Keywords

1 Introduction

The role of municipalities in reducing the diffuse greenhouse gas (GHG) emissions has become notably relevant. Among the types of diffuse GHG, transport, residential, residues, commercial and certain industrial activities are included.

There are two types of measures that municipalities can adopt to manage GHG emissions. First, the corrective measures directed to reduce the GHG emissions of the current activities in the city. The second type are preventive measures that can be executed through urban planning.

The first group of measures has often been promoted as shown in Sovacool and Brown (2010), Puliafito and Allende (2007) or Dhakal (2009). Conversely, preventive studies related to the GHG emissions that can be generated from urban projects are not common.

Urban planning has a decisive influence on GHG emissions in general (Engel et al. 2012) and on diffuse emissions in particular, because it puts in order transportation, urban uses and waste management.

The main advantage of linking the GHG emission assessment to urban planning is that it simplifies the implementation of preventive measures for their reduction and compensation based on urban design decisions. Examples of these types of measures can be found in Dong et al. (2013) regarding industrial developments, Kim and Kim (2013) regarding dwelling building intensity, Ho et al. (2013) regarding urban design, Wu et al. (2013) on coefficient cities, and La Roche (2010) examined certain building solutions.

Therefore, the main objective of this paper is to develop a methodology to calculate the carbon footprint that is linked to residential use and can be implemented through urban planning instruments.

2 Methodology

The urban planning master plan was selected because it is the urban planning basic standard that applies for the entire municipality area. After the planning instrument was selected, the methodology applied required the definition of theoretical framework (characterization of the GHG emission sources and determination of the consumption and the emission factors) and the assessment of GHG emissions. As a practical complement to this, the carbon footprint of the actual planning has been studied on a set of 31 municipalities in the south of the Community of Madrid.

2.1 Theoretical Framework

The theoretical framework require specifying the agent of the GHG emissions around which calculations can be made. Thus, ignoring the particularities in regulations and doing the necessary simplifications, a general urban development plan must define the land occupation model, other structuring determinations and the management conditions.

The land occupation model requires the assignment the entire municipality area to a land use category (urban, developable or non-developable land). This model evidently affects the resultant carbon footprint by identifying those urban lands where GHG will be generated, and those that will be excluded from urban planning and are potentially apt to fix emissions.

This land occupation model is tightly linked to managerial conditions, since the conditions for urbanizing developable land are established and those responsible for carrying on this development are identified (urban sector). They are responsible for the urban development and fit the role of responsible agent for the calculation of carbon footprint in the terms defined by the British Standards Institution (2008).

Regarding the other structuring determinations, as an overall plan, global uses and utilization and the public network should be defined. The edification intensities clearly affect GHG emissions because they usually determine the type of specific activities to be executed in every land use category and the intensity with which it used. The former have an evident influence on the carbon footprint and vouch for the identification of sectors as responsible agents, by defining the uses in each sector (residential, industrial, tertiary) and the intensity (utilization).

Communication systems, equipments, infrastructures and open spaces networks and green spaces are usually included among the public network.

It can be assumed that the green spaces do not generate GHG emissions, while the equipment, in any form it may be presented, can be considered as another land for the calculation of the carbon footprint. The communication systems and infrastructure have a clear impact on the carbon footprint; however, emissions are not caused by them but by the final customers.

2.2 Emission Sources

The identification of GHG emission sources must be centered on the agent responsible for the calculation of those emissions. Thus, each sector will be defined on the general plan by the use and utilization of the land and the conditions for its development. The first two, do not generate GHG emissions but affect the type of sources (uses) and the intensity of emissions (utilization). The development conditions do not generate emissions either and allow for the implementation of preventive measures.

The global land use and built intensity determine the GHG emissions, and the standard development allows one to implement preventive measures.

Then, in order to identify the GHG emission sources, the public networks that provide services to the sectors included on the general planning (road network, water supply, electricity, sanitation and gas), from which use emissions are generated, must be studied.

