Regional Environmental Accounts Denmark 2003

Peter Rørmose Jensen Thomas Olsen

This report has benefited from funding by the European Commission, GD Environment, by means to the grant agreement no. 200471401007, action 3 for the study entitled "Environmental Statistics and Accounts – Regional Environmental Accounts”.

Regional environmental accounts Denmark 2003

Statistics Denmark December 2005

Contact information:

Peter Rørmose Jensen

Head of section National Accounts - Environmental Accounts and Input-Output Statistics Denmark Sejrogade 11 DK-2100

Phone: +45 3917 3917 Direct: +45 3917 3862 E-mail: [email protected]

Thomas Olsen

Head of section National Accounts - Environmental Accounts and Input-Output Statistics Denmark Sejrogade 11 DK-2100

Phone: +45 3917 3917 Direct: +45 3917 3828 E-mail: [email protected]

Table of contents

1 INTRODUCTION...... 2 1.1 Regions in Denmark...... 2 2 REGIONAL ENERGY ACCOUNTS...... 5 2.1 Data sources for energy accounts ...... 7 2.1.1 Census on the use of energy in manufacturing industries ...... 8 2.1.2 Census on energy producers...... 10 2.1.3 Survey on the use of energy in trade and service ...... 10 2.1.4 Regional Economic Statistics...... 11 2.1.5 Data about regional distribution of energy consumption in households ...... 12 2.2 Method for energy accounts ...... 12 2.2.1 Agriculture and Fishing etc. (industries 1-5)...... 13 2.2.2 Extraction of crude oil and gas (industries 6)...... 13 2.2.3 Manufacturing industries (industries 7-62) ...... 13 2.2.4 Energy supply industries (industries 63-65)...... 15 2.2.5 Trade and service industries (industries 66-130) ...... 16 2.2.6 Households (industries 131 – 135)...... 17 2.3 Results for energy accounts...... 18 2.3.1 Industries ...... 18 2.3.2 Households...... 19 2.4 Summary and conclusions about energy accounts...... 22 3 REGIONAL AIR EMISSIONS ACCOUNTS ...... 23 3.1 Method for air emissions...... 23 3.2 Results for air emissions...... 24 3.3 Summary and conclusions for air emissions...... 26 4 REGIONAL WATER ACCOUNTS ...... 27 4.1 Data sources for water accounts...... 27 4.2 Method for water accounts...... 29 4.2.1 Extraction of water by industry and by region...... 29 4.2.1.1 Extraction of ground water ...... 29 4.2.1.2 Extraction of surface water ...... 30 4.2.2 Use of water by industry and by region ...... 30 4.2.2.1 The VAT-paying companies’ use of tap water...... 31 4.2.2.2 The entities’ not paying VAT use of tap water...... 31 4.2.2.3 Balancing the use of tap water ...... 32 4.2.2.4 Use of water extracted for own use...... 32 4.3 Results for water accounts ...... 32 4.4 Summary and conclusions: Regional water accounts ...... 39 5 REFERENCES ...... 40 6 APPENDIX ...... 41

1 Introduction

A system of national environmental accounts has been established in Denmark, and quite comprehensive statistics are published on an annual basis. The statistics comprise water accounts, energy accounts, air emission accounts, oil and gas balances, environmental taxes and forest accounts as major parts. New parts are gradually being added to the system, but a demand for a regionalized version of the accounts has already now been revealed. This requirement is obvious in the light of the regional or local nature connected with many environmental issues.

Regional statistics not So the purpose of this project is to investigate the possibilities for establishing readily available regional environmental accounts in Denmark.1 It is clear from the beginning that far from all of the statistics collected at a national level are readily available at the regional level. At least not in a form that is coherent with the national environmental accounts. Consequently, the job is to find out on the one hand, what statistics actually do exist at the regional level and how they compare to the national data, and on the other hand, some methods for regionalizing existing national data in a proper way.

The demarcation of what to include in the regionalization process is determined by the contents of the national environmental accounts in Denmark. We concentrate on energy and its related emissions and water accounts. Other environmental statistics are available at the county level but they will not be looked at in this project since we do not have them in the national environmental accounts.

Since the national environmental accounts are build as a true satellite account to the Danish National Accounts they contain the same 130 industries as the National Accounts. In appendix 1, a list of the 130 industries is presented. But in addition to the 130 industries, 5 rows of household use of the 40 energy carriers are included.

Although, the energy balances at the national level are fully balanced with regard to production, exports and imports of energy, we have decided to concentrate on the domestic use of energy only. There is no meaningful statistical coverage of the flows of energy between regions. Moreover, the exports of energy (electricity) come from a pool common to the producers of it, and there is no way to ascribe deliveries of electricity to certain producers.

1.1 Regions in Denmark

16 or 14 counties? Denmark is divided into 13 counties and 271 local authorities. In some situations, 3 of the largest local authorities, namely Copenhagen, Frederiksberg and Bornholm are treated as counties as well. In this report, we operate with 16 counties as far as it is possible. Thus, in the section on energy, we have data on the 16 counties level, but unfortunately in the section on water we need to aggregate Copenhagen and Frederiksberg Municipalities with Copenhagen County and therefore only operate with 14 counties.

Public responsibilities There is a fine-meshed administrative infrastructure. Official public duties are divided between the state, counties and local authorities. The state is responsible for the usual governmental activities, such as defence, police, the universities and the juridical system. The counties are responsible for the hospitals, the secondary schools, certain environmental activities and public transportation, while the local authorities or municipalities are responsible for the primary schools and the social activities, such as care for the elderly.

1 This report has benefited from funding by the European Commission, GD Environment, by means to the grant agreement no. 200471401007, action 3 for the study entitled "Environmental Statistics and Accounts – Regional Environmental Accounts”.

The figure below shows the placement and names of the counties in Denmark. Also, the figure shows the code numbers normally used as a label for the counties in statistical registers in Denmark. The same code is used throughout this report. Figure 1. Map of Danish Counties

As shown by the map, the counties vary somewhat in size, and the population density also varies as shown by the following table.

Table 1. A few statistical facts about Danish Counties

Code County Area Population Km2 People Total 43,097.9 5,387,071 1 Copenhagen Municipality 88.25 502,362 2 Frederiksberg Municipality 8.77 91,886 11 Copenhagen county 528.26 618,237 12 Frederiksborg County 1,347.44 375,705 13 Roskilde County 891.42 239,049 21 West Zealand County 2,983.77 304,761 22 Storstrøm County 3,398.02 262,144 23 Bornholm County 588.15 43,347 24 Funen County 3,485.84 476,580 31 South Jutland County 3,939.12 252,980 32 Ribe County 3,131.66 224,454 33 Vejle County 2,996.64 358,055 34 Ringkøbibg County 4,853.95 274,574 35 Århus County 4,560.74 657,671 36 Viborg County 4,122.50 234,430 37 North Jutland County 6,173.38 495,068

Copenhagen and Århus are the largest counties with more than 600,000 inhabitants. Most of the remaining counties have between 200,000 and 400,000 inhabitants.

Small country but.. Denmark is quite a small country so it can be questioned if it is worthwhile at all to regionalize our national energy accounts. a lot of variation at the But a number of variables e.g. availability of natural gas and district heating, number county level of cars per capita and types of dwellings etc. varies quite a lot between counties. Furthermore, the central power plants are so large that not every county has one, and agricultural production is quite different in the western and the eastern part of

Denmark due to the quality of the soil. The localization of industries across the country also varies quite a lot as it does in other countries. Some industries need to be close to where sufficiently skilled labour is found and other industries are dependent

on infrastructure, e.g. harbours and the railways. Naturally, emissions of CO2 also vary between counties as they are closely related to energy consumption. The presence of these differences makes it an interesting task to compile energy accounts at a regional level in Denmark.

But, the local authority But why not go one step further down in the administrative hierarchy to the local level too detailed authority level? In a country with only a little more than 5 million people, we think that the local authority level would encompass too many very small units with 5- 10,000 inhabitants. This administrative level is not as well covered statistically as the county level. Furthermore, many of the local authorities would be very similar to their neighbours because they are supplied with energy and district heating from the same sources, the types of dwellings will be equal and so on.

County level is most useful In conclusion, we find that the county level is the only useful regional level for this project because, on the one hand, we see quite some variation between the units at this level and, on the other hand, there is a quite good statistical coverage.

Future possibilities From the beginning of 2007, there will be a completely new regional administrative structure in Denmark. The number of counties will be reduced from 14 to 5 and the number of local authorities will be reduced from 271 to 99. In the future, the new local authorities might be sufficiently well covered statistically so this level can be used for regional accounts.

2 Regional Energy Accounts

The starting point for the establishment of regional energy accounts is the aggregated energy account or energy balance for Denmark.

National energy accounts Statistics Denmark collects and maintains quite large annual databases of energy use organized in energy accounts. Here, input of various energy types are balanced with the use of energy by industry and households.

Schematic of the Energy The Danish Energy Balances are organized in the following way, where supply of and Balance demand for energy is balanced for every year since 1980

------40 columns of energy carriers ------

Production (1 row) Supply Import (1 row)

Input,

Industries (130 rows) =

Input, Demand Households (5 rows)

Inventory changes (1 row)

Export (1 row)

The collection of these data is closely connected with compilation of the national accounts in Denmark. They are organised in such a way that they are directly compatible with the national accounts at the most detailed industry level. They describe the supply and use of energy in value units (DKK) as well as physical units (tonnes or m3 and joule). They keep account of 40 energy carriers, such as oil, gas, coal, gasoline and wood, straw and wind power.

The matrices are balanced so that the supply (production + imports) equals demand (input to industries + input to households + inventory changes + exports). In this project we are not working with the full balanced system, however, but only the inputs to industries and households. The reason for this is discussed in more detail below the table.

Table 2. Aggregated Danish Energy Account 2003.

Crude oil Coal and Gasoline Natural gas Other gas Sustainable Electricity District and refinery furnace coke and other oil energy heating feedstuff etc. products TJ Total 368,259 239,193 311,316 478,992 16,138 92,966 117,392 104,494 Households 0 120109,581 28,522 1,306 9,108 36,731 64,759 Total, industries 368,259 239,073 201,734 450,470 14,832 83,857 80,661 39,735 Agriculture, fishing and quarrying 0 1,022 36,922 29,511 218 3,853 7,389 1,889 Manufacturing 368,259 219,05752,433 85,389 13,632 44,449 33,8657,654 Electricity, gas and water supply 0 18,995 10,044 323,305 86 35,556 1,942 16 Construction 0 06,581 103 121 0 5340 Wholesale and retail trade; hotels, 0 0 16,598 4,122 119 0 14,749 10,180 restaurants Transport, storage and 0 0 65,185 223 499 0 4,279 555 communication Financial intermediation, business 0 0 5,873 2,552 54 0 6,264 6,347 activities Public and 0 0 8,099 5,264 104 0 11,639 13,095

Table 2 shows the Danish national energy accounts for 2003 in an aggregated form. In appendix 1 all 40 energy carriers are listed.

