- Email: [email protected]
The ecological footprint housing component: A geographic information system analysis
Ecological Indicators 16 (2012) 31–39
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
The ecological footprint housing component: A geographic information system analysis Les W. Kuzyk ∗ The City of Calgary, PO Box 2100 Stn M, Calgary, Alberta T2P 2M5, Canada
a r t i c l e
i n f o
Article history: Received 5 October 2010 Received in revised form 19 January 2011 Accepted 10 March 2011 Keywords: GIS analysis Ecological footprint Household Policy planners Decision support
a b s t r a c t The ecological footprint (EF) and its unit, the global hectare, share a reputation of effectively communicating the connection between local awareness and global impact. One use of a Geographic Information System (GIS) in urban planning is decision support, while the potential of the ecological footprint in GIS has not developed significantly. The smaller the spatial unit in GIS, the more accurate and flexible are the available GIS analyses. As urban planners are interested in showing sustainability at a local level and need accurate local data, the EF Housing component, of specific interest to planners is estimated here through a bottom up or component method to meet this need for a local measure. Average household energy use is purchased from local utilities companies in units of energy while the City Assessment department supplies dwelling size data for each household. Postal code areas approximating block faces are created for the City of Calgary in GIS and energy and household size is converted to GIS format allowing GIS analysis and map creation. A sample analysis is carried out that involves comparison between the sustainability of inner city single family Infill housing and older existing single family housing. This case study method involves a direct measure of housing energy and materials consumption, yet one that may be expressed in global hectares. Analytical output shows a net increase in the use of global hectares by Infill houses where improved building insulation standards and heating technology effecting gas consumption over time are more than offset by increased house size. Ecological footprint components, such as the Housing and perhaps Mobility, of specific interest to urban policy planners can be presented in GIS maps and tables to stimulate urban planner policy debate and potential decision-making support. GIS sourced household data while retaining units in EF global hectares makes sustainability analysis possible at a household scale. The GIS analysis here, which spatially and numerically shows the difference in sustainability between Infill housing and older existing housing, may allow planners to formulate effective policy. As well as the benefits of the land use measure and global data at a local level, the EF is effective in raising awareness, education and policy debate. Local ecological footprint measurements appear to be in a position to support urban planner policy decisions. Crown Copyright © 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction The ecological footprint and its unit, the global hectare, share a reputation of effectively communicating in an intuitively understood manner one quantifiable measure of sustainability. The ecological footprint (EF) depicts the connection between local activity and global impact and can reveal how impacts of a city or spatial area within the city extend far beyond its boundaries. It can also show how the trend over time of growing housing size contributes to the consumption of the global hectare (gha). Measurement of the ecological footprint in global hectares may be a sustainability metric of use to urban and regional policy
∗ Corresponding author. Tel.: +1 403 268 2321. E-mail address: [email protected]
planners for not just enhancing education, awareness and inspiration but also for policy debate and by extension, for direct policy decision-making support. Communicating in terms of the ecological footprint, local consumption can be translated into a numerical tabulation of local resource use for direct comparison with resource availability on the planet. Global hectares fit well into the language of planners, and council members and citizens with whom they engage also appear to find an intuitive understanding. Even fervent critics of the method defend its value for public engagement (Klinsky et al., 2010) which is one element of the policy development process carried out by policy planners. As a part of the move towards measuring sustainability in urban form using measures such as the ecological footprint, room is opening up to answer more questions regarding current and potential sustainability. Taking the ecological footprint measure into a Geographic Information System (GIS) allows maps, visual display output and
1470-160X/$ – see front matter. Crown Copyright © 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.03.009
32
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
numerical results from spatial and non-spatial analyses to be included in the support commonly sought from GIS by policy planners. The integration of the ecological footprint into GIS is a developing frontier according to Yue et al. (2006), and with development of the GIS policy planning tool described in this publication, that frontier is being opened up further. Decision support, one of the primary uses of GIS, answers more questions in the realm of urban sustainability as more data becomes available. As the level of detail of that data becomes finer, questions can be addressed at an increasingly local level including scales approximating the city block and in some cases the household. The ecological footprint is an accounting method for recording human consumption of and waste assimilation by globally available bioproductive land. The footprint measures global hectares of land consumed on the entire planet or by any nation, population, city, household or individual. Beginning with the most readily available and accurate EF source compiled for each nation by the Global Footprint Network global hectares per capita can be adjusted to local areas within the nation. This procedure has been followed by the GFN for the entire city of Calgary, Alberta, Canada to estimate not only the ecological footprint, but as well, a matrix of components and land uses that make up the ecological footprint. Following the same logic of extrapolating down to finer spatial areas such as nation to city and beginning with the Housing Component of the ecological footprint for the City of Calgary, the EF data has been adjusted here to even more local areas within the city. These local areas as well as a subset of the components that make up an ecological footprint are of extra interest to planners formulating policy. Policy may be formulated through analyses at the household level with a focus on the EF Housing Component, as well as potentially the Mobility Component and Energy Land use (the Carbon Footprint expressed in global hectares). The objectives of this paper are to first of all show that the ecological footprint and specifically the Housing component can be measured and analyzed at the fine geographic scale of household, secondly using GIS as an analytical tool allow local planner policy development to be quantitatively described for public engagement, raising of awareness and communication and finally to broach the possibility that the results of these GIS analyses may possibly directly affect policy decision-making. 2. Policy support in GIS Despite published limitations to the use of the EF for policy development (Wilson and Grant, 2009; Mcmanus and Haughton, 2006; Yue et al., 2006), the integration of a highly detailed scale of EF data into GIS may assist in challenging these restrictions. The results of GIS analyses inform policy planners on issues they are debating regarding the policy they propose to have implemented. For policy that is more locally focused there exists a greater need for analysis of local data. Urban planners need accurate local data (Klinsky et al., 2009) to support their interests at a local level in showing change in sustainability as a result of implemented policy. Empirically measured variation in sustainability over existing built form may be the basis of sustainability targets along with theoretical based targets. Hunter et al. (2006) discuss the increasing interest in the household level of ecological footprint measurement where leading edge work in urban EF measurement in Wales and Scotland recognizes the relevance of the household dimension of EF analysis. Very little work has been published on EF analysis at the household level where the potential educative impact of EF analysis is high (Hunter et al., 2006). It is speculated here that educative impact may apply to policy planners and the decision support they seek from GIS analyses. Planning policy involves designing, implementing and managing a desired urban form with an increasing interest in form that
is sustainable. Sustainability in this context is often expressed in the more subjective terms of housing type, density and mixed use based on general assumptions of trend directions that are more sustainable. The ecological footprint helps fill out the function of measuring the sustainability that is to be planned for or managed (Collins et al., 2009; Wackernagel et al., 2006; Mcmanus and Haughton, 2006). Cranston et al. (2010) discuss the footprint as a quantitative indicator that might assist in measurement of sustainability which suggests planning policy indicators such as housing type and density may be defined in terms of a numerical EF measurement for direct comparison during decision-making. One part of the decision-making process carried out by policy planners involves public engagement through open houses involving discussions with citizens of the community. The smaller the spatial unit involved during public engagement the more likely citizens will relate to local issues (Klinsky et al., 2009, 2010). A basic law of geography is in effect – entities closer to each other have a greater impact on each other than entities more distant from each other. Residents of an area are more interested in what happens in and what is affecting their own back yard, the block they live on and the community where they reside than a more distant city block or community. Yet when the discussion topics are based in a common language (Wackernagel et al., 2006) using terms such as ecological footprint and numbers expressed in global hectares, impact on even globally distal places does become apparent and global thinking (Klinsky et al., 2010) ensues. Coming together with this need for smaller areas to better engage citizens, the world of GIS also seeks out smaller areas for improved analysis. The smaller the spatial unit in GIS, the more accurate, detailed and flexible the available GIS mapping and analyses. Within a GIS, smaller spatial units can be aggregated into larger spatial units while maintaining accuracy of non-spatial attributes, while the disaggregation of larger spatial units into smaller looses accuracy and is more complex if at all possible. So starting with the smallest unit possible such as an individual, a household or a Canadian six digit (fewer digits are at times used to denote larger areas) postal code has its advantages. The household represents a typical unit used by policy planners as well as being the basis for data tabulations such as the accounting records kept by an urban property tax department. In GIS at the City of Calgary the household is associated with a street address and a parcel of registered land. All street addresses have an associated postal code by which data can be aggregated. As some data may be available only at a postal code level (household energy use in this case), and as a postal code approximates a city block, this spatial scale has been selected as appropriate. The needs of policy planners for more local data combined with the basic GIS improved functionality when smaller spatial units are involved converge into an ecological footprint based GIS layer that allows flexibility and accuracy at a high level of detail to provide policy support analyses. The fact that the ecological footprint can be broken down into a Consumption Land Use Matrix (CLUM) of Land Uses vs. Consumption Components adds value to policy planner support. This matrix includes Housing and Mobility Consumption on one axis as well as Energy Land on the opposing axis, each of which can be individually defined and analyzed in GIS. Ecological footprint Housing Consumption along with Mobility Consumption and Energy Land use are of primary interest to policy planners as these are factors over which their policy has direct practical influence. According to the GFN 2008 EF calculation for the City of Calgary, Housing and Mobility Consumption are made up of a high percentage of Energy Land use (85% and 92% respectively) which allows further definition of sustainability in terms of Energy Land or what equates to the increasingly recognized Carbon Footprint (Haq and Owen, 2009; Stiglitz et al., 2008) which can still be express in global hectares.