers to drive safer and environmentally friendlier. Workshops and trainings are
given to instruct people how to drive in this way. On average there is a reduction of 7,8% (R),
(SenterNovem 2010). The user is asked to estimate the percentage (P) of the population (In case
of the sector Traffic) or employees (in the case of the municipal organization) are participating.
Emission reduction (In both traffic and the municipal organization) = EC * P * R
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Improving public transport infrastructure
As with the measure 2.1.8, this measure is hard to quantify generically. Measures taken in this
field are for example: more bus stations, more bus-only lanes etc. Therefore the user is asked to
estimate the amount of person-car kilometers are replaced by person-public transport kilometers
(input). The average emission per person-km in a automobile is 180 grams of CO2 equivalents,
that for public transport is 100 grams of CO2 equivalents (den Boer, Brouwer & van Essen,
2008).
Emission reduction (Only in traffic) = Input * (180-100) grams CO2 per km
More use of public transport
Identical to the former one, but now aiming at the municipal organization. Input(1) is the total
amount of person-car kilometers replaced by person-public transport kilometers. Input(2) is the
total amount of person-car kilometers replaced by either bike or carpooling.
Emission reduction (Only in municipal organization) = Input(1) * (180-100) grams CO2 per km +
Input(2) * 180 grams CO2 per km
Improving biker infrastructure
As with the measure 2.1.8, this measure is hard to quantify generically. Measures taken in this
field are for example: more bikers roads, more bicycle sheds etc. Therefore the user is asked to
estimate the amount of person-car kilometers are replaced by person-bike kilometers (input). The
average emission per person-km in a automobile is 180 grams of CO2 equivalents (den Boer,
Brouwer & van Essen, 2008).
Emission reduction (Only in traffic) = Input * 180 grams CO2 per km
Strict demands on greenhouse reconstruction
Commonly, greenhouses have a lifetime of 20-40 years (Agriholland, 2010). The model assumes
30 years as the lifetime of a greenhouse. Obligating the construction of greenhouses to be climate
neutral will reduce the emissions of the greenhouses with 100% in 30 years. The input in this case
is the year of implementation of this measure (Y).
EG = Emissions by the gas use of the greenhouses (i.e. not of the total agricultural sector)
Emission reduction (Only in agriculture)(As a function of t ) =
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Better the efficiency of greenhouses
With this measure the user can denote which percentage (P, input) of the greenhouses is reducing
their emissions (R, input) with which percentage. Calculation is straightforward:
EG = Emissions by the gas use of the greenhouses (i.e. not of the total agricultural sector)
Emission reduction (Only in agriculture) = EG * R * P
Reduction of greenhouse acreage
Input in this measure is the percentage (P) with which the greenhouse acreage will be diminished.
Note: It is not plausible that municipalities will actually use this measure as a policy to reduce
greenhouse gas emissions. Purpose of this measure is to enable the user to correct the emissions
in the future, in case of serious diminishment of the greenhouse acreage.
EG = Emissions by the gas use of the greenhouses (i.e. not of the total agricultural sector)
Emission reduction (Only in agriculture) = EG * P
Use of rest heat originating from industrial processes
Input in this measure is the total amount of rest heat measured in GJ that is actually used to
replace heating processes fueled by fossil fuels. This need not be in the same industrial sector, but
can find its application in heating of homes, offices etc. The model assumes the replaced fossil
fuel to be natural gas, which is almost always the case. Wherever the replacement of fossil fuels
takes place is not taken into account, the reduction is attributed to the industry sector.
Emission reduction (Only in industry) = Input (Heat in GJ) * 56.1
Lower energy use of sector with fixed percentage per year
Municipalities often make agreements with a complete sector. In these agreements the sector
promises to lower their energy demand with a agreed percentage (P, input) per year. The year of
implementation (Y) is also used in the calculation. The model assumes that the reduction of
energy demand is divided equally over electricity and natural gas.
Emission reduction relevantly to the year before t = ( EG + EE ) * (t-1) * P
Emission reduction in a specific year (after the year of implementation) relevantly to the emission
that was predicted to be emitted in the autonomic situation =
+
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Less use and better efficiency of street lights
This measure is practically seen composed of two separate measures. The first is the less use of
the street lights. The latter is the more efficient use of street lights.
Less use of street lights
This can be done by dimming the lights. Input in this case is the percentage (P) of the street lights
that are begin dimmed. Second input is the reduction in electricity use (R) of these street lights.
SenterNovem states in 2007 that 50% of the electricity use of a municipality is accountable to
street lights.
