Methodology for determining the ecological footprint of the construction of residential buildings in Andalusia (Spain)

Ecological Indicators 25 (2013) 239–249

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Methodology for determining the ecological footprint of the construction of residential buildings in Andalusia (Spain) Jaime Solís-Guzmán ∗ , Madelyn Marrero, Antonio Ramírez-de-Arellano Department of Building Construction II, E.T.S. de Ingeniería de Edificación, University of Seville, 41012 Seville, Spain

a r t i c l e

i n f o

Article history: Received 20 January 2012 Received in revised form 7 October 2012 Accepted 9 October 2012 Keywords: Ecological footprint Resources Building Waste Construction Consumption

a b s t r a c t Construction activity is now a major consumer of natural resources. As a general rule, resource consumption has been evaluated through methodologies related to Life Cycle Analysis. This research is proposed to integrate the Ecological Footprint indicator into the building sector: to observe the difficulties and the benefits it can generate relative to other indicators. To this end, prior knowledge related to the Ecological Footprint indicator must first be adapted to the residential sector, by analyzing the construction of buildings, and secondly a calculation methodology is established in order to quantitatively determine what impacts are generated by industry according to the Ecological Footprint indicator. This methodology applies the indicator to resources used (energy, water, labour, construction materials, etc.) and to waste generated in the construction of residential buildings. The methodology is applied to a case study corresponding to the urbanization and building construction of a representative building type in Andalusia (Spain) when the building is in the planning stage. The final result is 0.384 gha/year/m2 . © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Within the industrial sector, construction activity, including its associated industry, is the largest consumer of such natural resources as timber, minerals, water, and energy. In the European Union, the construction of buildings consumes 40% of the total consumption of materials, 40% of the total consumption of primary energy and generates 40% of the total waste, taking particular responsibility for the ongoing deterioration of the environment the ˜ Nieva and Vigil-Escalera del Pozo, expansion of urban land (Bano 2005). Therefore, in the pursuit of improving the environmental performance of buildings, it is necessary to assess this through indicators, so that the weight of the environmental impacts can be qualified and quantified throughout their life cycle, from the extraction of raw materials to demolition. The tools that analyze these impacts generally follow the methodology of Life Cycle Analysis (LCA) (Zabalza Bribián et al., 2011; Malmqvist and Glaumann, 2009). However, no approaches exist that employ other methodologies, such as that of the Ecological Footprint (EF). The EF indicator was introduced by Mathis Wackernagel (Chambers et al., 2004), who measured the EF of humanity and compared it with the carrying capacity of the planet. According to

∗ Corresponding author. E-mail address: [email protected] (J. Solís-Guzmán). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.10.008

its definition, the EF is the amount of land that would be required to provide the resources (grain, feed, firewood, fish, and urban land) and absorb the emissions (CO2 ) of humanity (Wackernagel and Rees, 1996; WWF, 2008). By comparing the EF to the amount of land available, Wackernagel concluded that human consumption of resources currently stands 50% above the global carrying capacity (WWF, 2010). It is now considered one of the most relevant indicators for the assessment of impacts on the environment, and can also be used in conjunction with other indicators, such as the carbon footprint and water footprint (Galli et al., 2012). The strengths of the indicator include its provision of an aggregation of multiple anthropogenic pressures and its easily understood strong conservation message. On the other hand, its main weaknesses are that neither can it cover all aspects of sustainability nor all environmental concerns, and that certain underlying assumptions are controversial (Galli et al., 2012). The indicator has been used since its inception to determine impacts on greatly differing scales: to predict the impacts generated by mankind on Planet Earth (Meadows et al., 2006), for the periodic calculation of the footprint of mankind on Planet Earth (WWF, 2006, 2008, 2010), or for periodically calculating the EF of different countries (Fricker, 1998; Lenzen and Murray, 2001; Medved, 2006; van Vuuren and Smeets, 2000; von Stokar et al., 2006; Wackernagel et al., 2004), cities (Barrett et al., 2002; Acosta Bono et al., 2001), neighbourhoods (Li et al., 2010; Kuzyk, 2012), productive sectors (Gössling et al., 2002; Hunter and Shaw, 2007,

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J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249 Table 1 Constructed area of the two blocks. Floor area (m2 ) Constructed area Ground floor First floor Second floor Third floor Total Total area (m2 )

Fig. 1. Typology of residential building under analysis.

Peeters and Schouten, 2006) and industries (Holden and Høyer, 2005; Herva et al., 2008, 2012; Frey et al., 2006; Niccolucci et al., 2008; Domenech Quesada, 2007). The EF methodology is applied for the first time to evaluate the region of study, Andalusia, in the work of Acosta Bono et al. (2001). And in the work of Nye and Rydin (2008) an innovative analysis of EF per building component is proposed. Finally, following the procedure developed by Spanish researchers (Domenech Quesada, 2007; Cagiao et al., 2011) on corporate EF calculation, the process of building construction is studied. This methodology, adapted to the unique characteristics of the construction sector, has been chosen for its comprehensibility, transparency, and adaptability (Cagiao et al., 2011). This research, therefore, aims to bring all previous knowledge related to the EF indicator into the residential building sector in order to analyze the phase of construction of buildings, and to establish a methodology for calculation (Solís-Guzmán, 2011), and hence to determine the advantages and disadvantages that this indicator yields in the analysis of environmental impact on the building sector.

Block 1 1359.06 1359.15 1363.35 1363.35 5444.91 10243.69

Block 2 1197.86 1197.86 1201.53 1201.53 4798.78

the construction. In the event that the implementation period is longer than a year, then the impact or derivative use of the building process is assumed to be uniform. For example, consider that the construction lasts 18 months, then during the first year 2/3 of the total impact of the construction is produced and during the second year 1/3 of the total impact of the construction is produced. During the analysis the project is still in the design phase, and hence certain consumption data (water consumption, power consumption. . .) remains unavailable. 3. Methodology Ecological footprint analysis focuses on the implementation and construction phase of residential buildings due to the complexity of EF calculations: research into the other two phases of the life cycle of buildings, those of use and demolition, is not part of the present analysis. This analysis follows the flowchart described in Fig. 2. 1. Defining the impact factors. These are the generators of impact on the land (upper level of the tree of Fig. 2): direct consumption, indirect consumption, waste generation and built land. Direct consumption is that which causes direct use of resources on the construction site, through energy expenditure (in the form of fuel or electricity) or the use of water. Both are located in the second level of the tree, and are listed as resources (see the key to Fig. 2). Indirect consumption is caused by the indirect use of resources, such as material or energy resources from other previously used resources, which are in our case: • Manpower • Building material consumption

