Rexdale home with two bathrooms (Royale LePage , 2014), it is easy to see why the .... LePage: http://www.royallepage.ca/en/on/toronto/rexdale/properties#.
An Investigation into the Relationship between Socioeconomic Status and Water Conservation
Abstract The relationship between water consumption and socioeconomic status (SES) in Toronto hasn’t been researched or analyzed extensively. The purpose of this essay is to determine whether SES has a significant effect on the water consumption of Torontonians. This essay will examine the question, “To what extent does SES influence the water consumption of a population?” Average household income by ward and average post-secondary education levels by ward were mapped to display the levels of SES. Next, average residential water consumption was mapped by ward. All data was obtained from the Neighbourhood Ward Profiles, specifically from the National Household Survey conducted in 2011. This essay concludes that SES has a significant effect on the water consumption of a ward in Toronto, and that income has a larger effect on water consumption than the offset created by education in a ward. Acknowledging this, the City of Toronto should therefore seek to mitigate high levels of water consumption with higher SES through marketing campaigns or programs aimed at high SES households.
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Introduction The aim of this investigation and analysis is to determine whether SES, which combines a mix of education and income variables, has a significant effect on the water consumption of Torontonians. The relationship between the components of SES and water consumption have not been investigated fully in Toronto, and it is unclear whether education plays a larger role than income in the patterns of consumption. While discussions surrounding environmental sustainability in higher education have been absent in previous years, educators are currently pushing for more critical analysis of environmental problems in higher education (Jickling, 2002). As higher income provides the privilege of accessing higher education, an interesting paradox ensues, where a university graduate should know more about sustainability and aim to curb their consumption, but the fact they have a higher overall income leads to the assumption that they have more disposable income and are therefore able to consume more. The opposite is true for those of lower SES, they have less money to consume, but overall less access to higher education and less access to critical conversations regarding resource conservation. This paradox is important to investigate as it can allow certain policies and programs to be put into place where they are geographically needed, such as advertisement campaigns. This investigation is significant as it will determine what side of the paradox, income or education, has a larger effect on the water consumption of a population. The Tbilisi Declaration proclaims that “Environmental education should be provided for all ages, at all levels and in both formal and non-formal education” (Intergovernmental Conference on Environmental Education, 1977) and as a member of The United Nations Environment Programme, Canada should ensure that that value is upheld. In areas of Toronto that lack environmental education, efforts to reduce consumption and promote environmental sustainability should be concentrated to reduce Toronto’s environmental impact. Research Question:
To what extent does SES influence the water consumption of a population? Hypothesis:
In wards with higher SES, water consumption will be higher than wards with lower SES, even though access to higher education is higher in high SES wards, income will have a more prevailing effect on water consumption than education. This hypothesis is suggested because households with higher incomes have larger disposable incomes, which may be allocated to more bathrooms in the household, larger lawns to water and the presence of luxury items like pools, hot tubs or fountains. Justification and Geographical Context:
In 1990, the United Nation’s member states agreed upon the creation of the eight Millennium Development Goals (MDGs) to aid in the development of countries around the world. Development Goal 7.1 is to “integrate the principles of sustainable development into 1
