- Email: [email protected]
Quality based energy contents and carbon coefficients for building materials: A systems approach
Energy 29 (2004) 561–580 www.elsevier.com/locate/energy
Quality based energy contents and carbon coefficients for building materials: A systems approach W.P.S. Dias , S.P. Pooliyadda Department of Civil Engineering, University of Moratuwa, Moratuwa, Sri Lanka Received 21 December 2001
Abstract This paper describes a computerised relational database management system to represent and calculate (i) the energy inputs to a building material or element up to the point of utilisation or construction and (ii) the carbon emitted to the atmosphere up to that point. All building elements, materials and ‘‘primitive’’ raw materials are placed in an aggregation–decomposition hierarchy. The process analysis carried out here captures around 90% of the embedded energy in a product. The database can handle multiple sources of data and perform calculations to give the average, maximum and minimum embedded energies, which are also classified according to fuel type (i.e. biomass, fossil fuel and electricity) and process stage (i.e. production energy, transport energy for raw materials and energy embedded in raw materials). The embedded energy requirements are also calculated on the basis of the lowest quality energy, namely biomass energy (‘‘bio-equivalent’’ basis), in addition to the more conventional basis of tonnes of oil equivalent. Timber was found to be the preferred option and steel the least desirable, with concrete in between, from an energy consumption and carbon emission point of view. # 2003 Elsevier Ltd. All rights reserved.
1. Introduction Energy and environmental considerations are becoming increasingly important today. This is primarily because there is an appreciation of the non-renewable and limited nature of many of our energy resources and environmental assets. However, any sort of development inevitably consumes energy resources and depletes environmental assets. This is particularly true of industrial development, and the construction industry is one of the largest contributors to gross
Corresponding author. Fax: +94-11-2650622. E-mail address: [email protected] (W.P.S. Dias).
0360-5442/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2003.10.001
562
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
domestic capital formation in many countries. As such, the above concerns regarding energy and environmental issues should particularly be applied to this industry as well. Most of the construction industry energy considerations have been made with respect to the operational phase—e.g. energy saving buildings [1]. In cold countries such as UK and USA, the ratio between construction and annual operational energy is around 3–6; in a slightly warmer temperate-zone country (such as New Zealand), this ratio goes up to around 9 [2]. However, in Sri Lanka, the operational energy is likely to be very small. In fact, we have shown in a companion paper [3] that the ratio of embedded energy to annual operational energy can be as high as 35. This is because no space heating or air conditioning is used in such buildings, which account for around 65% of the capital formation through building construction in Sri Lanka [4]. Therefore, there is a clear need for research into energy inputs and environmental costs of building materials, in order for building construction industry professionals and policy makers to make choices regarding the type of building materials that should be constructed. This is particularly so for residential buildings, where operational energy consumption is likely to be less than in commercial buildings, and especially for countries where the housing stock continues to increase significantly. The significance of embedded energy in such residential buildings has been appreciated by other researchers too [5]. The building research establishment (BRE) in the UK has recently issued ‘‘environmental profiles’’ of selected raw materials [6]. This is in many ways a more comprehensive study than ours, as it deals with energy inputs throughout an anticipated 60 year building element life cycle (inclusive of maintenance and demolition). Adalberth [7] proposed a method for assessing life cycle energy use for buildings. Our study considered energy inputs only up to the construction stage. The BRE report [6] also gives a range of environmental impacts, covering emissions to air (such as carbon dioxide and acidic gases), water and land. Cole and Rousseau [8] have computed a composite air emission index, obtained by summing the ratio of emissions to ambient air quality limits for particulates, SO2, NO2 and CO; they also give non-energy related particulate and hydrocarbon emissions, in addition to carbon dioxide emissions for various materials. Harris [9] has attempted to quantify environmental impacts such as depletion of non-renewable resources, local disruption due to extraction, indoor toxicity effects and potential for recycling. Our study dealt only with carbon emissions as environmental impacts. However, the issues relating to construction or embedded energy of building materials will vary from one country to another, depending among other things considerably on the sources of energy used for manufacturing. In Sri Lanka, for example, there is a wide range of energy sources used for building materials manufacture, from firewood for brick and tile production to fossil fuel and electricity for cement and steel production. An energy policy would clearly need to take into account such differences in quality of the energy sources [10,11] that are used for manufacture, and not merely the quantity. This focus on energy quality is perhaps the main original contribution of this paper.
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
563
2. Objectives and approach In this paper, we describe some research carried out to obtain embedded energy contents and carbon emission coefficients for a variety of Sri Lankan building materials. The objectives of the research described in this paper are as follows: 1. To develop a computerised database for embedded energies and carbon coefficients of building materials in a hierarchical structure. 2. To compare the embedded energy requirements of building materials in a way that reflects the quality of the energy sources used. 3. To indicate how energy contents and carbon coefficients for alternative materials of construction can be compared; this is more fully dealt with elsewhere [3]. The ‘‘systems’’ nature of the approach adopted was reflected mainly in: (i)
The adoption of an aggregation–decomposition hierarchy for building materials and its implementation in a computerised database; and (ii) The implementation of the concept of ‘‘energy quality’’ through a ‘‘mutually consistent’’ scheme for three types of energy sources, namely biomass, fossil fuel and electricity.
3. Methodology 3.1. Energy analysis There are four commonly accepted methods of energy analysis [2], namely 1. 2. 3. 4.
Statistical analysis Input–output analysis Process analysis Eco-energetics
The method used will depend mainly on the overall objectives of the analysis and the availability of data. Statistical analysis is based on national statistics regarding energy supplies to and production outputs from industries, thus enabling an estimation of energy requirements per unit output. Input–output analysis uses input–output tables that reflect money value transactions between different sectors of the economy. If the energy cost for each industry is known, then their energy inputs (including indirect inputs for heating, lighting etc.) can be found, based on their purchases from the energy supply sector. Process analysis is the most frequently used method. It involves the study of the inputs and outputs in a process at the level of an individual industry (as opposed to the national level as in the first two methods above). The energy requirements of a process or product are determined from all the material, equipment and energy inputs into the process. Energy analyses are seldom better than 10% accurate [12]. The total energy requirement of a process or industry can vary
564
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
widely between manufacturers because of (i) the efficiency of energy, material and labour use; (ii) technology used for the process; (iii) difference in handling of wastes; and (iv) differences in raw materials input. Process analysis is used in this study too, following Honey and Buchanan [12], because it was convenient to determine the inputs and outputs for a process through visits to producers. Also, Sri Lankan national statistics were not very comprehensive, especially since many building materials are produced in the informal sector (e.g. brick manufacture and sand extraction). All the material and energy inputs for a given amount of output were identified and quantified. Also, following Baird and Chan [2] and most other workers, human energy and energy associated with environmental costs, have not been included in the energy analysis, mainly because of the complexity and ambiguity associated with such factors. The inclusion of such factors, advocated by Odum [10] for example, is what eco-energetics is about; it places an ‘‘energy equivalent’’ value on all forms of energy. However, our study does make an independent assessment of environmental costs in terms of carbon dioxide emissions. It also makes distinctions between the quality of various types of energy, albeit on a basis different to that of Odum [10]. 3.2. System boundaries It is important to define the system boundaries when calculating embedded energies of materials. The boundary adopted depends in part on the availability of data but mainly on the overall aims and thus the assumptions that are made. The IFIAS workshop [13] suggested that four boundary levels could be drawn for most materials, as illustrated in Fig. 1. The lower levels (e.g. Level 1) focus on the immediate production of a material, while the higher levels (e.g. Level 4) try to capture even the direct energy to machines for making (i) the capital equipment used at Level 1 and (ii) the capital equipment for generating the raw materials for Level 1. In general, a process will require decreasing amounts of additional energy with an increase in the boundary level. While process analyses are commonly used to calculate energy requirements
Fig. 1. IFIAS [13] definition of system boundaries.
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
565
at Levels 1 and 2, input–output tables at industry and national levels are required to extend the calculations to Levels 3 and 4, respectively [2]. Level 1 includes the production and transport energy inputs to the process, while Level 2 also includes the energy required to make the (raw) material inputs to the process. As much as 90% of the total energy sequestered in the process should have been included in the first two levels [13]. Our study was aimed at capturing energy inputs into building materials up to Level 2. 3.3. TOE values and carbon coefficients In order to compare energy values from different sources, a common unit, i.e., tonnes of oil equivalent (TOE), is used. For example, 1 metric tonne (mT) of diesel has a TOE value of 1.05, while 1 mT of fuel oil has one of 0.98. One TOE is equivalent to 41.84 GJ. The conversion of oil to electricity has a generally well established efficiency of 36%, with 0.24 TOE of oil required to generate 1 MWh of thermal electricity. Hydropower on the other hand is assumed to be ‘‘l00% efficient’’; hence its TOE value (for 1 MWh) is taken as 0.086. For the conversion of Sri Lankan electricity consumption into TOE values, it is taken that 66% is hydro-electricity and 34% is thermal [14], thus yielding a TOE value of 0.138 (or 5.78 GJ/ MWh). The TOE of biomass (mainly firewood) is taken as 0.38 [15]. The gross quantity of carbon (mostly in the form of carbon dioxide) emitted in the manufacture of each material per unit of weight or volume is termed the ‘‘carbon coefficient’’. Most of the carbon emissions arise as a result of the burning of fossil fuels. The conversion factors used in this study [16,17] are 20.3 kgC/GJ for oil type fossil fuels and 54 kgC/GJ for thermal electricity. Carbon emissions for hydro-electricity would arise from biomass decaying within reservoirs. This however would be an order of magnitude less than for thermal electricity and would be very small where deep reservoirs and large installed capacities are involved [18], as in Sri Lanka; hence the carbon coefficient for hydro-electricity is taken as 0. Since 34% of Sri Lankan electricity is taken as thermal, a value of 18.36 kgC/GJ of electricity was used in this study. Burning of wood emits carbon dioxide too, and a value of 30 kgC/GJ can be used [16]. However, a net value of 0 kg of carbon per unit of biomass energy was taken in this study, on the assumption that wood fuels are sustainable; i.e. trees cut down for fuel will be replenished. There is considerable interest in fuel wood farming in Sri Lanka at present [19]. Any timber used in construction is also considered to ‘‘lock up’’ carbon and contributes a negative carbon coefficient; this was taken as 250 kg of carbon per cubic metre of wood [12]. Here too, there is the assumption that trees cut down for timber will be replaced: once again, there is a considerable effort at reforestation in Sri Lanka now. The above carbon will be released at the end of a notional life span of say 50 years; it would have to be accounted for in the environmental cost of the demolition stage (which is not considered here). Such locking up is not ascribed to formwork, which has a very short life span and will hence release carbon to the atmosphere fairly soon by decay or other means. Carbon is locked up in steel too, but the amount is negligible and hence ignored. Carbon dioxide is also released to the atmosphere via chemical reactions in the aluminium and cement industries. Therefore, a figure of 130 kg of carbon per tonne of aluminium was included in the carbon coefficient for aluminium and one of 142 kg of carbon per tonne of cement was included in the carbon coefficient for cement [12].