Many sources used to A number of different sources are used in the compilation of the accounts. The compile the National foreign trade statistics are used to measure imports and exports of energy goods. The Energy Accounts amount and value of the production of crude oil and natural gas are determined by Statistics Denmark by means of a small-scale questionnaire-based survey. Consumption of energy in Danish industries is determined by a census, which is described later in this chapter. Information about reimbursement of energy taxes is an important source for the determination of the use of electricity, gas and oil in certain branches of the service industry. Data for determining the size of input in the energy supply sector stems from the Danish Energy Agency. It will also later be discussed in more detail later.

Supply and use of energy At the national level the energy accounts are balanced. It means that all flows of energy are accounted for. On the supply side, we find production and extraction in Denmark, along with imports of energy. On the use side, we have input to industries and households, together with exports and inventory. However, at the regional level, we are not able to account for imports and exports of energy. Exports of e.g. electricity are shipped through cables from Denmark to Sweden and Germany and it would not make very much sense to attribute these exports to one of the counties. Neither do we have very much knowledge about the internal Danish imports and exports of energy between counties. The companies that distribute energy inside Denmark are not bounded by the limits of the counties, so they have no reason to compile statistics at the county level. So when we look at supply of energy, we are not able to distinguish between energy produced in Denmark and imported energy, but for analyses of, e.g. emissions, it does not really matter. As regards the use of energy we are only concerned with the amount of energy actually used in Denmark.

Method The general methodological approach for the project is top down. The national energy account is used as a fix point and a way to spread these data to the regions is sought. One basic condition for the project is that the regional accounts must aggregate exactly to the national environmental account. We will obtain that by using the following methods:

– Disaggregation of the national matrix to the regional level by using appropriate indicators and keys.

– Putting together relevant statistics at the regional level and then subsequently balance the sum of the regional matrices against the national ones to obtain a match.

Both these methods will achieve the goal of equality between the sum of the regional accounts and the national account.

Keys and indicators The approach in the project is to locate additional statistical sources, which carry information about the regional distribution of either the specific cell in question (industry * energy carrier) or an indicator, which is as closely related to the cell in question as possible. The absolutely most common case is the latter. Thus, the regional economic accounts will be an important source for generating distribution keys for industries not covered by specific energy statistics, and statistics on housing and connected heating can be used as a location indicator for use of different types of energy carriers in households.

2.1 Data sources for energy accounts

Statistics for generating As mentioned earlier, there are 135 rows in the matrix that we will try to regionalize. keys So a major task in the project is to search for statistics suited for generating the keys that we need. The first place that we looked at was in Statistics Denmark’s own portfolio of statistics, where we have found a number of usable statistics. Secondly, we have gone through statistics from other governmental bodies, e.g. the Energy Authority, Environmental Protection Agency and other similar places. In addition to that the Internet is very useful in locating private institutes and associations that have put together statistics on specific areas of interest for their members or costumers.

Through the search for ways to generate keys it has become obvious that it seems relevant to group the industries in six groups

Table 3. Best available statistical sources for generation of regional distribution keys for groups of industries and households

Group Industries General description Best available regionalized statistics 1 1-5 Agriculture and fishing Intermediate input in Regional Economic Statistics 2 6 Extraction of crude oil and gas Cannot be attributed to a specific county 3 7-62 Manufacturing industries Census on use of energy in manufacturing industries Regional Economic Statistics 4 63-65 Supply of electricity, gas and heating Census on production of energy (including inputs used) 5 66-130 Trade and service industries Survey of use of energy in trade and service Employment in Regional Economic Statistics Car ownership statistics with regional dimension 6 131-135 Households Housing Statistics (with type of heating) Car ownership statistics with regional dimension

In the text below, there is some more argumentation leading to the conclusions reflected in table 3.

Agriculture and Fishing All industries are statistically covered some better than others. Group 1 “Agriculture and Fishing” accounts for as much as 20% of total energy consumption in Danish industries. However, it has not been possible to locate statistics describing the regional distribution energy use in agriculture and fishing directly. What we do know on a regional basis is the total intermediate input in these industries as it is reflected

in the regional economic statistics published by Statistics Denmark. Therefore, energy in these industries is distributed in agreement with the distribution of total inputs. Thus, it builds on the assumption that energy input in proportion to total input is the same all over Denmark. The only problem about this is that there is a clear tendency that agriculture in the western part of Denmark is more animal-oriented, and more based on crop in the eastern part, because of the different character of the soil. So if animal production is more energy intensive, we might be getting slightly biased results for the distribution of agricultural energy consumption. In this report, however, this is not considered to be a serious problem.

Oil and gas extraction The extraction of crude oil is the second group. It takes place in the North Sea and it industry cannot, therefore, be attributed to any particular county. For the sake of this and a few other items, we have included an extra non-existing county, where this and other non-distributable goods and services are placed.

Manufacturing industry Group number 3 is manufacturing industry, and here we have a comprehensive statistical coverage. In the next section, we review the primary source related to this area.

2.1.1 Census on the use of energy in manufacturing industries

Now we turn to the third group in table 3 above, namely manufacturing industries. Since 1980 Statistics Denmark has conducted a census on consumption of energy in manufacturing industries. The census has been carried out 12 times during the last 25 years, so it is not annually based and the data are not readily comparable between all years. Its purpose is to provide data on the volume and composition of energy used by manufacturing industries. The survey forms an important part of input to the total energy statistics at Statistics Denmark also known as the Energy Balance Sheets. The National Accounts Division and Environmental Accounts Unit at Statistics Denmark use, to a great extent, these balance sheets, and they are also used by a number of external users.

The census covers all local kind-of-activity units (LKAU’s) among companies in the manufacturing industry with more than 20 employees. A company with more than 20 employees may include a number of work units with less than 20 employees, but they are included in the survey as well. It may involve storehouses, regional distribution units and so on.

The LKAU’s asked accounts for approximately 90% of total energy consumption in manufacturing industry. The last 10% from small companies are estimated in the enumeration process.

One of the background variables in the census is the local authority code (271 different codes), which can be translated directly into the county codes that are displayed in table 1.

Almost all energy sources are covered as it can be seen from the following list of variables.

Table 4. Energy carriers included in the census on manufacturing industries

Main category Sub categories Solid fuel Hard coal (tonnes) Furnace coke, coke and brown coal (tonnes) Fuel wood, sawdust, straw inclusive of own production (tonnes) Waste, including paper, cardboard and wood (tonnes) Liquid fuel Motor gasoline for registered vehicles (m3) Other gasoline products, e.g. tax free gasoline (m3) Diesel oil for registered vehicles (m3) Gas oil and other diesel oil products (m3) Heavy fuel oil (tonnes) Petroleum coke (tonnes) Waste oil (tonnes) Refinery gas (tonnes) Gas Auto gas for registered vehicles (tonnes) Other liquid gas products, e.g. LPG, bottled gas (tonnes) Natural gas (1,000 m3) Town gas (1,000 m3) Biogas (1,000 m3) Electricity Purchase of electricity (kWh) Own production of electricity (kWh) Own consumption of self produced electricity (kWh) Sale of own production of electricity (kWh) District heating Purchase of district heating (GJ/m3/MWh) Own production of district heating (GJ/m3/MWh) Own consumption of self produced district heating (GJ/m3/MWh) Sale of own production of district heating (GJ/m3/MWh)

In recent years, it has become more common for companies to produce own electricity and heating, and it has to be taken into account when we form a picture of energy consumption. That is the reason why the LKAU’s have been asked quite detailed questions about their own production as reflected in table 4 above.

It is acknowledged that there are problems for some companies in reporting at this detailed level. One problem is that it can be difficult to distinguish between different types of oil. It has been dealt with to some extent in the compilation of the energy accounts.

All of the information gathered this way is in physical units and it is labelled as fuel consumption, but normally we like to measure it as energy consumption in giga joule (= 109 joule), which requires conversion from fuel use to energy consumption according to the following table compiled by the Danish Energy Agency.

Table 5. Heating values of various energy carriers

Amount Energy carrier Heating value (GJ) 1 tonne Hard coal 26.5 1 tonne Furnace coke, coke and brown coal 29.3 1 tonne Wood pills 17.5 1 tonne Fuel wood, sawdust, straw 12.6 1 tonne Waste (including paper, cardboard and wood) 14.7 1 tonne Heavy fuel oil 40.4 1 tonne Petroleum coke 31.4 1 tonne LPG 46.0 1 tonne Refinery gas 52.0 1 m3 Petrol 32.9 1 m3 Other petrol and petroleum products 33.3 1 m3 Gasoil 35.9 1000 m3 Town gas 16.7 1000 m3 Bio gas 23.5 1000 m3 Natural gas 39.9 1 MWh Electricity 3.6

A unique identification number is reported on all work units. With this number it is possible to draw background information from other registers. Thus, through a match by identification numbers in the energy survey and the business register, it is possible to identify, e.g. number of employees, total turnover and, naturally, the geographical location attached to the companies that have reported data to the energy survey. Statistics are published no later than eight months after the end of the reference year.

Manufacturing industry With this large census in hand on which the energy accounts are actually built, we well covered should be well covered, as far as the manufacturing industry is considered. It should be a straightforward task to distribute the national numbers on manufacturing industries to the counties. But now let us look at the most important industries, namely the energy supply.

Sources for energy supply The most obvious source for distribution of the input into energy production is the annual census on energy producers

2.1.2 Census on energy producers

Once a year the Danish Energy Authority carries out a census on the amount of energy produced by energy producers connected to the public net. Power as well as heating is covered. But what is more important for the present project is that the producers are also asked to report the input of energy used for production of electricity and district heating.

In addition to the usual producers of electricity and district heating the census covers a lot of different producers, e.g. prisons, schools, sewage treatment facilities etc. These units have only small but nevertheless measurable contributions to the public net. In counties with no major energy producers these units can have some effect on the totals.

A code for regional location is among the background variables in this census, so it is possible to regionally distribute the input of energy required by these operators. This census is also used by statistics Denmark to compile the National Energy Accounts. In conclusion this census must be said to be a very useful source in this project as well.

Survey on trade and Finally, the last 64 industries out of the 130 are trade and service industries, and we service will review the most obvious source for their distribution.

2.1.3 Survey on the use of energy in trade and service

Statistics Denmark has twice conducted a survey on the consumption of energy in trade and service industries. This survey is much smaller than the above-mentioned census for the manufacturing industries and is based only on a sample of the relevant work units. Also the number of types of energy is reduced, but since fewer types of energy are relevant in the trade and service sectors than in manufacturing it should not be a problem.

Naturally, also here information is given about the regional location of the work-unit responding to the questionnaire.

Survey 2002 The first of the two surveys was conducted in 2002 in a preliminary form. Only very aggregated results were published. However, some experience was gathered from this survey, and it was utilized in preparing the newest survey.

Survey 2005 In the spring of 2005, a new survey was carried out, gathering information about the year 2004. The survey was carried out on the basis of a sample of 5000 LKAU’s. The sample was selected on the basis of companies with more than 5 employees and then all work-units in these companies were asked to answer the questionnaire. Only 4205

of the work-units had an explicitly measured consumption of energy. The enumeration to the full population was carried out on the basis of regression estimates made with SASClan software.

Sample too small for However, this survey is quite difficult to use as a source for dividing the energy regionalizing consumption in the trade and service industries between counties. The reason for this is basically that the sample is too small. When the sample was chosen from the population it was done on the basis of the 130 industries, and the enumeration factor is based on employment in the particular industries. If a subdivision of the 16 counties should have been included the size of the sub-sample should have been particularly larger.

