Regional Environmental Accounts Denmark 2003 Peter Rørmose Jensen Thomas Olsen

Transcription

1 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 , action 3 for the study entitled "Environmental Statistics and Accounts Regional Environmental Accounts.

2 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: Direct: Prj@dst.dk Thomas Olsen Head of section National Accounts - Environmental Accounts and Input-Output Statistics Denmark Sejrogade 11 DK-2100 Phone: Direct: Tol@dst.dk

3 Table of contents 1 INTRODUCTION Regions in Denmark REGIONAL ENERGY ACCOUNTS Data sources for energy accounts Census on the use of energy in manufacturing industries Census on energy producers Survey on the use of energy in trade and service Regional Economic Statistics Data about regional distribution of energy consumption in households Method for energy accounts Agriculture and Fishing etc. (industries 1-5) Extraction of crude oil and gas (industries 6) Manufacturing industries (industries 7-62) Energy supply industries (industries 63-65) Trade and service industries (industries ) Households (industries ) Results for energy accounts Industries Households Summary and conclusions about energy accounts REGIONAL AIR EMISSIONS ACCOUNTS Method for air emissions Results for air emissions Summary and conclusions for air emissions REGIONAL WATER ACCOUNTS Data sources for water accounts Method for water accounts Extraction of water by industry and by region Extraction of ground water Extraction of surface water Use of water by industry and by region The VAT-paying companies use of tap water The entities not paying VAT use of tap water Balancing the use of tap water Use of water extracted for own use Results for water accounts Summary and conclusions: Regional water accounts REFERENCES APPENDIX

4 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 readily available So the purpose of this project is to investigate the possibilities for establishing 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? Public responsibilities 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. 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 , action 3 for the study entitled "Environmental Statistics and Accounts Regional Environmental Accounts. 2

5 Figure 1. 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. 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 Km Population People Total 43, ,387,071 Copenhagen Municipality Frederiksberg Municipality Copenhagen county Frederiksborg County Roskilde County West Zealand County Storstrøm County Bornholm County Funen County South Jutland County Ribe County Vejle County Ringkøbibg County Århus County Viborg County North Jutland County , , , , , , , , , , , ,362 91, , , , , ,144 43, , , , , , , , ,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 county level But a number of variables e.g. availability of natural gas and district heating, number 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 3

6 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 CO 2 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 level too detailed County level is most useful Future possibilities But why not go one step further down in the administrative hierarchy to the local 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. 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. 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. 4

7 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 Schematic of the Energy Balance 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. The Danish Energy Balances are organized in the following way, where supply of and demand for energy is balanced for every year since columns of energy carriers Production (1 row) Import (1 row) Supply Input, Industries (130 rows) = Input, Households (5 rows) Demand 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 m 3 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. 5

8 Table 2. Aggregated Danish Energy Account Crude oil and refinery feedstuff Coal and furnace coke etc. Gasoline and other oil products Natural gas Other gas Sustainable energy Electricity District heating TJ Total 368, , , ,992 16,138 92, , ,494 Households ,581 28,522 1,306 9,108 36,731 64,759 Total, industries 368, , , ,470 14,832 83,857 80,661 39,735 Agriculture, fishing and quarrying 0 1,022 36,922 29, ,853 7,389 1,889 Manufacturing 368, ,057 52,433 85,389 13,632 44,449 33,865 7,654 Electricity, gas and water supply 0 18,995 10, , ,556 1, Construction 0 0 6, Wholesale and retail trade; hotels, restaurants ,598 4, ,749 10,180 Transport, storage and , , communication Financial intermediation, business 0 0 5,873 2, ,264 6,347 activities Public and 0 0 8,099 5, ,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 compile the National Energy Accounts Supply and use of energy Method A number of different sources are used in the compilation of the accounts. The foreign trade statistics are used to measure imports and exports of energy goods. The 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. 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. 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. 6

9 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 keys As mentioned earlier, there are 135 rows in the matrix that we will try to regionalize. 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 Agriculture and fishing Intermediate input in Regional Economic Statistics 2 6 Extraction of crude oil and gas Cannot be attributed to a specific county Manufacturing industries Census on use of energy in manufacturing industries Regional Economic Statistics Supply of electricity, gas and heating Census on production of energy (including inputs used) Trade and service industries Survey of use of energy in trade and service Employment in Regional Economic Statistics Car ownership statistics with regional dimension 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 7

10 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 industry Manufacturing industry The extraction of crude oil is the second group. It takes place in the North Sea and it 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. 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 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. 8