566
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
In order to obtain carbon coefficients from energy intensities for imported items, the embedded energy was multiplied by a factor of 20 kgC/GJ, based on a report by Honey and Buchanan [12]. This figure is based on a world consumption of 260,000 PJ of fossil fuel releasing 5.2 billion tonnes of carbon in 1984. 4. Hierarchical structuring of building materials All building elements (e.g. brick wall) and materials (e.g. bricks) and raw materials (e.g. clay) can be placed in an aggregation–decomposition hierarchy. Each stage of a building entity (e.g. brick wall, bricks, clay etc.) can be considered to have both proximate and remote energy inputs. Hence, we can say that the distinction between elements, materials and raw materials is blurred. The proximate energy comprises (i) the energy for the actual production process, (ii) the transport of raw materials and (iii) the transport of the energy sources. The remote energy comprises (i) the energy embedded in raw materials and (ii) the energy for extracting the energy sources used as direct energy. Although most researchers have included the energy costs of transporting building materials and elements [2,5,7,12], as we have done, they have not included the energy costs for transporting energy sources. Hence, we have also neglected values for the extraction and transport of energy sources: this includes transmission costs for electricity. They probably do not contribute very much to embedded energy, but should be subjected to future research. The above analysis basically captures most of the energy inputs associated with Levels 1 and 2 in the IFIAS scheme [13]—see Fig. 1. These two levels account for around 90% of the embedded energy in a product. The indirect energy of production (i.e. energy for lighting etc.) was omitted in most cases other than where production energy has been computed via electricity meter readings. Also, production processes in the informal sector (e.g. brick making, roof carpentry etc.) consume little if any indirect energy. Such energy can be neglected in Sri Lanka because energy for space heating (which is the greatest contribution to the indirect energy of production) is not required. The energy inputs into building entities can be summed incrementally, resulting in a hierarchical scheme. Thus, bricks are raw materials for the brick wall, and clay is a raw material for the bricks—see Fig. 2. The analysis can be represented by a table for each material, the total energy for a material at a given level becoming the embedded energy of raw material at the next higher level. Therefore, these tables can be termed ‘‘hierarchical tables’’. In this hierarchical structure, the lowest level any building material can reach is a raw material stage which cannot be divided further. These are called ‘‘primitives’’. The highest level is the finished building. Intermediate levels are the building material stage (e.g. cement) and element stage (e.g. brick wall), although the distinction between materials and elements is blurred, as explained before. Examples of hierarchical tables for a brick wall are given in Fig. 3. Although the final level considered in this research is the finished building [20], the hierarchy is open-ended (at least at the upper end) as it can be expanded to find the embedded energy of a city or even a country. It must be emphasised that the hierarchy proposed here is one of materials or products, whereas the IFIAS hierarchy is one of processes. They can be considered mutually ‘‘orthogonal’’ to each other. At every level of these material hierarchies, it is sought to incorporate the
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
567
Fig. 2. Hierarchical arrangement of materials for brickwork.
IFIAS hierarchy up to Level 2. The process (or time) span therefore extends from ‘‘the cradle’’ to construction. As discussed above, the energy embedded in a building material at intermediate levels in the hierarchy will in general comprise (i) production energy, (ii) energy to transport its component materials and (iii) the energy embedded in its component raw materials (with the energy to extract and transport energy sources being ignored). All these energies can also be classified according to input energy type as biomass, fossil fuel and electrical energy. Fig. 2 shows this schematically for the brick wall element, while the two-dimensional matrix type arrangement in the hierarchical tables (Fig. 3) enables energy contents of materials to be classified both according to process and fuel type. In addition to biomass, fossil fuel and electricity, the database (described below) has a field for ‘‘imports’’ (i.e. raw materials imported to the country), and
568
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 3. Hierarchical tables for energy analysis.
another one to account for carbon emissions (or storage) not related to the fuel type (see Section 3.3). These fields are not represented in Figs. 2 or 3 for the sake of simplicity. Each energy component (whether classified by process or fuel type) also has a minimum, average and maximum value since a number of samples were used to arrive at the energy contents. The embedded energies of materials are found for a convenient unit (e.g. 1000 nos. for bricks—see Fig. 3). However, the fraction of this energy to be carried forward as embedded energy in a brick wall will depend on the unit chosen for brick wall. These units are based on the Standard Method of Measurement of Building Works [21], in this case 10 m2, and the number of bricks required for it (equal to 1173). Hence, the embedded energy of 1000 bricks has to be factored by 1173/1000 (see Fig. 3). Such calculations are performed via an ‘‘amount’’ column in the tables. The tables also differentiate the energy input by energy type, whether biomass, fossil fuel or electricity. Similar hierarchical tables can be constructed for carbon emissions as well [20].
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
569
5. Database design 5.1. Requirements of the database In order to computerize this database of energy inputs and carbon emissions, some requirements need to be fulfilled. First, it must be possible for a common scheme to be applied to all materials at any level in the hierarchical structure described above, and also for user defined materials to be added. Although the scheme would be common, the list length of component raw materials would be different from one material to another. The second requirement, where the materials are concerned, is that the list entries would not merely be ‘‘text’’ type entries. The entry would not merely be a description, but would also be a material in its own right. Since the figures of the various energy values in many cases would be average values obtained from a number of establishments, the database must be capable of storing the original primary data, and also calculating the summary statistics, of which the mean (and, as described later the minimum and maximum values as well) would be used in the energy computations. The database was implemented using a relational database management system (RDBMS). i.e. Microsoft Access, to satisfy the above requirements. 5.2. Structure of the database There are three tables used to structure the database, namely (i) data table; (ii) samples’ table; and (iii) relations’ table. The data table is used to define the items (i.e. primitives, materials and elements) in the database. Each item can have data from many sources (i.e. from a number of manufacturers). These are defined in the samples’ table. Here, samples are related to the data table by the item code, where each item can have several samples. This samples’ table consists of a sample number and description with all its relevant data. The components that make up a given sample (for example, blocks, cement and sand in the case of blockwork) are defined in the relations’ table. Different quantities of raw materials may be needed by different manufacturers, and indeed even different types of raw materials; such variations can be accommodated in this scheme. There are one to many relationships between the data and samples’ tables and also between the samples’ and relations’ table. The advantage of this structure is that one item can accommodate a multiple number of samples. One disadvantage is that the relations have to be defined for each sample. This ‘‘disadvantage’’ however does allow the flexibility of defining different relations for different samples, although the need for that may be infrequent. Another feature of this structure is that the calculated values will not strictly represent a particular manufacturer. For example, even though a certain manufacturer of bricks obtains clay with a particular value of embedded energy, the embedded energy of clay used for the calculation of the embedded energy of these bricks will be an average value for the embedded energy of clay and not the manufacturer’s particular value (see Fig. 4). The database calculations are also performed based on maximum and minimum values of embedded energy (from across the range of manufacturers sampled), which are combined with the maximum and minimum transport energies (from across the range of transporters sampled) as well. In this way, the
570
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 4. Aggregation of energy costs in the database structure.
ranges obtained for embedded energies are likely to include most of the possibilities. This means that the ranges for items higher up in hierarchies will be wider. 5.3. Database calculations Queries were used to perform simple summary type calculations, i.e. to summarise all sample data into average, minimum and maximum values. The calculations were done by the implementation of a recursive algorithm, which stops when it reaches the basic raw materials (i.e. primitives). Once data entry (i.e. data from several sources for a particular item) is done, the average, maximum and minimum energies of that item are calculated. If the item is a primitive, the calculations are stopped and the final result is given as the total embedded energy. If the item is not a primitive, a recursive calculation is performed. It will calculate the energies for each sub-item of that item (for example, in the case of brickwork, the sub-items are bricks, cement and sand). If the sub-item is a primitive (e.g. sand), it will stop the calculations and will give the final result, but if it is not primitive (say bricks), it will again search for its sub-items (i.e. clay). This process continues until all the sub-items are primitives, and finally results in the aggregation of the average, minimum and maximum total embedded energies of the initial item. For carbon coefficient calculations, the average energy values entered will be multiplied by the factors given in Section 3.3. These calculations were also performed using a query. In addition to these fuel consumption based calculations for carbon coefficients, an additional amount is added or deducted for certain types of materials (as discussed in Section 3.3), accounting for locking up or releasing of carbon directly by the production process. The same recursive algorithm used for energy calculations is adopted to find the carbon coefficients of non-primitive materials.