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
3. Bottom up and top down methods The ecological footprint can be calculated at a sub national level by two methods; the component or bottom up method and the compound or top down method (Simmons et al., 2000; Klinsky et al., 2010). The bottom up method appears to be of interest to Calgary policy planners as it is used in one city wide plan recently (adopted in 2009) published by the City of Calgary. An alternate measure of sustainability in units other than the global hectare, the Calgary Energy Map (Gilmour et al., 2009/2010) which makes up part of Calgary’s new Municipal Development Plan uses gas and electric energy as well as building floor space to calculate energy use in Gigajoules per hectare for a citywide map – a ground up measure. Ground up data may be more sensitive to underlying data variations, less restrictive and less prone to error when proxies for consumption are used (Simmons et al., 2000), and this may explain the added interest to planners. The bottom up method has at least a psychological advantage as people including planners relate most easily to concrete and localized information (Klinsky et al., 2009; Collins and Flynn, 2007) discusses the improved accuracy possible for the bottom up method when minimizing the use of proxy data which constitutes the basis of the top down method. Simmons et al. (2000), however, directly compares the top down to the bottom up method by carrying out total ecological footprint calculations using both methods for an identical area, a self-governing European island. This explicit comparison shows a difference of only 3% between the two methods. The two methods each have advantages and are increasingly used in conjunction with each other as well as having results measured by either compared directly. The top down method can be utilized in combination with bottom up data when data availability is the deciding factor, just as coarser scales may be used when data at finer scales are not available (Wilson and Grant, 2009; Klinsky et al., 2009). The variation of ecological footprint Housing Component, of specific interest to planners (Kuzyk, 2010), is estimated here by a bottom up method. The initial value of the Housing Component, however, is derived from numbers calculated for Calgary by GFN mostly using Statistics Canada’s annual Consumption/Spending survey to define the footprint and its components – a top down calculation. The combined top down/bottom up source used here allows an accurate estimate of the Housing Component at a postal code level of spatial detail, and using the dynamics of GIS analysis, suggests analysis at the household level is possible and may allow planner GIS policy decision-making support. Once a global hectare based EF GIS layer has been compiled, many sustainability questions can be answered. For a sample case, this study sought out a question or series of questions from the minds of City of Calgary policy planners. One initial question posed by a policy planner in an informal conversation on sustainability was: what would the environmental impact of single family Infill housing in the inner city be compared to original single family stock housing? Infill housing generally refers to the insertion of additional housing units into an already approved subdivision or neighbourhood. Typically in Calgary, an older stock housing unit is demolished most likely in an inner city community where they would be common, the lot is subdivided if it meets the requirements of the land use bylaw and two or more new housing units are constructed. Stock housing refers to the original housing construction, typically over 65 years ago in Calgary, built when a subdivision or community was undeveloped and first coming to be developed.
4. GIS analysis methodology As a starting point for this methodology the ecological footprint calculated for a geography containing the area to be studied is first
33
obtained. The EF calculated for each nation by the Global Footprint Network (GFN, 2008) is one potential starting point. Then global hectares per capita are adjusted to local areas within the nation following the direction of the Global Footprint Network suggested procedure (GFN, 2005). If an ecological footprint has been calculated for a city, that city EF measure is the starting point for estimation of the EF at a sub-city level. A Consumption Land Use Matrix (CLUM) of consumption components (including Housing and Mobility) and land uses (including Energy Land or carbon footprint in global hectares) that make up the ecological footprint is also required to allow segregation of the Housing and Mobility components. Data that represent the ecological footprint well are housing unit size and household energy use as well as government travel to work surveys. Local government tax departments track the floor area of local housing if this is the basis of the local land tax system and if this data exists it will be available at a household scale. Utilities companies may supply energy to local housing units and these utilities companies are approached for data. Data release may be restricted for privacy reasons at a household level for although it is recorded for billing purposes, it may only be released at a larger geography such as a postal code that groups several households together. Having obtained housing size and energy consumption data, this non-spatial data is then linked to the appropriate spatial data in a GIS. Travel to work survey data will also need to be linked to the spatial geography at which it is available. Then, to address local planner policy issues, a planning question is defined. A GIS analysis procedure is then designed, tested and carried out in GIS to output results that address the question and supply GIS maps and charts on which decisions can be made. Fine local geography at a household and block face scale as well as the Housing, Mobility and Energy Land Use components that constitute a part of an ecological footprint are of added interest to planner policy.
5. Case study methodology To allow analysis of the ecological footprint Housing Component in GIS for the City of Calgary, data is created in and converted to GIS format. The City of Calgary maintains a spatial land ownership parcel layer with a six digit postal code attributed to an address for each parcel. GIS is used to merge ownership parcels with the same postal code into spatial postal code areas or shapes. The City of Calgary, with a population just over one million, contains over three hundred thousand ownership parcels, which aggregate to just over twenty five thousand postal code areas. With over 19,000 of these being residential (EF is a measure of consumption, so where people reside is considered to be the point of consumption) and with an average of 17 households per postal code, the GIS postal code layer is created. Residential energy consumption in the City of Calgary is almost entirely made up of natural gas for heat and electric power for appliances and other household use. For this study, average household energy use per six-digit postal code is purchased from local utilities companies. This data is available in units of energy; Gigajoules for gas and Kilowatt hours for electricity. A simple conversion factor between these two energy units allows expression in a common unit. In GIS, this energy use data is imported and attributed to the postal code layer. The City of Calgary Assessment (property tax) department collects dwelling size in square meters of floor space for each household along with other useful data including building type and construction year. Assessment data is available at the City of Calgary in GIS format and can be spatially aggregated to a postal code scale (see Fig. 1 for sample Infill household data shown with hypothetical postal codes). The Housing component of the ecological footprint CLUM calculated for the City of Calgary by the Global
34
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Fig. 1. Inner City Infill household Assessment data used in conjunction with utilities companies household energy use to estimate the mean Housing EF in global hectares for each postal code.
Footprint Network gives city average global hectares based on each of electric energy dollars spent, gas energy dollars spent and gross rent dollars spent. These numbers are scaled up or down for each postal code by the above or below scale factor for each of electric energy, gas energy and housing unit size. The construction year in the Assessment database is used to separately select the two housing types: Infill vs. Stock; and the Assessment building type attribute allows only houses and duplexes to be selected. To analyze at a household scale, Infill housing data is selected from the Assessment database within inner city communities (see Fig. 2) only where the entire postal code is made up of housing units with an Assessment construction year of 1996 or later and that are classified as houses or duplexes. Houses comprise one unit in one detached building structure while duplexes have two housing units in one detached building structure; together these are classified as single family housing in this case. The same search is done for houses and duplexes with a construction year before 1946 within inner city communities. The 68 postal codes in the 1996 and after category which can accurately be classified as Infills are analytically compared to the 223 postal codes with original housing stock in the pre 1946 category which would be originally constructed Stock housing (see Fig. 3). Calgary is a newer western Canadian city, incorporated as a town in 1884 and as a city in 1894 where single family housing is made up mostly of wood frame construction with a typical housing life cycle of 70 years, helping to define this classification as appropriate.