2. Case study For the determination of which building type to analyze, the most representative types of buildings for the residential sector in Andalusia (Mercader, 2010; Mercader et al., 2010) were first studied. This study concluded that the predominant residential buildings were 2-storey semi-detached houses and 4-storey blocks of flats. To begin the ecological footprint analysis, it was decided to choose the typology that theoretically generates a smaller impact on the area per m2 built (Holden, 2004), by selecting blocks of flats of 4 floors, although it would be necessary to apply the methodology to various dwelling types to compare variations in the EF indicator. A building and urbanization project of two purpose-built blocks were studied, each block containing 4 floors above ground level and two below ground level, amounting to a total of 107 dwellings, with their parking spaces, storerooms and shops (Fig. 1). This project was initiated in the province of Huelva (Andalusia, Spain) and was completed in 2008, which is the year to be taken as reference. The total constructed area is shown in Table 1. In the initial assumptions for the case study of the paper, it is considered that the only activity that exerts an impact on the study area is that which corresponds to the construction of the residential buildings considered above. This impact will be continued for a period of 12 months; the time-span considered necessary for

The manpower consumption in building construction produces on the one hand, food expenditure by the operators, and secondly, the use of fuel derived from mobile operators (trips to the construction site). For their part, building materials, through manufacturing processes, transportation and installation (see Fig. 2) consume fuel (transport of materials to the workplace) or energy (necessary for the manufacture of materials and commissioning). For EF material analysis, a quantitative study is performed on the building materials that amount is then translated into resources expressible in terms of EF. As an interim step, this becomes a quantity in terms of primary energy consumption, in the same way as with electricity and mobility. The third factor is the impact of waste generated in the construction phase, which mostly corresponds to the so-called construction and demolition waste (CDW). The last factor of impact is the land on which the construction itself is built, which causes the consumption of land, and consequently, a footprint. Therefore, each of the impact factors uses resources (energy, water, manpower, materials) or generates waste. 2. Definition of intermediate elements (see the key to Fig. 2). Through these elements, consumption is transformed into elements that allow us to define the various footprints that make up the total footprint of the system under study. The intermediate

J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249

Fig. 2. Methodology flowchart.

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Table 2 Equivalence factors (WWF, 2006).

Table 3 Summary of the overall costs. e (gha/ha)

Primary cropland Marginal cropland Forest Permanent pasture Marine Inland water Built-up area

2.21 1.79 1.34 0.49 0.36 0.36 2.21

elements are: fuel, electricity, mobility, manufacture, transport and installation of construction materials, CO2 emissions, and land necessary for the absorption of CO2 emissions (CO2 land). 3. Definition of the coefficients. The various coefficients that enable consumption and the intermediate elements to be transformed into partial footprints are: electric mix, forest productivity, food performance factor, mobility coefficient, transport coefficient, embodied energy coefficient, waste generation coefficient, waste conversion factor, consumed land, emission factor, absorption factor, and equivalence factor. 4. Definition of partial and total footprints: by means of the intermediate elements and the coefficients, the partial and global footprints that are generated in the construction phase of the dwelling sector are obtained. These are located in the bottom level of Fig. 2, and represented by green globes: forest footprint, food footprint, energy footprint, built land footprint, and total ecological footprint. For these calculations, up-to-date equivalence factors (e) must be considered for the project, to turn the results in hectares into global hectares (gha). These equivalence factors are shown in Table 2. To apply the above methodology, a budget must be used in accordance with a building cost system. For this analysis, the Andalusian Construction Cost Database (ACCD, 2008) is used. This base has been developed over the past 25 years in Andalusia, and is the most widespread in this region. Its use is mandatory in public developments in Andalusia. Not only is ACCD valid as an estimation of cost, but it also provides a common method to manage information during the design and construction of buildings (Marrero and Ramirez-de-Arellano, 2010). This database defines a cost structure that distinguishes between direct costs and indirect costs, thereby allowing a clear definition of all costs for each project type. The ACCD structure is arborescent and hierarchical, with clearly defined levels from the apex of the hierarchy down to lower levels, whereby each group is divided into subgroups of similar characteristics. For this analysis, the levels used in this structure are: 1. The Total Production Cost (TPC): covers all production costs incurred by the tasks necessary for the projected work. 2. Basic Cost (BC): refers to elements that are a resource: manpower, materials, and machinery. In our study, all costs are allocated directly, since the Indirect Costs (IC) are previously analyzed and integrated into the budget in a direct way. Hence all the costs of the construction process are clearly defined. Therefore, in order to determine the TPC, it is necessary to calculate not only the Direct Costs of Production (PDC) but also the Indirect Costs of Production (PIC) and Health and Safety Costs (HSC), which are usually analyzed separately. Furthermore, in order to make a detailed calculation of the materials, a budget is assumed in accordance with the ACCD for the year 2008, the year taken for this study. Therefore, the procedure for the determination of the total budget is:

Cost (D ) PDCB PDCU PIC HSCB HSCU TPC

5,067,139.67 187,613.37 380,726.02 51,867.43 938.07 5,688,284.55

1. Obtain the TPC for the construction of the blocks and the urbanization. 2. Recalculate the costs to adjust them to the ACCD (2008). 3. Integrate IC into the TPC. 4. Integrate HSC into the TPC. 5. Calculate the TPC (adjusted to ACCD, 2008). TPC = PDCB + PDCU + PIC + HSCB + HSCU

(1)

where TPC is the Total Production Costs, PDCB is the Building Production Direct Costs, PDCU is the Urbanization Production Direct Costs, PIC is the Production Indirect Costs, HSCB is the Building Health and Safety Costs, HSCU is the Urbanization Health And Safety Costs. The overall costs are shown in Table 3. 3.1. Determination of the EF of energy consumption To predict the amount of energy consumed in construction work, data provided by polynomial formulae is used (Spain MP, 1970, 1981), which estimate the resources used in the work as a percentage of the total costs for 48 types of construction work (roads, canals, railways, buildings, etc.), both for public and private initiatives (Table 4). For this case study, type 18 of these formulae is employed: “Those buildings with reinforced concrete structure and facilities that cost less than 20% of total costs”. Furthermore, as the case study is a public development, it is considered a public initiative. The coefficients in Table 4 represent the percentage of the TPC, which does not include VAT, industrial profit, general costs and an additional 15% of IC. In our case, IC are allocated directly, and hence the percentages in Table 4 are increased to obtain 100% of the costs (TPC), thereby obtaining the corrected coefficients, multiplying by 1.15, which are those used for the calculations. Each of the initials of Table 4 refers to the following: m: manpower cost; e: energy; c: cement; s: steel; w: wood and cr: ceramics. Therefore, in this example, the energy consumption of the work could be estimated as 9% of TPC. As a hypothesis, the total energy consumption of the execution of the work is considered to be shared out between electricity and fuel consumption, since this is a footprint analysis at the project design stage and therefore the consumption cannot be determined. Therefore, once the total energy consumption and fuel consumption are determined, then the difference between these quantities can be considered to be the electricity consumption. Now that the energy cost of work construction (in Euros) is defined, the next step is to determine the fuel consumption in the work, which is due to the use of machinery. First, the calculation is carried out, through measurements of the project, of the hours