Avg
Max
Min
Avg
Imported Max
Min
Avg
Total energy Max
96 601 192 1038 1082
0
0
0
96 0 0 601 0 0 192 0 0 1038 29,000 29,000 1642 0 0
0
5307
8576 10,605 0 6968 10,893 13,301 0 4282 4282 4282 0 1558 1664 1770 29,000 32,686 32,686 32,686 0 1191 1402 2026
0
111 0 0 0 0 0 0 87 99 111 9 17,968 17,968 17,968 129,500 129,500 129,500 147,477 147,477 147,477
96 601 192 1038 957
48
99 9
6201 10,003 12,345 671 794 923 0 0 0 3681 3681 3681 0 0 0 1366 1472 1578 0 0 0 2648 2648 2648 0 0 0 234 321 384
21
87 9 0
8528 10,525
0 0.00
Max Min
Electricity
80
5286
1000 Nos Brickwork (225 mm) 10 m2 Cement 1 mT Concrete (1:2:4) 1 m3 Steel 1 mT Timber sections 1 m3 75 mm 100 mm
Aggregates Aluminium extrusions Bricks
0 0.00
Min Avg
Min Avg
Max
Fossil fuel
Biomass
0 0.00
3
Qtty
1m 1 mT
Description
Table 1 Energy breakdown by fuel type (MJ/Qtty)
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580 571
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Steel Timber sections 75 mm 100 mm
Description
9 9 40 148 29 176 12 321
Avg
Min
Max
Min
Avg
Transport of raw materials
Production
36 36 36 2 1m 1 mT 17,968 17,968 17,968 9 1000 Nos 5286 8528 10,525 19 10 m2 0 0 0 58 1 mT 4195 4195 4195 29 1 m3 31 31 31 81 1 mT 3674 3674 3674 12 1 m3 773 878 1365 234
3
Qtty
Table 2 Energy breakdown by process (MJ/Qtty)
16 9 65 239 29 271 12 384
Max
Avg
49 54 129,500 129,500 2 8 6910 10,745 58 58 1446 1457 29,000 29,000 184 204
Min
Embedded in raw materials
Min 59 87 129,500 147,477 15 5307 13,124 6968 58 4282 1468 1558 29,000 32,686 277 1191
Max
Max 99 111 147,477 147,477 8576 10,605 10,893 13,363 4282 4282 1664 1770 32,686 32,686 1403 2026
Avg
Total energy
572 W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
573
Table 3 Carbon emission calculations (KgC/Qtty) Description
Qtty
From fuel
From imports
From material
Net value
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Steel Timber sections (75 mm 100 mm)
1 m3 1 mT 1000 Nos 10 m2 1 mT 1 m3 1 mT 1 m3
2 330 1 18 86 33 73 26
0 2590 0 0 0 0 580 0
0 130 0 23 142 45 0 307
2 3050 1 41 228 78 653 281
5.4. Database outputs Reports were used to show summary information relating to the data. These reports summarise the embedded energy of each item for a defined quantity in terms of: (i)
different fuel types, i.e. biomass, fossil fuel, electricity and imports (see Table 1 for some selected items), and (ii) different processes, i.e. production, transport of raw material and the energy embedded in raw materials (see Table 2 for some selected items).
In both reports, these different energy categories are shown as minimum, average and maximum values, and the total minimum, average and maximum embedded energies of each item are given in the last column. One of the problems in a comprehensive approach such as this is that the magnitudes of energy inputs would have large disparities. For example, in Fig. 3 (bricks), the production energy contributes 8528 MJ, while the embedded energy in the raw material contributes only 8 MJ. As such, it is difficult to ‘‘round up’’ values to reflect a level of accuracy that is justified. So, when the energy total in Fig. 3 (bricks) is given as 8576 MJ, it is not meant to indicate an accuracy to 4 significant digits, but rather to capture even the small contributions from all sources considered. Reports were also used to give the carbon coefficients (see Table 3 for some selected items), consisting of the carbon emissions from (i) all the fuels (i.e. biomass, fossil fuel, electricity), (ii) imports, (iii) particular carbon liberations or locking up (i.e. from the ‘‘material’’), and finally (iv) the net value of each item for a defined quantity. The database outputs can also be exported to a spreadsheet that contains a bill of quantities (BOQ) of a building. In this way, the variations in energy for alternative types of building materials (e.g. use of a block wall instead of a brick wall) can also be studied (see Section 7 below). The embedded energy of an entire building can be computed using the complete BOQ [3]. Table 4 gives a comparison between the minimum, average and maximum embedded energy values obtained in this study, and the ranges of values reported by other researchers, for selected commonly used building materials. The comparison shows that values obtained in this study
Aggregates Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Timber sections (75 mm 100 mm)
Description
1m 1000 Nos 10 m2 1 MT 1 m3 1 m3
3
Qtty
4 5 BSR 1 BSR 7
No. of samples
87 5307 6968 4282 1558 1191
99 8576 10,893 4282 1664 1402
111 10,605 13,364 4282 1770 2065
29–115 6300–10,000 10,000–11,920 3400–6100 1060–2410 848
Energy (MJ)
Max
Min
Avg
Other researchers
Total energy (MJ)
Table 4 Comparison of embedded energies with other researchers (MJ/Qtty)
Clough and Martyn [24] Baird and Chan [2] Baird and Chan [2] Clough and Martyn [24] Baird and Chan [2] Baird and Chan [2]
Source
574 W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
575
are broadly consistent with the findings of others. Table 4 also indicates the number of establishments included for each building material in the process analysis. Cement production is carried out in only one factory. The values for some composite elements have been obtained through raw material quantities in the building schedule of rates [22] used in Sri Lanka.
6. Energy quality analysis Though it may appear that the least energy intensive material is the preferred option, there are other issues that have to be considered too. One of the main issues is that of the differences in energy quality. For the purpose of this study, a ‘‘high quality’’ energy or energy source is considered as one that can be used for a variety of end-uses with relative ease [11]. For example, electricity is a high quality energy because it can be used for a range of uses such as cooking and heating, cooling (through air conditioners and refrigerators), locomotion (via motors) and powering up equipment (e.g. television, computers etc.), while biomass is a low quality energy because it can only be used for heating and cooking by burning; fossil fuel is in between, because although it has to be burnt too, it can be used for locomotion via engines. The next step should be to find a methodology for comparing different energy qualities. One approach to this could be to assess the amount of the lower quality energy that is required to obtain a given quantity of the higher quality energy via established processes. The following processes were considered: (i) electricity generation by burning fossil fuels, (ii) obtaining electricity by burning fuel wood (dendro-electricity). All the energy contents for building materials can then be compared on the basis of the total equivalent amount of lowest quality energy (i.e. biomass energy). The resulting energy contents will then have a ‘‘bio-equivalent’’ basis. It must be appreciated that the above analyses are essentially efficiency based analyses. The disparity between the qualities of biomass, fossil fuel and electricity based energy is likely to be greater than that reflected by these efficiency calculations. For example, Odum [10] values a unit of electrical energy at four times that of fossil fuel energy, whereas a purely efficiency based approach will result in a factor of only 2.78 (see below). This study adopts the efficiency based approach, since it has a clear basis and will not distort the original energy values too much. Since electricity has the highest energy quality, it is used as a reference, and is assigned an ‘‘efficiency’’ of 100%, whatever the primary fuel used for its generation; this is similar to the ideas associated with assigning a TOE value for hydro-electricity (see Section 3.3). In obtaining electricity by burning fossil fuels, the thermal efficiency is assumed as 36%. Therefore, 2.78 GJ of fossil fuel energy are equivalent to 1 GJ of electrical energy. The process of obtaining electricity by burning firewood (dendro-electricity) can be used to find the relationship between electricity and biomass. When 10 million metric tonnes of fuel wood are used, 10,000 GWh of electricity could be generated [19]. Therefore, 1 mT of fuel wood will yield 1 MWh. But, the fuel wood (which is drier than the conventional firewood) had a TOE value of 0.43 per mT [19], i.e. an energy content of 18 GJ. Now, for electricity that is
576
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
100% efficient, 1 MWh can be produced with (or is equivalent to) only 3.6 GJ. Therefore, in order to obtain 1 GJ of electrical energy, ð18=3:6 ¼Þ5 GJ of biomass energy have to be consumed. For the system in the mutually consistent scheme, the difference in quality associated with the conversion from biomass to electrical energy through fossil fuel energy must be identical to that associated with the direct conversion from biomass to electrical energy. Since there is a ‘‘quality factor’’ of 2.78 between fossil fuel and electricity, and one of five between biomass and electricity, the quality factor between biomass and fossil fuel can be obtained as 1.8 (i.e. 5/2.78). More studies are required to study these differences in quality (including their discrepancies), perhaps using exergy analyses, as suggested below. For the moment, the above factors are adopted as a first approximation. The final database outputs of average embedded energies of selected building items on a bioequivalent basis are shown in Table 5, together with their average energy contents on a TOE basis. The values in the ‘‘fossil fuel’’ column in Table 5 are obtained by factoring by 1.8 (see above) the values in the ‘‘fossil fuel (Avg)’’ column in Table 1. Where the ‘‘electricity’’ column in Table 5 is concerned, it should be noted that the Sri Lankan electricity mix (66% hydro and 34% thermal) was found to have a TOE value of 0.138 (or 5.78 GJ/MWh). On the other hand, the generation of electricity using biomass required 18 GJ for 1 MWh. Hence, the factor for converting the Table 1 ‘‘electricity (Avg)’’ values to the ‘‘electricity’’ values in Table 5 is (18=5:78 ¼Þ3:11. The embedded energy on a bio-equivalent basis will always be greater than (or equal to) those on the conventional TOE basis, because the former is based on the biomass energy that would have had to be used in order to produce a building material. If another (higher quality) energy source was actually used, the equivalent biomass energy would be greater, as described above, and hence the material is ‘‘penalised’’ for the use of that higher quality energy. Thus, the energies (on a bio-equivalent basis) for materials produced using industrialised processes (such as cement, steel and aluminium) are almost double their energies on a TOE basis. On the other hand, for materials (such as bricks) that require predominantly biomass energy inputs, the difference between their energies on a bio-equivalent and a TOE basis is negligible. Table 5 Energy breakdown—on a bio-equivalent basis (MJ/Qtty) Description
Qtty
Biomass
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2.4) Steel Timber sections 75 mm 100 mm
0 1 m3 1 mT 0 1000 Nos 8528 10 m2 10,003 1 mT 0 1 m3 0 1 mT 0 1 m3 0
Fossil fuel
Electricity Imported Total energy Total energy (bio-eq) (TOE)
178 17 86 1429 6625 2649 4767 577
0 55,881 0 299 1869 598 3227 3364
0 233,100 0 0 0 0 52,200 0
178 288,998 8614 11,731 8494 3247 60,194 3941
99 147,477 8576 10,893 4282 1664 32,686 1403
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
577
The energy quality analysis adopted in this research is an efficiency based approach essentially using the first law of thermodynamics. However, the first law is incapable of properly characterising the thermodynamic losses in a process. This can be remedied by introducing a parameter called exergy through the combination of the first and second laws. The exergy analysis [23] resulting from the combination of the first and second laws provides a measure of the maximum work that can be extracted from a given thermodynamic system during a process; in other words, a way of quantifying the thermodynamic potential of the system. Such an analysis, however, was outside the scope of this work.