Emission reduction (Only in the municipal organization) = EE * 50% * R * P
Efficient use of street lights
The model assumes that replacing each conventional street light for LED-alternatives reduces the
electricity demand with 20% (R = 20%). Input is the percentage (P) of the conventional street
lights that are being replaced with LED lights.
Emission reduction (Only in the municipal organization) = EE * 50% * R * P
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Production or purchase of sustainable energy
In this chapter the emission reduction is generally calculated using a protocol made by
Agentschap NL (2010). Whenever there is reference to Agentschap NL in this chapter, the
protocol is meant.
Photovoltaic systems
Photovoltaic (PV) systems have a defined maximum capacity (CPV). This capacity is reached in
summer on a sunny day (Assuming that the system is correctly placed). This maximum capacity
is measured in kWp, and is also the input. Agentschap NL uses 700 (A) hours as a representative
amount of hours that the PV-systems in the Netherlands work on maximum capacity.
Emission reduction (In all sectors) = CPV * A * 0.6086
Reduction of emissions emitted by the use of electricity.
In some occasions the user may want to state the capacity of the PV-system in the form of a
covered surface. This possibility is offered in the model. The model uses the conversion factor of
0,125 kWp per square meter of solar panel (Milieucentraal.nl).
Solar heaters
Solar heaters do not have a defined way of describing their capacity. Solar heaters heat up water
that is used (sometimes after reheating by gas) to heat up a building or to serve as DHW.
Agentschap NL uses a standard solar heater. This standard solar heater has a reduction potential
of 233 kilograms of CO2 equivalents. Input in this case is the amount of solar heaters will be
installed.
Emission reduction (In all sectors) = Input * 233
Reduction of emissions emitted by the use of natural gas.
In some occasions the user may want to state the amount of solar heaters in the form of a covered
surface. This possibility is offered in the model. The model uses the conversion factor of 3 square
meters used by one solar panel (Milieucentraal.nl).
Wind turbines
Wind turbines are defined by their capacity (CW = input ). Agentschap NL uses 2200 hours (A) as
their average working time per year.
Emission reduction (In all sectors) = CW * A * 0.6086
Reduction of emissions emitted by the use of electricity.
There are no standard forms of wind turbines. Wind turbines reach in capacity from 500 W to 2
kW for home-application and from 5 kW to 5 MW for application for companies. For this reason
the user is always asked to define the capacity of the wind turbine.
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Subsoil thermal storage
Subsoil thermal storage finds it application in different ways. Two main categories of thermal
storage underground can be distinguished:
- Closed thermal storage system
- Open thermal storage system
Closed thermal storage system
Closed thermal storage systems are almost always applied in dwellings and consists of a two
directions-tube being inserted into the ground. This tube reaches depths of 30-40 meters. Water is
circulated in this tube to heat up or cool the water. In summer it is used for cooling, and in winter
it is used for heating. The returning water generally has a temperature of 10 to 15 degrees Celsius.
The temperature is scaled up in winter by a heat pump to be able to warm up the building. The
electricity consumed by the heat pump will is seen as negative emission reduction. Agentschap
NL only bestows a reduction potential to the heating of the dwelling, as they consider cooling
dwellings in the Netherlands as undeveloped. The input for heating is the capacity (CHP) of the
heat pump. Assumed is that the flow of water will be suffice for the heat pump.
Other factors here are (Agentschap NL):
SPFHP = 4.1 = Seasonal Performance Factor of the heat pump in this case (Soil - Water,

1. Open systems without heat pump
Input is the amount of water being pumped back and forth between the cold and warm storage
(V). Obviously, the same amount of water has to be pumped from the cold to the heat as there is
pumped vice versa. So half of V is used for cooling, half for heating (θ = 0.5). Additional
constants are the average energy used per cubic meter (λ) of heating water (or cooling) water and
the utilization factor (β). The last indicates how effective the water flow is used. (Agentschap NL)
λH = 23 MJ / m3 = Used energy per cubic meter of water pumped for heating
λC = 23 MJ / m3 = Used energy per cubic meter of water pumped for cooling
βH = 0.3 = Utilization factor of water pumped for heating
βC = 1 = Utilization factor of water pumped for cooling
Emission reduction (All sectors) = V * θ * λH * βH * 56.1 + V * θ * λC * βC * 0.001 * 68.9
Cooling deminishes electricity use, while heating diminishes the use of natural gas.
2. Open systems with heat pump
The difference between this version and the previous is that now there is a heat pump in place to
use the heating water more efficient. The calculation for cooling remains the same. Extra input in
this case is the capacity (CHP) of the heat pump. Calculation is the same as in case of the closed
thermal systems.