Table 4 Polynomial formulae of type 18 (public initiative). Type

m

e

c

s

w

cr

Total

18 18 (corrected)

36 42

8 9

12 14

12 14

7 8

10 12

85 100

J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249 Table 5 Example of calculation of machinery consumption associated with PDCB .

Loader Dump truck Backhoe Bulldozer Vibratory roller Manual mechanical tamper

Hours

Cost (D /h)

272.29 1298.44 40.93 0.74 178.00 311.09

23.87 25.60 34.98 30.30 23.28 3.01

3.2. Determination of the EF of water consumption

Cost (D ) 6499.56 33,240.06 1431.73 22.42 4143.84 936.38

of machinery used, and then the economic cost of the machinery used can be calculated (Table 5). To obtain the fuel consumption in units of volume (litres), the cost of fuel used must be determined and the data must be updated to the year of the project, which in this case is the year 2008. Once the fuel consumption is defined in units of volume (litres), the footprint of fuel consumption can be expressed as: EFwf =

F ·e EP f

(2)

where EFwf is the Weighted ecological footprint of fuel consumption (gha/year), F is the Fuel consumption (GJ), EP is the Energy productivity of fuel (GJ/ha/year), which is considered as oil (Table 6). Energy productivity of fuel is expressed as: EP =

A E

(3)

where A is the absorption factor and E is the emission factor. ef is the equivalence factor of forests (gha/ha). When the fuel consumption has been determined, the electricity consumption in the construction work can be calculated. To express this data in energy consumption units, the billing model of electricity in Andalusia is used. After obtaining this information, it becomes necessary to determine the electric mix in Andalusia. Since renewable energies have very high energy productivity (EP) (Wackernagel and Rees, 1996), then it is assumed that their energy footprint is irrelevant as compared to the energy footprint from fossil fuels and nuclear energy. The EP of fuels (Table 6) and the efficiency factor for electricity production are then considered. The absorption factor is taken as A = 5.21 tonnes of CO2 /ha per year (Domenech Quesada, 2007) and the efficiency factor for electricity production is assumed to be 0.3 (IDAE, 2007). The formula used is: EFwe =

P i i

EPi

· ef

(4)

where EFwe is the Weighted ecological footprint of electricity consumption (gha/year), Pi is the Primary energy consumption (GJ), EPi is the Energy productivity (GJ/ha/year). The consumption is calculated from the primary energy consumption i and the energy productivity, applied to each of the sources.

Table 6 Emission factors (E) and EP of energy sources. Energy sources Fossil fuels Coal Oil Natural gas Nuclear

E (kg C/GJ) (Acosta Bono et al., 2001) 26.00 20.00 15.30 20.00

EP (GJ/ha/year) 55 71 93 71

243

Although the footprint of water consumption is not reflected in EF methodology, this contribution is included here in order to account for such consumption. The procedure is: 1. Determine the ranges of water consumption and the ranges of costs in work of similar dimensions to that which is to be analyzed so that the ratio of the cost of the work to water consumption can be established. 2. Define the average water consumption of the work to be analyzed, by interpolating with the data obtained in the previous section. Interpolation is based on the TPC. 3. Determine the EF. This is defined by the calculation procedure which considers the forest as a water producer, whereby the consumption of this resource is included in that of forest land. In order to calculate forest productivity (m3 /ha/year), the hypothesis that a forest of wetlands can produce 1500 m3 of fresh water per hectare per year (Domenech Quesada, 2007) is assumed. Therefore, the formula employed for the calculation of the EF of water consumption is: EFww =

W ·e FP f

(5)

where EFww is the Weighted ecological footprint of water consumption (gha/year), W is the Water consumption (m3 ) and FP is the Forest productivity (m3 /ha/year). 3.3. Determination of the EF of food consumption The initial hypothesis of this section is that workers’ food is attributed to the EF of the building construction since this activity takes place on the worksite, in the same way as in the methodology developed by Domenech Quesada (2007) where business meals are allocated to the Corporate Ecological Footprint. To this end, the total number of manpower hours worked for the entire work must first be calculated, which is obtained by measuring the project. Such manpower is broken down with ACCD Systematic Classification (ACCD, 2008). This classification also gives the economic cost of the manpower (D /h). The footprint is calculated using the expression: EFwfd =

EFm · Nh hm

(6)

where EFwfd is the Weighted ecological footprint of food (gha/year), EFm is the Ecological footprint expressed as gha/year/meal, hm is the 8 h/meal. One meal per working day is assumed. Nh is the Total number of hours worked. Therefore, it is necessary to obtain the EFm of the various types of food that make up the daily meal of every worker. On accordance with the methodology used, four types of footprints are generated: 1. Fossil Footprint: generated by all types of food, due to their required processing, or, as in the case of fish, this factor represents the fuel consumed for the capture of the fish. This translates into CO2 land. The formula is: EFmf =

C · EI · ef EP

(7)

where EFmf is the Fossil EF (gha/year/meal), C is the consumption (t/meal), EI is the energy intensity (GJ/t), EP is the energy productivity (oil) (GJ/ha/year).