7. Comparison of building material alternatives This paper will give only a single example of comparing building materials on an energy content and carbon emission basis; the subject is treated more extensively elsewhere [3]. The energy inputs and carbon emissions corresponding to purlins made out of three different materials, i.e. timber, steel and prestressed concrete, were analysed. The cross-section of each purlin was selected so that it could carry a uniformly distributed load of 1 kN/m over a span of 3 m, without violating deflection or strength criteria. The cross-section required for timber purlins is 75 mm 100 mm. The raw material required for a timber purlin is air-dry treated timber of 0.02 m3. The cross-section required for steel purlins is a 75 mm 75 mm 6 mm angle section. The raw material required is 20 kg of steel. The cross-section required for prestressed concrete purlins is 60 mm 120 mm with 3 nos. of 6 mm diameter high tensile wires. The raw material requirement for a prestressed concrete purlin is 2.3 kg of steel, 9.3 kg of cement, 0.02 m3 of aggregates and 0.01 m3 of sand. The energy inputs corresponding to timber, prestressed concrete and steel purlins, which have similar spans and approximately similar bending capacities, are 29, 121 and 654 MJ, respectively, on a TOE basis, indicating that steel is around 23 times more energy intensive than timber and that prestressed concrete is around four times more energy intensive than timber. However, on a bio-equivalent basis, i.e. using values like those in the penultimate column of Table 5, the ratios are 15 and 3, respectively (see Fig. 5).
Fig. 5. Embedded energy comparison for purlins.
578
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 6. Carbon emission comparison for purlins.
Where carbon emissions are concerned Fig. 6 shows that 5.61 kg of carbon is stored in the timber purlin compared to 3.73 kg of carbon and 13.06 kg of carbon released to the atmosphere via prestressed concrete and steel purlins, respectively. Therefore, it appears that timber is the preferred option, and that steel is the least desirable one from an energy consumption and carbon emission point of view. This is consistent with other research findings [5,12]. However, because of the worldwide phenomenon of deforestation, the use of timber will be possible only with aggressive reforestation. 8. Conclusion 1. All building elements (e.g. brick wall), materials (e.g. bricks) and ‘‘primitive’’ raw materials (e.g. clay) can be placed in an aggregation–decomposition hierarchy. Each stage of a building material can be considered to have energy inputs that are defined as ‘‘proximate’’ (i.e. production energy and transport of raw materials) and ‘‘remote’’ (i.e. embedded energy of raw materials). This product oriented hierarchy complements the IFIAS [13] process oriented hierarchy and captures most of the energy inputs associated with Levels 1 and 2 in the IFIAS [13] scheme, where these two levels account for around 90% of the embedded energy in a product. 2. The relational database that was designed and the recursive algorithm that was developed can be used to represent and calculate the embedded energies and carbon coefficients of building materials and elements that are hierarchically arranged, such that some materials are raw materials of others. It can also handle multiple sources of data and perform calculations to give the energies corresponding to average, maximum and minimum values. The energy data are also classified in two ways in the database—i.e. (i) by process (production, transport of raw materials and embedded energy of raw materials) and (ii) by fuel type (biomass, fossil fuel, electricity and imports). The database outputs can be exported to a spreadsheet to perform other analyses. 3. According to energy quality calculations carried out (based on efficiency considerations), 1 GJ of electricity is equivalent to 5 GJ of biomass energy on the one hand, and to 2.78 GJ of fossil fuel energy on the other. For the conversion system to be mutually consistent, the biomass to fossil fuel factor is obtained as 1.8 (i.e. 5/2.78). The energy contents of building materials could thus be compared on the basis of an equivalent amount of the lowest quality
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
579
energy (i.e. biomass energy), and the energy contents were obtained on a ‘‘bio-equivalent’’ basis. 4. The analysis carried out indicates that among the more common Sri Lankan construction materials considered, the lowest energy option is timber, while the highest is steel, with concrete in between. Timber products have negative carbon coefficients as well, i.e. they store more carbon than is emitted in their use for construction, while the carbon emissions from steel products will be greater than those from concrete products.
Acknowledgements The funding received from the Energy Conservation Fund for this research is greatly appreciated. The assistance for database programming given by Mr. G.N. Kodagoda is also gratefully acknowledged. References [1] Energy in the built environment. Report G16: 1988. Stockholm: Swedish Council for Building Research; 1988. p. 184. [2] Baird G, Chan SA. Energy cost of houses and light construction buildings. Report 76. New Zealand Energy Research and Development Committee. Auckland: University of Auckland; 1983. [3] Pooliyadda SP, Dias WPS. The significance of embedded energy for buildings in a tropical country. Engineer, Sri Lanka 2001;34(3):25–35. [4] Statistical abstract of the democratic socialist republic of Sri Lanka. Dept. of Census and Statistics, . Colombo: Dept. of Census and Statistics; 1995. [5] Chen TY, Burnett J, Chau CK. Analysis of embodied energy in the residential building of Hong Kong. Energy 2001;26(4):323–40. [6] Howard N, Edwards S, Anderson J. BRE Methodology for environmental profiles of construction materials, components and buildings. London: CRC; 1999. [7] Adalberth K. Energy use during the life cycle of buildings: a method. Building Environ 1997;32(4):317–20. [8] Cole RJ, Rousseau D. Environmental auditing for building construction: energy and air pollution indices for building materials. Building Environ 1992;27(1):23–30. [9] Harris DJ. A quantitative approach to the assessment of the environmental impact of building materials. Building Environ 1999;34(6):751–8. [10] Odum HT. Environment, power and society. New York: Wiley Interscience; 1971. [11] Perera KKYW. Energy status of Sri Lanka. Colombo: Institute of Policy Studies; 1992. [12] Honey BG, Buchanan AH. Environmental impacts of the New Zealand building industry, Dept. of Civil Engineering Research Report 92/2. Christchurch: University of Canterbury; 1992. [13] Energy analysis workshop on methodology and convention, Workshop Report No. 6. Stockholm: International Federation of Institute for advanced Study; 1974. [14] Siyambalapitiya T. Electricity pricing policy in Sri Lanka. Colombo: Institute of Policy Studies; 1997. [15] Folcy G, Barnard G. Biomass gasification in developing countries. Technical Report No. 1, London: International Institute for Environment and Development; 1983. [16] Guidelines for national greenhouse gas inventories: Workbook Vol. 2, Intergovernmental Panel on Climate Change; 1996. [17] Turbott C, Pool F, Vickers G. Energy management and the greenhouse effect—a background paper on energy management and the 1988 Toronto goal. Wellington: Ministry of Commerce; 1991.
580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
[18] Dams and development: A new framework for decision-making. Report on the World Commission on Dams. Earthscan Publishers; 2000. [19] Joseph PG. Energy conservation and biomass energy. LIFE—World Bank Seminar on Energy and Environment. Colombo: Lanka Institute for Environment; 1998. [20] Pooliyadda SP. Energy content and carbon emission audit of building materials. M. Phil. Thesis, Moratuwa: University of Moratuwa; 2000. [21] Standard method of measurement of building works, 7th ed.. . London: The Royal Institute of Chartered Surveyors; 1988 [SMM7]. [22] Building schedule of rates. . Colombo: Ministry of Local Government; 1988. [23] Costa MM, Schaeffer R, Worrell E. Exergy accounting of energy and materials flows in steel production systems. Energy 2001;26(4):363–84. [24] Clough R, Martyn R. Environmental impact of building and construction materials. Project Report 12, Vol. B— Mineral products. London: Construction Industry Research and Information Association; 1995.