country policies and programmes and reverse the loss of environmental resources” (General Assembly of the United Nations, 2014). This target is under MDG 7 which is “to ensure environmental sustainability”. Sustainable development as defined by the World Commission on Environment and Development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs (Guinness, 2011). Responsible use of resources, including water is crucial to sustainable development of countries all over the world. Sustainable development has multiple theoretical models and definitions. This investigation will adopt the nested dependencies model of sustainability that states that “human society is a wholly-owned subsidiary of the environment” (Willard, 2010) – that without the proper environment, human society cannot survive, nonetheless thrive. Economist Herman Daly suggests a theory consisting of three rules to help define environmental sustainability: 1. For a renewable resource, the sustainable rate of use can be no greater than the rate of regeneration of its source. 2. For a non-renewable resource, the sustainable rate of use can be no greater than the rate at which a renewable resource, used sustainably can be substituted for it. 3. For a pollutant, the sustainable rate of emission can be no greater than the rate at which the pollutant can be recycled, absorbed, or rendered harmless in the environment. (Stein, 2010) The first rule relates the most to the variable of water consumption discussed in this essay, as water is a renewable resource. Water consumption as defined by the Organization for Economic Co-operation and Development refers to freshwater taken from ground or surface water sources, either permanently or temporarily, and conveyed to the place of use (OECD, 2005). Water consumption is used in this examination due to its availability in the Toronto data archives and its relationship to an individual’s impact on the environment. Other indicators (such as air quality indices) lack the ability to create a direct link between the data and the individual. Per Herman Daly’s theory, the rate of consumption must not exceed the rate of regeneration, and while there are limited ways to measure the rate of regeneration of water’s source, large rates of consumption most likely mean that the resource is not being used in a sustainable manner. Considering that Toronto lies in the richest fresh water area in the world (U.S. Environmental Protection Agency, 2012), it wouldn’t seem like the lack of fresh water is a potential problem. However, Toronto’s population is growing, mainly due to immigration (Toronto Foundation, 2016). If Toronto’s population cannot learn how to properly manage its water sources, the availability of fresh water to future generations may be affected. Furthermore, as water in the City of Toronto must be heavily treated before being used, reduced overall water consumption could lower the strain on the water sanitation system. Without sustainable consumption efforts in place to mitigate consumption, Toronto may find itself in a water-tight situation in the future. 2
The City of Toronto has tried promoting the conservation of water. Advertisement campaigns combined with an online portal that anyone can access, are teaching Torontonians about water saving strategies. Multiple programs like trading in inefficient toilets and receiving rebates for water-saving toilets have also helped reduce water consumption in some areas (Toronto Water, 2013). Those of higher SES do have an element of privilege associated with access to these programs, including having access to multiple bathrooms while one is retrofitted with a new water efficient toilet, for example. However, there is no empirical evidence whether these programs favour certain socioeconomic classes over others. This essay focuses on the relationship between SES and water consumption. SES is defined as individual’s position within a hierarchical social structure that depends on a combination of variables, including occupation, education, income, wealth, and place of residence (American Heritage New Dictionary of Cultural Literacy, 2005). This essay will accept this definition, with a spotlight on education and income. To answer the research questions, maps will be created to investigate the relationship between SES and water consumption. These maps will seek to illustrate whether income or education has a greater propensity to affect water consumption. One map will illustrate the ward boundaries, the next will map education and income with SES and the last will illustrate the average residential water consumption with average household income and percentage of post-secondary education recipients, by ward. The creation of these maps, and the data that accompany them, will allow critical analysis and conclusions to be made. Spatial Theory:
This essay uses the distance-decay model as the theory behind the relationship between SES and water consumption. Distance decay theory applied in this context contends that as distance increases away from the Central Business District (CBD) we should see a decline in variables mentioned. In the case of Toronto, the central area comprises the CBD and the Yonge Street Corridor (YSC), as seen in map 1. The YSC, as the name suggests is an area surrounding Yonge Street in central Toronto. It is an area with high access to services and commercial areas, as well as public transport. The CBD is an area of financial institutions, and considered the downtown of Toronto. The high cost of living in the YSC and CBD means that individuals of lower SES will live further from these areas in accordance with Distance-Decay Theory. Therefore, in relation to my hypothesis, the central areas of Toronto should have higher water consumption compared to the outer regions of the city (the suburbs).