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Table 7 Parameters for the calculation of the food footprint (Domenech Quesada, 2007). Foods

Ci (t/1000 D )

Fi % 25 25 12 10 8 6 5 5 4

Meat Fish Cereals Beverages Vegetables Sweets Oil Dairy Coffee

0.65 0.50 4.69 0.34 1.45 0.70 0.71 0.93 0.54

EIi (GJ/t) 80 100 15 7 10 15 40 37 75

NPi (t/ha/year) 0.033 0.029 2.264 22.500 6.730 4.893 1.485 0.276 0.566



EFwm =



F% i

i

100

· Ci · EIi

(8)

where each of the factors considered would be: Cm: cost per meal. This is assumed at a cost of 10 D per meal. Fi %: Percentage of the meal cost that each type of food represents. Ci : Consumption in tonnes per 1000 D . EIi : Energy intensities. 2. Pasture Footprint: generated by meat and dairy products. 3. Cropland Footprint: generated by cereals, beverages, vegetables, sweets, oil and coffee. 4. Productive sea Footprint: generated by fish. For these three types of footprint, the data of natural productivity (NP) is used (Table 7), whereby the three types of consumed land are determined: pasture, cropland, and productive sea. For example, in the case of pasture: EFmp =

C · ep NP

(9)

where EFmp is the Pasture EF (gha/year/meal), NP is the Natural productivity (t/ha/year), ep is the equivalence factor of pastures. The expanded expression becomes: C Cm = NP 1000



 (F % · C )/100 i

i



i

NPi

The footprint of construction materials is determined using the following expression



If we develop this expression: Cm C · EI = 1000

3.5. Determination of the EF of construction materials

(10)

where Fi % represents the percentage of the cost of the meal of each type of food that belongs to pasture (Table 7). In this case, these are meat and dairy products. 3.4. Determination of the mobility EF In order to determine the EF related to the mobility of workers, the following assumptions are made 1. Private vehicles are established as the only means of transport, since it is assumed that the construction work is placed in a remote area away from the city centre. 2. The average distance travelled by the vehicles is established. It assumes an average distance of 15–30 km. 3. The average vehicle occupancy is 4 people per vehicle. In order to determine the number of workers, the total number of hours worked must be known (calculated in the previous section on food) and the effective duration of the work (in hours). Both items can be obtained from the ACCD (ACCD, 2008). 4. For the calculation of the fuel consumption, consumption coefficients of cars in Spain (IDAE, 2007) is used. 5. The mobility footprint is determined by following the procedure in the energy section.

Cmi · Esemi

i

EP

· ef

(11)

where EFwm is the Weighted EF of construction materials (gha/year), Cmi is the Material consumption (kg); Esemi is the Specific embodied energy of material i (MJ/kg) and EP is the Energy productivity (oil) (MJ/ha/year). The footprint of the construction materials is allocated to CO2 absorption land, since the wooden construction materials used in the work represent only a tiny percentage of the total. The embodied energy values were obtained from various sources (Berge, 2009; Nye and Rydin, 2008; DGVAU et al., 1999; ITEC, 2005; Cuchí, 2005), by taking the average of the values available as the energy value, provided there is no great disparity between those values. This embodied energy includes the manufacture, transportation and installation of construction materials. Based on these values, the consumption of materials (by weight) is determined through measurements of the project studied. Basic Costs (BC) of the ACCD (2008) are used. In order to convert units of measurement of BC (m, m2 , m3 , etc.) into weight, the coefficients calculated by Mercader (Mercader, 2010) are used (Table 8). The example shown in Table 8 corresponds to the study of the construction of our building project, and features a number of the most representative materials of the work from a quantitative point of view. The grouping of BC is based on representative materials or those whose information of embodied energy is available. The second column of Table 8 shows the unit in which the BC is measured. The remaining columns represent: Mmi : Measurement of the Basic Cost of the material i of the project concerned BCmi : Basic Cost of the material i (according to ACCD, 2008) TCmi : Total Cost of the construction material i (D ) TCmi = Mmi · BCmi

(12)

Mmbi is the Measurement of the material i that is integrated into the building. It relates to Mmi through a loss coefficient which takes into account those materials that are not integrated into the building. Cci : Conversion coefficient of the unit measure of the Basic Cost into weight (kg). For this purpose, those coefficients calculated by Mercader (2010) are used. Cmi : Consumption of the material i (kg) Cmi = Mmbi · Cci

(13)

Esemi : Specific embodied energy of the material i. Esem values come from the sources referenced above. Eemi : Embodied energy of the material i (MJ). Eemi = Cmi · Esemi

(14)

By performing a similar analysis with all the materials measured in the design project, the consumption and the embodied energy of the materials are obtained, which leads to the EF of the construction materials. 3.6. Determination of the EF of waste In this section, the environmental impact of waste is evaluated, with the focus on those types of waste which are most relevant to our research: municipal solid waste (MSW), and construction and demolition waste (CDW).

J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249

245

Table 8 Examples of the calculation of embodied energy of the most representative materials in the case study.

Steel B 500S Concrete HA25/B/40 Brick 24/11.5/9 cm Gypsum board 13 mm thickness Cement II/A-L 32.5 N Coated aluminum sliding door

u

Mmi (u)

Mmbi (u)

BCmi (D /u)

TCmi (D )

Cci (kg/u)

Cmi (kg)

Esemi (MJ/kg)

Eemi (MJ)

kg m3 mu m2 t m2

234,915.31 1271.37 239.61 21,253.45 173.07 327.60

223,728.87 1234.34 226.05 20,241.38 164.83 327.60

0.77 69.32 98.28 4.55 92.54 69.60

180,884.79 88,131.37 23,548.87 96,703.20 16,015.81 22,800.96

1.00 2500.00 1550.00 10.00 1,000.00 20.00

223,728.87 3,085,849.51 350,373.11 202,413.81 164,827.65 6,552.00

40.00 1.00 2.90 7.00 7.00 200.00

8,949,154.86 3,085,849.51 1,016,082.03 1,416,896.67 1,153,793.53 1,310,400.00

The types of waste generated throughout the life cycle of a building are varied in content and origin. By focusing on the construction phase of the building, one must consider, on one hand, the MSW generated in the workplace, and secondly, the CDW generated during this phase. Municipal solid waste can be broken down into four types: organic matter, paper/cardboard, plastics, and glass. In the case of the construction and demolition waste, two types of waste are considered in accordance with the management models that exist in the CDW treatment plants in Andalusia: excavated earth and mixed CDW. Mixed CDW groups the remains of materials generated during the execution of the work unit and the packaging used in the transport of the materials. In new construction work, excavated earth may represent over 80% of CDW, while the mixed CDW is distributed among the remains of materials and packaging (Solís-Guzmán et al., 2009). The determination of the EF of waste is based on the methodology of Wackernagel (Wackernagel et al., 2000), which states that the footprint associated with waste disposal and emissions is calculated in the same way as for materials: with the same energy intensity but subtracting the percentage of energy that can be recovered for recycling. It should be borne in mind that in the methodology used here, all consumption is allocated to the fossil footprint, except in the case of paper where consumption also affects the forest footprint. ˜ The procedure uses conversion rates (Maranón et al., 2008) already incorporated into the research of Domenech Quesada (2007). These conversion rates can refer to various types of waste from very different origins (hazardous, non-hazardous, paper, etc.). For our case study, non-hazardous waste and paper waste are of interest. For non-hazardous waste, the procedure is based on the energy intensity (EI) of the production of the material from which the waste is made, with a deduction of the percentage of energy that can be recovered by recycling. Some of these types of non-hazardous waste are organic, excavated earth, or mixed CDW. The conversion rate is calculated by using the formula CRx =

EIx %Rx %SEx · (1 − · ) · ef EP 100 100

(15)

where each of these terms is: CRx : Weighted Conversion Rate of non-hazardous waste (gha/year/t), EIx : Energy Intensity of the production of the material from which the waste is made. For these values, the energy intensities of the materials to be recycled must be known. The data is summarized in Table 9. EP: Energy Productivity of the waste (assumed to be equal to that of fossil fuels).