6. Results With data classified and then analyzed, a tentative answer to the policy planner question is presented. It turns out that between construction years of 1945 and 1995, variation exists in the global hectares consumed by housing. Use of electric energy in Infill households varies little (a 1.4% increase) while the use of gas heating energy decreases significantly (by 23.4%) in spite of an offsetting increase in building size. The decreased use of gas for heating is likely due to improved building standards and the associated improvement in insulation as well as more efficient heating systems. Housing floor area changed the most (a 54.7% increase) reflecting the largest change in the EF Housing footprint for each household. The increase in house size offset any gains in insulation and heating system efficiency and left the total Housing ecological footprint of Infill housing 11.0% larger than that of pre 1945 stock housing according to this GIS analysis. Planners, when presented with this first tentative answer in a formal meeting, ask for further comparison between Greenfield housing and inner city Infill housing (see Fig. 4). Greenfield refers to land that has not been developed before which for the City of Calgary exists mostly on the perimeter of the developed part of the city. The highest ecological footprint is suggested to be in the suburbs and rural areas close to a city where low-density development exists along with high levels of consumption (Mcmanus and Haughton, 2006; Klinsky et al., 2010). The same GIS analysis procedure is followed and a count of 1649 postal codes with only houses
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
35
Fig. 2. Inner City communities showing distribution of Infill versus Stock housing based on year of construction.
and duplexes built in 1996 and after are selected. Results show very little difference between Infill housing and Greenfield housing; a difference of 1.6% (see Fig. 5). When mapping and graphic results originating from GIS analysis are presented to policy planners both formally and informally, debate ensues, concurring with the findings of Yue et al. (2006) and Klinsky et al. (2010), while this study brings out that debate at a much finer spatial scale and a narrowed focus on ecological footprint Components such as Housing.
Fig. 3. Inner City Infill versus Stock housing gas and electric energy and floor space combined to estimate and compare total Housing EF. Note Mobility EF does not vary based on mode of travel to work.
What was also intuitively requested by planners for further analysis was the Mobility Component of the ecological footprint. Klinsky et al. (2009) have shown a trend towards higher footprint in communities located further from the downtown core of Montreal, Canada. For the Mobility footprint, ground up data is not available at a level of detail comparable to that available for the Housing footprint (yet, although Alberta vehicle registries have been approached). However a top down method can be substituted. Using Statistics Canada’s 2006 Census data at a Dissemination Area (DA) or what might be called Census Village (mean population in Calgary is 670) level also converted to GIS format, mode of travel to work is used to estimate a 36% increase in the Mobility footprint for Greenfield housing. Combining the Housing Component from a bottom up source and the Mobility Component from a top down source, and making the tenuous assumption that they are equally accurate, total Housing and Mobility compared between Infill and Greenfield housing are calculated. The combined results show Infill housing to use 9.2% less global hectares than Greenfield housing (see Fig. 6). This case study analysis shows that sustainability analysis using the ecological footprint units of global hectares is possible at a household level and should be possible in other urban contexts. The finer level of detail involved allows GIS analysis to be carried out on questions such as those posed on Infill, stock and Greenfield housing. It would also allow planners to engage the public armed with data at the household scale where policy discussion impact may be high and to potentially use these results to support decisions made regarding policy.
36
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Fig. 4. Greenfield communities housing versus Inner City communities Infill housing.
7. Discussion Urban and regional planners have an interest in measuring local sustainability (Mcmanus and Haughton, 2006; Wilson and Grant, 2009; Kuzyk, 2010) with the ecological footprint and the components that make it up being one measure available. As well as the benefits of the land area based measure and global data
Fig. 5. Inner City Infill versus Greenfield gas and electric energy and floor space combined to estimate and compare total Housing EF. Note Mobility EF does vary based on mode of travel to work.
being measured at a local level, the ecological footprint is effective in raising awareness, education and policy debate (Klinsky et al., 2010; Mcmanus and Haughton, 2006). The quantitative nature of the measure adds effectiveness to the decision-making process of policy development. The answers to the policy planner question(s) are that Infill housing consumes more global hectares than stock inner city housing when based only on the Housing Component. When inner city
Fig. 6. Inner City Infill versus Greenfield Housing and Mobility EF combined and compared.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Infill housing is compared with Greenfield housing, Infill housing consumes about the same number of Housing global hectares. So where a newly constructed house or duplex is built in the city has little influence on its ecological footprint by a measure composed of housing unit floor space and household energy use. If the measure of Mobility (from a statistics based top down source) is assumed to have the same accuracy as the Housing Component measure, the total Housing and Mobility components of ecological footprint show a trend in the intuitively expected direction. There is a decrease in global hectares consumed if a single family house is constructed as an Infill in the inner city rather than in a community on the edge of the City of Calgary. There are several shortfalls to this methodology including that of the case study. Along with the lack of bottom up data for the Mobility Component, all other ecological footprint Components (Food, Goods, Services and Government) have no source at the postal code scale. This analysis does not include a cost analysis of underground facilities and roadways development in the Greenfields nor does it included a measure of the cost associated with demolition of inner city stock housing for replacement by Infill housing. The tendency of Infills to develop on subdivided lots, where two Infills typically replace one stock house creating an associated increase in density is not considered. Pertaining to the data used, the electric utility company suppresses data when there are fewer than 5 households per postal code for freedom of information reasons so for this analysis, that data was estimated based on persons per household. One assumption made for this case study methodology is that a housing unit double the size will use double the part of the ecological footprint it represents. Also, the energy and construction materials reduction of the common wall of duplexes is not taken into account when converting to global hectares. The assumption that doubling the household energy use does equate to double that the ecological footprint represented is likely a solid assumption. Basing postal code level ecological footprint on a GFN top down calculated CLUM for the entire city of Calgary makes the assumption that the GFN numbers are correct on a citywide basis. The ecological footprint of Calgary is well above the EF of Canada (Wilson and Mark, 2005), one contributing factor being Calgary’s source of electric power. In the province of Alberta, 40% of electricity is generated from natural gas fired generators and 50% from coal-fired generators (Alberta Energy, 2010), the remaining being hydroelectric and other renewable sources including the wind farms of southern Alberta. That some households in Calgary may be purchasing electricity from renewable sources such as wind power or the fact that a portion of the Alberta electric grid is sourced from hydroelectric, though these sources are relatively small, is not considered and the assumption is made that household electric energy use is equitable among all households. Assumption is also made here that the GFN took in to account the various sources of Alberta’s electric energy in their CLUM Housing electric energy component for Calgary. Measuring the ecological footprint at varying scales can be problematic. The most accurate measure is at the national level where national accounts are the basis of measure. EF calculated back to 1961 for many nations as shown by the Global Footprint Network (GFN, 2010) shows fluctuation on a year by year basis somewhat challenging the accuracy of data during any given year even at the national level. These variations are due to methodology improvements according to GFN and the general trend over time, which can be smoothed out, is consistent. If the original national data is somewhat in question, data at a finer scale also might be challenged. Primary problems of data at a finer scale than national are the availability of data as well as a record of imports and exports. Wilson and Grant (2009) describes the various approaches to data collection at a sub-national level including the indicator method, the resource flow models extensively developed in Europe by the Stockholm
37
Environmental Institute, the use of community questionnaires and the Household Expenditure survey approach available in Canada. The method presented here is a combination of the Household Expenditure survey approach, as this is the basis of the EF CLUM calculated for the City of Calgary by the GFN and the indicator method where electricity, gas and house size are used. Collection of bottom up data that is a direct measure of consumption such as housing size and household energy use circumvent the import export problem. As methodologies at a sub-national scale are still being developed, the method presented here claims to be another option and an improved one with bottom up consumption data being used. A distinct benefit to the purchase of energy use data from utilities companies is the availability in units of Gigajoules or Kilowatt hours which is a direct measure of energy use; a preferable measure over dollars spent when converting into global hectares. This can quite correctly be assumed to be an improvement over the dollars spent basis of the Global Footprint Network calculation for the entire city. The building floor space is also assumed to be an improvement over the gross imputed rent in dollars spent used by GFN. Further research may allow direct translation of house size and household energy consumed into global hectares without depending on the GFN citywide numbers. In one broader context, debate continues as to whether technology or efficiency improvement will solve or assist in alleviating increasing human pressure on the global natural economy. Wackernagel and Rees (1996) see a need to focus on improving human welfare by means other than sheer growth, concurred with by Rees (2008) discussion of growth gradually fading away as an option. Rees points out that “material growth in even the most efficient economies overwhelms the positive gains from efficiency”. In this study, growth in housing floor space or house size over the several decades age difference between Infill and stock housing overrides energy efficiency gains resulting from improved building insulation and heating systems over the same time period. Toth et al. (2009) point out a future projected pattern of growth in household energy consumption in spite of efficiency improvements. Making reference to technology as well as increased efficiency as being the solution to the sustainability challenge (Victor, 2008) details the lack of supporting evidence for these proposals. The increase over time of housing unit size is a contributing factor to unsustainability. According to a study done in Seattle, smaller footprint would likely result from a reduction in house size (Redefining Progress, 2005). Housing size may become one of the targets of influence by planner policy and this may be associated with increased density. Planner policy includes maintenance of the City land use bylaw which can stipulate density and allowable housing type. Policy planners can have an influence on sustainability through a city land use bylaw. Wolf and Meyer (2010) look at the use of sustainability measurement in GIS for the purpose of land use decision-making including a measure of compactness or density, concluding that scientific discussion can be bridged with practical application from a planning perspective. It is of interest to note here that from Calgary’s 2009 Global Footprint Network total ecological footprint CLUM (Consumption Land Use Matrix), the Housing component makes up 18.5% of which 85% is Energy land and the Mobility component makes up 11.0% of which 92% is Energy land. Policy planners have, within the scope of influence of the policy they develop, the potential to have a large impact on the sustainability of future urban built form. As mentioned, a GIS layer once created can provide a wealth of new information. Further analysis of City of Calgary Assessment data allows the EF Housing Component by postal code to be classified by housing type. Results show a trend across housing types where single family housing uses the most global hectares and
38
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
the embedded energy or Energy Land use portion of the Housing Component.