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Map 1
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Methods of Investigation Data was collected from the Toronto Ward Profiles available online through the City of Toronto’s website (City of Toronto, 2011). The City of Toronto organizes each data set by ward, which contains data from the 2011 Census Data and National Household Survey data. The data was agglomerated onto a spreadsheet, and visualized in ArcMap. Water Consumption:
Water consumption was used as an accurate indicator of environmental sustainability as Herman Daly’s theory suggests, and due to the availability of the data on a ward-by-ward basis. Income:
Average household income was used as it allowed for a more comprehensive representation of a ward. Crucially, it provides the income of a household, accounting for multiple sources of income to be represented, as opposed to the use of just average income, which breaks average income into a person-by-person basis. Education:
Percentage of residents with post-secondary education was used because postsecondary education provides a more accurate look at the association to income, as people with postsecondary education have a higher tendency to be paid more than those without postsecondary education (Porter, 2014). According to the UNESCO Global Monitoring Report on Education and Attitudes towards the environment “levels of education frequently link with levels of environmental concern in individual countries, even when other socio-demographic characteristics are controlled for” (Clery & Rhead, 2013).
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Data Presentation and Analysis Map 2:
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In map 2, the percentage of post-secondary education recipients and average household income were mapped together to show the relationship between them, and therefore the justification for the use of SES. As is evident in the map, as the percentage of post-secondary education recipients increases, the average household income increases as well. This follows the definition of SES discussed earlier, and therefore areas with higher household income and higher percentage of post-secondary education recipients will from here on out be referred to as areas of high SES, and vice versa for areas of low SES. In map 2, areas of high economic status are concentrated around the CBD and the YSC, with post-secondary education percentage ranging from 61% to 79%, and average household income ranging from $140,000-$200,000. Areas of low SES are dispersed around in the periphery of the city, outside of the CBD and YSC seen in map 1. Here post-secondary education percentage range from 37% to 52%, and average household income ranges from $50,000 to $100,000. This is reflective of distance decay theory mentioned previously and this theory is representative of the pattern seen in Toronto. As you travel further away from the core of the city (CBD and YSC) the relative SES decreases. It can be inferred that as you move away from the core of the city, lower quality of housing and infrastructure polarizes lower SES residents out of the expensive core and into the cheaper periphery of the city.
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Map 3:
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Map 4:
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In maps 3 and 4, average residential water usage, with income and education, was mapped on a ward by ward basis to allow the relationship between SES and water consumption to be established. Areas of high residential water consumption are concentrated in the core of the city, with consumption ranging from 360m3 to 700m3 in the CBD and along the southern part of the YSC. Areas of low residential water consumption lie in the inner suburbs immediately surrounding the CBD and the YSC with consumption ranging from 240m3 to 310m3. It should be noted that wards on the outer edge of the city have higher average residential water consumption than the inner-city suburbs surrounding the YSC. A clear relationship is established; in areas of high SES there is higher average water consumption. Relationship between Maps 3 and 4:
In map 3, it is evident that water consumption is higher in areas of higher income. In map 4, you can see that in areas of higher post-secondary education percentage, water consumption is higher as well. As the overall trend shows that water consumption is still high in areas of higher education, a conclusion can be made that higher education does not offset the increase in consumption bolstered by higher income. Sample neighbourhood analysis:
As mentioned in the introduction, the main relationship between a higher SES and higher water consumption is the ability to consume more. Wards with the highest water consumption in the city are Ward 22 St. Paul’s, Ward 20 Trinity-Spadina, and Ward 27 TorontoCentre Rosedale. These wards encompass some of the wealthiest neighbourhoods in Toronto, including Yorkville, Rosedale and Forest Hill. The largest trait that contributes to water consumption in these areas is the sheer size of houses, and luxury features such as pools and larger outdoor areas that require extensive and water consuming maintenance. The following photo analysis will compare features of the neighbourhood of Forest Hill (a high SES area) with features of the neighbourhood of Rexdale (a low SES area).
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Image 1, Satellite image of a portion of Forest Hill (Google, 2012)
Above is an annotated satellite image of a small portion of the neighbourhood of Forest Hill in central Toronto. As can be seen, there are a large number of pools scattered throughout the neighbourhood. This would lead to a higher water consumption due to the filling of the pool, approximately 70m3 (Sierra Club, n.d.) and water loss due to splashing, evaporation, and general maintenance throughout the summertime. It is evident that the number and
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maintenance of pools, as well as lawn care can significantly increase water consumption compared to lower SES wards. A comparison of the image above with the image of the Rexdale neighbourhood of Ward 9 York-Centre below, shows a clear difference between the exterior features.