%Rx : Recycling rate of waste x. In the case of organic waste, nationwide information (OSE, 2008) is used, by determining the percentage given in Table 9 (13%) for composted organic waste. For the other flows, (paper, plastic, and glass), data from the Regional Government (Andalusia ME, 2009) on recycling rates in Andalusia is used. For excavated earth 50% reuse on site and 80% recycling on treatment plant is estimated, although all material can be recycled. For mixed CDW, a recycling rate of 15% (GERD, 2009) is considered, which is well below the national and European objectives. %SEx : percentage of energy saved by recycling. This formulation is applied to all waste as fossil footprint, except for paper and cardboard. In the case of paper and cardboard waste, the forest footprint must be added to the fossil footprint, and hence, in order to calculate the conversion rate, the following equation ˜ et al., 2008), must be considered (Maranón CRx =

EIx %Rx %SEx 1 %Rx · (1 − · ) · ef + · (1 − · 0.8) · ef EP 100 100 NP 100

(16)

where each of these terms is: NP is the Natural Productivity of paper, considered as 1.01 t/ha per year (Domenech Quesada, 2007) (Table 9). %Rx is the Recycling rate (Table 9). Finally, the following expression is used for the determination of the total footprint of the waste, EFwws =



CRxi · Gi

(17)

i

where EFwws is the Weighted EF of the waste (gha/year), CRxi is the Weighted Conversion Rate (gha/year/t), Gi is the Waste Generation (t). In short, the procedure is as follows: 1. Determination of the generation of MSW and CDW. These calculations are either based on statistical data (Spain ME, 2001; Andalusia ME, 2009), or on a software tool (Ramirez-de-Arellano Agudo et al., 2008; Solís-Guzmán et al., 2009). 2. Determination of conversion rates (CR) for each type of waste, following the previously described formulation. 3. Calculation of the EF of the Waste (from forestry and energy footprint). 3.7. Determination of the built land EF This footprint is obtained by calculating the area consumed by the urbanization and by the building under study, through the planning of each project analyzed. As defined by the EF

Table 9 Parameters for the calculation of conversion rates.

EIx (GJ/t) EP (GJ/ha/year) %Rx %SEx ef (gha/ha) NP (t/ha/year)

Organic

Paper

Plastic

Glass

Earth

Mixed CDW

20 71 13 100 1.34

30 71 50 50 1.34 1.01

43.75 71 40 70 1.34

20 71 40 40 1.34

0.10 71 80 90 1.34

5 71 15 90 1.34

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Table 10 Overall costs and EF of machinery. Machinery Building Urbanization IC Total cost

Cost (D ) 167,708.63 16,588.42 71,152.76 255,449.81

Table 12 Total cost of manpower. EF of Fuel consumption (gha/year)

Task

Manpower hours

Building Urbanization Building health and safety Urbanization health and safety IC Total

147.84

methodology, the allocated surface is in the form of “productive land used directly.” The EF calculation becomes, EFwb = S · eb

(18)

where EFwb is the Weighted EF of built land (gha/year), S is the Surface area consumed (ha/year), eb is the Equivalence factor of built land. The land used directly is considered to have the productivity of agricultural land, since most of the infrastructure and built environment are located in areas of agricultural quality. 4. Results 4.1. Energy consumption EF The fuel consumption by machinery is determined by measurements and costs. The results appear in Table 10. By means of polynomial formulae, Table 4, the percentage of overall costs that correspond to the energy consumption is computed. Since the energy consumption of machinery is already calculated, the difference between energy consumption and fuel consumption is therefore the electricity consumption. The electricity consumption in kWh is obtained from the billing model used by the electricity supplier. In order to obtain the electricity footprint, it is necessary to determine the source of electricity in Spain (Spain MI, 2008). As explained above, only the footprint from fossil fuels and nuclear energy is considered. The results appear in Table 11. 4.2. Water consumption EF The results of consumption are 2599.48 m3 of water, thereby resulting in an EF (forest footprint) of water consumption of 2.32 gha/year. 4.3. Food consumption EF First, the total number of manpower hours worked for the entire project is calculated, obtained by measuring the project. Such manpower is broken down according to the ACCD Systematic Classification (ACCD, 2008). The manpower costs (D /h) are also obtained in this classification. The results appear in Table 12. The EFm from the different foods that make up the daily meals of the workers are then obtained, by using the data in Table 7. The results are shown in Fig. 3.

98,686.05 4,280.57 604.46 10.93 15,836.82 119,418.84

2,322,194.11 8,359.90 0.30 27,866.33 417.59

1,470,946.35 62,590.07 8,752.26 158.29 264,474.95 1,806,921.92

4.4. Mobility EF Following the guidelines raised in Section 3.4., a mobility EF (fossil) of 0.03 gha/year is obtained. 4.5. Construction materials EF The Total Embodied Energy for building and for urbanization are determined, and the results are, respectively, 114,521,390.01 and 10,313,394.16 MJ. The Total Embodied Energy is 124,834,784.18 MJ. Once the Total Embodied Energy is calculated in the process of building construction and urbanization, then the EF of construction materials can be determined using the methodology proposed, giving a result of an EF of consumption of construction material (fossil) of 2357.77 gha/year. 4.6. Waste EF The generation of MSW and CDW are determined through statistical databases and tools. Conversion rates are calculated using the methodology proposed. In the case of CDW, a software tool enabled the result of 22,400 m3 of excavated earth (of which 50% is reused) and 1920 m3 of mixed CDW to be obtained. The results are shown in Table 13. 4.7. Built Land EF For this section, the total area consumed must be indicated. To this end, the surface area for blocks and surface area for roads are computed, giving a total area of 7123.78 m2 /year. By means of an equivalence factor of 2.21 gha/ha, the resulting EF of built land becomes 1.57 gha/year. 4.8. Total EF Tables 14 and 15 show the overall results, expressed in gha/year and gha/year/m2 . In Table 15, the constructed area considered is that of blocks, not the built land. Therefore, the data used is 10,243.69 m2 (Table 1). In order to determine the ecological footprint per capita, the average number of inhabitants per dwelling is 2.7 (Spain MI, 2011) and the total number of dwellings is 107 which gives up 288.9 cap living in the buildings. Finally, the total ecological footprint per capita is 13.63 gha/year/cap.