It would be possible with a special effort to construct some regional figures on the basis of the approximately 4000 answers, but they would not be very reliable. Using regionally distributed employment figures, one could distribute the total energy consumption in those industries. Although, the reported figures from the 4205 answers are not an adequate representative of the full population, it would even be possible to make some limited use of them, because they do have a regional code attached to them.

The survey is not really So we must conclude that this survey is not really usable for regional distribution. useful as a source for this Instead, we must turn to the Regional Economic Accounts that are published by projectl Statistics Denmark.

2.1.4 Regional Economic Statistics

Statistics Denmark produces regional economic accounts as a part of the National Accounts. Since 1999, the Danish regional accounts have been compiled in full accordance with SNA93 and ENS95. The Danish version is fully comparable with accounts in other European countries. The regional distribution is done in accordance with the principles in NUTS III, which resembles the Danish counties. At higher levels, Denmark becomes one single region. The statistics are primarily based on the same sources as the ordinary national accounts, here in a regionalized version though. In the regional accounts information is available at both current and fixed prices for the following variables:

• Production • Intermediate input • Gross value added

Furthermore, a number of variables are accounted for in current prices only

• Other production taxes and subsidies, net • Wages • Gross surplus of production and mixed income

Finally, the number of employees and the number of employed people are published and also the Gross Domestic Product in total and in a per capita is published.

Indicators for agriculture As it was mentioned earlier, there is not really any background data available for etc. distributing the energy consumption in the industries 1-6, which are “Agriculture and Fishing”. Therefore, it has been decided to use the data on “Intermediate input” from the regional economic accounts, because energy is a quite important part of the total input in production. For agriculture it is close to 20%. Thus, “intermediate consumption” is considered to be a better indicator for the energy input than, e.g. employment.

Indicators for trade and The opposite situation occurs in relation to the trade and service industries, where service energy plays a less important role. Use of energy is mostly related to heating, lighting,

power for computers and is as such quite closely connected to the number of people employed. Therefore, employment is considered to be a better indicator for the use of energy in these industries than intermediate input is.

2.1.5 Data about regional distribution of energy consumption in households

There is only scattered and sporadic information about the actual regional distribution of the energy use that takes place in households. There are no official statistics about consumption of heating and electricity by county or local authority. Also, there are no regionalized statistics about use of gasoline for motor vehicles.

Consequently, in the absence of a direct statistical description of the energy consumption by households, we need to look for indicators in the form of statistics that cover the states and processes for which we know require various types of energy as input.

Since the size of the consumption of energy by households is considerable, compared to the total energy consumption, it is worthwhile to spend some time looking for appropriate indicators for distribution among the counties. Thus, what we are able to get is indirect statistical information such as indicators, which can be used under some assumptions which we will return to later.

2.2 Method for energy accounts

The method in this project is really straightforward. We use direct or indirect sources of data as keys to distribute the national data to the 16 counties (plus 1 county for the non-distributable data). Thus, for every single cell in the matrix, we are (in principle) looking for the best available key for the distribution. The key for the single cell must fulfil the following equation

17 17 = = ⋅ X ij ∑ X ijk ∑ X ij vijk (1) k=1 k=1

where Xij is the consumption of energy carrier j in industry i and Xijk is the consumption of energy carrier j in industry i in county k. The keys vijk are the ones we are looking for. They can be generated directly on the basis of statistics concerning the particular cell in question or indirectly on the basis of statistics describing matters that are as closely related to the cell in question as possible. It should be obvious that for each county the key must sum to one over the counties in order to get a complete distribution

17 = ∑vijk 1 (2) k=1

It also means that to get the regional energy accounts we just use the formula

= ⋅ X ijk X ij vijk (3)

This is a top-down approach that secures consistency in all directions as described by the following identities

135 135 17 = ∑∑∑X ij X ijk (4) iik===1 1 1

where Xij is the consumption of energy carrier j in industry i and Xijk is the consumption of energy carrier j in industry i in county k.

Equation (4) shows that the sum of the 40 regional energy carrier column sums equals the column sums in the national matrix. In the other dimension we have completely the same picture

40 40 17 = ∑∑∑X ij X ijk (5) jjk===1 1 1

Now equation (5) shows that the row sums of the 135 industries and consumers energy consumption in the regions are equal to the row sums in the national matrix. This project has been worked out with the aim to secure this consistency.

In the following sections, we will dig a little deeper into how the keys vijk are generated for the cells in the various sub-sections of the national energy matrix.

2.2.1 Agriculture and Fishing etc. (industries 1-5)

As mentioned previously, there are no available direct statistics in this sub-section, so we use a regionally distributed vector of intermediate input as the key. A drawback related to this method is that we only have a common vector of intermediate inputs that we have to use for all 40 columns of energy carriers. Formally, it just means that we get

17 = ⋅ X ij ∑ X ij vik (6) k=1 where there is no j in the key. Thus, this calculation is based on the assumption that the regional distribution of the use of all energy carriers is the same. There is no doubt that this is an erroneous assumption, but we can do no better for the moment without initializing a further study of the matter.

2.2.2 Extraction of crude oil and gas (industries 6)

The extraction activities take place in the North Sea, and they are not related to a specific county. Therefore, a “dummy” county no. 17 is created and the key is one in this county, and zero in all other counties.

2.2.3 Manufacturing industries (industries 7-62)

The basis for the generation of keys related to manufacturing industries is the census discussed above. For this project the basic SAS-data set was acquired from the energy unit at Statistics Denmark. Since this data set is the basis for the compilation of the energy accounts, the strategy was to use it to recreate the national energy accounts and then to have the regional dimension in addition to that, because it is “built into” the data from the beginning.

However, it turns out, that the original data are further treated and improved in the process of compiling them into the energy accounts. Energy consumption is moved between energy carriers and between industries on the basis of more reliable additional statistics. So it is not possible to recognize all of the original data in the final energy balances. So we were faced with an aggregation of the new regional matrices that were unlike the official matrix in a number of cells. So the situation was that

17 ≠ new X ij ∑ X ijk (7) k=1

for a number of cells, where Xnew is data directly from the census data set. In principle, we were ready to accept this but we could not accept the fact that the column and row sums did not match the official ones. Then we scaled the data drawn from the original census data set so that at the aggregated level the column sums were equal to the column sums in the official matrix. But, it was not possible to match the row sums at

the same time. So what we did then was to create keys vijk in the following way

X new = ijk vijk 17 (8) new ∑ X ijk k=1

Then the key generated in (8) could be used in a new calculation to regionalize the aggregated national energy accounts.

X new =⋅ijk =⋅ XXijk ij17 Xv ij ijk (9) new ∑ X ijk k =1

So in this way we can generate keys on the basis of the census data to regionalize the national energy accounts despite the fact that the census data and the energy accounts data are not really concurrent.

The last remaining problem was then that for some 15 cells we did not have a vijk key, because in the original data set these cells were empty, even though these particular cells are not empty in the official energy accounts matrix. The solution for these cells was to copy a key from another cell that resembles the one in question in the best possible way.

Table 6. Substitutes for missing keys

Key missing in the ”new” matrix Substituted with [15,4] LPG in ”Bakeries” [75,4] LPG used in “Retail trade with food” [15,5] LPG, other in ”Bakeries” [75,4] LPG used in “Retail trade with food” [26,5] LPG, other in ”Printing activities” [26,4] LPG used in ”Printing activities” [63,5] LPG, other in ”Electricity production” [63,9] Gasoline used in ”Electricity production” [15,9] Gasoline in ”Bakeries” [75,9] Gasoline used in retail trade with food [30,9] Gasoline in ”Manufacture of pesticides” [30,31] Electricity in ”Manufacture of pesticides” [42,9] Gasoline in ”Steel production” [42,31] Electricity in ”Steel Production” [15,14] Gasoil in ”Bakeries” [75,14] Gasoil in “Retail trade with food” [15,15] Gasoil, transp. in ”Bakeries” [75,15] Gasoil, transport in ”Retail trade with food” [30,15] Gasoil, transp. ”Manufacture of pesticides” [30,31] Electricity in ”Manufacture of pesticides” [42,15] Gasoil, transp. in ”Steel production” [42,31] Electricity in ”Steel Production” [15,18] Heavy fuel oil in “Bakeries” [75,18] Heavy fuel oil in “Retail trade with food” [76,18] Heavy fuel oil in “Department Stores” [76,31] Electricity in “Department Stores” [15,31] Electricity in “Bakeries” [75,31] Electricity in “Retail trade with food” Note: The numbers in the cells are addresses of the particular cells in the matrices

There might be better substitutes for some of the missing cells, but the fact that they are actually missing might indicate that these are not the most important cells in the system. So the possible error that is made by not choosing the best substitute may not be very serious in a broader perspective.

Actually, industry number 62 “Recycling of waste and scrap” is not a part of the survey on manufacturing industries. We do not have data available to construct special keys for this industry, so it has been decided to regionalize the national data

with the assistance of the regional economics statistics as with for the industriess 1-6 above.

2.2.4 Energy supply industries (industries 63-65)

For the generation of keys for this group of industries we have the good census data set on Energy Producers from the Danish Energy Authority. In the dataset, the respondents report their production of electricity and heating and what amount of 21 different energy carriers they have just to generate their energy output. The Danish Energy Authority has provided every record in the dataset with a key that links a specific amount of inputs to production of heating and another specific amount is linked to the production of electricity.

So for every record (every energy producing unit in Denmark) we know the amount of each of the 21 energy carriers they used in 2003, and we know in which county this consumption took place.

We also know from the census in which of the 130 industries they consider themselves to be. In the census on energy producers, the respondents are asked to classify their activity according to the Danish Industry Classification DB93. Almost all of the respondents have classified their unit according to their main activity, which can be agriculture, marked gardening, various kinds of manufacturing and so on, because energy production is only a by-product for them. However, it is necessary to classify production of energy in the industries where it rightfully belongs. According to the classification system used by the Danish National Accounts, energy production takes place in one of the two (three with gas production) energy producing industries and not in any other industry, even though the main activity of many of the energy producing units is not energy production.

Thus, all activities should be classified as belonging to one of the following two industries

401000 “Production and distribution of electricity” 402000 “Steam and hot water supply”

Many of the units in the census produce both electricity and heating to the public net, so it has to be decided to which of the two industries the energy input should be ascribed.

To help with this question we turn to another variable in the data set, namely “Type of production plant”. There are six different types available in the questionnaire

– Central works – Decentralized works – Business works – District heating – Local works – Unknown type of works

When the data is sorted into these 6 categories some kind of pattern emerges. Now some of the aggregated figures from this census can be recognized in the national energy accounts. The reason why this is interesting is that there is not full correspondence between the 21 energy carriers in this census and the 40 energy carriers in the national energy accounts, so the more figures that are equal between the two sets of data the easier it is to handle the remaining differences. So what we have now is the following line of calculation

Input used for energy production

App. 2000 records * 21 energy carriers

Application of key for distribution of inputs between electricity and district heating by record

Aggregation of records by county

Input used for electricity production Input used for heating production 16 counties * 21 energy carriers 16 counties * 21 energy carriers

Now we can generate the keys for the regional distribution of the use of the 21 energy carriers for production of electricity (industry 63) and for production of district heating (industry 65).