11 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 (m 3 ) Other gasoline products, e.g. tax free gasoline (m 3 ) Diesel oil for registered vehicles (m 3 ) Gas oil and other diesel oil products (m 3 ) 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 m 3 ) Town gas (1,000 m 3 ) Biogas (1,000 m 3 ) 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/m 3 /MWh) Own production of district heating (GJ/m 3 /MWh) Own consumption of self produced district heating (GJ/m 3 /MWh) Sale of own production of district heating (GJ/m 3 /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 (= 10 9 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 tonne Furnace coke, coke and brown coal tonne Wood pills tonne Fuel wood, sawdust, straw tonne Waste (including paper, cardboard and wood) tonne Heavy fuel oil tonne Petroleum coke tonne LPG tonne Refinery gas m 3 Petrol m 3 Other petrol and petroleum products m 3 Gasoil m 3 Town gas m 3 Bio gas m 3 Natural gas MWh Electricity 3.6 9

12 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 well covered Sources for energy supply With this large census in hand on which the energy accounts are actually built, we 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. The most obvious source for distribution of the input into energy production is the annual census on energy producers 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 service Finally, the last 64 industries out of the 130 are trade and service industries, and we will review the most obvious source for their distribution 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 Survey 2005 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. In the spring of 2005, a new survey was carried out, gathering information about the year 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

13 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 regionalizing However, this survey is quite difficult to use as a source for dividing the energy 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 useful as a source for this projectl So we must conclude that this survey is not really usable for regional distribution. Instead, we must turn to the Regional Economic Accounts that are published by Statistics Denmark 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 etc. Indicators for trade and service As it was mentioned earlier, there is not really any background data available for 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. The opposite situation occurs in relation to the trade and service industries, where energy plays a less important role. Use of energy is mostly related to heating, lighting, 11

14 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 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 X = X = X v (1) ij ijk k= 1 k= 1 ij ijk where X ij is the consumption of energy carrier j in industry i and X ijk is the consumption of energy carrier j in industry i in county k. The keys v ijk 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 k= 1 v = 1 (2) ijk It also means that to get the regional energy accounts we just use the formula X ijk = X v (3) ij ijk This is a top-down approach that secures consistency in all directions as described by the following identities X = (4) ij X ijk i= 1 i= 1 k= 1 12

15 where X ij is the consumption of energy carrier j in industry i and X ijk 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 X = (5) ij X ijk j= 1 j= 1 k= 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 v ijk are generated for the cells in the various sub-sections of the national energy matrix 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 = X v (6) ij k= 1 ij ik 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 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 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 13

16 17 new ij X ijk k= 1 X (7) for a number of cells, where X new 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 v ijk in the following way ijk = 17 X k= 1 new ijk v (8) X new ijk Then the key generated in (8) could be used in a new calculation to regionalize the aggregated national energy accounts. X X X X v new ijk ijk = ij = 17 ij ijk new X ijk k = 1 (9) 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 v ijk 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 14

17 with the assistance of the regional economics statistics as with for the industriess 1-6 above 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 Production and distribution of electricity 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 15

18 Input used for energy production App 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 16 counties * 21 energy carriers Input used for heating production 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). 63 j new X 21 X 63 j k = and v 21 = k j k 16 new X X k= j k k= 1 new 21 j k v (10) new j k where the j 21 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 Trade and service industries (industries ) As we cannot use the trade and service industry survey for regionalizing the energy consumption in industries we must turn to the economic regional accounts. This time we choose the regional distribution of employment as our indicator key. 16

19 X ijk = X v (11) ij ik where v ik is the vector of county i s share of total national employment Households (industries ) 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 m 2 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 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. 17

20 2.3 Results for energy accounts The basic form of the data that is a result of this work is, of course a threedimensional 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 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 Total Crude oil and refinery feedstuff Coal and Gasoline Natural gas furnace and other coke etc. oil products Other gas Sustainable energy Electricity District heating Terra Joule Total 1,728, , , , ,992 16,138 92, , ,494 1 Copenghagen Municipality 115, ,548 38,099 20, ,797 9,735 16,325 2 Frederiksberg Municipality 6, , ,349 2, Copenhagen County 97, ,797 41,963 12, ,409 12,288 11, Frederiksborg County 40, ,224 13, ,526 5,807 4, Roskilde County 27, ,153 5, ,403 3,850 3, West Zealand County 315, ,918 56,999 22,373 6,934 7,106 4,475 7,172 4, Storstrøm County 38, ,257 5, ,732 5,144 3, Bornholm Municipality 5, , Funen County 83, ,384 21,092 15, ,138 10,147 9, South Jutland County 71, ,678 13,613 10, ,384 5,879 3, Ribe County 60, ,337 14,328 6, ,886 6,150 4, Vejle County 232, , ,068 22,327 5,684 3,629 9,459 6, Ringkøbing County 50, ,086 12, ,171 7,922 5, Århus County 117, ,787 29,238 10, ,377 13,273 15, Viborg County 39, ,181 10, ,876 6,046 3, North Jutland County 115, ,435 33,148 15, ,304 12,283 9, Not distributable 312, , , 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. 18