Table 11 Electricity EF. Electricity consumption (kWh) Electricity consumption (GJ) Efficiency factor Primary energy consumption (GJ) Electricity EF(fossil) (gha/year)

Cost (D )

Fig. 3. Weighted EF of food (%).

J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249

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Table 13 Waste EF.

G (t) CR (gha/year/t) EF (fossil) (gha/year) Total EF

Organic

Paper

Plastic

Glass

17.71 0.3284 5.82

8.45 1.2207 10.32

4.43 0.5945 2.63

2.83 0.3171 0.89

Earth 13,440 0.0005 7.10

Mixed CDW 1,920 0.0816 156.72 183.48

Table 14 Total EF. Footprint type (gha/year) Impact Machinery Electricity Water Food Mobility Materials Waste Built land Totals Grand total

CO2 absorption land

Forest

Pasture

Productive Sea

Cropland

372.50

231.63

111.50

372.50

231.63

111.50

Built land

147.84 417.59 2.32 114.75 0.03 2356.04 183.48 3219.73 3939.26

2.32

1.57 1.57

Table 15 Total EF (with respect to constructed area). Footprint type (gha/year/m2 ) Impact

CO2 absorption land

Machinery Electricity Water Food Mobility Materials Waste Built land Totals Grand total

0.014432 0.040766

Forest

Pasture

Productive Sea

Cropland

0.036364

0.022612

0.010885

0.036364

0.022612

0.010885

Built land

0.000227 0.011202 0.000003 0.229999 0.017912 0.314314 0.384555

0.000227

In order to calculate the standard productive land (Lsp ) in gha/year the following equation is used, Lsp = Lp · Y · e

0.000154 0.000154

Table 16 Sensitivity analysis of the indicator EF. Ecological Footprint (gha/year)

(19)

Lp is the productive land (ha/year). In the present case study the productive land correspond to the constructed surface which was determined previously in Section 4.7, giving up 7123.78 m2 /year (0.7124 ha/year). Y is the yield factor establishes the relation between the local productivity of the land under study with respect to the average world productivity for this type of land. In the case study the productivity correspond to farming land in Andalusia which is 1.22 (Calvo, 2007). e is the Equivalence factor of built land (see Table 2).

Impact

Scenario 1

Scenario 2

Scenario 3

Waste Total

2004.82 5873.00

183.48 3939.26

86.70 3842.47

Scenario 2: 50% of the excavated soil is reused and the remaining 50% goes to a treatment plant which recycles 80% out of it. Other types of CDW are 15% recycled (see Table 9). Scenario 3: 100% of the excavated soil is reused, 70% of the CDW is recycled.

Lsp = 0.7124 × 1.22 × 2.21 = 1.91 gha/year

5. Conclusions

The standard productive land is 1.91 gha/year due to the land occupied by the buildings, on the other hand the total ecological footprint is 3939.26 gha/year. Moreover, a sensitivity analysis should be done to observe the behavior of the variables. In Table 16 three different scenarios are presented in order to determine the model sensitivity by three waste management procedures.

1. Footprint studies are primarily focused on an urban scale, thereby making it difficult to extrapolate information to the scale of individual buildings. Furthermore, the definition of the measurement units of the indicator for buildings is complicated due to the peculiarities of construction activity. Moreover, the dependence of analysis on charts and graphs necessitates a periodic review thereof. 2. An in-depth study into the innovative aspects of research is necessary, such as research into the impacts caused by water consumption, the study of the embodied energy of building materials, and that of waste generation.

Scenario 1: the excavated soil is not reused and the waste is not separated neither recycled.

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3. The difficulty of establishing the overall costs of a project as adjusted to a standard cost base, in this case ACCD, is evident since most construction companies often have their own cost databases. Furthermore, for the calculation of the overall costs, it has become necessary to determine the Direct Costs and Indirect Costs in full, with the subsequent difficulty of integrating these costs into the methodology of calculation of the indicator, since the literature on EF has always avoided the calculation of IC associated with any business. 4. The inclusion of the time factor has been shown to be a critical factor since it determines hypothesis testing throughout the entire methodology. Furthermore, the assumption of gha/year as the calculation unit allows for a greater generalization of results. 5. Based on the overall results it is clearly noticeable that the most representative type of footprint is that of fossil fuels (CO2 absorption land). Within this footprint, the effect of consumption of construction materials is highly significant. For this type of activity, mobility carries no decisive impact. Other sources leading to the fossil footprint are machinery, electricity, and food. Regarding food, it must be said that its calculation involves making assumptions that lead to various types of footprint that correspond to its origin, all of which are sufficiently representative. Finally, the footprint of water usage and land consumption has little appreciable effect in this study. All these results require further review towards the improvement of the current model.