X new X new 63 j21k 64 j21k = = (10) v63 j21k 16 and v64 j 21k 16 new new X 21 X 21 ∑ 63 j k ∑ 64 j k k=1 k=1

where the j21 index is just to differentiate it from the j index with 40 energy carriers used elsewhere. As mentioned before, the 21 energy carriers in the census cannot be directly matched with the 40 energy carriers we have in the energy accounts. But the problem is not as serious as it may seem, first of all because only 16 out of 40 columns are non-empty in industries 63 and 65, so we only need 16 different indicators. Furthermore, some of the 21 column sums are identical to the numbers in industries 63 and 65 in the aggregate energy accounts so the keys can be used directly in these cases. For the remaining cells in industries 63 and 65 that are not directly covered by a key, we can find column sums or combinations of column sums from the 21 columns that come quite close to the values in industries 63 and 65 so we can use the keys from these cells as quite competent indicators of the real values.

There is input of electricity in the production in all three industries electricity, gas and district heating despite the fact that nothing is reported in the survey. So for these three cells we apply the vector of regionalized economic intermediate input.

Industry 64 is the gas industry. It uses 7 different energy inputs for producing gas. However, it is not only production of gas but also the distribution of it. So for maintaining the net of natural gas pipelines, there is a need for electricity, gasoline and that kind of stuff. However, it has not been possible to regionalize this consumption, so it has been put in the “99th county.

2.2.5 Trade and service industries (industries 66-130)

As we cannot use the trade and service industry survey for regionalizing the energy consumption in industries 66-130 we must turn to the economic regional accounts. This time we choose the regional distribution of employment as our indicator key.

= ⋅ X ijk X ij vik (11)

where vik is the vector of county i’s share of total national employment

2.2.6 Households (industries 131 – 135)

Households use quite a large portion of the total energy supply. Households need energy input for heating and as power supply for various electrical appliances. As mentioned in the data section there is not really any official statistics about this consumption, so we must look for various indicators.

In total, there are 16 cells with positive values in the 5*40 = 200 cell sub matrix with energy consumption by households. We will discuss four of these indicators

• Distribution of the stock of motor vehicles as an indicator for use of gasoline and diesel • A weighted distribution of dwellings as an indicator for the use of electricity • Distribution of town gas, statistical information from three Danish cities • Heating of dwellings as indicator for the use of the 13 remaining energy carriers.

Unfortunately, there are no statistics available about the consumption or sale of gasoline and diesel oil on a regional basis. It might be possible to get some information from NERI (National Environmental Research Institute) about the transportation habits of Danes specified on a regional basis. But it has been considered too time-consuming for the present project to try to translate information into usable indicators. Instead, it has been decided to use statistics showing the regional distribution of car ownership.

A key of regional shares was calculated, and then it was multiplied by the total national consumption of gasoline. So it is assumed that cars only drive in the county where they belong. That is obviously wrong, but the question is how big a mistake it is. The counties with big cities may be underrepresented. Copenhagen municipality has a quite small amount of cars per inhabitant, but on the other hand Copenhagen has a lot of traffic coming from the suburbs. So probably the consumption of gasoline in Copenhagen Municipality is larger than its stock of owned cars indicates. Statistics about the regional distribution of dwellings measured in m2 was used to distribute the total energy consumption. Thus it is assumed that the larger the dwelling the more electricity is used in it.

The quite insignificant column with “Other gas” is a category that has diminished a lot over the years. The town gas has been replaced by either district heating or natural gas. We find some information about it in the statistics on heating of dwellings, and only three towns have reported substantial use of it.

Information about regional distribution of dwellings and how they are heated is an important first step towards a regional distribution of total household consumption of energy. At www.statbank.dk/bol1 a table with the title “Occupied dwellings by region, type of dwelling, tenure, heating and number of rooms” can be found. With aid from this table we can distinguish dwellings and their type of heating by region. The number of dwellings with ovens installed is used to distribute the supply of wood and wood pills, and the distribution of dwellings heated with natural gas is used to distribute the use of natural gas by county. And the same method and same statistics has been used to generate indicators for some of the other energy input in households.

2.3 Results for energy accounts

The basic form of the data that is a result of this work is, of course a three- dimensional matrix with 135 rows, 40 columns split by 16+1 counties. That is a lot of numbers but we will show only aggregated tables along with the most important and interesting results in the text. Refer to the appendices or the authors of this report for more detailed information.

Results are presented both in actual levels and on a per capita basis, because it is often more relevant to compare counties this way. But, of course, there are cases where it is important to look at just the total for a county and to compare totals between counties.

2.3.1 Industries

The consumption of energy by Danish industries varies very much county by county. The reason is that Denmark is a small country and the central units producing power and refining crude oil are so large that there is only room for a few of them and they dominate the picture to a large extent.

Table 7. Energy Consumption by Danish Industries. 2003.

Crude oil Coal and Gasoline Natural gas Other gas Sustainable Electricity District and furnace and other energy heating refinery coke etc. oil products Total feedstuff Terra Joule Total 1,728,750 368,259 239,193 311,316 478,992 16,138 92,966 117,392 104,494 1 Copenghagen Municipality 115,856 0 21,548 38,099 20,492 860 8,797 9,735 16,325 2 Frederiksberg Municipality 6,554 0 6 2,192 291 129 2 1,349 2,585 11 Copenhagen County 97,513 0 12,797 41,963 12,087 453 6,409 12,288 11,515 12 Frederiksborg County 40,643 0 43 15,224 13,601 180 1,526 5,807 4,263 13 Roskilde County 27,264 0 60 9,153 5,367 106 5,403 3,850 3,324 21 West Zealand County 315,484 205,918 56,999 22,373 6,934 7,106 4,475 7,172 4,507 22 Storstrøm County 38,388 0 1462 14,257 5,396 155 8,732 5,144 3,241 23 Bornholm Municipality 5,583 0 731 2,493 94 17 857 830 561 24 Funen County 83,486 0 18,384 21,092 15,986 197 8,138 10,147 9,541 31 South Jutland County 71,695 0 31,678 13,613 10,514 104 6,384 5,879 3,522 32 Ribe County 60,375 0 22,337 14,328 6,945 127 5,886 6,150 4,602 33 Vejle County 232,048 162,342 499 22,068 22,327 5,684 3,629 9,459 6,041 34 Ringkøbing County 50,017 0 378 16,086 12,922 158 7,171 7,922 5,380 35 Århus County 117,200 0 36,787 29,238 10,397 377 11,377 13,273 15,751 36 Viborg County 39,159 0 48 14,181 10,326 166 4,876 6,046 3,515 37 North Jutland County 115,458 0 35,435 33,148 15,164 304 9,304 12,283 9,821 99 Not distributable 312,027 0 0 1,806 310,150 14 0 57 0

West Zealand is the county with the largest input of energy. The paramount reason for this is the localization of one of Denmark’s two refineries along with a major power plant in this county. Likewise, Vejle County is huge in energy consumption due to the large refinery in Frederecia. The second column concerning coal etc. has large numbers for some counties and almost nothing for other counties. This is due to the fact that large power plants that use coal are producing in some counties and not in others. The last row is the non-distributable. The major part of this input is the natural gas from the North Sea that is distributed further out in Denmark.

A large number of different tables can be made on the basis of dataset created so far. As an example a surprising picture can be obtained when looking at consumption of “Natural gas” in “Agriculture etc.”

Figure 2. Consumption of Natural Gas in the industry “Agriculture etc.”

3000

2500 Natural gas 2000

1500

1000

500

0 1234567891011121314151617 County

Marked Gardening Obviously this industry does not use very much natural gas except for the two counties 9 (Funen) and 14 (Århus). In the first case we are dealing with a number of large “Marked Gardening” companies , which are situated on the island Funen close to the main pipeline of natural gas through Denmark.

The other very high bar in Århus County is due to one company producing salt, through a process of that requires a lot of energy.

Figure 3. Distribution of total Energy Consumption in Denmark 2003 by county.

This picture shows the same results as table 5 organized in a geographic presentation form. For technical reasons it has been necessary to add the two municipalities Copenhagen and Frederiksberg and Copenhagen County to one unit Therefore the Copenhagen group is now almost as large as the two largest counties.

2.3.2 Households

The Danish households are accountable for about 15% of the total direct energy consumption in Denmark, which is what we are dealing with in this project. In the table below, the head of the table i.e. the columns, have been slightly changed when compared to the tables above for the total economy. The reason is that this

aggregation is of greater interest in relation to households than the previous aggregation. There are basically three groups of energy consumption by households,

– gasoline/diesel oil for motor vehicles, – electricity for various appliances in the home – heating of the homes.

Of course, some people may still use electricity for heating purposes, but it is probably of minor importance. This is reflected in the table.

Table 8. Energy Consumption by Households. 2003.

Gasoline Other oil Natural Other gas Wood and Straw Electricity District Total and diesel and coal gas wood pills heating oil products Terra Joule Total 249,042 78,544 30,072 28,522 1,305 4,395 4714 36,731 64,759 1 Copenghagen Municipality 21,059 4,732 1,393 0 698 138 0 2,823 11,274 2 Frederiksberg Municipality 3,849 849 348 0 121 2 0 504 2,024 11 Copenhagen County 29,384 9,597 3,518 5,753 252 243 20 3,867 6,133 12 Frederiksborg County 17,217 6,154 2,255 3,468 55 385 151 2,384 2,365 13 Roskilde County 11,106 3,507 1,460 2,717 13 213 106 1,554 1,535 21 West Zealand County 14,489 4,667 2,492 2,194 34 788 397 2,116 1,802 22 Storstrøm County 13,078 4,030 2,377 1,367 37 1,088 328 1,980 1,871 23 Bornholm Municipality 2,133 639 567 0 1 139 85 365 337 24 Funen County 22,516 6,797 2,489 2,896 14 334 500 3,524 5,962 31 South Jutland County 12,320 4,049 1,438 2,226 8 113 385 1,870 2,231 32 Ribe County 10,185 3,349 950 895 1 54 270 1,616 3,050 33 Vejle County 16,850 5,435 1,993 2,451 14 163 352 2,476 3,965 34 Ringkøbing County 12,266 4,274 1,456 420 2 56 430 1,960 3,668 35 Århus County 28,094 9,306 2,586 1,411 13 290 510 4,327 9,650 36 Viborg County 11,790 3,789 1,879 1,572 8 176 476 1,755 2,135 37 North Jutland County 22,705 7,369 2,872 1,151 32 212 703 3,611 6,756

The table shows huge differences between the counties. It appears from the table that the largest amount of district heating is consumed in Copenhagen, Ålborg, Funen and Århus counties, which is where the four largest cities in Denmark are. The district heating is clearly something, which is connected with the large cities, because it keeps down the costs per joule of heating delivered, the closer together the costumers are situated. On the contrary, some of the most “rural” and densely populated counties, e.g. Storstrøms County, have a large consumption of straw and wood and wood pills.

Per Capita In a number of instances it is useful to know the actual size of energy consumption like shown in table 8. However, for many analytical purposes it is more enlightening to see these numbers on a pr. capita basis. The following table displays the same as table 8 except for the fact that the numbers in each cell in the table is divided by the number of inhabitants in the county in question.