Quality based energy contents and carbon coefficients for building materials: A systems approach W.P.S. Dias , S.P. Pooliyadda Department of Civil Engineering, University of Moratuwa, Moratuwa, Sri Lanka Received 21 December 2001
Abstract This paper describes a computerised relational database management system to represent and calculate (i) the energy inputs to a building material or element up to the point of utilisation or construction and (ii) the carbon emitted to the atmosphere up to that point. All building elements, materials and ‘‘primitive’’ raw materials are placed in an aggregation–decomposition hierarchy. The process analysis carried out here captures around 90% of the embedded energy in a product. The database can handle multiple sources of data and perform calculations to give the average, maximum and minimum embedded energies, which are also classified according to fuel type (i.e. biomass, fossil fuel and electricity) and process stage (i.e. production energy, transport energy for raw materials and energy embedded in raw materials). The embedded energy requirements are also calculated on the basis of the lowest quality energy, namely biomass energy (‘‘bio-equivalent’’ basis), in addition to the more conventional basis of tonnes of oil equivalent. Timber was found to be the preferred option and steel the least desirable, with concrete in between, from an energy consumption and carbon emission point of view. # 2003 Elsevier Ltd. All rights reserved.
1. Introduction Energy and environmental considerations are becoming increasingly important today. This is primarily because there is an appreciation of the non-renewable and limited nature of many of our energy resources and environmental assets. However, any sort of development inevitably consumes energy resources and depletes environmental assets. This is particularly true of industrial development, and the construction industry is one of the largest contributors to gross
Corresponding author. Fax: +94-11-2650622. E-mail address: [email protected] (W.P.S. Dias).
0360-5442/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2003.10.001
562
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
domestic capital formation in many countries. As such, the above concerns regarding energy and environmental issues should particularly be applied to this industry as well. Most of the construction industry energy considerations have been made with respect to the operational phase—e.g. energy saving buildings [1]. In cold countries such as UK and USA, the ratio between construction and annual operational energy is around 3–6; in a slightly warmer temperate-zone country (such as New Zealand), this ratio goes up to around 9 [2]. However, in Sri Lanka, the operational energy is likely to be very small. In fact, we have shown in a companion paper [3] that the ratio of embedded energy to annual operational energy can be as high as 35. This is because no space heating or air conditioning is used in such buildings, which account for around 65% of the capital formation through building construction in Sri Lanka [4]. Therefore, there is a clear need for research into energy inputs and environmental costs of building materials, in order for building construction industry professionals and policy makers to make choices regarding the type of building materials that should be constructed. This is particularly so for residential buildings, where operational energy consumption is likely to be less than in commercial buildings, and especially for countries where the housing stock continues to increase significantly. The significance of embedded energy in such residential buildings has been appreciated by other researchers too [5]. The building research establishment (BRE) in the UK has recently issued ‘‘environmental profiles’’ of selected raw materials [6]. This is in many ways a more comprehensive study than ours, as it deals with energy inputs throughout an anticipated 60 year building element life cycle (inclusive of maintenance and demolition). Adalberth [7] proposed a method for assessing life cycle energy use for buildings. Our study considered energy inputs only up to the construction stage. The BRE report [6] also gives a range of environmental impacts, covering emissions to air (such as carbon dioxide and acidic gases), water and land. Cole and Rousseau [8] have computed a composite air emission index, obtained by summing the ratio of emissions to ambient air quality limits for particulates, SO2, NO2 and CO; they also give non-energy related particulate and hydrocarbon emissions, in addition to carbon dioxide emissions for various materials. Harris [9] has attempted to quantify environmental impacts such as depletion of non-renewable resources, local disruption due to extraction, indoor toxicity effects and potential for recycling. Our study dealt only with carbon emissions as environmental impacts. However, the issues relating to construction or embedded energy of building materials will vary from one country to another, depending among other things considerably on the sources of energy used for manufacturing. In Sri Lanka, for example, there is a wide range of energy sources used for building materials manufacture, from firewood for brick and tile production to fossil fuel and electricity for cement and steel production. An energy policy would clearly need to take into account such differences in quality of the energy sources [10,11] that are used for manufacture, and not merely the quantity. This focus on energy quality is perhaps the main original contribution of this paper.
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
563
2. Objectives and approach In this paper, we describe some research carried out to obtain embedded energy contents and carbon emission coefficients for a variety of Sri Lankan building materials. The objectives of the research described in this paper are as follows: 1. To develop a computerised database for embedded energies and carbon coefficients of building materials in a hierarchical structure. 2. To compare the embedded energy requirements of building materials in a way that reflects the quality of the energy sources used. 3. To indicate how energy contents and carbon coefficients for alternative materials of construction can be compared; this is more fully dealt with elsewhere [3]. The ‘‘systems’’ nature of the approach adopted was reflected mainly in: (i)
The adoption of an aggregation–decomposition hierarchy for building materials and its implementation in a computerised database; and (ii) The implementation of the concept of ‘‘energy quality’’ through a ‘‘mutually consistent’’ scheme for three types of energy sources, namely biomass, fossil fuel and electricity.
3. Methodology 3.1. Energy analysis There are four commonly accepted methods of energy analysis [2], namely 1. 2. 3. 4.
Statistical analysis Input–output analysis Process analysis Eco-energetics
The method used will depend mainly on the overall objectives of the analysis and the availability of data. Statistical analysis is based on national statistics regarding energy supplies to and production outputs from industries, thus enabling an estimation of energy requirements per unit output. Input–output analysis uses input–output tables that reflect money value transactions between different sectors of the economy. If the energy cost for each industry is known, then their energy inputs (including indirect inputs for heating, lighting etc.) can be found, based on their purchases from the energy supply sector. Process analysis is the most frequently used method. It involves the study of the inputs and outputs in a process at the level of an individual industry (as opposed to the national level as in the first two methods above). The energy requirements of a process or product are determined from all the material, equipment and energy inputs into the process. Energy analyses are seldom better than 10% accurate [12]. The total energy requirement of a process or industry can vary
564
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
widely between manufacturers because of (i) the efficiency of energy, material and labour use; (ii) technology used for the process; (iii) difference in handling of wastes; and (iv) differences in raw materials input. Process analysis is used in this study too, following Honey and Buchanan [12], because it was convenient to determine the inputs and outputs for a process through visits to producers. Also, Sri Lankan national statistics were not very comprehensive, especially since many building materials are produced in the informal sector (e.g. brick manufacture and sand extraction). All the material and energy inputs for a given amount of output were identified and quantified. Also, following Baird and Chan [2] and most other workers, human energy and energy associated with environmental costs, have not been included in the energy analysis, mainly because of the complexity and ambiguity associated with such factors. The inclusion of such factors, advocated by Odum [10] for example, is what eco-energetics is about; it places an ‘‘energy equivalent’’ value on all forms of energy. However, our study does make an independent assessment of environmental costs in terms of carbon dioxide emissions. It also makes distinctions between the quality of various types of energy, albeit on a basis different to that of Odum [10]. 3.2. System boundaries It is important to define the system boundaries when calculating embedded energies of materials. The boundary adopted depends in part on the availability of data but mainly on the overall aims and thus the assumptions that are made. The IFIAS workshop [13] suggested that four boundary levels could be drawn for most materials, as illustrated in Fig. 1. The lower levels (e.g. Level 1) focus on the immediate production of a material, while the higher levels (e.g. Level 4) try to capture even the direct energy to machines for making (i) the capital equipment used at Level 1 and (ii) the capital equipment for generating the raw materials for Level 1. In general, a process will require decreasing amounts of additional energy with an increase in the boundary level. While process analyses are commonly used to calculate energy requirements
Fig. 1. IFIAS [13] definition of system boundaries.
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
565
at Levels 1 and 2, input–output tables at industry and national levels are required to extend the calculations to Levels 3 and 4, respectively [2]. Level 1 includes the production and transport energy inputs to the process, while Level 2 also includes the energy required to make the (raw) material inputs to the process. As much as 90% of the total energy sequestered in the process should have been included in the first two levels [13]. Our study was aimed at capturing energy inputs into building materials up to Level 2. 3.3. TOE values and carbon coefficients In order to compare energy values from different sources, a common unit, i.e., tonnes of oil equivalent (TOE), is used. For example, 1 metric tonne (mT) of diesel has a TOE value of 1.05, while 1 mT of fuel oil has one of 0.98. One TOE is equivalent to 41.84 GJ. The conversion of oil to electricity has a generally well established efficiency of 36%, with 0.24 TOE of oil required to generate 1 MWh of thermal electricity. Hydropower on the other hand is assumed to be ‘‘l00% efficient’’; hence its TOE value (for 1 MWh) is taken as 0.086. For the conversion of Sri Lankan electricity consumption into TOE values, it is taken that 66% is hydro-electricity and 34% is thermal [14], thus yielding a TOE value of 0.138 (or 5.78 GJ/ MWh). The TOE of biomass (mainly firewood) is taken as 0.38 [15]. The gross quantity of carbon (mostly in the form of carbon dioxide) emitted in the manufacture of each material per unit of weight or volume is termed the ‘‘carbon coefficient’’. Most of the carbon emissions arise as a result of the burning of fossil fuels. The conversion factors used in this study [16,17] are 20.3 kgC/GJ for oil type fossil fuels and 54 kgC/GJ for thermal electricity. Carbon emissions for hydro-electricity would arise from biomass decaying within reservoirs. This however would be an order of magnitude less than for thermal electricity and would be very small where deep reservoirs and large installed capacities are involved [18], as in Sri Lanka; hence the carbon coefficient for hydro-electricity is taken as 0. Since 34% of Sri Lankan electricity is taken as thermal, a value of 18.36 kgC/GJ of electricity was used in this study. Burning of wood emits carbon dioxide too, and a value of 30 kgC/GJ can be used [16]. However, a net value of 0 kg of carbon per unit of biomass energy was taken in this study, on the assumption that wood fuels are sustainable; i.e. trees cut down for fuel will be replenished. There is considerable interest in fuel wood farming in Sri Lanka at present [19]. Any timber used in construction is also considered to ‘‘lock up’’ carbon and contributes a negative carbon coefficient; this was taken as 250 kg of carbon per cubic metre of wood [12]. Here too, there is the assumption that trees cut down for timber will be replaced: once again, there is a considerable effort at reforestation in Sri Lanka now. The above carbon will be released at the end of a notional life span of say 50 years; it would have to be accounted for in the environmental cost of the demolition stage (which is not considered here). Such locking up is not ascribed to formwork, which has a very short life span and will hence release carbon to the atmosphere fairly soon by decay or other means. Carbon is locked up in steel too, but the amount is negligible and hence ignored. Carbon dioxide is also released to the atmosphere via chemical reactions in the aluminium and cement industries. Therefore, a figure of 130 kg of carbon per tonne of aluminium was included in the carbon coefficient for aluminium and one of 142 kg of carbon per tonne of cement was included in the carbon coefficient for cement [12].