8. Conclusion
Fig. 7. Housing EF trend across housing types.
apartment style residences consume the least (see Fig. 7). Single family housing tends to exist in lower density areas and apartments in higher density areas, supporting the tendency of higher density to be more sustainable and in this case also assigning a quantitative and globally comparable measure. Policy planners create policy in terms of density where household units per hectare are defined and increasing density is considered to be a step in the direction of improved sustainability. As mentioned, suburban communities are typically made up of the single family housing type and are low-density. Along with housing type, a direct measure of density in units of global hectares can be carried out. The EF postal code layer in GIS, with an area and a count of households for each postal code is used to define this trend in global hectares. The trend found here confirms planner sustainability thought and makes numerical comparison in global hectares available when higher density is applied to urban built form (see Fig. 8). There is a common interest in residential electric and gas use for ecological footprint calculations in Europe (Hunter et al., 2006; Simmons et al., 2000) with the use of these sources being a part of the highly collaborative and university reviewed procedure used by Cardiff, Wales (Collins and Flynn, 2007). Though this interest extends to North America (Wilson and Grant, 2009; Klinsky et al., 2010), availability of this data has been limited for studies carried out to date. The method appears to be solid with several advantages as it involves a direct measure of housing energy and materials consumption, yet one that may be expressed in global hectares. The utilities companies’ sources are in the more relevant units of energy consumed rather than dollars spent and were collected at the finest level of detail that would be released for this study. Wilson and Grant (2009) specifically lists an interest in dwelling size as a measure of the non-energy Housing Component and it is suggested here that square meters of floor space also well represent a measure of
Analysis of the ecological footprint Housing Component at a household level is possible for certain analyses when data is compiled in GIS at a postal code or approximate block face scale. Ecological footprint components, such as Housing and perhaps Mobility as well as the Energy Land use of specific interest to urban policy planners can be used to stimulate policy debate. From this inspired debate, policy development decision-making is potentially possible. Bottom up data is of primary interest to urban planners; data they can measure and influence with policy. The GIS analysis here, which spatially and numerically shows the difference in sustainability between Infill, older existing stock and newer Greenfield housing makes up an example of what may assist planners in formulating sustainability policy. The analysis carried out in this study shows that inner city Infill housing brings about a sustainability improvement over Greenfield development due to a shorter travel to work distance and therefore a reduced EF Mobility. Inner city community development also results in a more sustainable development pattern due to the tendency of inner city development to be made up of higher density as well as lower impact housing types. The study here also shows that EF Housing does not vary between Infill and Greenfield development and that Infill Housing actually consumes more global hectares than older stock housing in spite of improved energy efficiency. Infill development of more sustainable housing types and higher density neighbourhoods are shown to bring about more sustainable urban development. If Infill housing shifted to construction of housing units with smaller floor areas and still retained the improved construction standards that result in more energy efficiency, even greater sustainability could be achieved. These factors all point towards the benefits of the concentration of housing units in the inner city and a reduction in urban sprawl on a city’s perimeter. There appear to be several options available to policy planners, all of which can be quantitatively defined, measured and perhaps have targets set for in terms of the ecological footprint in global hectares. The objectives in this study of analyzing the ecological footprint measurement at a household geography, allowing GIS presentation material to raise awareness and assist in communication for policy planners as well as making available direct decision-making support based on the EF have been met. Data from combined bottom up and top down sources, when compiled into GIS format allows analytical output from the traditionally used GIS source of policy planner support to be extended into the realm of global sustainability measurements. The method shown here is based on minimal data collection and should be replicable for any city or region with a property tax department and utilities companies supplying household energy. Measurements such as density and housing type in global hectares fit within the policy development sphere of urban planners. Valuable policy debate and decision-making support is available through GIS map creation and analysis while retaining units in the quantitative measure of EF global hectares.
References
Fig. 8. Housing EF trend across housing unit density.
Alberta Energy, 2010. http://www.energy.alberta.ca/Electricity/electricity.asp. Collins, Andrea, Cowell, Richard, Flynn, Andrew, 2009. Evaluation and environmental governance: the institutionalisation of ecological footprinting. Environment and Planning 41, 1707–1725. Collins, Andrea, Flynn, Andrew, 2007. Engaging with the ecological footprint as a decision-making tool: process and responses. Local Environment 12 (3), 295–312.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39 Cranston, G.R., Hammond, G.P., Johnson, R.C., 2010. Ecological debt: exploring the factos that affect national footprints. Journal of Environmental Policy and Planning 12 (2), 121–140. Global Footprint Network, 2005. National Footprint and Biocapacity Accounts 2005: the underlying calculation method. Global Footprint Network, 2008. Living Planet Report 2008 [Online] Available from www.assets.panda.org. Global Footprint Network, 2010. Footprint for Nations [Online] http://www.foot printnetwork.org/en/index.php/GFN/page/footprint for nations/. Gilmour, Brent, Leach, Tonja, Salisny, Ralph, 2009/2010. Resonding to the energy challenge of Canadian communities: key lessons for applying the QUEST principles to Calgary’s Municipal Development Plan. AACIP Planning Journal Winter (3). Haq, Gary, Owen, Ann, 2009. Green Streets: The Neighbourhood Carbon Footprint of York. Stockholm Environment Institute. Hunter, C., Carmichael, K., Pangbourne, K., 2006. Household ecological footprinting using new diary-based data-gathering approach. Local Environment 11 (3), 307–327. Klinsky, S., Sieber, R., Meredith, T., 2009. Creating local ecological footprints in a North American context. Local Environment 14 (6), 495–513. Klinsky, S., Sieber, R., Meredith, T., 2010. Connecting local to global: geographic information systems and ecological footprints as tools for sustainability. The Professional Geographer 62 (1), 84–102, First published on: 11 December 2009 (iFirst). Kuzyk, L., 2010. Infill housing sustainability analysis. AACIP Planning Journal Summer (4). Mcmanus, P., Haughton, G., 2006. Planning with ecological footprints: a sympathetic critique of theory and practice. Environment and Urbanization 18 (1), 113–127. Redefining Progress, 2005. Seattle Ecological Footprint Assessment December 2005, www.rprogress.org.
39
Rees, Willam E., 2008. Human nature, eco-footprints and environmental injustice. Local Environment 13 (8), 685–701. Statistics Canada, 2008. Survey of Household Spending in 2006, RY2006 62FPY0031 Geographic.xlsx. Statistics Canada, Government of Canada, Ottawa, Ontario. Simmons, C., Lewis, K., Barret, J., 2000. Two feet – two approaches; a componentbased model of ecological footprinting. Ecological Economics 32, 375–380. Stiglitz, Joseph E., Sen, Amartya, Firoussi, Jean-Paul, 2008. Report by the Commission on the Measurement of Economic Performance and Social Progress. Toth, Agnes Hofmeister, Kelemen, Kata, Piskoti, Marianna, 2009. Changes in consumer behavior patters in the light of sustainability. In: Sustainable Consumption 2009 Conference Proceedings , Corvinus University of Budapest (September 24, 2009), pp. 75–85. Victor, Peter A., 2008. Managing Without Growth: Slower by Design, Not Disaster, Advances in Ecological Economics. Edward Elgar, Northampton, MA, USA. Wolf, Torsten, Meyer, Burghard C., 2010. Suburban scenario development based on multiple landscape assessments. Ecological Indicators 10, 74–86. Wackernagel, Mathis, Rees, William, 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers, B.C., Canada. Wackernagel, Mathis, et al., 2006. The Ecological Footprint of cities and regions: comparing resource availability with resource demand. Environment and Urbanization 18, 103–112. Wilson, J., Grant, J.L., 2009. Calculating ecological footprints at the municipal level: what is a reasonable approach for Canada? Local Environment 14 (10), 963– 979. Wilson, Jeffrey, Mark, Anielski, 2005. Ecological Footprints of Canadian Municipalities and Regions. Anielski Management Inc., Edmonton, Alberta, Canada. Yue, D., Xu, X., Li, Z., Hui, C., Li, W., Yang, H., Ge, J., 2006. Spatiotemporal analysis of ecological footprint and biological capacity of Gansu, China 1991–2015: down from the environmental cliff. Ecological Economics 58, 393–406.