Image 2, Satellite image of a portion of Forest Hill (Google, 2012)
In this sample of Rexdale, the houses are significantly smaller than in Forest Hill. As well, there is a distinct lack of pools or outdoor water features while the front and back yards are much smaller than in Forest Hill. All of these factors would contribute to lower water consumption. In continuation of the differences in exterior features of homes in Rexdale and Forest Hill, the size of yards plays a significant role in water consumption. The water used to maintain large lawns, garden areas and multiple trees of a large house in Forest Hill is notable compared to a house in Rexdale where the only upkeep is a small grass backyard. Furthermore, income plays a large part in consumption. It can be assumed that a family will most likely try to save money if they are in a less economically secure situation, and this may lead to cut backs on water consumption to lower their water bill.
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As is also evident in the images above, the difference in the size of houses is significant. Evidently the difference in house size is caused by a large difference in average household income. In relation to water consumption, the size of houses is an important factor due to the presence of an excessive number of bathrooms and luxury bathroom features like Jacuzzi tubs and multiple sinks in high earning households. A real estate listing for a house in the heart of the Forest Hill neighbourhood advertised six bathrooms featuring multiple sinks, multiple-head shower and large bath tubs (Forest Hill Real Estate, 2014). In comparison to the average Rexdale home with two bathrooms (Royale LePage , 2014), it is easy to see why the difference in average residential water consumption is so great. The presence of these features may contribute to greater water consumption compared to a smaller house in Rexdale. Spatial Theory Analysis:
Based on the distance-decay theory, it can be concluded that water consumption follows a spatial pattern. In the case of Toronto, as you move further away from the core areas of the city, average residential water consumption decreases, as does average household income and percentage of population with post-secondary education.
Conclusion Income and Water Consumption:
This factor was examined through the spatial representation of data. Based on the maps created and data analyzed, as average household income of a ward increased, the average residential water consumption increased as well. Education and Water Consumption:
This factor was examined through the spatial representation of data. Based on the maps created, as percentage of population with post-secondary education increased, the water consumption increased as well. Evidently, higher education did not offset the effect of having a larger disposable income. SES and Environmental Sustainability:
Accepting that average residential water consumption is a viable method of measuring environmental sustainability of an area, and based on secondary data collected (data analysis and presentation), this study illustrates that higher SES tends to be associated with lower environmental sustainability concerning water usage.
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Evaluation: This investigation is significant because it provides relevant information about patterns of water consumption in Toronto. As well, it provides the basis for more comprehensive studies into the patterns of water consumption. It can provide city planners and government officials with the insight to focus water consumption reduction strategies in specific areas of high SES across Toronto. Multiple errors and limitations were present in the creation of this essay, specifically dealing with the use of secondary information. The conclusion and data analysis of this essay is entirely based off the maps created using data from The National Household Survey from 2011 that was accompanied by the Census and devised by Statistics Canada. The use of secondary information accepts that there could be multiple errors with the method of creating the census and the way it was distributed. Certain errors stated by Statistics Canada include, coverage errors where dwellings or individuals are missed, non-response errors when some or all information is not provided, response errors when a question is misunderstood or a characteristic is misreported by the respondent, processing errors including keying errors, when valid but not correct values are inserted into a record to replace missing or invalid data (Statistics Canada, 2012). As well, one of the largest errors associated with the National Household Survey that made the 2011 Census less accurate than the previous Census was the inaugural decision to make the NHS voluntary, meaning that if you didn’t have the time or just felt like it was unnecessary, you were not obligated to complete it, something that was not present in the 2006 Census. This decision could have corrupted data by giving people the choice to opt-out, creating discrepancies in who answers the Census. There was no realistic way to improve on this limitation, as data from 2006, while more comprehensive and extensive, is eight years old and therefore inaccurate, therefore the 2011 NHS data was used. As well, this investigation only looked at the water consumption of Toronto in the year of 2011. Multiple changes could have occurred within the data and patterns associated with environmental sustainability since then, as sustainability does change over time with prevailing technological advancements, such as the availability of more efficient washing machines and toilets. It is also possible that other factors besides income or education influence water consumption, including but not limited to aging infrastructure in the CBD such as leaking pipes or water main bursts. Lastly, there are multiple different ways to measure and quantify environmental sustainability. In this study, only one part of one definition was assessed. To improve on the general accuracy of this study, more than one definition or indicator could have been used. However, certain indicators such as ecological footprint required extensive and elaborate calculation paired with multiple different data sets. Due to time constraints and lack of reliable and relevant data, ecological footprint and other such indicators were not assessed.