References Acosta Bono, G., González Daimiel, J., Calvo Salazar, M., Sancho Royo, F., 2001. Estimación de la Huella Ecológica en Andalucía y Aplicación a la Aglomeración Urbana de Sevilla (Estimation of the Ecological Footprint in Andalusia and Application to the Urban Agglomeration of Seville). Dirección General de Ordenación del Territorio y Urbanismo, Consejería de Obras Públicas de la Junta de Andalucía, Seville, Spain. ISBN 84-8095-270-9. http://hdl.handle.net/10326/974 (accessed 15.07.11). Andalusia ME (Ministry of Enviroment), 2009. Informe de Medio Ambiente 2008. http://www.juntadeandalucia.es/medioambiente/site/web/menuitem. 318ffa00719ddb10e89d04650525ea0/6?vgnextoid=3b32db0dee134210Vgn VCM1000001325e50aRCRD (accessed 15.08.11). Andalusian Construction Cost Database (ACCD), 2008. (Base de Costes de la Construcción de Andalucía, 2008. Consejería de Obras Pública y Vivienda de la Junta de Andalucía. http://www.juntadeandalucia.es/ obraspublicasyvivienda/portalweb/web/areas/vivienda/texto/bcfbb3af-ee3a11df-b3d3-21796ae5a548 (accessed 15.11.10).5 ˜ Nieva, A., Vigil-Escalera del Pozo, A., 2005. Guía de construcción sostenible Bano (Sustainable Building Guide). Instituto sindical de Trabajo, Ambiente y Salud (ISTAS), Spain. http://www.ecohabitar.org/PDF/CCConsSost.pdf (accessed 15.07.11). Barrett, J., Vallack, H., Jones, A., Haq, G., 2002. A Material Flow Analysis and Ecological Footprint of York. Technical Report. Stockholm Environment Institute, Stockholm, Sweden. Berge, B., 2009. The Ecology of Building Materials. Architectural Press, Amsterdam, Holland. Cagiao, J., Gómez, B., Domenech, J.L., Gutiérrez Mainar, S., Gutiérrez Lanza, H., 2011. Calculation of the corporate carbon footprint of the cement industry by the application of MC3 methodology. Ecological Indicators 11, 1526–1540. Calvo, M., 2007. Informe de síntesis: análisis preliminary de la Huella Ecológica en ˜ (Synthesis report: preliminary analysis of the Ecological Footprint in Espana Spain). Secretaría General para el Territorio y la Biodiversidad. Ministerio de Medio Ambiente, Spain. Chambers, N., Simmons, C., Wackernagel, M., 2004. Sharing Nature’s Interest: Ecological Footprints as an Indicator of Sustainability. Sterling Earthscan, London, Great Britain. Cuchí, A., 2005. Arquitectura i Sostenibilitat (Architecture and Sustainability). Universitat Politécnica de Catalunya (UPC), Barcelona, Spain. Dirección General de la Vivienda, la Arquitectura y el Urbanismo (DGVAU), Institut Cerdá, IDAE, 1999. Guía de la Edificación Sostenible, Calidad Energética y Medio Ambiental en Edificación (Sustainable Building, Energy and Environmental Quality Guide in Building Construction). Ministerio de Fomento. Madrid, Spain. Domenech Quesada, J.L., 2007. Huella Ecológica y Desarrollo Sostenible (Ecological Footprint and Sustainable Development). AENOR, Madrid, Spain. Frey, S.D., Harrison, D.J., Billett, E.H., 2006. Ecological footprint analysis applied to mobile phones. J. Ind. Ecol. 10 (1–2), 199–216. Fricker, A., 1998. The ecological footprint of New Zealand as a step towards sustainability. Futures 30, 559–567.

Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S., 2012. Integrating ecological carbon and water footprint into a footprint family of indicators: definition and role in tracking human pressure on the planet. Ecol. Ind. 16, 100–112. Gössling, S., Borgström Hansson, C., Horstmeier, O., Saggel, S., 2002. Ecological footprint analysis as a tool to assess tourism sustainability. Ecol. Econ. 43, 199–211. Gremio de Entidades del Reciclaje de Derribos, (GERD), 2009. IV Congreso Nacional de Demolición y Reciclaje (IV National Congress of Demolition and Recycling). Zaragoza, Spain. http://www.congresorcd.com/ponencias.cfm (accessed 15.08.11). Herva, M., Franco, A., Ferreiro, S., Álvarez, A., Roca, E., 2008. An approach for the application of the ecological footprint as environmental indicator in the textile sector. J. Hazard. Mater. 156, 478–487. Herva, M., García-Diéguez, C., Franco-Uría, A., Roca, E., 2012. New insights on ecological footprinting as environmental indicator for production processes. Ecol. Ind. 16, 84–90. Holden, E., 2004. Ecological Footprints and Sustainable Urban Form. J. Hous Built Environ. 19, 91–109. Holden, E., Høyer, K.G., 2005. The ecological footprints of fuels. Transport. Res. Part D 10, 395–403. Hunter, C., Shaw, J., 2007. The ecological footprint as a key indicator of sustainable tourism. Tour. Manag. 28, 46–57. Instituto para la Diversificación y Ahorro de la Energía (IDAE), 2007. Guía Práctica de la Energía: Consumo Eficiente y Responsable (Practical Energy Guide: Efficient and Responsible Consumption). IDAE. Madrid, Spain. http://www.idae.es/index.php/mod.documentos/mem.descarga?file=/ documentos 11406 Guia Practica Energia 3ed A2010 509f8287.pdf (accessed 15.08.11). ITEC, 2005. Metabase-TCQ 2000: Datos Ambientales (Environmental Data). ITEC. Barcelona, Spain. http://www.itec.es/programas/tcq/ (accessed 15.08.11). Kuzyk, L.W., 2012. The ecological footprint housing component: A geographic information system analysis. Ecol. Ind. 16, 31–39. Lenzen, M., Murray, S.A., 2001. A modified ecological footprint method and its application to Australia. Ecol. Econ. 37, 229–255. Li, D.Z., Hui, E.C.M., Leung, B.Y.P., Li, Q.M., Xu, X., 2010. A methodology for ecoefficiency evaluation of residential development at city level. Build. Environ. 45, 566–573. Malmqvist, T., Glaumann, M., 2009. Environmental efficiency in residential buildings: a simplified communication approach. Build. Environ. 44, 937–947. ˜ Maranón, E., Iregui G., Domenech J.L., Fernández Nava Y., González M., 2008. Propuesta de índices de conversión para la obtención de la huella de los residuos y los vertidos (Proposed conversion rates to obtain the footprint of waste and spills). Observatorio iberoamericano del desarrollo local y la economía social, 4 (1) (Abril-Junio 2008). Marrero, M., Ramirez-de-Arellano, A., 2010. The building cost system in Andalusia: application to construction and demolition waste management. Construct. Manag. Econ. 28, 495–507. ˜ Meadows, D.H., Randers, J., Meadows, D.L., 2006. Los Límites del Crecimiento 30 Anos después. (Limits to Growth: A 30-year Update). Galaxia Gutenberg, Barcelona, Spain. Medved, S., 2006. Present and future ecological footprint of Slovenia – the influence of energy demand scenarios. Ecol. Model. 192, 25–36. Mercader, P., 2010. Cuantificación de los recursos consumidos y emisiones de CO2 producidas en las construcciones de Andalucía y sus implicaciones en el Protocolo de Kyoto. (Quantification of the resources consumed and of CO2 emissions on the construction sites of Andalusia and its implications for the Kyoto Protocol), Ph. D. thesis, Universidad de Sevilla, Seville, Spain. http://fondosdigitales.us.es/tesis/tesis/1256/cuantificacion-de-los-recursosconsumidos-y-emisiones-de-co2-producidas-en-las-construcciones-deandalucia-y-sus-implicaciones-en-el-protocolo-de-kioto/ Mercader, P., Marrero, M., Solís-Guzmán, J., Montes, M.V., Ramírez de Arellano, A., 2010. Cuantificación de los recursos materiales consumidos en la ejecución de la Cimentación (Quantification of material resources consumed during concrete slab construction). Inform. Construc. 62, 125–132. Niccolucci, V., Galli, A., Kitzes, J., Pulselli, R.M., Borsa, S., Marchettini, N., 2008. Ecological footprint analysis applied to the production of two Italian wines. Agric. Ecosyst. Environ. 128, 162–166. Nye, M., Rydin, Y., 2008. The contribution of ecological footprinting to planning policy development: using REAP to evaluate policies for sustainable housing construction. Environ. Plan. B: Plan. Des. 35 (2), 227–247. ˜ (OSE), 2008 Sostenibilidad en Observatorio de la Sostenibilidad en Espana ˜ 2007 (Sustainability in Spain 2007). http://www. sostenibilidad-es. org Espana (accessed 15.08.11). Peeters, P., Schouten, F., 2006. Reducing the ecological footprint of inbound tourism and transport to Amsterdam. J. Sustain. Tour. 14, 157–171. Ramirez-de-Arellano Agudo, A., Solís-Guzmán, J., Pérez Monge, J., 2008. Generación de RCD versión 2. 0 (Software de Evaluación de RCD para Tramitación de Licencias Municipales) (CDW Generation 2. 0: CDW Evaluation Software for Processing of Municipal Licences). Universidad de Sevilla, Spain. Solís-Guzmán, J., Marrero, M., Montes-Delgado, M.V., Ramírez-de-Arellano, A., 2009. A Spanish model for quantification and management of construction waste. Waste Manag. 29 (9), 2542–2548. Solís-Guzmán, J., 2011. Evaluación de la huella ecológica del sector edificación (uso residencial) en la comunidad andaluza. (Assessing the ecological footprint of the building sector (residential use) in Andalusia), Ph.D. thesis, Universidad de Sevilla, Seville, Spain.