566
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
In order to obtain carbon coefficients from energy intensities for imported items, the embedded energy was multiplied by a factor of 20 kgC/GJ, based on a report by Honey and Buchanan [12]. This figure is based on a world consumption of 260,000 PJ of fossil fuel releasing 5.2 billion tonnes of carbon in 1984. 4. Hierarchical structuring of building materials All building elements (e.g. brick wall) and materials (e.g. bricks) and raw materials (e.g. clay) can be placed in an aggregation–decomposition hierarchy. Each stage of a building entity (e.g. brick wall, bricks, clay etc.) can be considered to have both proximate and remote energy inputs. Hence, we can say that the distinction between elements, materials and raw materials is blurred. The proximate energy comprises (i) the energy for the actual production process, (ii) the transport of raw materials and (iii) the transport of the energy sources. The remote energy comprises (i) the energy embedded in raw materials and (ii) the energy for extracting the energy sources used as direct energy. Although most researchers have included the energy costs of transporting building materials and elements [2,5,7,12], as we have done, they have not included the energy costs for transporting energy sources. Hence, we have also neglected values for the extraction and transport of energy sources: this includes transmission costs for electricity. They probably do not contribute very much to embedded energy, but should be subjected to future research. The above analysis basically captures most of the energy inputs associated with Levels 1 and 2 in the IFIAS scheme [13]—see Fig. 1. These two levels account for around 90% of the embedded energy in a product. The indirect energy of production (i.e. energy for lighting etc.) was omitted in most cases other than where production energy has been computed via electricity meter readings. Also, production processes in the informal sector (e.g. brick making, roof carpentry etc.) consume little if any indirect energy. Such energy can be neglected in Sri Lanka because energy for space heating (which is the greatest contribution to the indirect energy of production) is not required. The energy inputs into building entities can be summed incrementally, resulting in a hierarchical scheme. Thus, bricks are raw materials for the brick wall, and clay is a raw material for the bricks—see Fig. 2. The analysis can be represented by a table for each material, the total energy for a material at a given level becoming the embedded energy of raw material at the next higher level. Therefore, these tables can be termed ‘‘hierarchical tables’’. In this hierarchical structure, the lowest level any building material can reach is a raw material stage which cannot be divided further. These are called ‘‘primitives’’. The highest level is the finished building. Intermediate levels are the building material stage (e.g. cement) and element stage (e.g. brick wall), although the distinction between materials and elements is blurred, as explained before. Examples of hierarchical tables for a brick wall are given in Fig. 3. Although the final level considered in this research is the finished building [20], the hierarchy is open-ended (at least at the upper end) as it can be expanded to find the embedded energy of a city or even a country. It must be emphasised that the hierarchy proposed here is one of materials or products, whereas the IFIAS hierarchy is one of processes. They can be considered mutually ‘‘orthogonal’’ to each other. At every level of these material hierarchies, it is sought to incorporate the
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
567
Fig. 2. Hierarchical arrangement of materials for brickwork.
IFIAS hierarchy up to Level 2. The process (or time) span therefore extends from ‘‘the cradle’’ to construction. As discussed above, the energy embedded in a building material at intermediate levels in the hierarchy will in general comprise (i) production energy, (ii) energy to transport its component materials and (iii) the energy embedded in its component raw materials (with the energy to extract and transport energy sources being ignored). All these energies can also be classified according to input energy type as biomass, fossil fuel and electrical energy. Fig. 2 shows this schematically for the brick wall element, while the two-dimensional matrix type arrangement in the hierarchical tables (Fig. 3) enables energy contents of materials to be classified both according to process and fuel type. In addition to biomass, fossil fuel and electricity, the database (described below) has a field for ‘‘imports’’ (i.e. raw materials imported to the country), and
568
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 3. Hierarchical tables for energy analysis.
another one to account for carbon emissions (or storage) not related to the fuel type (see Section 3.3). These fields are not represented in Figs. 2 or 3 for the sake of simplicity. Each energy component (whether classified by process or fuel type) also has a minimum, average and maximum value since a number of samples were used to arrive at the energy contents. The embedded energies of materials are found for a convenient unit (e.g. 1000 nos. for bricks—see Fig. 3). However, the fraction of this energy to be carried forward as embedded energy in a brick wall will depend on the unit chosen for brick wall. These units are based on the Standard Method of Measurement of Building Works [21], in this case 10 m2, and the number of bricks required for it (equal to 1173). Hence, the embedded energy of 1000 bricks has to be factored by 1173/1000 (see Fig. 3). Such calculations are performed via an ‘‘amount’’ column in the tables. The tables also differentiate the energy input by energy type, whether biomass, fossil fuel or electricity. Similar hierarchical tables can be constructed for carbon emissions as well [20].
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
569
5. Database design 5.1. Requirements of the database In order to computerize this database of energy inputs and carbon emissions, some requirements need to be fulfilled. First, it must be possible for a common scheme to be applied to all materials at any level in the hierarchical structure described above, and also for user defined materials to be added. Although the scheme would be common, the list length of component raw materials would be different from one material to another. The second requirement, where the materials are concerned, is that the list entries would not merely be ‘‘text’’ type entries. The entry would not merely be a description, but would also be a material in its own right. Since the figures of the various energy values in many cases would be average values obtained from a number of establishments, the database must be capable of storing the original primary data, and also calculating the summary statistics, of which the mean (and, as described later the minimum and maximum values as well) would be used in the energy computations. The database was implemented using a relational database management system (RDBMS). i.e. Microsoft Access, to satisfy the above requirements. 5.2. Structure of the database There are three tables used to structure the database, namely (i) data table; (ii) samples’ table; and (iii) relations’ table. The data table is used to define the items (i.e. primitives, materials and elements) in the database. Each item can have data from many sources (i.e. from a number of manufacturers). These are defined in the samples’ table. Here, samples are related to the data table by the item code, where each item can have several samples. This samples’ table consists of a sample number and description with all its relevant data. The components that make up a given sample (for example, blocks, cement and sand in the case of blockwork) are defined in the relations’ table. Different quantities of raw materials may be needed by different manufacturers, and indeed even different types of raw materials; such variations can be accommodated in this scheme. There are one to many relationships between the data and samples’ tables and also between the samples’ and relations’ table. The advantage of this structure is that one item can accommodate a multiple number of samples. One disadvantage is that the relations have to be defined for each sample. This ‘‘disadvantage’’ however does allow the flexibility of defining different relations for different samples, although the need for that may be infrequent. Another feature of this structure is that the calculated values will not strictly represent a particular manufacturer. For example, even though a certain manufacturer of bricks obtains clay with a particular value of embedded energy, the embedded energy of clay used for the calculation of the embedded energy of these bricks will be an average value for the embedded energy of clay and not the manufacturer’s particular value (see Fig. 4). The database calculations are also performed based on maximum and minimum values of embedded energy (from across the range of manufacturers sampled), which are combined with the maximum and minimum transport energies (from across the range of transporters sampled) as well. In this way, the
570
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 4. Aggregation of energy costs in the database structure.
ranges obtained for embedded energies are likely to include most of the possibilities. This means that the ranges for items higher up in hierarchies will be wider. 5.3. Database calculations Queries were used to perform simple summary type calculations, i.e. to summarise all sample data into average, minimum and maximum values. The calculations were done by the implementation of a recursive algorithm, which stops when it reaches the basic raw materials (i.e. primitives). Once data entry (i.e. data from several sources for a particular item) is done, the average, maximum and minimum energies of that item are calculated. If the item is a primitive, the calculations are stopped and the final result is given as the total embedded energy. If the item is not a primitive, a recursive calculation is performed. It will calculate the energies for each sub-item of that item (for example, in the case of brickwork, the sub-items are bricks, cement and sand). If the sub-item is a primitive (e.g. sand), it will stop the calculations and will give the final result, but if it is not primitive (say bricks), it will again search for its sub-items (i.e. clay). This process continues until all the sub-items are primitives, and finally results in the aggregation of the average, minimum and maximum total embedded energies of the initial item. For carbon coefficient calculations, the average energy values entered will be multiplied by the factors given in Section 3.3. These calculations were also performed using a query. In addition to these fuel consumption based calculations for carbon coefficients, an additional amount is added or deducted for certain types of materials (as discussed in Section 3.3), accounting for locking up or releasing of carbon directly by the production process. The same recursive algorithm used for energy calculations is adopted to find the carbon coefficients of non-primitive materials.