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
The ecological footprint housing component: A geographic information system analysis Les W. Kuzyk ∗ The City of Calgary, PO Box 2100 Stn M, Calgary, Alberta T2P 2M5, Canada
a r t i c l e
i n f o
Article history: Received 5 October 2010 Received in revised form 19 January 2011 Accepted 10 March 2011 Keywords: GIS analysis Ecological footprint Household Policy planners Decision support
a b s t r a c t The ecological footprint (EF) and its unit, the global hectare, share a reputation of effectively communicating the connection between local awareness and global impact. One use of a Geographic Information System (GIS) in urban planning is decision support, while the potential of the ecological footprint in GIS has not developed significantly. The smaller the spatial unit in GIS, the more accurate and flexible are the available GIS analyses. As urban planners are interested in showing sustainability at a local level and need accurate local data, the EF Housing component, of specific interest to planners is estimated here through a bottom up or component method to meet this need for a local measure. Average household energy use is purchased from local utilities companies in units of energy while the City Assessment department supplies dwelling size data for each household. Postal code areas approximating block faces are created for the City of Calgary in GIS and energy and household size is converted to GIS format allowing GIS analysis and map creation. A sample analysis is carried out that involves comparison between the sustainability of inner city single family Infill housing and older existing single family housing. This case study method involves a direct measure of housing energy and materials consumption, yet one that may be expressed in global hectares. Analytical output shows a net increase in the use of global hectares by Infill houses where improved building insulation standards and heating technology effecting gas consumption over time are more than offset by increased house size. Ecological footprint components, such as the Housing and perhaps Mobility, of specific interest to urban policy planners can be presented in GIS maps and tables to stimulate urban planner policy debate and potential decision-making support. GIS sourced household data while retaining units in EF global hectares makes sustainability analysis possible at a household scale. The GIS analysis here, which spatially and numerically shows the difference in sustainability between Infill housing and older existing housing, may allow planners to formulate effective policy. As well as the benefits of the land use measure and global data at a local level, the EF is effective in raising awareness, education and policy debate. Local ecological footprint measurements appear to be in a position to support urban planner policy decisions. Crown Copyright © 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction The ecological footprint and its unit, the global hectare, share a reputation of effectively communicating in an intuitively understood manner one quantifiable measure of sustainability. The ecological footprint (EF) depicts the connection between local activity and global impact and can reveal how impacts of a city or spatial area within the city extend far beyond its boundaries. It can also show how the trend over time of growing housing size contributes to the consumption of the global hectare (gha). Measurement of the ecological footprint in global hectares may be a sustainability metric of use to urban and regional policy
∗ Corresponding author. Tel.: +1 403 268 2321. E-mail address: [email protected]
planners for not just enhancing education, awareness and inspiration but also for policy debate and by extension, for direct policy decision-making support. Communicating in terms of the ecological footprint, local consumption can be translated into a numerical tabulation of local resource use for direct comparison with resource availability on the planet. Global hectares fit well into the language of planners, and council members and citizens with whom they engage also appear to find an intuitive understanding. Even fervent critics of the method defend its value for public engagement (Klinsky et al., 2010) which is one element of the policy development process carried out by policy planners. As a part of the move towards measuring sustainability in urban form using measures such as the ecological footprint, room is opening up to answer more questions regarding current and potential sustainability. Taking the ecological footprint measure into a Geographic Information System (GIS) allows maps, visual display output and
1470-160X/$ – see front matter. Crown Copyright © 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.03.009
32
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
numerical results from spatial and non-spatial analyses to be included in the support commonly sought from GIS by policy planners. The integration of the ecological footprint into GIS is a developing frontier according to Yue et al. (2006), and with development of the GIS policy planning tool described in this publication, that frontier is being opened up further. Decision support, one of the primary uses of GIS, answers more questions in the realm of urban sustainability as more data becomes available. As the level of detail of that data becomes finer, questions can be addressed at an increasingly local level including scales approximating the city block and in some cases the household. The ecological footprint is an accounting method for recording human consumption of and waste assimilation by globally available bioproductive land. The footprint measures global hectares of land consumed on the entire planet or by any nation, population, city, household or individual. Beginning with the most readily available and accurate EF source compiled for each nation by the Global Footprint Network global hectares per capita can be adjusted to local areas within the nation. This procedure has been followed by the GFN for the entire city of Calgary, Alberta, Canada to estimate not only the ecological footprint, but as well, a matrix of components and land uses that make up the ecological footprint. Following the same logic of extrapolating down to finer spatial areas such as nation to city and beginning with the Housing Component of the ecological footprint for the City of Calgary, the EF data has been adjusted here to even more local areas within the city. These local areas as well as a subset of the components that make up an ecological footprint are of extra interest to planners formulating policy. Policy may be formulated through analyses at the household level with a focus on the EF Housing Component, as well as potentially the Mobility Component and Energy Land use (the Carbon Footprint expressed in global hectares). The objectives of this paper are to first of all show that the ecological footprint and specifically the Housing component can be measured and analyzed at the fine geographic scale of household, secondly using GIS as an analytical tool allow local planner policy development to be quantitatively described for public engagement, raising of awareness and communication and finally to broach the possibility that the results of these GIS analyses may possibly directly affect policy decision-making. 2. Policy support in GIS Despite published limitations to the use of the EF for policy development (Wilson and Grant, 2009; Mcmanus and Haughton, 2006; Yue et al., 2006), the integration of a highly detailed scale of EF data into GIS may assist in challenging these restrictions. The results of GIS analyses inform policy planners on issues they are debating regarding the policy they propose to have implemented. For policy that is more locally focused there exists a greater need for analysis of local data. Urban planners need accurate local data (Klinsky et al., 2009) to support their interests at a local level in showing change in sustainability as a result of implemented policy. Empirically measured variation in sustainability over existing built form may be the basis of sustainability targets along with theoretical based targets. Hunter et al. (2006) discuss the increasing interest in the household level of ecological footprint measurement where leading edge work in urban EF measurement in Wales and Scotland recognizes the relevance of the household dimension of EF analysis. Very little work has been published on EF analysis at the household level where the potential educative impact of EF analysis is high (Hunter et al., 2006). It is speculated here that educative impact may apply to policy planners and the decision support they seek from GIS analyses. Planning policy involves designing, implementing and managing a desired urban form with an increasing interest in form that
is sustainable. Sustainability in this context is often expressed in the more subjective terms of housing type, density and mixed use based on general assumptions of trend directions that are more sustainable. The ecological footprint helps fill out the function of measuring the sustainability that is to be planned for or managed (Collins et al., 2009; Wackernagel et al., 2006; Mcmanus and Haughton, 2006). Cranston et al. (2010) discuss the footprint as a quantitative indicator that might assist in measurement of sustainability which suggests planning policy indicators such as housing type and density may be defined in terms of a numerical EF measurement for direct comparison during decision-making. One part of the decision-making process carried out by policy planners involves public engagement through open houses involving discussions with citizens of the community. The smaller the spatial unit involved during public engagement the more likely citizens will relate to local issues (Klinsky et al., 2009, 2010). A basic law of geography is in effect – entities closer to each other have a greater impact on each other than entities more distant from each other. Residents of an area are more interested in what happens in and what is affecting their own back yard, the block they live on and the community where they reside than a more distant city block or community. Yet when the discussion topics are based in a common language (Wackernagel et al., 2006) using terms such as ecological footprint and numbers expressed in global hectares, impact on even globally distal places does become apparent and global thinking (Klinsky et al., 2010) ensues. Coming together with this need for smaller areas to better engage citizens, the world of GIS also seeks out smaller areas for improved analysis. The smaller the spatial unit in GIS, the more accurate, detailed and flexible the available GIS mapping and analyses. Within a GIS, smaller spatial units can be aggregated into larger spatial units while maintaining accuracy of non-spatial attributes, while the disaggregation of larger spatial units into smaller looses accuracy and is more complex if at all possible. So starting with the smallest unit possible such as an individual, a household or a Canadian six digit (fewer digits are at times used to denote larger areas) postal code has its advantages. The household represents a typical unit used by policy planners as well as being the basis for data tabulations such as the accounting records kept by an urban property tax department. In GIS at the City of Calgary the household is associated with a street address and a parcel of registered land. All street addresses have an associated postal code by which data can be aggregated. As some data may be available only at a postal code level (household energy use in this case), and as a postal code approximates a city block, this spatial scale has been selected as appropriate. The needs of policy planners for more local data combined with the basic GIS improved functionality when smaller spatial units are involved converge into an ecological footprint based GIS layer that allows flexibility and accuracy at a high level of detail to provide policy support analyses. The fact that the ecological footprint can be broken down into a Consumption Land Use Matrix (CLUM) of Land Uses vs. Consumption Components adds value to policy planner support. This matrix includes Housing and Mobility Consumption on one axis as well as Energy Land on the opposing axis, each of which can be individually defined and analyzed in GIS. Ecological footprint Housing Consumption along with Mobility Consumption and Energy Land use are of primary interest to policy planners as these are factors over which their policy has direct practical influence. According to the GFN 2008 EF calculation for the City of Calgary, Housing and Mobility Consumption are made up of a high percentage of Energy Land use (85% and 92% respectively) which allows further definition of sustainability in terms of Energy Land or what equates to the increasingly recognized Carbon Footprint (Haq and Owen, 2009; Stiglitz et al., 2008) which can still be express in global hectares.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