The scheme in Fig. 1 summarizes the aforementioned theoretical framework.

Fig. 1
figure 1

Theoretical framework of GHG emission calculation in the general urban development plan

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2.3 Carbon Footprint Calculation

The carbon footprint is calculated using the following equation:

$$ {\text{HC}}\left( {{\text{kgCO}}_{{ 2 {\text{eq}}}} } \right) =\Sigma {\text{C}}_{\text{i}} \left( {\text{units}} \right) \times {\text{FE}}_{\text{i}} \left( {{\text{kgCO}}_{{ 2 {\text{eq}}}} /{\text{units}}} \right) $$
(1)

Consumption data (Ci) were collected from secondary information sources, which required contrasting data from different sources. The emission factors (EFi) were referred to the CO2 equivalent, (CO2eq) which includes the equivalence in terms of CO2 for all GHG.

The estimated emission factor published by the Oficina Catalana de Cambio Climático (OCCC 2014) for 2013, was used, according to which the emission factor of the peninsular electricity generation mix was 0.248 kgCO2eq/kWh. The considered emission factor for direct consumption of natural gas in housing is 0.2 kgCO2eq/kWh (MARM 2011), whereas the emission factor for vehicles is 0.20487 kgCO2eq/km (Zubelzu et al. 2011). A rate of 2.9 residents per house (INE 2013) was considered to homogenize the data that were measured with different units. This rate was calculated for the Community of Madrid in the population and housing census by the Instituto Nacional de Estadística (INE 2013).

3 Results and Discussion

The following sections show the results using the theoretical framework for the carbon footprint calculation linked to residential use and the analysis made for the studied region.

3.1 Theoretical Calculation

The results of the carbon footprint calculation of every analyzed source are shown in the following sections.

3.1.1 Potable Water Supply

Figure 2 shows the procedure to calculate the carbon footprint of potable water consumption.

Fig. 2
figure 2

Methodological scheme for the calculation of the carbon footprint of a potable water supply

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According to the Instituto Nacional de Estadística (INE 2014) the average consumption of potable water is 145 l/inhabitant. The energy cost of the potable water supply was analyzed by several authors (Table 1).

Table 1 Energy cost of the drinking water supply by different authors
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The average value proposed by Hardy and Garrido (2010) was used, because it is best adapted to the local conditions in the analyzed area. Therefore, the annual energy cost of the potable water supply per person in the Community of Madrid is 53.98 kWh. Then, the resulting carbon footprint is 13.78 kgCO2eq/yr.

3.1.2 Wastewater Management

The process to calculate the carbon footprint of the wastewater management is shown in Fig. 3.

Fig. 3
figure 3

Methodological scheme for calculating the carbon footprint of domestic wastewater management

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The amount of treated wastewater per inhabitant and day in Madrid is 0.22 m3 according to INE (2014), which fits approximately the published results of IECM (2014) that calculates it as 0.2 m3/d. The amount of reused water is 0.0022 m3/inhab. d (INE 2014). The calculated energy costs of wastewater treatment and reuse are shown in Table 2.

Table 2 Energy cost of wastewater management according to different authors
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There are other detailed works that allow the analysis of the carbon footprint of different wastewater treatments (RodríguezGarcía et al. 2012), but the results are usually close to the data in Table 2. The average values proposed by Hardy and Garrido (2010) were again chosen for being the ones best adapted to local conditions.

Therefore, the annual carbon footprint of the wastewater treatment is 11.55 kgCO2eq/yr, and the carbon footprint of reusing is 11.66 kgCO2eq/yr.

3.1.3 Electricity

The calculation of the carbon footprint resulting from electricity consumption requires applying the corresponding emission factor to the measured consumption. (Figure 4).