Table 9. Pr. Capita Energy Consumption in Households.

Gasoline Other oil Natural Other gas Wood and Straw Electricity District Total and diesel and coal gas woodpills heating oil products Mega Joule / person Average 46,045 14,515 5,579 5,271 241 812 871 6,788 11,967 1 Copenghagen Municipality 41,987 9,420 2,772 0 1,390 274 0 5,620 22,443 2 Frederiksberg Municipality 41,954 9,242 3,782 0 1,320 25 0 5,484 22,031 11 Copenhagen County 47,553 15,524 5,691 9,306 407 394 33 6,255 9,920 12 Frederiksborg County 45,835 16,379 6,002 9,231 148 1,024 401 6,346 6,294 13 Roskilde County 46,473 14,671 6,108 11,366 56 893 444 6,499 6,423 21 West Zealand County 47,551 15,313 8,177 7,200 111 2,585 1,302 6,943 5,912 22 Storstrøm County 49,901 15,373 9,066 5,216 143 4,149 1,251 7,553 7,139 23 Bornholm Municipality 49,223 14,749 13,078 0 28 3,206 1,972 8,422 7,764 24 Funen County 47,261 14,262 5,222 6,077 30 702 1,049 7,395 12,510 31 South Jutland County 48,711 16,005 5,686 8,798 33 445 1,523 7,391 8,818 32 Ribe County 45,393 14,920 4,230 3,989 4 242 1,205 7,198 13,590 33 Vejle County 47,078 15,180 5,567 6,846 39 455 982 6,916 11,075 34 Ringkøbing County 44,685 15,567 5,302 1,531 6 204 1,567 7,137 13,360 35 Århus County 42,743 14,150 3,933 2,145 20 442 775 6,580 14,673 36 Viborg County 50,305 16,163 8,016 6,704 36 751 2,031 7,486 9,108 37 North Jutland County 45,880 14,884 5,801 2,324 65 428 1,420 7,293 13,646

Columns 2-6 and 8 comprise almost all of what is used by households for heating of dwellings, and if we add them together, we will see a set of quite equal numbers. It means that we need to heat our homes no matter what method we have at hand

Figure 4 Sources of heating in households

30000

25000

20000 District heating Straw 15000 Wood and wood pills 10000 Other gas

Mega joule / person Natural gas 5000 Other oil products 0 1 3 5 7 9 111315 County

County number 8 is Bornholm, which is a small island quite remote from the mainland of Denmark. At Bornholm dwellings are not heated more or less than in other parts of Denmark, but there is no supply of “Natural Gas” at the island, which is why the use of “Other Oil and Coal Products” per capita is the highest in the country. Also the use of “Wood and Wood Pills” per capita is among the highest in the country.

County number 7 “Storstrøms Amt”, which is situated at the bottom of Sealand shows some of the same tendencies as Bornholm. Natural Gas is not very widely spread here and that is probably the reason why the use of “Other Oil Products” as well as “Wood and Wood Pills” per capita is among the highest here.

Copenhagen and Frederiksberg Municipalities are very different from the rest of the country in respect to heating. Here, “District Heating” is by far the most dominating source of heating. These are also almost the only counties where we can still find the category “Other Gas”, which is used for heating and cooking. It has been phased out in most other cities.

Gasoline and diesel oil Table 9 also reveals that energy used for driving private motor vehicles is very evenly distributed across counties except for Copenhagen and Frederiksberg, which are somewhat lower. We must bear in mind that these numbers are based on statistics about car-ownership and not actually the amount of gasoline used. In Copenhagen and Frederiksberg fewer people, in particular, own a car. If we were able to measure the actual amount of gasoline used and where it was used, we would probably see higher consumption in the more remote counties, where many people must commute to larger cities to work. On the other hand, consumption could also be higher in the cities because there are not many car-owners, but a lot of people go there to work. So in all, it may not be very different from the picture we get with the suggested method.

2.4 Summary and conclusions about energy accounts

The aim of this section was to be able to regionalize the aggregated Danish energy accounts at the county level. The method has been to find indicators for - in principle – all cells in the original matrix. A lot of efforts have been put into finding the best available indicators. There are surveys that help to distribute energy consumption by manufacturing industries, energy supply industries and households in a satisfactory way. But for “Agriculture etc.” and “Trade and service industries” we needed to use general economic indicators, which is a non satisfying solution. However since these industries (except from agriculture) do not mean very much in the general picture we got some quite good results. A large variety between the counties was revealed in the results.

3 Regional air emissions accounts

At Statistics Denmark the total emissions of 8 different substances such as CO2, Nox and SO2 etc. are calculated by multiplying the energy matrix by a set of emission factors that are obtained from the Danish National Environmental Research Institute (NERI). Thus, in determining the air emissions it is important to get the emission factors right. The emission factors reflect the technology used for the combustion of the fossil fuels. The emission factors generally correspond to the factors available in the CORINAIR (COoRdination of Information on AIR emissions) database. The emission factors from NERI are all connected to technical conditions, e.g. size and type of combustion plants. In order for Statistics Denmark to use those emission factors, it has been necessary to allocate and to some extent assume which 130 industries use what kind of combustion plants. Thus, Statistics Denmark creates for each year a matrix with the same dimension as the energy matrices containing the associated emissions factors obtained from NERI.

During the time from 1980 to 2001, some changes in the emission factors occurred. These changes were mainly due to changes in combustion technology and legislation.

However, in this project we concentrate on CO2, which has NOT changed its emission factors at all.

3.1 Method for air emissions

Because the emission coefficients are constant for CO2 we can allow ourselves to use the full matrix of regional distribution keys or indicators that were used for dividing the aggregated energy accounts into counties for the emissions as well. At the national level, we have a full matrix of CO2 emissions, and when we multiply this matrix by the regional keys we get 16 matrices of CO2 emissions by county. This is a similar procedure to the one described by equation (9). It is a straightforward method, but only possible because the emissions of CO2 are the same for all uses.

Thus, we cannot create regionalized emission accounts for any other substance with this method. Other substances such as Nox and SO2 would require local emission coefficients because the emissions are dependent on the technology used for production.

3.2 Results for air emissions

Table 10. CO2 emissions from energy use in Denmark. 2003.

Crude oil Coal and Gasoline Natural gas Other gas Sustainable Electricity District and furnace and other energy heating refinery coke etc. oil products Total feedstuff 1000 tonnes Total 68,038 0 23,135 23,451 11,193 1,151 9,108 0 0 1 Copenghagen Municipality 7,337 0 2,084 2,962 1,171 50 1,070 0 0 2 Frederiksberg Municipality 195 0 1 171 14 8 1 0 0 11 Copenhagen County 5,709 0 1,238 3,163 675 25 608 0 0 12 Frederiksborg County 2,278 0 4 1,188 779 11 297 0 0 13 Roskilde County 2,007 0 6 708 304 6 982 0 0 21 West Zealand County 8,540 0 5,512 1,413 396 524 695 0 0 22 Storstrøm County 2,602 0 140 1,092 306 10 1054 0 0 23 Bornholm Municipality 394 0 71 191 5 1 126 0 0 24 Funen County 5,062 0 1,780 1,626 917 13 727 0 0 31 South Jutland County 5,176 0 3,064 1,035 602 8 467 0 0 32 Ribe County 3,942 0 2,159 1,094 397 8 283 0 0 33 Vejle County 3,546 0 53 1,456 1,285 419 333 0 0 34 Ringkøbing County 2,448 0 36 1,229 740 11 431 0 0 35 Århus County 7,443 0 3,557 2,253 584 25 1025 0 0 36 Viborg County 2,032 0 5 1,079 594 12 343 0 0 37 North Jutland County 7,626 0 3,425 2,647 868 20 666 0 0 99 Not distributable 1,701 0 0 144 1,557 1 0 0 0

Table 10 shows the total energy related CO2 emissions that was generated in Denmark in 2003 consuming energy. When looking at the table it becomes clear

rather fast that the main source of CO2 emissions in Denmark is the burning of coal on the central power plants. About one third of the total Danish Emissions are generated here.

Even though emissions follow the use of energy we do get some results that are different from the energy results in table 5 when we look at the regional distribution

of CO2 emissions. The reason clearly is that different counties rely on different

amounts of the various energy types and the CO2 emission coefficients vary among those different energy types.

Table 10 show the CO2 emissions pr capita in Danish households 2003. The average

emission pr. person is 2,437 Kg CO2 . In Copenhagen Municipality this is only 1,207 Kg. pr. person which is just about half as much as the average. The reason is that the main source of heating in Copenhagen is district heating and that no emissions are

attached to this consumption. The cause for that is that CO2.is emitted from the district heating generation plants and it should not be counted twice.

Table 11. Pr. Capita CO2 emissions by Households 2003.

Gasoline Other oil Natural Other gas Wood and Straw Electricity District Total and diesel and coal gas woodpills heating oil products Kg / person Average 2,437 1,142 442 302 17 444 89 0 0 1 Copenghagen Municipality 1,207 741 228 0 87 150 0 0 0 2 Frederiksberg Municipality 1,124 727 300 0 82 14 0 0 0 11 Copenhagen County 2,446 1,222 446 534 26 215 3 0 0 12 Frederiksborg County 2,906 1,289 474 529 12 561 41 0 0 13 Roskilde County 2,828 1,155 482 652 5 489 45 0 0 21 West Zealand County 3,814 1,205 639 413 9 1,415 133 0 0 22 Storstrøm County 4,628 1,210 710 299 11 2,271 128 0 0 23 Bornholm Municipality 4,132 1,161 1,012 0 4 1,755 201 0 0 24 Funen County 2,387 1,123 421 349 4 384 107 0 0 31 South Jutland County 2,625 1,260 457 505 4 243 155 0 0 32 Ribe County 2,007 1,174 346 229 2 132 123 0 0 33 Vejle County 2,386 1,195 444 393 4 249 100 0 0 34 Ringkøbing County 2,015 1,225 428 88 2 112 160 0 0 35 Århus County 1,880 1,114 320 123 3 242 79 0 0 36 Viborg County 2,912 1,272 632 385 4 411 207 0 0 37 North Jutland County 2,153 1,172 464 133 6 234 145 0 0

Notice that the two column Electricity” and “District heating” have no values for CO2 emissions. The reason is that it creates no emissions right on the spot where electricity is used, but at the place of its production instead.

Statistics Denmark compile a set of energy matrices called “Gross Energy Consumption” and here all final use of energy is traced back to where it was generated. Then it becomes possible to ascribe the emissions exactly to those energy users who cause the emissions.

Figure 5 Households Emissions of CO2 by County

It is very clear from this figure as well as from the table that Bornholm County is the

most CO2 polluting county pr. capita. Second most polluting is Storstrøms County. The reason can be found by looking at table 11 in combination with table 9. It is revealed that there is no Natural Gas on Bornholm County at all, so they have to rely on the much more polluting oil products and wood and wood pills for heating.

3.3 Summary and conclusions for air emissions

Only CO2 emissions have been included in this project. The reason is that these emissions comes from the burning of fossil fuel and they are always the same for the various types of fuel no matter how and where they are burned and independent of the technology used. Therefore the emission coefficients are the same for all counties, which is not the case for other types of pollution. The calculation then is very straightforward, because we just multiply the entire matrix of regional coefficients by the national matrix of CO2 emissions.