J. Solís-Guzmán et al. / Ecological Indicators 25 (2013) 239–249 Spain ME (Ministry of Environment), 2001. Plan Nacional de Residuos de Construcción y Demolición 2001–2006 (National C&D Waste Plan 2001–2006). Ministry of the Environment. Madrid, Spain. ˜ Spain MI (Ministry of Industry), 2008. Estructura de generación eléctrica en Espana. ˜ 2007 (Structure of generation of electricity in Spain. La Energía en Espana Energy in Spain 2007). http://www.aven.es/pdf/la energia en espana 2007.pdf (accessed 15.08.11). Spain MI (Ministry of Industry), 2011. Proyecto SECH-SPAHOUSEC. Análisis del ˜ (SECH-SPAHOUSEC consumo energético del sector residencial en Espana Proyect. Analysis of energy consumption in the residential sector in Spain). http://www.idae.es/index.php/mod.documentos/mem.descarga?file=/ documentos Informe SPAHOUSEC ACC f68291a3.pdf (accessed 20.09.12). Spain MP (Ministry of the Presidency), 1970. Real Decreto 3650/1970, de 19 de Diciembre, por el que se aprueba el cuadro de fórmulas-tipo generales de revisión de precios de los contratos de obras del Estado y Organismos autónomos ˜ 1971 (Royal Decree 3650/1970, of December 19, through which is para el ano approved the set of general formulae of the revision of costs for works contracts of the State and autonomous bodies for the year 1971). Madrid, Spain. Spain MP (Ministry of the Presidency), 1981. Real Decreto 2167/1981, de 20 de agosto, por el que se complementa el Decreto 3650/1970, de 19 de diciembre, sobre fórmulas-tipo generales de revisión de precios de los contratos de obras del Estado y Organismos autónomos (Royal Decree 2167/1981 of 20 August, through which Decree 3650/1970 of 19 December is supplemented on general formulae for the revision of costs for works contracts of the State and autonomous bodies. Madrid, Spain. van Vuuren, D.P., Smeets, E.M.W., 2000. Ecological footprints of Benin, Bhutan Costa Rica and the Netherlands. Ecol. Econ. 34, 115–130. von Stokar, T., Steinemann, M., Rüegge, B. and Schmill, J., 2006. Switzerland’s ecological footprint: A contribution to the sustainability debate. Published by Federal

249

Office for Spatial Development (ARE), Agency for Development and Cooperation (SDC), Federal Office for the Environment (FOEN), Federal Statistical Office (FSO), Neuchåtel. Wackernagel, M., Rees, W., 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society, Gabriola Island, British Columbia. Wackernagel, M., Dholakia, R., Deumling, D., Richardson, D. 2000. Redefining Progress, Assess your Household’s Ecological Footprint. v 2.0. (accessed http://greatchange.org/ng-footprint-ef household evaluation.xls 10.08.11). Wackernagel, M., Monfreda, C., Erb, K.-H., Haberl, H., Schulz, N.B., 2004. Ecological footprint time series of Austria, the Philippines, and South Korea for 1961–1999: comparing the conventional approach to an ‘actual land area’ approach. Land Use Policy 21, 261–269. WWF (WWF International), Global Footprint Network, ZSL (Zoological Society of London), 2006. Living Planet Report 2006. WWF, Gland, Switzerland. ISBN 2-88085-272-2. http://assets.panda.org/downloads/lpr2006.pdf (accessed 10.08.11). WWF (WWF International), Global Footprint Network, ZSL (Zoological Society of London), 2008. Living Planet Report 2008. WWF, Gland, Switzerland. ISBN 978-2-88085-292-4. http://assets.panda.org/downloads/lpr2008.pdf (accessed 10.08.11). WWF (WWF International), Global Footprint Network, ZSL (Zoological Society of London), 2010. Living Planet Report 2010. WWF, Gland, Switzerland. ISBN 978-2-940443-08-6. http://assets.panda.org/downloads/lpr2010.pdf (accessed 10.08.11). Zabalza Bribián, I., Valero Capilla, A., Aranda Usón, A., Ramírez-de-Arellano, A., 2011. Life cycle assessment of building materials: Comparative analysis of energy and environmental impacts and evaluation of the eco-efficiency improvement potential. Build. Environ. 46, 1133–1140.