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Bibliography Internet: City of Toronto. (2011). Ward Profiles. Retrieved from City of Toronto Urban Planning: http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=2394fe17e5648410VgnVCM10000 071d60f89RCRD Clery, E., & Rhead, R. (2013). Education and Attitudes Towards the Environment. UNESCO. D'Andrea, M. (2013). Connecting Water Resources. Retrieved from Big City Water Woes: http://www.cwn-rce.ca/assets/resources/powerpoint/CWR-2013-Presentations/DAndreaMichael-FINAL.pdf Forest Hill Real Estate. (2014). 260 Warren Rd. Retrieved from Forest Hill Real Estate: http://www.foresthill.com/treblistings/specs/1236 General Assembly of the United Nations. (2014). Sustainable Development. Retrieved from General Assembly of the United Nations President of the 65th Session: http://www.un.org/en/ga/president/65/issues/sustdev.shtml Google. (2012). Google Maps. Retrieved from Google Maps: https://www.google.ca/maps/preview?q=google+maps&ie=UTF8&ei=mlsoVPmWB4aZyASt8ILADQ&ved=0CAgQ_AUoAQ Guinness, P. (2011). In P. Guinness, Geography for the IB Diploma: Patterns and Change (pp. 211-223). Cambridge: Cambridge University Press. Intergovernmental Conference on Environmental Education. (1977). The Tbilisi Declaration. Retrieved from http://resources.spaces3.com/a30712b7-da01-43c2-9ff0-b66e85b8c428.pdf Jickling, A. E. (2002). "Sustainability" in higher education. International Journal of Sustainability in Higher Education, 221-232. Literacy, American Heritage New Dictionary of Cultural. (2005). American Heritage New Dictionary of Cultural Literacy. Martel, L. (2006). 2006 Census: Portrait of the Canadian Population in 2006: Findings. Retrieved from Statistics Canada: http://www12.statcan.ca/census-recensement/2006/as-sa/97-550/indexeng.cfm OECD. (2005, March 15). Economic, Environmental and Social Statistics. Retrieved from OECD Factbook 2005: http://www.oecd-ilibrary.org/economics/oecd-factbook-2005_factbook-2005-en Porter, E. (2014, September 10). A Simple Equation: More Education = More Income . The New York Times. Revenue Services. (2013). Water Billing by Ward. Retrieved from Open Data Toronto: http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=289bd103cd8b1310VgnVCM10000 03dd60f89RCRD 15
Royale LePage . (2014). Rexdale, Toronto, Ontario Real Estate and Homes for Sale. Retrieved from Royal LePage: http://www.royallepage.ca/en/on/toronto/rexdale/properties#.VChWAmddWSo, 2014 Sierra Club. (n.d.). How much water does it take to have a swimming pool? Retrieved from Sierra Club of Texas: http://texas.sierraclub.org/press/facts3.pdf Stein, M. (2010). What is True Sustainability? Retrieved from Mother Earth News: http://www.motherearthnews.com/nature-and-environment/what-is-truesustainability.aspx#axzz3ENOkM9b7 Statistics Canada. (2012). Chapter 8 – Data quality Assessment, Retrieved from Statistics Canada: h ttp://www12.statcan.gc.ca/census-recensement/2011/ref/overview-apercu/pop8-eng.cfm Thwink.org. (2012). The Three Pillars of Sustainability. Retrieved from Thwink.org: http://www.thwink.org/sustain/glossary/ThreePillarsOfSustainability.htm Toronto Foundation. (2016). Toronto's Vital