3. Bottom up and top down methods The ecological footprint can be calculated at a sub national level by two methods; the component or bottom up method and the compound or top down method (Simmons et al., 2000; Klinsky et al., 2010). The bottom up method appears to be of interest to Calgary policy planners as it is used in one city wide plan recently (adopted in 2009) published by the City of Calgary. An alternate measure of sustainability in units other than the global hectare, the Calgary Energy Map (Gilmour et al., 2009/2010) which makes up part of Calgary’s new Municipal Development Plan uses gas and electric energy as well as building floor space to calculate energy use in Gigajoules per hectare for a citywide map – a ground up measure. Ground up data may be more sensitive to underlying data variations, less restrictive and less prone to error when proxies for consumption are used (Simmons et al., 2000), and this may explain the added interest to planners. The bottom up method has at least a psychological advantage as people including planners relate most easily to concrete and localized information (Klinsky et al., 2009; Collins and Flynn, 2007) discusses the improved accuracy possible for the bottom up method when minimizing the use of proxy data which constitutes the basis of the top down method. Simmons et al. (2000), however, directly compares the top down to the bottom up method by carrying out total ecological footprint calculations using both methods for an identical area, a self-governing European island. This explicit comparison shows a difference of only 3% between the two methods. The two methods each have advantages and are increasingly used in conjunction with each other as well as having results measured by either compared directly. The top down method can be utilized in combination with bottom up data when data availability is the deciding factor, just as coarser scales may be used when data at finer scales are not available (Wilson and Grant, 2009; Klinsky et al., 2009). The variation of ecological footprint Housing Component, of specific interest to planners (Kuzyk, 2010), is estimated here by a bottom up method. The initial value of the Housing Component, however, is derived from numbers calculated for Calgary by GFN mostly using Statistics Canada’s annual Consumption/Spending survey to define the footprint and its components – a top down calculation. The combined top down/bottom up source used here allows an accurate estimate of the Housing Component at a postal code level of spatial detail, and using the dynamics of GIS analysis, suggests analysis at the household level is possible and may allow planner GIS policy decision-making support. Once a global hectare based EF GIS layer has been compiled, many sustainability questions can be answered. For a sample case, this study sought out a question or series of questions from the minds of City of Calgary policy planners. One initial question posed by a policy planner in an informal conversation on sustainability was: what would the environmental impact of single family Infill housing in the inner city be compared to original single family stock housing? Infill housing generally refers to the insertion of additional housing units into an already approved subdivision or neighbourhood. Typically in Calgary, an older stock housing unit is demolished most likely in an inner city community where they would be common, the lot is subdivided if it meets the requirements of the land use bylaw and two or more new housing units are constructed. Stock housing refers to the original housing construction, typically over 65 years ago in Calgary, built when a subdivision or community was undeveloped and first coming to be developed.
4. GIS analysis methodology As a starting point for this methodology the ecological footprint calculated for a geography containing the area to be studied is first
33
obtained. The EF calculated for each nation by the Global Footprint Network (GFN, 2008) is one potential starting point. Then global hectares per capita are adjusted to local areas within the nation following the direction of the Global Footprint Network suggested procedure (GFN, 2005). If an ecological footprint has been calculated for a city, that city EF measure is the starting point for estimation of the EF at a sub-city level. A Consumption Land Use Matrix (CLUM) of consumption components (including Housing and Mobility) and land uses (including Energy Land or carbon footprint in global hectares) that make up the ecological footprint is also required to allow segregation of the Housing and Mobility components. Data that represent the ecological footprint well are housing unit size and household energy use as well as government travel to work surveys. Local government tax departments track the floor area of local housing if this is the basis of the local land tax system and if this data exists it will be available at a household scale. Utilities companies may supply energy to local housing units and these utilities companies are approached for data. Data release may be restricted for privacy reasons at a household level for although it is recorded for billing purposes, it may only be released at a larger geography such as a postal code that groups several households together. Having obtained housing size and energy consumption data, this non-spatial data is then linked to the appropriate spatial data in a GIS. Travel to work survey data will also need to be linked to the spatial geography at which it is available. Then, to address local planner policy issues, a planning question is defined. A GIS analysis procedure is then designed, tested and carried out in GIS to output results that address the question and supply GIS maps and charts on which decisions can be made. Fine local geography at a household and block face scale as well as the Housing, Mobility and Energy Land Use components that constitute a part of an ecological footprint are of added interest to planner policy.
5. Case study methodology To allow analysis of the ecological footprint Housing Component in GIS for the City of Calgary, data is created in and converted to GIS format. The City of Calgary maintains a spatial land ownership parcel layer with a six digit postal code attributed to an address for each parcel. GIS is used to merge ownership parcels with the same postal code into spatial postal code areas or shapes. The City of Calgary, with a population just over one million, contains over three hundred thousand ownership parcels, which aggregate to just over twenty five thousand postal code areas. With over 19,000 of these being residential (EF is a measure of consumption, so where people reside is considered to be the point of consumption) and with an average of 17 households per postal code, the GIS postal code layer is created. Residential energy consumption in the City of Calgary is almost entirely made up of natural gas for heat and electric power for appliances and other household use. For this study, average household energy use per six-digit postal code is purchased from local utilities companies. This data is available in units of energy; Gigajoules for gas and Kilowatt hours for electricity. A simple conversion factor between these two energy units allows expression in a common unit. In GIS, this energy use data is imported and attributed to the postal code layer. The City of Calgary Assessment (property tax) department collects dwelling size in square meters of floor space for each household along with other useful data including building type and construction year. Assessment data is available at the City of Calgary in GIS format and can be spatially aggregated to a postal code scale (see Fig. 1 for sample Infill household data shown with hypothetical postal codes). The Housing component of the ecological footprint CLUM calculated for the City of Calgary by the Global
34
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Fig. 1. Inner City Infill household Assessment data used in conjunction with utilities companies household energy use to estimate the mean Housing EF in global hectares for each postal code.
Footprint Network gives city average global hectares based on each of electric energy dollars spent, gas energy dollars spent and gross rent dollars spent. These numbers are scaled up or down for each postal code by the above or below scale factor for each of electric energy, gas energy and housing unit size. The construction year in the Assessment database is used to separately select the two housing types: Infill vs. Stock; and the Assessment building type attribute allows only houses and duplexes to be selected. To analyze at a household scale, Infill housing data is selected from the Assessment database within inner city communities (see Fig. 2) only where the entire postal code is made up of housing units with an Assessment construction year of 1996 or later and that are classified as houses or duplexes. Houses comprise one unit in one detached building structure while duplexes have two housing units in one detached building structure; together these are classified as single family housing in this case. The same search is done for houses and duplexes with a construction year before 1946 within inner city communities. The 68 postal codes in the 1996 and after category which can accurately be classified as Infills are analytically compared to the 223 postal codes with original housing stock in the pre 1946 category which would be originally constructed Stock housing (see Fig. 3). Calgary is a newer western Canadian city, incorporated as a town in 1884 and as a city in 1894 where single family housing is made up mostly of wood frame construction with a typical housing life cycle of 70 years, helping to define this classification as appropriate.
6. Results With data classified and then analyzed, a tentative answer to the policy planner question is presented. It turns out that between construction years of 1945 and 1995, variation exists in the global hectares consumed by housing. Use of electric energy in Infill households varies little (a 1.4% increase) while the use of gas heating energy decreases significantly (by 23.4%) in spite of an offsetting increase in building size. The decreased use of gas for heating is likely due to improved building standards and the associated improvement in insulation as well as more efficient heating systems. Housing floor area changed the most (a 54.7% increase) reflecting the largest change in the EF Housing footprint for each household. The increase in house size offset any gains in insulation and heating system efficiency and left the total Housing ecological footprint of Infill housing 11.0% larger than that of pre 1945 stock housing according to this GIS analysis. Planners, when presented with this first tentative answer in a formal meeting, ask for further comparison between Greenfield housing and inner city Infill housing (see Fig. 4). Greenfield refers to land that has not been developed before which for the City of Calgary exists mostly on the perimeter of the developed part of the city. The highest ecological footprint is suggested to be in the suburbs and rural areas close to a city where low-density development exists along with high levels of consumption (Mcmanus and Haughton, 2006; Klinsky et al., 2010). The same GIS analysis procedure is followed and a count of 1649 postal codes with only houses
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
35
Fig. 2. Inner City communities showing distribution of Infill versus Stock housing based on year of construction.
and duplexes built in 1996 and after are selected. Results show very little difference between Infill housing and Greenfield housing; a difference of 1.6% (see Fig. 5). When mapping and graphic results originating from GIS analysis are presented to policy planners both formally and informally, debate ensues, concurring with the findings of Yue et al. (2006) and Klinsky et al. (2010), while this study brings out that debate at a much finer spatial scale and a narrowed focus on ecological footprint Components such as Housing.