Emission reduction (All sectors) = CHP * A * 0.0036 * 56.1 + V * θ * λC * βC * 0.001 * 68.9
Emission reduction (All sectors) = - (CHP * A / SPFHP) * 0.6086
Again we see the negative reduction due to the electricity use of the heat pump.
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Geothermal installation
Geothermal installations are used for heating purposes and sometimes also for electricity
production. Input for the geothermal installation is the thermal capacity (CT) and the net electrical
capacity (CE). The net electrical capacity incorporates the use of the geothermal installation.
Additional factors: Hours of full capacity (A = 5000) and the common efficiency of gas powered
heaters in households (η = 0.95).
Emission reduction (All sectors) = CT * A * 56.1 / η + CE * A * 0.6086
Whenever CE is set to 0 by the user, the model assumes the geothermal installation thus uses
electricity. This is calculated as following:
Emission reduction (All sectors) = - ( CT * A / COPG ) * 68.9 * 0.001
This is thus again a negative emission reduction, due to the net use of electricity. The COPG
factor in the equation stands for Coefficient of Performance. It stands for the ratio of energy
transported divided by the energy needed to perform the task. Here COPG = 30.
Digesters for biomass
Digesters are vessels where natural fermentation is speeded up. Fermentation also takes place in
more anaerobe conditions then it would normally do in free nature. Having the fermentation in
closed and controlled environments has the following benefits:
- Harvest of heat produced in the vessel (Q)
- Harvest of 'green gas' produced by the biomass (G)
- Production of electricity (fueled by the green gas). (E)
Per GJ of heat produced and used for heating of processes or buildings there is 56.1 kg of CO2
equivalents reduction. Each cubic meter of 'green gas' avoids 1.78 kg of CO2 equivalents that
would have been emitted in the combustion of conventional natural gas. If the gas is used to
produce electricity, the net production in kWh is multiplied with 0.6086.
Emission reduction (For TSU and agriculture) = Q * 56.1 + G * 1.78 + E * 0.6086
Other ways of producing sustainable energy
The model offers the user to fill in the revenues of other types of sustainable energy production if
when it is absent in the model. The model asks for the net year production of electricity in kWh
(E) and the net year production of heat in GJ (Q)
Emission reduction (All sectors) = Q * 56.1 + E * 0.6086
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Purchase of green electricity
Green electricity is electricity produced in ways which do not incorporate the combustion of
fossil fuels. No greenhouse gasses are emitted during the production of green electricity. The
option to buy green electricity is offered to the municipal organization, households and the TSU
sector
Emission reduction (All sectors) = EE * P
Purchase of green gas
Although still in its infancy, the market for green gas is developing quickly and could become a
significant player in the future. Important in the concept of green gas is the carbon cycle. The idea
is that all CO2 that originates from the combustion of green gas is reabsorbed by biomass, which
is then used to produce green gas
Emission reduction (All sectors) = EG * P
Sustainable fuels for vehicles
Sustainable fuels for vehicles are available in different forms. An electric vehicle can drive on
'green electricity', CNG fueled vehicles can run on 'green gas' and conventional vehicles can be
fueled with biodiesel or bio-ethanol for example. As with the two former measures, input is the
percentage (P) of the sector that buys sustainable fuels for the vehicles in 2009, in 2010, in 2015
and in 2020. In total four inputs. For each year, the reduction is calculated as following:
Emission reduction (municipal organization and traffic) = EV * P
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Efficient use of fossil fuels
Higher efficiency conventional vehicles
This measure enables the user to indicate that when the municipality is planning to replace the
vehicles that are used by the organization, they are replaced by high efficient vehicles. Denoting
the efficiency of vehicles is done by labeling in the Netherlands. The model assumes all vehicles
are replaced by the A-label type vehicles. By definition, A-label vehicles are 20% (R = 20%)
more efficient than the "standard" vehicle.
Emission reduction (Only in the municipal organization) = EM * R
Use of CNG-fueled vehicles
CNG fueled vehicles are more efficient than the conventional diesel or gasoline fueled vehicles.
Conventional vehicles emit on average 180 grams of CO2 equivalents per km, while CNG fueled
vehicles emit 120 grams of CO2 equivalents per km (den Boer, Brouwer & van Essen, 2008).
Input in the case of the traffic sector is the amount of person-car kilometers (K) are being
changed from conventional vehicles to CNG-fueled vehicles. Input in the case of the municipal
organization is the percentage (P) of the vehicles used by the organization are being replaced by
CNG-fueled alternatives.