Avg
Max
Min
Avg
Imported Max
Min
Avg
Total energy Max
96 601 192 1038 1082
0
0
0
96 0 0 601 0 0 192 0 0 1038 29,000 29,000 1642 0 0
0
5307
8576 10,605 0 6968 10,893 13,301 0 4282 4282 4282 0 1558 1664 1770 29,000 32,686 32,686 32,686 0 1191 1402 2026
0
111 0 0 0 0 0 0 87 99 111 9 17,968 17,968 17,968 129,500 129,500 129,500 147,477 147,477 147,477
96 601 192 1038 957
48
99 9
6201 10,003 12,345 671 794 923 0 0 0 3681 3681 3681 0 0 0 1366 1472 1578 0 0 0 2648 2648 2648 0 0 0 234 321 384
21
87 9 0
8528 10,525
0 0.00
Max Min
Electricity
80
5286
1000 Nos Brickwork (225 mm) 10 m2 Cement 1 mT Concrete (1:2:4) 1 m3 Steel 1 mT Timber sections 1 m3 75 mm 100 mm
Aggregates Aluminium extrusions Bricks
0 0.00
Min Avg
Min Avg
Max
Fossil fuel
Biomass
0 0.00
3
Qtty
1m 1 mT
Description
Table 1 Energy breakdown by fuel type (MJ/Qtty)
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580 571
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Steel Timber sections 75 mm 100 mm
Description
9 9 40 148 29 176 12 321
Avg
Min
Max
Min
Avg
Transport of raw materials
Production
36 36 36 2 1m 1 mT 17,968 17,968 17,968 9 1000 Nos 5286 8528 10,525 19 10 m2 0 0 0 58 1 mT 4195 4195 4195 29 1 m3 31 31 31 81 1 mT 3674 3674 3674 12 1 m3 773 878 1365 234
3
Qtty
Table 2 Energy breakdown by process (MJ/Qtty)
16 9 65 239 29 271 12 384
Max
Avg
49 54 129,500 129,500 2 8 6910 10,745 58 58 1446 1457 29,000 29,000 184 204
Min
Embedded in raw materials
Min 59 87 129,500 147,477 15 5307 13,124 6968 58 4282 1468 1558 29,000 32,686 277 1191
Max
Max 99 111 147,477 147,477 8576 10,605 10,893 13,363 4282 4282 1664 1770 32,686 32,686 1403 2026
Avg
Total energy
572 W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
573
Table 3 Carbon emission calculations (KgC/Qtty) Description
Qtty
From fuel
From imports
From material
Net value
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Steel Timber sections (75 mm 100 mm)
1 m3 1 mT 1000 Nos 10 m2 1 mT 1 m3 1 mT 1 m3
2 330 1 18 86 33 73 26
0 2590 0 0 0 0 580 0
0 130 0 23 142 45 0 307
2 3050 1 41 228 78 653 281
5.4. Database outputs Reports were used to show summary information relating to the data. These reports summarise the embedded energy of each item for a defined quantity in terms of: (i)
different fuel types, i.e. biomass, fossil fuel, electricity and imports (see Table 1 for some selected items), and (ii) different processes, i.e. production, transport of raw material and the energy embedded in raw materials (see Table 2 for some selected items).
In both reports, these different energy categories are shown as minimum, average and maximum values, and the total minimum, average and maximum embedded energies of each item are given in the last column. One of the problems in a comprehensive approach such as this is that the magnitudes of energy inputs would have large disparities. For example, in Fig. 3 (bricks), the production energy contributes 8528 MJ, while the embedded energy in the raw material contributes only 8 MJ. As such, it is difficult to ‘‘round up’’ values to reflect a level of accuracy that is justified. So, when the energy total in Fig. 3 (bricks) is given as 8576 MJ, it is not meant to indicate an accuracy to 4 significant digits, but rather to capture even the small contributions from all sources considered. Reports were also used to give the carbon coefficients (see Table 3 for some selected items), consisting of the carbon emissions from (i) all the fuels (i.e. biomass, fossil fuel, electricity), (ii) imports, (iii) particular carbon liberations or locking up (i.e. from the ‘‘material’’), and finally (iv) the net value of each item for a defined quantity. The database outputs can also be exported to a spreadsheet that contains a bill of quantities (BOQ) of a building. In this way, the variations in energy for alternative types of building materials (e.g. use of a block wall instead of a brick wall) can also be studied (see Section 7 below). The embedded energy of an entire building can be computed using the complete BOQ [3]. Table 4 gives a comparison between the minimum, average and maximum embedded energy values obtained in this study, and the ranges of values reported by other researchers, for selected commonly used building materials. The comparison shows that values obtained in this study
Aggregates Bricks Brickwork (225 mm) Cement Concrete (1:2:4) Timber sections (75 mm 100 mm)
Description
1m 1000 Nos 10 m2 1 MT 1 m3 1 m3
3
Qtty
4 5 BSR 1 BSR 7
No. of samples
87 5307 6968 4282 1558 1191
99 8576 10,893 4282 1664 1402
111 10,605 13,364 4282 1770 2065
29–115 6300–10,000 10,000–11,920 3400–6100 1060–2410 848
Energy (MJ)
Max
Min
Avg
Other researchers
Total energy (MJ)
Table 4 Comparison of embedded energies with other researchers (MJ/Qtty)
Clough and Martyn [24] Baird and Chan [2] Baird and Chan [2] Clough and Martyn [24] Baird and Chan [2] Baird and Chan [2]
Source
574 W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
575
are broadly consistent with the findings of others. Table 4 also indicates the number of establishments included for each building material in the process analysis. Cement production is carried out in only one factory. The values for some composite elements have been obtained through raw material quantities in the building schedule of rates [22] used in Sri Lanka.
6. Energy quality analysis Though it may appear that the least energy intensive material is the preferred option, there are other issues that have to be considered too. One of the main issues is that of the differences in energy quality. For the purpose of this study, a ‘‘high quality’’ energy or energy source is considered as one that can be used for a variety of end-uses with relative ease [11]. For example, electricity is a high quality energy because it can be used for a range of uses such as cooking and heating, cooling (through air conditioners and refrigerators), locomotion (via motors) and powering up equipment (e.g. television, computers etc.), while biomass is a low quality energy because it can only be used for heating and cooking by burning; fossil fuel is in between, because although it has to be burnt too, it can be used for locomotion via engines. The next step should be to find a methodology for comparing different energy qualities. One approach to this could be to assess the amount of the lower quality energy that is required to obtain a given quantity of the higher quality energy via established processes. The following processes were considered: (i) electricity generation by burning fossil fuels, (ii) obtaining electricity by burning fuel wood (dendro-electricity). All the energy contents for building materials can then be compared on the basis of the total equivalent amount of lowest quality energy (i.e. biomass energy). The resulting energy contents will then have a ‘‘bio-equivalent’’ basis. It must be appreciated that the above analyses are essentially efficiency based analyses. The disparity between the qualities of biomass, fossil fuel and electricity based energy is likely to be greater than that reflected by these efficiency calculations. For example, Odum [10] values a unit of electrical energy at four times that of fossil fuel energy, whereas a purely efficiency based approach will result in a factor of only 2.78 (see below). This study adopts the efficiency based approach, since it has a clear basis and will not distort the original energy values too much. Since electricity has the highest energy quality, it is used as a reference, and is assigned an ‘‘efficiency’’ of 100%, whatever the primary fuel used for its generation; this is similar to the ideas associated with assigning a TOE value for hydro-electricity (see Section 3.3). In obtaining electricity by burning fossil fuels, the thermal efficiency is assumed as 36%. Therefore, 2.78 GJ of fossil fuel energy are equivalent to 1 GJ of electrical energy. The process of obtaining electricity by burning firewood (dendro-electricity) can be used to find the relationship between electricity and biomass. When 10 million metric tonnes of fuel wood are used, 10,000 GWh of electricity could be generated [19]. Therefore, 1 mT of fuel wood will yield 1 MWh. But, the fuel wood (which is drier than the conventional firewood) had a TOE value of 0.43 per mT [19], i.e. an energy content of 18 GJ. Now, for electricity that is
576
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
100% efficient, 1 MWh can be produced with (or is equivalent to) only 3.6 GJ. Therefore, in order to obtain 1 GJ of electrical energy, ð18=3:6 ¼Þ5 GJ of biomass energy have to be consumed. For the system in the mutually consistent scheme, the difference in quality associated with the conversion from biomass to electrical energy through fossil fuel energy must be identical to that associated with the direct conversion from biomass to electrical energy. Since there is a ‘‘quality factor’’ of 2.78 between fossil fuel and electricity, and one of five between biomass and electricity, the quality factor between biomass and fossil fuel can be obtained as 1.8 (i.e. 5/2.78). More studies are required to study these differences in quality (including their discrepancies), perhaps using exergy analyses, as suggested below. For the moment, the above factors are adopted as a first approximation. The final database outputs of average embedded energies of selected building items on a bioequivalent basis are shown in Table 5, together with their average energy contents on a TOE basis. The values in the ‘‘fossil fuel’’ column in Table 5 are obtained by factoring by 1.8 (see above) the values in the ‘‘fossil fuel (Avg)’’ column in Table 1. Where the ‘‘electricity’’ column in Table 5 is concerned, it should be noted that the Sri Lankan electricity mix (66% hydro and 34% thermal) was found to have a TOE value of 0.138 (or 5.78 GJ/MWh). On the other hand, the generation of electricity using biomass required 18 GJ for 1 MWh. Hence, the factor for converting the Table 1 ‘‘electricity (Avg)’’ values to the ‘‘electricity’’ values in Table 5 is (18=5:78 ¼Þ3:11. The embedded energy on a bio-equivalent basis will always be greater than (or equal to) those on the conventional TOE basis, because the former is based on the biomass energy that would have had to be used in order to produce a building material. If another (higher quality) energy source was actually used, the equivalent biomass energy would be greater, as described above, and hence the material is ‘‘penalised’’ for the use of that higher quality energy. Thus, the energies (on a bio-equivalent basis) for materials produced using industrialised processes (such as cement, steel and aluminium) are almost double their energies on a TOE basis. On the other hand, for materials (such as bricks) that require predominantly biomass energy inputs, the difference between their energies on a bio-equivalent and a TOE basis is negligible. Table 5 Energy breakdown—on a bio-equivalent basis (MJ/Qtty) Description
Qtty
Biomass
Aggregates Aluminium extrusions Bricks Brickwork (225 mm) Cement Concrete (1:2.4) Steel Timber sections 75 mm 100 mm
0 1 m3 1 mT 0 1000 Nos 8528 10 m2 10,003 1 mT 0 1 m3 0 1 mT 0 1 m3 0
Fossil fuel
Electricity Imported Total energy Total energy (bio-eq) (TOE)
178 17 86 1429 6625 2649 4767 577
0 55,881 0 299 1869 598 3227 3364
0 233,100 0 0 0 0 52,200 0
178 288,998 8614 11,731 8494 3247 60,194 3941
99 147,477 8576 10,893 4282 1664 32,686 1403
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
577
The energy quality analysis adopted in this research is an efficiency based approach essentially using the first law of thermodynamics. However, the first law is incapable of properly characterising the thermodynamic losses in a process. This can be remedied by introducing a parameter called exergy through the combination of the first and second laws. The exergy analysis [23] resulting from the combination of the first and second laws provides a measure of the maximum work that can be extracted from a given thermodynamic system during a process; in other words, a way of quantifying the thermodynamic potential of the system. Such an analysis, however, was outside the scope of this work.