Signs. Toronto: Toronto Foundation. Toronto Water. (2013). Water Efficiency Program. Retrieved from Canaroma: http://www.canaroma.ca/pdf/rtrp_application.pdf U.S. Environmental Protection Agency. (2012). The Great Lakes An Environmental Atlas and Resource Book. Retrieved from Great Lakes: http://www.epa.gov/glnpo/atlas/index.html UNData. (2013). City population by sex, city and city type. Retrieved from UNData: http://data.un.org/Data.aspx?d=POP&f=tableCode%3A240 United Nations. (2013). Millenium Development Goals. Retrieved from United Nations : http://www.un.org/millenniumgoals/ United Nations General Assembly. (2005). 2005 World Summit Outcome. United Nations World Summit, (pp. 11-12). New York. Willard, B. (2010, July 20). Sustainability Models. Retrieved from Sustainability Advantage : http://sustainabilityadvantage.com/2010/07/20/3-sustainability-models/ World Commision on the Environment and Development. (n.d.). Report of the World Commission on Environment and Development: Our Common Future. Retrieved from UN Documents: http://www.un-documents.net/wced-ocf.htm
Books: Guinness, P. (2011). In P. Guinness, Geography for the IB Diploma: Patterns and Change (pp. 211-223). Cambridge: Cambridge University Press.
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Appendix Table of Data Collected and Used:
Ward 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Ward Name Etobicoke North Etobicoke North Etobicoke Centre Etobicoke Centre Etobicoke-Lakeshore Etobicoke-Lakeshore York West York West York Centre York Centre York South-Weston York South-Weston Parkdale-High Park Parkdale-High Park Eglinton-Lawrence Eglinton-Lawrence Davenport Davenport Trinity-Spadina Trinity-Spadina St. Paul's St. Paul's Willowdale Willowdale Don Valley West Don Valley West Toronto CentreRosedale Toronto CentreRosedale Toronto-Danforth Toronto-Danforth Beaches-East York Beaches-East York
AvgHousehold_income %PS_Education 66001 42.80% 72100 46.60% 95259 59.50% 106513 57.40% 110919 65.40% 79279 59.80% 62541 38.20% 52280 39.60% 60550 37.70% 78704 63.80% 61883 43.00% 61271 38.10% 105852 70.40% 67305 62.20% 71529 52.50% 175528 73.40% 71279 43.60% 67392 57.10% 85294 64.90% 84382 73.00% 116008 68.60% 128973 79.10% 83641 71.70% 91433 68.70% 202111 74.20% 88404 61.00%
average residential usage 382.13 339.56 260.73 312.78 296.76 292.83 335.16 409.47 287.27 314.96 289.17 307.97 240.96 314.69 348.12 340.12 321.78 313.19 284.37 524.76 385.60 506.38 266.61 286.37 420.54 313.42
109667
76.50%
706.00
75284 84631 89388 72471 108092
68.40% 61.50% 59.60% 56.70% 66.30%
443.96 256.02 393.06 240.34 332.07 17
33 Don Valley East 34 Don Valley East Scarborough 35 Southwest Scarborough 36 Southwest 37 Scarborough Centre 38 Scarborough Centre Scarborough39 Agincourt Scarborough40 Agincourt Scarborough-Rouge 41 River Scarborough-Rouge 42 River 43 Scarborough East 44 Scarborough East
73194 74008
65.00% 59.10%
277.70 294.65
59212
50.70%
289.57
80620 65749 64378
52.60% 46.30% 52.30%
260.57 288.28 283.10
71264
50.40%
354.86
64464
52.70%
324.34
75275
48.10%
352.34
74662 67686 100626
50.70% 50.30% 60.20%
363.52 289.61 291.85
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