Emission reduction (In the case of traffic) = K * (180 - 120) * 0.001
Emission reduction (In the case of the municipal organization) = EM * P * ( 120 / 180 )
Use of electric vehicles
Electrically powered vehicles are more efficient than the conventional diesel or gasoline fueled
vehicles. Conventional vehicles emit on average 180 grams of CO2 equivalents per km, while
electric vehicles emit 100 grams of CO2 equivalents per km (den Boer, Brouwer & van Essen,
2008).
Input in the case of the traffic sector is the amount of person-car kilometers (K) are being
changed from conventional vehicles to electric vehicles. Input in the case of the municipal
organization is the percentage (P) of the vehicles used by the organization are being replaced by
electric alternatives.
Emission reduction (In the case of traffic) = K * (180 - 100) * 0.001
Emission reduction (In the case of the municipal organization) = EM * P * ( 100 / 180 )
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Higher efficiency in the heating of buildings
Heating installations for buildings are generally boilers that heat up the buildings as well as
provide warm tap water. Beside of these boilers there are various other types of heating
installations used. For simplicity sake we use the conventional boilers. These come in
generations. The current generation is called HR ketel (High efficiency boiler), where the
previous generation is called VR ketel (Higher efficiency boilers). The improvement in efficiency
is 16% (R = 16%) from the previous generation to the current. This measure is only sensible to
apply whenever the heating installation in place is outdated.
In the case of the municipal organization the model assumes that all the heating installations are
being replaced (As it does in the case of insulation).
Fraction TSU --> FT = Percentage (Input) of buildings in this sector where the insulation is placed
Fraction Households --> FH = Amount of households applied (Input) / Total amount of
households in the municipality.
Emission reduction in the case of:
Municipal organization = EG * R
Households = EG * R * FH
TSU = EG * R * FT
Demand higher efficiency public transport
Whenever (In large municipalities) the license of the former public transport company expires,
the municipality can add demands to the concession. These demands can be about the type of
vehicles used in the public transport and the percentage of the fuel that is sustainable. Input in this
case is whether the public transport, instead of using conventional vehicles, will use electric or
CNG fueled vehicles (R) . Second input is the demanded sustainability of the fuel used in
percentages (P).
RC = 0% = Reduction if public transport is continued with conventional vehicles
RE = (100/180) % = Reduction of public transport is continued with electric vehicles.
RG = (120/180) % = Reduction if public transport is continued with CNG gas fueled vehicles.
Emission reduction (Only in traffic) = EM-PT * ( 100 - ( 100- RX ) * ( 100 - P ))
EM-PT stands for the absolute share in the greenhouse gas emissions of the public transport in the
total traffic emission.
Non energy related measures
Lower the amount of animals in the municipality
Each animals emits a certain amount of methane gasses. This is partly due to internal
fermentation and partly due to manure displacement. If a municipality decides to replace a
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number of farms for nature, this would find its consequence in the methane emissions. The
emissions per animal are (IPCC, 1997,XX):
Milk giving cattle: M = 165.5 kg of methane per year per animal
Young cattle: M = 40.9 kg of methane per year per animal
Remaining cattle: M = 76.5 kg of methane per year per animal
Horses: M = 19.6 kg of methane per year per animal
Sheep's: M = 8.2 kg of methane per year per animal
Goats: M = 8.1 kg of methane per year per animal
Pigs: M = 11.3 kg of methane per year per animal
Chickens: M = 0.02 kg of methane per year per animal
The user gives the type of animal and the total amount of animals (T) as input.
Emission reduction (Only in the case of agriculture) = M * T * 21
21 is the global warming potential of methane. This is according to the Second Assessment
Report (SAR) of the IPCC.
Lower the acreage where manure is spread
Nitrous oxide (N2O) is emitted when manure is moved or disturbed. This happens primarily when
the manure is spread over farmland. As almost on all the farmland manure is spread and the
model does not know the composition of the soil and manure in each municipality, the model will
assume all the farmland to the same. The model asks the user to give as input the surface with
which the farmland acreage will be diminished.
F = Surface in square kilometers that will be transformed from farmland to nature / Total original
farmland surface
Emission reduction = EA-N2O * F * 310
EA-N2O stands for the emissions in a certain municipality in the form of nitrous oxide. 310 is the
global warming potential of nitrous oxide. This is according to the SAR.
Capture of methane originating from manure or waste treatment
Methane is a strong greenhouse gas. It is primarily emitted by animals, manure and waste.
Capture of methane would reduce the emission. Input is the total amount of captured cubic
meters of methane (M = Input * 1.78 kilograms of methane per cubic meter).
Emission reduction (In agriculture and TSU) = M * 21
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