7. Comparison of building material alternatives This paper will give only a single example of comparing building materials on an energy content and carbon emission basis; the subject is treated more extensively elsewhere [3]. The energy inputs and carbon emissions corresponding to purlins made out of three different materials, i.e. timber, steel and prestressed concrete, were analysed. The cross-section of each purlin was selected so that it could carry a uniformly distributed load of 1 kN/m over a span of 3 m, without violating deflection or strength criteria. The cross-section required for timber purlins is 75 mm 100 mm. The raw material required for a timber purlin is air-dry treated timber of 0.02 m3. The cross-section required for steel purlins is a 75 mm 75 mm 6 mm angle section. The raw material required is 20 kg of steel. The cross-section required for prestressed concrete purlins is 60 mm 120 mm with 3 nos. of 6 mm diameter high tensile wires. The raw material requirement for a prestressed concrete purlin is 2.3 kg of steel, 9.3 kg of cement, 0.02 m3 of aggregates and 0.01 m3 of sand. The energy inputs corresponding to timber, prestressed concrete and steel purlins, which have similar spans and approximately similar bending capacities, are 29, 121 and 654 MJ, respectively, on a TOE basis, indicating that steel is around 23 times more energy intensive than timber and that prestressed concrete is around four times more energy intensive than timber. However, on a bio-equivalent basis, i.e. using values like those in the penultimate column of Table 5, the ratios are 15 and 3, respectively (see Fig. 5).
Fig. 5. Embedded energy comparison for purlins.
578
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
Fig. 6. Carbon emission comparison for purlins.
Where carbon emissions are concerned Fig. 6 shows that 5.61 kg of carbon is stored in the timber purlin compared to 3.73 kg of carbon and 13.06 kg of carbon released to the atmosphere via prestressed concrete and steel purlins, respectively. Therefore, it appears that timber is the preferred option, and that steel is the least desirable one from an energy consumption and carbon emission point of view. This is consistent with other research findings [5,12]. However, because of the worldwide phenomenon of deforestation, the use of timber will be possible only with aggressive reforestation. 8. Conclusion 1. All building elements (e.g. brick wall), materials (e.g. bricks) and ‘‘primitive’’ raw materials (e.g. clay) can be placed in an aggregation–decomposition hierarchy. Each stage of a building material can be considered to have energy inputs that are defined as ‘‘proximate’’ (i.e. production energy and transport of raw materials) and ‘‘remote’’ (i.e. embedded energy of raw materials). This product oriented hierarchy complements the IFIAS [13] process oriented hierarchy and captures most of the energy inputs associated with Levels 1 and 2 in the IFIAS [13] scheme, where these two levels account for around 90% of the embedded energy in a product. 2. The relational database that was designed and the recursive algorithm that was developed can be used to represent and calculate the embedded energies and carbon coefficients of building materials and elements that are hierarchically arranged, such that some materials are raw materials of others. It can also handle multiple sources of data and perform calculations to give the energies corresponding to average, maximum and minimum values. The energy data are also classified in two ways in the database—i.e. (i) by process (production, transport of raw materials and embedded energy of raw materials) and (ii) by fuel type (biomass, fossil fuel, electricity and imports). The database outputs can be exported to a spreadsheet to perform other analyses. 3. According to energy quality calculations carried out (based on efficiency considerations), 1 GJ of electricity is equivalent to 5 GJ of biomass energy on the one hand, and to 2.78 GJ of fossil fuel energy on the other. For the conversion system to be mutually consistent, the biomass to fossil fuel factor is obtained as 1.8 (i.e. 5/2.78). The energy contents of building materials could thus be compared on the basis of an equivalent amount of the lowest quality
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
579
energy (i.e. biomass energy), and the energy contents were obtained on a ‘‘bio-equivalent’’ basis. 4. The analysis carried out indicates that among the more common Sri Lankan construction materials considered, the lowest energy option is timber, while the highest is steel, with concrete in between. Timber products have negative carbon coefficients as well, i.e. they store more carbon than is emitted in their use for construction, while the carbon emissions from steel products will be greater than those from concrete products.
Acknowledgements The funding received from the Energy Conservation Fund for this research is greatly appreciated. The assistance for database programming given by Mr. G.N. Kodagoda is also gratefully acknowledged. References [1] Energy in the built environment. Report G16: 1988. Stockholm: Swedish Council for Building Research; 1988. p. 184. [2] Baird G, Chan SA. Energy cost of houses and light construction buildings. Report 76. New Zealand Energy Research and Development Committee. Auckland: University of Auckland; 1983. [3] Pooliyadda SP, Dias WPS. The significance of embedded energy for buildings in a tropical country. Engineer, Sri Lanka 2001;34(3):25–35. [4] Statistical abstract of the democratic socialist republic of Sri Lanka. Dept. of Census and Statistics, . Colombo: Dept. of Census and Statistics; 1995. [5] Chen TY, Burnett J, Chau CK. Analysis of embodied energy in the residential building of Hong Kong. Energy 2001;26(4):323–40. [6] Howard N, Edwards S, Anderson J. BRE Methodology for environmental profiles of construction materials, components and buildings. London: CRC; 1999. [7] Adalberth K. Energy use during the life cycle of buildings: a method. Building Environ 1997;32(4):317–20. [8] Cole RJ, Rousseau D. Environmental auditing for building construction: energy and air pollution indices for building materials. Building Environ 1992;27(1):23–30. [9] Harris DJ. A quantitative approach to the assessment of the environmental impact of building materials. Building Environ 1999;34(6):751–8. [10] Odum HT. Environment, power and society. New York: Wiley Interscience; 1971. [11] Perera KKYW. Energy status of Sri Lanka. Colombo: Institute of Policy Studies; 1992. [12] Honey BG, Buchanan AH. Environmental impacts of the New Zealand building industry, Dept. of Civil Engineering Research Report 92/2. Christchurch: University of Canterbury; 1992. [13] Energy analysis workshop on methodology and convention, Workshop Report No. 6. Stockholm: International Federation of Institute for advanced Study; 1974. [14] Siyambalapitiya T. Electricity pricing policy in Sri Lanka. Colombo: Institute of Policy Studies; 1997. [15] Folcy G, Barnard G. Biomass gasification in developing countries. Technical Report No. 1, London: International Institute for Environment and Development; 1983. [16] Guidelines for national greenhouse gas inventories: Workbook Vol. 2, Intergovernmental Panel on Climate Change; 1996. [17] Turbott C, Pool F, Vickers G. Energy management and the greenhouse effect—a background paper on energy management and the 1988 Toronto goal. Wellington: Ministry of Commerce; 1991.
580
W.P.S. Dias, S.P. Pooliyadda / Energy 29 (2004) 561–580
[18] Dams and development: A new framework for decision-making. Report on the World Commission on Dams. Earthscan Publishers; 2000. [19] Joseph PG. Energy conservation and biomass energy. LIFE—World Bank Seminar on Energy and Environment. Colombo: Lanka Institute for Environment; 1998. [20] Pooliyadda SP. Energy content and carbon emission audit of building materials. M. Phil. Thesis, Moratuwa: University of Moratuwa; 2000. [21] Standard method of measurement of building works, 7th ed.. . London: The Royal Institute of Chartered Surveyors; 1988 [SMM7]. [22] Building schedule of rates. . Colombo: Ministry of Local Government; 1988. [23] Costa MM, Schaeffer R, Worrell E. Exergy accounting of energy and materials flows in steel production systems. Energy 2001;26(4):363–84. [24] Clough R, Martyn R. Environmental impact of building and construction materials. Project Report 12, Vol. B— Mineral products. London: Construction Industry Research and Information Association; 1995.