Fig. 3. Inner City Infill versus Stock housing gas and electric energy and floor space combined to estimate and compare total Housing EF. Note Mobility EF does not vary based on mode of travel to work.
What was also intuitively requested by planners for further analysis was the Mobility Component of the ecological footprint. Klinsky et al. (2009) have shown a trend towards higher footprint in communities located further from the downtown core of Montreal, Canada. For the Mobility footprint, ground up data is not available at a level of detail comparable to that available for the Housing footprint (yet, although Alberta vehicle registries have been approached). However a top down method can be substituted. Using Statistics Canada’s 2006 Census data at a Dissemination Area (DA) or what might be called Census Village (mean population in Calgary is 670) level also converted to GIS format, mode of travel to work is used to estimate a 36% increase in the Mobility footprint for Greenfield housing. Combining the Housing Component from a bottom up source and the Mobility Component from a top down source, and making the tenuous assumption that they are equally accurate, total Housing and Mobility compared between Infill and Greenfield housing are calculated. The combined results show Infill housing to use 9.2% less global hectares than Greenfield housing (see Fig. 6). This case study analysis shows that sustainability analysis using the ecological footprint units of global hectares is possible at a household level and should be possible in other urban contexts. The finer level of detail involved allows GIS analysis to be carried out on questions such as those posed on Infill, stock and Greenfield housing. It would also allow planners to engage the public armed with data at the household scale where policy discussion impact may be high and to potentially use these results to support decisions made regarding policy.
36
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Fig. 4. Greenfield communities housing versus Inner City communities Infill housing.
7. Discussion Urban and regional planners have an interest in measuring local sustainability (Mcmanus and Haughton, 2006; Wilson and Grant, 2009; Kuzyk, 2010) with the ecological footprint and the components that make it up being one measure available. As well as the benefits of the land area based measure and global data
Fig. 5. Inner City Infill versus Greenfield gas and electric energy and floor space combined to estimate and compare total Housing EF. Note Mobility EF does vary based on mode of travel to work.
being measured at a local level, the ecological footprint is effective in raising awareness, education and policy debate (Klinsky et al., 2010; Mcmanus and Haughton, 2006). The quantitative nature of the measure adds effectiveness to the decision-making process of policy development. The answers to the policy planner question(s) are that Infill housing consumes more global hectares than stock inner city housing when based only on the Housing Component. When inner city
Fig. 6. Inner City Infill versus Greenfield Housing and Mobility EF combined and compared.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
Infill housing is compared with Greenfield housing, Infill housing consumes about the same number of Housing global hectares. So where a newly constructed house or duplex is built in the city has little influence on its ecological footprint by a measure composed of housing unit floor space and household energy use. If the measure of Mobility (from a statistics based top down source) is assumed to have the same accuracy as the Housing Component measure, the total Housing and Mobility components of ecological footprint show a trend in the intuitively expected direction. There is a decrease in global hectares consumed if a single family house is constructed as an Infill in the inner city rather than in a community on the edge of the City of Calgary. There are several shortfalls to this methodology including that of the case study. Along with the lack of bottom up data for the Mobility Component, all other ecological footprint Components (Food, Goods, Services and Government) have no source at the postal code scale. This analysis does not include a cost analysis of underground facilities and roadways development in the Greenfields nor does it included a measure of the cost associated with demolition of inner city stock housing for replacement by Infill housing. The tendency of Infills to develop on subdivided lots, where two Infills typically replace one stock house creating an associated increase in density is not considered. Pertaining to the data used, the electric utility company suppresses data when there are fewer than 5 households per postal code for freedom of information reasons so for this analysis, that data was estimated based on persons per household. One assumption made for this case study methodology is that a housing unit double the size will use double the part of the ecological footprint it represents. Also, the energy and construction materials reduction of the common wall of duplexes is not taken into account when converting to global hectares. The assumption that doubling the household energy use does equate to double that the ecological footprint represented is likely a solid assumption. Basing postal code level ecological footprint on a GFN top down calculated CLUM for the entire city of Calgary makes the assumption that the GFN numbers are correct on a citywide basis. The ecological footprint of Calgary is well above the EF of Canada (Wilson and Mark, 2005), one contributing factor being Calgary’s source of electric power. In the province of Alberta, 40% of electricity is generated from natural gas fired generators and 50% from coal-fired generators (Alberta Energy, 2010), the remaining being hydroelectric and other renewable sources including the wind farms of southern Alberta. That some households in Calgary may be purchasing electricity from renewable sources such as wind power or the fact that a portion of the Alberta electric grid is sourced from hydroelectric, though these sources are relatively small, is not considered and the assumption is made that household electric energy use is equitable among all households. Assumption is also made here that the GFN took in to account the various sources of Alberta’s electric energy in their CLUM Housing electric energy component for Calgary. Measuring the ecological footprint at varying scales can be problematic. The most accurate measure is at the national level where national accounts are the basis of measure. EF calculated back to 1961 for many nations as shown by the Global Footprint Network (GFN, 2010) shows fluctuation on a year by year basis somewhat challenging the accuracy of data during any given year even at the national level. These variations are due to methodology improvements according to GFN and the general trend over time, which can be smoothed out, is consistent. If the original national data is somewhat in question, data at a finer scale also might be challenged. Primary problems of data at a finer scale than national are the availability of data as well as a record of imports and exports. Wilson and Grant (2009) describes the various approaches to data collection at a sub-national level including the indicator method, the resource flow models extensively developed in Europe by the Stockholm
37
Environmental Institute, the use of community questionnaires and the Household Expenditure survey approach available in Canada. The method presented here is a combination of the Household Expenditure survey approach, as this is the basis of the EF CLUM calculated for the City of Calgary by the GFN and the indicator method where electricity, gas and house size are used. Collection of bottom up data that is a direct measure of consumption such as housing size and household energy use circumvent the import export problem. As methodologies at a sub-national scale are still being developed, the method presented here claims to be another option and an improved one with bottom up consumption data being used. A distinct benefit to the purchase of energy use data from utilities companies is the availability in units of Gigajoules or Kilowatt hours which is a direct measure of energy use; a preferable measure over dollars spent when converting into global hectares. This can quite correctly be assumed to be an improvement over the dollars spent basis of the Global Footprint Network calculation for the entire city. The building floor space is also assumed to be an improvement over the gross imputed rent in dollars spent used by GFN. Further research may allow direct translation of house size and household energy consumed into global hectares without depending on the GFN citywide numbers. In one broader context, debate continues as to whether technology or efficiency improvement will solve or assist in alleviating increasing human pressure on the global natural economy. Wackernagel and Rees (1996) see a need to focus on improving human welfare by means other than sheer growth, concurred with by Rees (2008) discussion of growth gradually fading away as an option. Rees points out that “material growth in even the most efficient economies overwhelms the positive gains from efficiency”. In this study, growth in housing floor space or house size over the several decades age difference between Infill and stock housing overrides energy efficiency gains resulting from improved building insulation and heating systems over the same time period. Toth et al. (2009) point out a future projected pattern of growth in household energy consumption in spite of efficiency improvements. Making reference to technology as well as increased efficiency as being the solution to the sustainability challenge (Victor, 2008) details the lack of supporting evidence for these proposals. The increase over time of housing unit size is a contributing factor to unsustainability. According to a study done in Seattle, smaller footprint would likely result from a reduction in house size (Redefining Progress, 2005). Housing size may become one of the targets of influence by planner policy and this may be associated with increased density. Planner policy includes maintenance of the City land use bylaw which can stipulate density and allowable housing type. Policy planners can have an influence on sustainability through a city land use bylaw. Wolf and Meyer (2010) look at the use of sustainability measurement in GIS for the purpose of land use decision-making including a measure of compactness or density, concluding that scientific discussion can be bridged with practical application from a planning perspective. It is of interest to note here that from Calgary’s 2009 Global Footprint Network total ecological footprint CLUM (Consumption Land Use Matrix), the Housing component makes up 18.5% of which 85% is Energy land and the Mobility component makes up 11.0% of which 92% is Energy land. Policy planners have, within the scope of influence of the policy they develop, the potential to have a large impact on the sustainability of future urban built form. As mentioned, a GIS layer once created can provide a wealth of new information. Further analysis of City of Calgary Assessment data allows the EF Housing Component by postal code to be classified by housing type. Results show a trend across housing types where single family housing uses the most global hectares and
38
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39
the embedded energy or Energy Land use portion of the Housing Component.