We have found that the large emission coefficients related to burning of hard coal and oil products means a lot of emissions from the Danish power plants. For households it is very easy to see where the supply of natural gas is missing because the emissions pr. capita in these areas are much higher due to more substantial use of oil and wood and wood pills.

It has been shown in this report that a regionalization of the Danish Energy Accounts is absolutely possible.

In general there is not enough data to build a regional version of the accounts through a bottom-up approach where the sum of the regional accounts equals the aggregated version. But it has been possible to collect enough and sufficiently good statistics to be used as indicators in a top-down approach. Thus, it has been possible to generate keys that enable a distribution of the national accounts by Danish counties. Naturally, some of the keys are better founded statistically, than others, but in general the results seem to be very sensible.

For the part concerning energy the weakness lies in the fact that it has been necessary to use regional economic statistics as indicators for the generation of keys for agriculture etc. and for the trade and service industries. It means that detailed analysis of the industries in question may reveal the underlying economic information more than structural environmental information that one would want to obtain.

Only a minor sub-sample of the total results has been shown in this report. More detailed results can be obtained from the authors of the report.

4 Regional water accounts

Water accounts The Danish water accounting system consists of a physical and a monetary part. The system shows the supply and use of water with a breakdown by 130 industries and households.

Three dimensions The structure of the Danish water figures is three-dimensional, when we consider the physical amounts. The first dimension is the water type, i.e. tap water, ground water, surface water and seawater. The second dimension is the grouping of NACE2 industries into 130 industries and the households. The third dimension is regional, and is a categorization according to municipalities.

The third dimension is important because the price of water-related services varies among the municipalities. Therefore, this information is especially important during the calculation of the Danish national monetary water accounts.

Until now the regional dimension has not been reflected in the standard Danish physical water tables but has only been used in the calculations.

Goal: Regional accounts The section describes the possibilities for establishing regional physical water accounts.

Furthermore, based on the present data sources, the results of this first attempt to establish regional water accounts for Denmark are shown.

Spatial issues The river basin level is the recommended spatial unit for integrated water management. Analyses at the river basin level are necessary to fully understand the impact of human activity on the ecological and hydrological systems. See United Nations (2005).

However the spatial units in the regional water accounts established in this section are the local administrative units, i.e. the Danish counties. The reason for this is that the administrative units correspond to the spatial units within which, the economic activity as described in the traditional national accounts is described.

Furthermore, in relation to the regional aspect all data sources are only made up by administrative units. The regional water accounts by administrative units provide a first step towards regional water accounts by river basin level though.

4.1 Data sources for water accounts

The Danish water accounts are primarily based on the physical data on water extraction obtained from the Danish Water and Waste Water Association (DANVA) and the Geological Survey of Denmark and Greenland (GEUS). Bie and Simonsen (1999) and Olsen (2005) offer a comprehensive overview of the sources.3

The most important Many data sources are used in computing the extraction of ground water, but two of sources them are of major importance.

DANVA The first concerns the water supply industry's extractions, where data from DANVA are used. The data from DANVA also includes information on economic and administrative conditions, which is used in the calculation of the monetary water accounts.

2 NACE: Nomenclature generale des Activités economiques dans les Communautes Européennes. Classification of Economic Activities in the European Community. 3 The institution referred to as DVF in Bie and Simonsen (1999) has changed its name and is therefore referred to as DANVA in this report.

The data from DANVA offer very detailed data on the level of municipalities. In 2003, the material covers 109 municipalities, which is equivalent to 40 pct. of all municipalities in Denmark. This corresponds to 61 pct. of the total water distribution.

For each municipality we have data on the number of inhabitants in the area of distribution. The area of distribution is always smaller than (or equal to) the municipality that it covers.

Data on extraction of water on own site and on consumption on own site is available. These figures do not necessarily equal each other, since distribution between water suppliers do occur. The distribution pattern is given (in percentages) between the four different types of consumers as well. That is:

– Private households – Industry – Institutions – Losses

Furthermore, we have a variety of information at the municipality level on the monetary aspects of the water distribution.

Data is generalised The information on the monetary aspects broken down by municipalities is absolutely crucial. However, to obtain data covering all 271 municipalities in Denmark, we have to generalise and enumerate the DANVA data. In order to do this, we assume that regional location is the primary factor when determining the price of water and sewage. This assumption is used to generalise the DANVA data using the Danish system of geo codes as the key to enable the generalisation. The generalisation is described in detail in Bie and Simonsen (1999).

Regional dimension in This means that we have information on the water supply industry’s supply of ground the DANVA data water broken down by municipalities.

GEUS The second source is GEUS, which concerns almost all other extractions. That is, the industries and households extraction for their own use.

In the GEUS data the units extracting water is broken down by 25 categories. The GEUS categories are then assigned to the 130 national accounts industries by Statistics Denmark. Irrigation requires special permits and is, as such, a specific GEUS category. In the national accounts and thus in the water accounts, however, it is part of the total extraction within agriculture.

Regional dimension GEUS no longer publishes information on extraction of water broken down by regions in GEUS data and by categories.

However, from GEUS it has been possible to obtain information on the extraction of ground water aggregated to 10 categories broken down by counties. Most of the 10 categories relate to the water supply industry, i.e. they correspond to the generalised DANVA data, whereas the rest relates to the industries and the households. That is, ground water extracted for irrigation purposes, ground water extracted for use in fresh water fish farming and finally ground water extracted by other industries.

Furthermore, the total extraction of surface water by county is available in this data material.

The water In order to calculate the amount of water actually supplied to the industries and the supply industry households by the water supply industry information on the water supply industry’s

use of water for filter backwashing4, protectionary drillings5 and losses broken down by counties are also obtained from GEUS and DANVA.

Information on purchase Another source is a survey conducted by Statistics Denmark on the manufacturing of raw materials industries’ purchases of raw materials including extraction of water. Connected to this survey is information on regional location and industry.

Green Accounts Additional sources are large companies’ green accounts. Information on regional location and industry can also be obtained from these accounts.

Water statistics Finally, Statistics Denmark’s water statistics are used. This statistics show the extraction of water broken down by counties without the industry dimension though. The water statistics are also based on the same data sources as mentioned above.

Target totals Thus, the water statistics provide the target totals6 for the regional dimension whereas the already existing physical water accounts provide the target total for the industry dimension.

4.2 Method for water accounts

This chapter offers a description of how regional Danish water accounts are established. Bie and Simonsen (1999) and Olsen (2005) offer a comprehensive description of the methods used to establish the already existing physical water accounts.

For the purpose of this project all calculations necessary for the breakdowns by counties have been carried out at an eight industry level.

A traditional or real balancing between supply and use is used only with respect to tap water - the remaining three types of water are characterized by having the supply side determined by the total use of industries and households.

Ground water is dealt with from a perspective of use or extraction. By adding the quantities extracted by different sectors the total supply is calculated at the same time. Surface water is dealt with in the same way.

4.2.1 Extraction of water by industry and by region

4.2.1.1 Extraction of ground water The water Information on the water supply industry’s extraction of ground water and surface supply industry water broken down by counties is based on the water statistics, i.e. data from GEUS and DANVA.

Other industries Information on the own extraction broken down by counties in the industry agriculture, fishing and quarrying is based on information obtained from GEUS.

Information on the own extraction broken down by counties in the industry manufacturing is based on information obtained from the survey on the manufacturing industries purchases of raw materials and green accounts.

Information on the own extraction broken down by counties in the industry wholesale and retail trade; hotels, restaurants is based on information obtained from GEUS.

4 In Danish: filterskylning. 5 In Danish: afværgeboring. 6 The meaning of the expression target total is just that it refers to something exogenously given.

Information on the own extraction broken down by counties in the industry public and personal services is based on information obtained from GEUS.

Households Information on the households own extraction broken down by counties is based on information obtained from GEUS.

Balancing The physical water accounts, do as already mentioned provide us with the target total for the extraction by industries whereas the water statistics provide us with the target total for the extraction by county.

To make sure that both target totals are met we have to balance the breakdown of the extraction of ground water by industry and by county to the target totals.

Initially, the industry dimension is correct because the information on regions obtained from GEUS, DANVA, the survey on purchases of raw materials and green accounts have only been used as a key for the regional breakdown of the extraction by industries and households. In contrast, the regional dimension does not meet the target total for the counties as published in the water statistics.

To make the total extraction of ground water by county equal, the total published in the water statistics an iterative balancing procedure is carried out. The breakdown by counties of the household’s and the water supply industry’s extraction of ground water is already given in the water statistics though, and is therefore kept constant in the balancing process.

4.2.1.2 Extraction of surface water The water supply industry Information on the water supply industry’s extraction of surface water is obtained from DANVA.

Other industries Information on the own extraction of surface water broken down by counties in the industry agriculture, fishing and quarrying is based on information obtained from GEUS. Initially the extraction of surface water by county follows the same distribution as the own extraction of ground water by county.

Information on the own extraction of surface water broken down by counties in the industry manufacturing is based on information obtained from the survey on the manufacturing industries purchases of raw materials and green accounts.

Information on the own extraction of surface water broken down by counties in the industry public and personal services is based on information obtained from GEUS.

Balancing To make the total extraction of surface water by county equal, the total published in the water statistics the same iterative balancing procedure as described above could be carried out.

However, the result of this procedure is that it is not possible to make the extraction equal both the industry dimension as well as the regional dimension. Therefore, the initial breakdown of the extraction of surface water is used.

4.2.2 Use of water by industry and by region

Water supply industry The water supply industry extracts water in order to supply it to the households and the industries. What is left is to break down this supply of tap water by households, industries and by region, since all other uses of ground water and surface water are allocated to the industries or households extracting the water.

The method used to break down the use of tap water on industries and municipalities is described below.

“Industries + Institutions” DANVA operates in their statistics with three consumer types plus losses, i.e. losses in = the industries in the distribution, plus their own uses, i.e. water used for filter backwashing and national accounts protectionary drilling. DANVA’s definition of households is directly compatible with the national accounts. What remains to be dealt with concerning the regional water accounts is the water used by institutions and industries. These two groups are unfortunately, not well defined in relation to the national accounts. As a consequence, the tap water use of these two groups is to be broken down by industries and by region.

Two sources of Basically, two sources of information are used. The first one utilizes the facts that tap information water since 1994 has been subject to taxation in Denmark, and that VAT-paying industries, with some known exceptions, have their water taxes reimbursed from the tax authorities.

The second source of information is employment statistics by industries and municipalities. The two sources are used simultaneously on each block of industries to fully exhaust the (enumerated) water supply to the two DANVA categories, industries and institutions.

4.2.2.1 The VAT-paying companies’ use of tap water VAT-paying companies Studying the law concerning the water tax, 98 of the 130 industries of the national accounts are entitled to reimbursement of their water tax. The method applied to these industries follows, in short, the procedure listed below:

1) From the central tax authorities, year-specific data on 300,000 VAT-registered companies’ reimbursed water taxes are obtained. The key variable is the unique company registration number, the so-called CVR-number;

2) Making use of the size of the water tax, measured in DKK per m3 (5 DKK/m3 in 2003) the reimbursed water taxes are converted into physical amounts of consumed water for each company;

3) Matching the VAT-registers’ approximately 300,000 companies with the Central Register of Enterprises and Establishments of Statistics Denmark by the CVR-number, each company is assigned an (NACE-) industry code and a code indicating the municipality in which, the company is located;

4) Aggregating the companies' use of tap water by industry and region to the national accounts industry level.