Fig. 4
figure 4

Methodological scheme for calculating the carbon footprint of the electricity consumption

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According to Comisión Nacional de la Energía (2011) and IDAE (2011), electricity provides energy to household appliances, lighting and air-conditioning systems, whereas the kitchen, heating and hot water are supplied with natural gas. Thus, a typical dwelling so defined, consumes 2,746.47 kWh/yr in peninsular Spain (IDAE 2011) which produces a carbon footprint of 234.87 kgCO2eq/yr.

3.1.4 Gas Supply

The scheme to determine the carbon footprint derived from gas consumption is shown in Fig. 5.

Fig. 5
figure 5

Calculation scheme of the carbon footprint of the gas consumption

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According to the Comisión Nacional de la Energía (2011), the average gas consumption per year in the Community of Madrid varied in 2010 between 10.26 and 13.5 MWh/dwelling, whereas The Federación de la Energía de la Comunidad de Madrid (FENERCOM 2011) calculated it at 8,9 MWh/dwelling (considering a total consumption of 1,347 ktep (15,103,827 kWh) and 1,691,467 clients).

Thus, considering as valid the intermediate consumption of 10.26 MWh the resulting carbon footprint is 707.58 kgCO2eq/yr for each inhabitant.

3.1.5 Transportation Infrastructure (Road Traffic)

The carbon footprint of transportation was calculated by using the information referring to the characteristics of movements and an estimate is made by applying the corresponding emission factors. According to Monzón de Cáceres and De la Hoz (2009), the average length of mandatory travel rose in 2004 up to 10.3 km while the non-mandatory rose up to 5.7 km, both referring to the Community of Madrid. Using those distances, and supposing that the mandatory movements are made on working days (250 days/yr), the resulting annual emissions due to road traffic was 661.18 kgCO2eq/yr.

3.1.6 Other Emissions

  1. 1.

    Waste treatment

The scheme to determine the carbon footprint of waste treatment is shown in Fig. 6.

Fig. 6
figure 6

Methodological scheme to calculate the carbon footprint of waste management

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There are certain problems at this point because of the various available treatment technologies, the diversity of residues generated, the difficulty of typifying waste generation and the scarcity of reliable information sources regarding waste management. Thus, the aggregated data is used, on total emission generation and GHG generation, included in the Greenhouse Gases Emission Inventory (MARM 2011), which reports in 2008 a value of 15,560,000 tCO2eq as a result of 27,462,704 tons of treated waste. Those values imply a rate of 0.56 tCO2eq/t of treated waste, which is slightly higher than the values reported by Romero et al. (2010) (0.341 tCO2eq/t in Barcelona) or Mühle et al. (2010) 0.2844tCO2eq/t in United Kingdom and 0.103 tCO2eq/t in Germany. The average of those data referring to Spain will be taken, resulting in 0.45 tCO2eq/t of residue.

Thus, the resulting carbon footprint generated from residue treatment is 198 kgCO2eq/yr per inhabitant, considering that every inhabitant produces 0.44 tons of waste per year in the Community of Madrid (INE 2013).

  1. 2.

    Building

Several authors have studied the carbon footprint of the building process (Suzuki and Oka 2011; La Roche 2010; Onat et al. 2014, Strobele 2013). Given their nature, the total carbon footprint must be distributed along the building lifetime (50 years). La Roche (2010) calculated an annual total carbon footprint of 1.061 tCO2eq/dwelling, which represents 0.365 tCO2eq per inhabitant. Espelt and Adarve (2009), calculated a carbon footprint for the edification and urbanization construction processes of 15,221 kg/inhab. and 871 kg/inhab. respectively, for an urban model in Barcelona. These values indicate a total carbon footprint of 0.321 tCO2eq/inhab. yr.

The average of the pair the mentioned values, which is 343.32 kgCO2eq/inhab. yr. can then be taken as valid.

  1. 3.