8. Conclusion
Fig. 7. Housing EF trend across housing types.
apartment style residences consume the least (see Fig. 7). Single family housing tends to exist in lower density areas and apartments in higher density areas, supporting the tendency of higher density to be more sustainable and in this case also assigning a quantitative and globally comparable measure. Policy planners create policy in terms of density where household units per hectare are defined and increasing density is considered to be a step in the direction of improved sustainability. As mentioned, suburban communities are typically made up of the single family housing type and are low-density. Along with housing type, a direct measure of density in units of global hectares can be carried out. The EF postal code layer in GIS, with an area and a count of households for each postal code is used to define this trend in global hectares. The trend found here confirms planner sustainability thought and makes numerical comparison in global hectares available when higher density is applied to urban built form (see Fig. 8). There is a common interest in residential electric and gas use for ecological footprint calculations in Europe (Hunter et al., 2006; Simmons et al., 2000) with the use of these sources being a part of the highly collaborative and university reviewed procedure used by Cardiff, Wales (Collins and Flynn, 2007). Though this interest extends to North America (Wilson and Grant, 2009; Klinsky et al., 2010), availability of this data has been limited for studies carried out to date. The method appears to be solid with several advantages as it involves a direct measure of housing energy and materials consumption, yet one that may be expressed in global hectares. The utilities companies’ sources are in the more relevant units of energy consumed rather than dollars spent and were collected at the finest level of detail that would be released for this study. Wilson and Grant (2009) specifically lists an interest in dwelling size as a measure of the non-energy Housing Component and it is suggested here that square meters of floor space also well represent a measure of
Analysis of the ecological footprint Housing Component at a household level is possible for certain analyses when data is compiled in GIS at a postal code or approximate block face scale. Ecological footprint components, such as Housing and perhaps Mobility as well as the Energy Land use of specific interest to urban policy planners can be used to stimulate policy debate. From this inspired debate, policy development decision-making is potentially possible. Bottom up data is of primary interest to urban planners; data they can measure and influence with policy. The GIS analysis here, which spatially and numerically shows the difference in sustainability between Infill, older existing stock and newer Greenfield housing makes up an example of what may assist planners in formulating sustainability policy. The analysis carried out in this study shows that inner city Infill housing brings about a sustainability improvement over Greenfield development due to a shorter travel to work distance and therefore a reduced EF Mobility. Inner city community development also results in a more sustainable development pattern due to the tendency of inner city development to be made up of higher density as well as lower impact housing types. The study here also shows that EF Housing does not vary between Infill and Greenfield development and that Infill Housing actually consumes more global hectares than older stock housing in spite of improved energy efficiency. Infill development of more sustainable housing types and higher density neighbourhoods are shown to bring about more sustainable urban development. If Infill housing shifted to construction of housing units with smaller floor areas and still retained the improved construction standards that result in more energy efficiency, even greater sustainability could be achieved. These factors all point towards the benefits of the concentration of housing units in the inner city and a reduction in urban sprawl on a city’s perimeter. There appear to be several options available to policy planners, all of which can be quantitatively defined, measured and perhaps have targets set for in terms of the ecological footprint in global hectares. The objectives in this study of analyzing the ecological footprint measurement at a household geography, allowing GIS presentation material to raise awareness and assist in communication for policy planners as well as making available direct decision-making support based on the EF have been met. Data from combined bottom up and top down sources, when compiled into GIS format allows analytical output from the traditionally used GIS source of policy planner support to be extended into the realm of global sustainability measurements. The method shown here is based on minimal data collection and should be replicable for any city or region with a property tax department and utilities companies supplying household energy. Measurements such as density and housing type in global hectares fit within the policy development sphere of urban planners. Valuable policy debate and decision-making support is available through GIS map creation and analysis while retaining units in the quantitative measure of EF global hectares.
References
Fig. 8. Housing EF trend across housing unit density.
Alberta Energy, 2010. http://www.energy.alberta.ca/Electricity/electricity.asp. Collins, Andrea, Cowell, Richard, Flynn, Andrew, 2009. Evaluation and environmental governance: the institutionalisation of ecological footprinting. Environment and Planning 41, 1707–1725. Collins, Andrea, Flynn, Andrew, 2007. Engaging with the ecological footprint as a decision-making tool: process and responses. Local Environment 12 (3), 295–312.
L.W. Kuzyk / Ecological Indicators 16 (2012) 31–39 Cranston, G.R., Hammond, G.P., Johnson, R.C., 2010. Ecological debt: exploring the factos that affect national footprints. Journal of Environmental Policy and Planning 12 (2), 121–140. Global Footprint Network, 2005. National Footprint and Biocapacity Accounts 2005: the underlying calculation method. Global Footprint Network, 2008. Living Planet Report 2008 [Online] Available from www.assets.panda.org. Global Footprint Network, 2010. Footprint for Nations [Online] http://www.foot printnetwork.org/en/index.php/GFN/page/footprint for nations/. Gilmour, Brent, Leach, Tonja, Salisny, Ralph, 2009/2010. Resonding to the energy challenge of Canadian communities: key lessons for applying the QUEST principles to Calgary’s Municipal Development Plan. AACIP Planning Journal Winter (3). Haq, Gary, Owen, Ann, 2009. Green Streets: The Neighbourhood Carbon Footprint of York. Stockholm Environment Institute. Hunter, C., Carmichael, K., Pangbourne, K., 2006. Household ecological footprinting using new diary-based data-gathering approach. Local Environment 11 (3), 307–327. Klinsky, S., Sieber, R., Meredith, T., 2009. Creating local ecological footprints in a North American context. Local Environment 14 (6), 495–513. Klinsky, S., Sieber, R., Meredith, T., 2010. Connecting local to global: geographic information systems and ecological footprints as tools for sustainability. The Professional Geographer 62 (1), 84–102, First published on: 11 December 2009 (iFirst). Kuzyk, L., 2010. Infill housing sustainability analysis. AACIP Planning Journal Summer (4). Mcmanus, P., Haughton, G., 2006. Planning with ecological footprints: a sympathetic critique of theory and practice. Environment and Urbanization 18 (1), 113–127. Redefining Progress, 2005. Seattle Ecological Footprint Assessment December 2005, www.rprogress.org.
39
Rees, Willam E., 2008. Human nature, eco-footprints and environmental injustice. Local Environment 13 (8), 685–701. Statistics Canada, 2008. Survey of Household Spending in 2006, RY2006 62FPY0031 Geographic.xlsx. Statistics Canada, Government of Canada, Ottawa, Ontario. Simmons, C., Lewis, K., Barret, J., 2000. Two feet – two approaches; a componentbased model of ecological footprinting. Ecological Economics 32, 375–380. Stiglitz, Joseph E., Sen, Amartya, Firoussi, Jean-Paul, 2008. Report by the Commission on the Measurement of Economic Performance and Social Progress. Toth, Agnes Hofmeister, Kelemen, Kata, Piskoti, Marianna, 2009. Changes in consumer behavior patters in the light of sustainability. In: Sustainable Consumption 2009 Conference Proceedings , Corvinus University of Budapest (September 24, 2009), pp. 75–85. Victor, Peter A., 2008. Managing Without Growth: Slower by Design, Not Disaster, Advances in Ecological Economics. Edward Elgar, Northampton, MA, USA. Wolf, Torsten, Meyer, Burghard C., 2010. Suburban scenario development based on multiple landscape assessments. Ecological Indicators 10, 74–86. Wackernagel, Mathis, Rees, William, 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers, B.C., Canada. Wackernagel, Mathis, et al., 2006. The Ecological Footprint of cities and regions: comparing resource availability with resource demand. Environment and Urbanization 18, 103–112. Wilson, J., Grant, J.L., 2009. Calculating ecological footprints at the municipal level: what is a reasonable approach for Canada? Local Environment 14 (10), 963– 979. Wilson, Jeffrey, Mark, Anielski, 2005. Ecological Footprints of Canadian Municipalities and Regions. Anielski Management Inc., Edmonton, Alberta, Canada. Yue, D., Xu, X., Li, Z., Hui, C., Li, W., Yang, H., Ge, J., 2006. Spatiotemporal analysis of ecological footprint and biological capacity of Gansu, China 1991–2015: down from the environmental cliff. Ecological Economics 58, 393–406.