Some uncertainty The reimbursement method has been evaluated as fairly good. However, the method involved though involves some uncertainties. Big companies engaged in different industries often only have one (common) reimbursement, which may cause one industry’s or region’s consumption to appear too high and another industries or regions consumption to appear too low. Also, some reimbursement actually takes place within industries dominated by public institutions. However, the take-it-or-leave-it option, which characterizes the reimbursement data, implies that we have to ignore these facts.

Other important issues involve uncertainty connected to the periodizing of the payment of the reimbursement. To deal with the potential problems connected to the periodizing problem, a special calculation which weighs the reimbursement data has been implemented. See Olsen (2005).

4.2.2.2 The entities’ not paying VAT use of tap water Entities not paying VAT Only VAT-paying entities are entitled to reimbursement of their water taxes. Industries dominated by public institutions, e.g. hospitals and schools, are not VAT- registered. Besides, certain liberal professions, e.g. architectural and engineering

consultancies are due to the legislation not entitled to have their water taxes reimbursed.

To calculate the consumption of tap water in these remaining 32 industries, each industry’s employment broken down by municipality has been multiplied by a yearly unit consumption of water per employee. However, the consumption of hospitals, primary schools, secondary schools and other industries among the 32 in question exceeds this minimum. To compensate for this, the relevant number of people, e.g. patients or pupils are added to the employment number before it is multiplied by the unit consumption.

In this way, a minimum consumption of water for kitchens and bathrooms is obtained. The yearly unit consumption of water per employee is 6.3 m3 in 2003. The yearly unit consumption is based on the daily amount of water per head used to flush the toilets. The argument for that is that this use of water is assumed to be the most predominant use of water caused by the employees etc. in the relevant industries. See Olsen (2005). The unit consumption is assumed to be the same among the municipalities.

4.2.2.3 Balancing the use of tap water Proportionate distribution As described above, distributing the total amount of tap water between industries and of the difference municipalities is undertaken in two independent steps. The residual is then broken down by industries and by municipalities in proportion to the use of tap water. In 2003, the residual accounts for less than one percent of the total use of tap water. See Olsen (2005).

The spatial unit in this first attempt to establish a regional water account is, as already mentioned, the Danish counties.

When aggregating the use of tap water from the level of municipalities to the level of counties and, subsequently, comparing the use of water in the counties with our target total given in the water statistics, there is a small difference.

This difference is due to the fact that we use the reimbursement data as one of the means to break down the use of tap water by industries and by municipality. Big companies engaged in different activities or with more than one workplace often receive only one common reimbursement. In the reimbursement data, the reimbursement is typically registered as a payment to the head office of the company. This may cause the regional dimension not to be fully correct, whereas the industry dimension is correct with the reservations mentioned above.

Therefore, it is necessary also to make a balancing with respect to the regional level, without changing the industry level.

Consequently, an iterative balancing procedure as the one described above is carried out, cf. section 1.2.1.1.

4.2.2.4 Use of water extracted for own use The use of ground water and surface water extracted for own use is, as already mentioned, allocated to the industries extracting the water.

4.3 Results for water accounts

All information is broken down by 14 regional units – the counties. The regional water accounts show extraction of ground water and surface water, use of water extracted for own use and use of water supplied by the water supply industry.

Extraction of ground Table 12 shows the extraction of ground water broken down by counties and water industries. The water supply industry naturally accounts for the largest amount, but agriculture, fishing and quarrying also extract a large amount of water for their own use.

Water supply industry The water supply industry in Copenhagen extracts 15 pct. of the water supply industry’s total extraction.

Irrigation The large extraction of ground water by agriculture etc. in Ribe and Ringkøbing counties is due to extraction for irrigation purposes, whereas the extraction in Vejle County is mainly due to freshwater fish farming.

Table 12 Extraction of ground water 2003*

County Total Total supply services services quarrying Households Construction Manufacturing supply industry communication Total industries industries Total business activities activities business Of which the water Public and personal Wholesale and retail Transport, storage and and storage Transport, Agriculture, fishing and and fishing Agriculture, Financial intermediation, intermediation, Financial trade; hotels, restaurants Electricity, gas and water

Mill. m3

Total 632.5 10.4 622.2 166.6 32.8 420.8 417.5 0.0 0.2 0.0 0.0 1.8 Copenhagen 62.1 0.0 62.1 0.0 0.6 61.5 61.5 0.0 0.0 0.0 0.0 0.0 12 Frederiksborg County 31.4 0.0 31.4 1.2 0.6 29.6 29.6 0.0 0.0 0.0 0.0 0.0 13 Roskilde County 37.8 0.6 37.2 0.1 3.1 34.0 34.0 0.0 0.0 0.0 0.0 0.0 21 West Zealand County 32.1 0.2 32.0 0.3 1.5 30.1 29.9 0.0 0.0 0.0 0.0 0.0 22 Storstrøm County 20.5 0.1 20.4 0.4 2.8 17.2 17.2 0.0 0.0 0.0 0.0 0.0 23 Bornholm Municipality 3.8 0.0 3.8 0.0 0.1 3.7 3.7 0.0 0.0 0.0 0.0 0.0 24 Funen County 46.7 4.0 42.8 3.5 1.5 37.8 37.5 0.0 0.0 0.0 0.0 0.1 31 South Jutland County 45.2 0.1 45.1 22.4 0.7 21.6 21.5 0.0 0.0 0.0 0.0 0.4 32 Ribe County 59.2 0.2 59.0 36.8 0.8 20.8 20.7 0.0 0.1 0.0 0.0 0.5 33 Vejle County 76.0 4.4 71.6 40.7 1.9 28.7 28.4 0.0 0.0 0.0 0.0 0.1 34 Ringkøbing County 66.4 0.0 66.4 31.2 7.4 27.3 27.3 0.0 0.0 0.0 0.0 0.5 35 Århus County 53.4 0.4 53.0 5.1 2.1 45.7 45.2 0.0 0.0 0.0 0.0 0.0 36 Viborg County 28.8 0.2 28.5 7.3 0.4 20.6 20.6 0.0 0.0 0.0 0.0 0.1 37 North Jutland County 69.2 0.2 69.0 17.5 9.3 42.1 40.4 0.0 0.0 0.0 0.0 0.0

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

Table 13 shows the extraction of surface water. The extraction of surface water only makes up 3 pct. of the total extraction in Denmark.

West Zealand County is the only county where the water supply industry extracts surface water.

Table 13 Extraction of surface water 2003*

Mill. m3

Total 18.6 0.0 18.6 2.7 9.5 6.4 4.8 0.0 0.0 0.0 0.0 0.1 Copenhagen 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 Frederiksborg County 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 Roskilde County 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 West Zealand County 5.1 0.0 5.1 0.0 0.0 5.0 4.8 0.0 0.0 0.0 0.0 0.0 22 Storstrøm County 1.9 0.0 1.9 0.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 Bornholm Municipality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24 Funen County 6.5 0.0 6.5 0.1 6.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31 South Jutland County 0.6 0.0 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 32 Ribe County 0.7 0.0 0.7 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 33 Vejle County 0.4 0.0 0.4 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 34 Ringkøbing County 0.8 0.0 0.8 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35 Århus County 0.6 0.0 0.6 0.1 0.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 36 Viborg County 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 37 North Jutland County 1.0 0.0 1.0 0.1 0.1 0.8 0.0 0.0 0.0 0.0 0.0 0.0

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

Water supply industry The connection between the water supply industry’s extraction of ground water and surface water and the water supply industry’s supply of tap water is shown in table 14.

Table 14 Extraction and supply by the water supply industry 2003*

County water water Losses Losses drillings drillings Protectionary Total extraction Filter backwashing backwashing Filter Supply of tap water water suppliers, net Received from other Extraction of ground Extraction of surface

Mill. m3

Total 417.5 4.8 422.3 7.5 11.9 0.0 28.2 374.8 Copenhagen 61.5 0.0 61.5 1.7 9.4 30.5 4.4 76.5 12 Frederiksborg County 29.6 0.0 29.6 0.5 0.0 -6.2 1.6 21.3 13 Roskilde County 34.0 0.0 34.0 0.1 1.0 -17.1 1.6 14.2 21 West Zealand County 29.9 4.8 34.7 0.2 0.1 -8.0 1.724.7 22 Storstrøm County 17.2 0.0 17.2 0.2 0.3 0.9 1.5 16.0 23 Bornholm Municipality 3.7 0.0 3.7 0.1 0.0 0.0 0.1 3.5 24 Funen County 37.5 0.0 37.5 0.5 1.1 0.0 2.8 33.1 31 South Jutland County 21.5 0.0 21.5 0.3 0.0 0.0 1.7 19.5 32 Ribe County 20.7 0.0 20.7 0.6 0.0 0.0 1.4 18.8 33 Vejle County 28.4 0.0 28.4 0.7 0.0 0.0 1.8 25.9 34 Ringkøbing County 27.3 0.0 27.3 0.6 0.0 0.0 1.7 25.0 35 Århus County 45.2 0.0 45.2 1.1 0.0 0.0 3.5 40.5 36 Viborg County 20.6 0.0 20.6 0.3 0.0 0.0 1.8 18.5 37 North Jutland County 40.4 0.0 40.4 0.6 0.0 0.0 2.6 37.1

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

The total extraction by the water supply industry was 422.3 mill. m3 in 2003. However, there is a loss in the water pipe lines. In addition to this, water is used for filter backwashing on the common waterworks, and finally water is used as a consequence of protectionary drillings to protect the ground water reserves against pollution. Thus, the final users received a total of 374.8 mill. m3 in 2003.

Table 14 also shows the amounts of water used for filter backwashing and protectionary drillings. 79 pct. of the water used as a consequence of protectionary drillings was used in Copenhagen in 2003. The reason for this is that the activity in the manufacturing industry until recent decades was very high in Copenhagen. This activity resulted in the pollution, which makes the protectionary drillings necessary today.

Intra-industry deliveries The table also shows intra-industry deliveries. The table shows that Copenhagen and Storstrøm County are the only regions that use a larger amount of water than what is extracted within the region. The table shows that 40 pct. of the amount of tap water supplied by the water supply industry in Copenhagen originates from the water supply industry in other counties.

Tap water In 2003, the households used 237.7 mill. m3 corresponding to 63 pct. of the total use of tap water, cf. table 15. The breakdown by industries shows that public and personal services, manufacturing and agriculture, fishing and quarrying account for the largest use of tap water. These three industries separately account for approximately a quarter of the use of tap water in the industries.