    Other emissions

Several GHG emissions are not attributable to the aforementioned sources: public lighting, street cleaning, and city maintenance, among others. For their calculation, assigning a percentage of the total emissions is accepted as valid. Some authors (Lin et al. 2013; Ramaswami et al. 2008) consider a value of 10 % which includes every GHG emission source and not only the urban-planning-related ones, whereas on this study a value of 5 % was considered.

3.1.7 Summary of Emissions

Finally, the carbon footprint of the urban land for dwelling construction use is calculated as follows, considering number of dwellings and expected occupation.

$$ \begin{aligned} {\text{HC}}\left( {{\text{kgCO}}_{{ 2 {\text{eq}}}} /{\text{yr}}.} \right) & = 1.0 5\times \left[ {{\text{HC}}_{\text{water}} + {\text{HC}}_{\text{waste water}} + {\text{HC}}_{{{\text{elect}}.}} } \right. \\ & \left. {\quad \, + {\text{HC}}_{\text{gas}} + {\text{HC}}_{{{\text{build}}.}} + {\text{HC}}_{\text{road traffic}} + {\text{HC}}_{{{\text{inhab}}.}} } \right] \\ & = 1.0 5\times 2, 1 70. 5\left( {{\text{kgCO}}_{{ 2 {\text{eq}}}} /{\text{inhab}}.} \right) \\ & \quad \, \times {\text{Oc}}\left( {{\text{inhab}}./{\text{dwelling}}} \right) \times {\text{Number of dwellings}} \\ \end{aligned} $$
(3.1)

The final results of the carbon footprint calculation for the analyzed area are shown in Fig. 7.

Fig. 7
figure 7

Summary of the carbon footprint calculation

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Figure 7 shows that the most important source is the gas, followed by transportation, being water the least influential one. The deduced global carbon footprint value per inhabitant is consistent with the results of other authors. Lin et al. (2013) and Ramaswami et al. (2008) stated that the carbon footprint of the sources studied in their work, being 12 big cities, (20.97 % of the total carbon footprint calculated by these authors) oscillated between 4.3 and 0.996 tCO2eq/yr for Denver and Tokyo. Minx et al. (2013) calculated the carbon footprint of 434 municipalities in the United Kingdom, reaching an average per inhabitant of 12.5 tCO2eq which implies 2.61 tCO2eq/yr considering the 20.97 % from the sources that are comparable to this study. Petsch et al. (2011) obtained 19.5 tCO2eq/yr, whereas Jones and Kammen (2013) obtained 20 tCO2eq/yr in the United States.

The similarity among the results of the present study and the aforementioned consumption-based works confirms the validity of the theoretical calculations.

3.2 Case Study in the Community of Madrid

The deduced methodology was applied to a set of municipalities in the south of the Community of Madrid (Fig. 8).

Fig. 8
figure 8

Location of the analyzed municipalities

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Table 3 shows the results, obtained by applying the deduced methodology to the studied municipalities.

Table 3 Urban planning parameters for the analyzed municipalities
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The results in Table 3 show that higher GHG emissions come from Valdemoro, Navalcarnero and Aranjuez (exceeding the amount of 100,000 tCO2eq/yr), whereas the lower amounts do not exceed 1,000 tCO2eq/yr (Valdaracete, Villamanrique de Tajo and Cadalso de los Vidrios).

4 Conclusions

A methodology to calculate the carbon footprint of residence land use integrated in the urban planning was presented. The methodology was developed based on a research on emission sources attributable to urban planning.

The consumption was quantified and the corresponding emission factors were applied and the resulting carbon footprint was calculated to be 6.60918 tCO2eq/yr per planned dwelling unit on the general urban development plan. The consumption of natural gas and transportation are the most pollutant sources, both comprising nearly 60 % of the total GHG emissions.

Moreover, as a complement, an equation to calculate the carbon footprint was deduced that incorporates the amount of dwellings and expected occupation as the only independent variables.

Regarding the case study, the results show that the emissions exceed 100,000 tCO2eq/yr on those municipalities with greater growth.