Table 15 Use of tap water 2003*

Mill. m3

Total 374.8 237.7 137.1 39.4 40.6 4.3 0.1 0.7 13.5 3.8 3.8 31.0 11 Copenhagen County 76.5 57.1 19.4 0.1 7.1 1.3 0.0 0.1 4.0 0.5 0.9 5.5 12 Frederiksborg County 21.3 17.2 4.1 0.3 0.8 0.0 0.0 0.0 0.7 0.1 0.2 2.1 13 Roskilde County 14.2 9.5 4.7 0.1 2.4 0.0 0.0 0.0 0.3 0.0 0.1 1.6 21 West Zealand County 24.7 9.4 15.4 5.2 3.9 0.2 0.0 0.1 1.2 0.1 0.3 4.2 22 Storstrøm County 16.0 11.0 5.0 1.6 1.6 0.0 0.0 0.0 0.4 0.0 0.1 1.2 23 Bornholm Municipality 3.5 2.5 1.0 0.4 0.4 0.0 0.0 0.0 0.1 0.0 0.0 0.0 24 Funen County 33.1 20.3 12.8 5.0 2.6 0.2 0.0 0.1 1.1 0.1 0.4 3.4 31 South Jutland County 19.5 13.9 5.6 3.2 0.7 0.1 0.0 0.0 0.4 0.1 0.1 1.0 32 Ribe County 18.8 9.8 9.1 3.5 2.3 0.1 0.0 0.0 0.6 0.9 0.2 1.4 33 Vejle County 25.9 13.6 12.3 2.6 4.1 1.9 0.0 0.0 0.9 0.3 0.3 2.2 34 Ringkøbing County 25.0 14.2 10.8 4.9 3.2 0.1 0.0 0.0 0.7 0.2 0.3 1.4 35 Århus County 40.5 27.7 12.8 2.1 6.9 0.1 0.0 0.1 1.0 0.2 0.3 2.1 36 Viborg County 18.5 11.2 7.4 3.6 1.6 0.2 0.0 0.0 0.4 0.3 0.2 1.1 37 North Jutland County 37.1 20.3 16.9 6.8 3.0 0.2 0.0 0.1 1.8 0.9 0.5 3.6

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

Table 15 shows that 20 pct. of the use of tap water is used in Copenhagen and that 30 pct. of the use of water within the industry wholesale and retail trade; hotels, restaurants is used in Copenhagen.

Final use of water The final use of water corresponds to the amount of water the households and industries extract for their own use and the amount of tap water they consume. The

final use of water in 2003 by county appears in table 16, which is a permutation of the data on water extraction in tables 12 and 13 and the use of tap water in table 14. The fourth column shows the amount of tap water that the water supply industry received from other water supply industries.

Table 16 Final use of water by region 2003*

County water water Total extraction Final us of water water suppliers, net Received from other Extraction of ground Extraction of surface

Mill. m3

Total 632.5 18.6 651.2 0.0 651.2 Copenhagen 62.1 0.0 62.2 30.5 92.7 12 Frederiksborg County 31.4 1.0 32.4 -6.2 26.1 13 Roskilde County 37.8 0.0 37.8 -17.1 20.7 21 West Zealand County 32.1 5.1 37.2 -8.0 29.2 22 Storstrøm County 20.5 1.9 22.4 0.9 23.2 23 Bornholm Municipality 3.8 0.0 3.8 0.0 3.8 24 Funen County 46.7 6.5 53.2 0.0 53.2 31 South Jutland County 45.2 0.6 45.8 0.0 45.8 32 Ribe County 59.2 0.7 59.9 0.0 59.9 33 Vejle County 76.0 0.4 76.4 0.0 76.4 34 Ringkøbing County 66.4 0.8 67.1 0.0 67.1 35 Århus County 53.4 0.6 54.0 0.0 54.0 36 Viborg County 28.8 0.1 28.9 0.0 28.9 37 North Jutland County 69.2 1.0 70.2 0.0 70.2

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

Figure 6: Final use of water by region 2003

In 2003, the households’ final consumption of water was 248.1 mill m3. Agriculture etc. consumed 52 pct. (208.6 mill. m3) of the final use of water in the industries, whereas they cf. table 15, only accounted for 29 pct. (39.4 mill. m3) of the industries consumption of tap water. The difference lies in the agriculture’s large extraction of ground water for own use.

Copenhagen accounts for the largest share of the final use of water. In 2003, this share amounted to 14 pct. of the total. The reason for this is partly the use of water as

a consequence of protectionary drillings, but it is also due to the fact that the population density is very high in Copenhagen. It is important to notice that Copenhagen receives a rather large amount of water from water suppliers in other counties.

Table 17 Final use of water by region and industry 2003*

Mill. m3

Total 651.2 248.1 403.1 208.6 82.9 56.7 47.6 0.7 13.7 3.8 3.8 32.8 11 Copenhagen 92.7 57.2 35.5 0.1 7.7 16.9 15.5 0.1 4.0 0.5 0.9 5.5 12 Frederiksborg County 26.1 17.2 8.9 1.5 2.3 2.1 2.1 0.0 0.7 0.1 0.2 2.1 13 Roskilde County 20.7 10.1 10.6 0.1 5.5 2.7 2.7 0.0 0.3 0.0 0.1 1.6 21 West Zealand County 29.2 9.5 19.6 5.5 5.5 2.6 1.9 0.1 1.2 0.1 0.3 4.2 22 Storstrøm County 23.2 11.2 12.1 2.0 6.3 2.1 2.0 0.0 0.4 0.0 0.1 1.2 23 Bornholm Municipality 3.8 2.5 1.2 0.4 0.5 0.2 0.2 0.0 0.1 0.0 0.0 0.0 24 Funen County 53.2 24.3 28.9 8.6 10.5 4.8 4.4 0.1 1.1 0.1 0.4 3.5 31 South Jutland County 45.8 13.9 31.8 26.2 1.4 2.2 2.0 0.0 0.5 0.1 0.1 1.4 32 Ribe County 59.9 9.9 50.0 41.0 3.1 2.1 1.9 0.0 0.7 0.9 0.2 2.0 33 Vejle County 76.4 18.0 58.4 43.5 6.1 4.9 2.5 0.0 0.9 0.3 0.3 2.4 34 Ringkøbing County 67.1 14.2 53.0 36.9 10.5 2.4 2.3 0.0 0.7 0.2 0.3 1.9 35 Århus County 54.0 28.1 25.9 7.3 9.1 5.6 4.7 0.1 1.0 0.2 0.3 2.2 36 Viborg County 28.9 11.4 17.5 11.1 2.0 2.3 2.1 0.0 0.4 0.3 0.2 1.2 37 North Jutland County 70.2 20.5 49.7 24.4 12.4 6.0 3.2 0.1 1.8 0.9 0.5 3.6

Copenhagen is made up of Copenhagen Municipality (1), Frederiksberg Municipality (2) and Copenhagen County (11).

Per capita Figure 7 shows the final use of water compared to the mid-year population by counties. The use of water per capita is especially high in the western parts of Denmark. This is due to the fact that the population density is lower and that the use of water for irrigation purposes especially takes place in this part of Denmark.

Employment Figure 8 shows the industries’ final use of water compared to the employment in the industries by region. The figure is thus an example of how the regional water accounts can be used together with the regional national accounts.

As it can be seen the figures more or less show the same pattern.

Figure 7: Final use of water per capita by region 2003

Figure 8: The industries’ final use of water compared to employment by region 2003

Figure 9: The industries’ final use of water compared to gross value added by region 2003

Regional water accounts Figure 9 shows the final use of water compared to gross value added by region. The compared to regional figure is thus another example of how the regional water accounts can be used national accounts together with the regional national accounts.

4.4 Summary and conclusions: Regional water accounts

This section has shown that on the basis of the already existing data sources, it is possible to establish regional water accounts.

By comparing the regional water accounts with the regional national accounts, a variety of different kinds of analysis become possible. For instance, the final use of water compared to output or gross value added, i.e. water intensity. This kind of analysis could be carried out at the total industry level as well as at the industry level.

However, in order to increase the data quality of the regional water accounts it is necessary to take a closer look at the data describing the industries extraction of ground water and surface water for own use.

In this project, the extraction and use of water has been broken down by eight industries and the households. The spatial unit has been the counties.

During the first part of the calculation of the water supply industry’s extraction of water and supply of tap water the level of detail has been 130 industries and the municipalities though.

Therefore, in order to make a further breakdown by more industries and by municipalities it is necessary to take a closer look at the data describing the industries extraction of ground water and surface water for own use.

The breakdown by municipalities would be a first step towards regional water accounts at the river basin level.

5 References

Bie, Thomas. Simonsen, Bo: NAMEA with Water Extraction and Use. Statistics Denmark. 1999.

Geological Survey of Denmark and Greenland (GEUS): Ground water 2004 Status and Development 1989 – 2004. 2005. Publication in the Danish language. Title in Danish: Grundvand 2004 Status og udvikling 1989 – 2004.

Olsen, Thomas: Integrated Environmental and Economic Accounting for Water and Waste Water. Denmark 1999 – 2003. Statistics Denmark. 2005.

6 Appendix

Types of energy accounted for in the Danish Energy Accounts

Column Type of energy Aggregated to in Aggregated to in industry tables: household tables

1 Crude oil Crude oil and refinery feedstuff 0 2 Refinery feedstocks Crude oil and refinery feedstuff 0 3 Refinery gas Other gas Other gas 4 LPG Other gas Other gas 5 LPG for road transport Other gas Other gas 6 Light Virgin Naphta Gasoline and other oil products Other oil products and coal 7 Tax free petrol Gasoline and other oil products Other oil products and coal 8 Aviation gasoline Gasoline and other oil products Other oil products and coal 9 Petrol, unleaded Gasoline and other oil products Gasoline and diesel 10 Petrol, leaded Gasoline and other oil products Other oil products and coal 11 JP4 Gasoline and other oil products Other oil products and coal 12 Kerosene Gasoline and other oil products Other oil products and coal 13 JP1 Gasoline and other oil products Other oil products and coal 14 Gasoil Gasoline and other oil products Other oil products and coal 15 Gasoil for road transport Gasoline and other oil products Gasoline and diesel 16 Marine gasoil Gasoline and other oil products Other oil products and coal 17 Light fueloil Gasoline and other oil products Other oil products and coal 18 Heavy fueloil Gasoline and other oil products Other oil products and coal 19 Waste oil Gasoline and other oil products Other oil products and coal 20 Petroleum coke Gasoline and other oil products Other oil products and coal 21 Orimulsion Gasoline and other oil products Other oil products and coal 22 Natural Gas Natural Gas Natural Gas 23 Natural Gas Natural Gas Natural Gas 24 Natural Gas Natural Gas Natural Gas 25 Coal Coal and furnace coke etc. Coal and furnace coke etc. 26 Coal Coal and furnace coke etc. Coal and furnace coke etc. 27 Coke Coal and furnace coke etc. Coal and furnace coke etc. 28 Furnace coke Coal and furnace coke etc. Coal and furnace coke etc. 29 Brown coal Coal and furnace coke etc. Coal and furnace coke etc. 30 Brown coal Coal and furnace coke etc. Coal and furnace coke etc. 31 Electricity Electricity Electricity 32 District heating District heating District heating 33 Town gas Other gas Other gas 34 Wood Sustainable energy Wood and wood pills 35 Wood waste Sustainable energy Wood and wood pills 36 Straw Sustainable energy Straw 37 Waste Sustainable energy 0 38 Biogas Sustainable energy 0 39 Wind power Sustainable energy 0 40 Water power Sustainable energy 0