Nikki Thompson
Recycling and Waste
Evidence and recommendations for high quality recycling programs.
Canada, along with the rest of the world, is continually growing and expanding. Increased
production and urbanization results in increased waste production leading to many negative
environmental impacts. When faced with these ever increasing environmental concerns, Canada's focus
needs to shift towards promoting sustainable recycling behaviours in order to mitigate these impacts.
There is a link between high quality recycling programs and waste reduction, as shown in the multiple
regression models discussed in this paper. Expanding on this, we take an in depth look at the impact of
various factors on recycling programs. This can be used to build the basis of a successful and effective
recycling program. As Canada continues to develop as a global economic player, the country also needs
to address the by-products of this growth by developing a high quality recycling program. Such a
program would support continued sustainable production and consumption by alleviating the negative
side effects associated with the creation of more waste.
Increased production and consumption results in increased waste; most commonly waste is
disposed through the use of landfills. The impacts of growing landfills vary from minor inconveniences
to much more serious issues. On the smaller scale, landfills create odour and noise and are not
aesthetically pleasing. Landfills also require the use of large tracks of land. Larger impacts include
local pollution, such as the contamination of ground water and soil. Electronic waste, such as
televisions or computers, release hazardous substances as they begin to break down in landfills. These
toxins are then absorbed into the soil, creating long term environmental impacts (Environment
Victoria). The most concerning issue linked to landfills is the production of methane that results from
the breakdown of organic material.
Methane is a component of natural gas and is also a greenhouse gas. It is created when organic
materials, such as paper, food scraps or yard waste begin to decompose (Howard, 2009). While carbon
dioxide is commonly associated with the issue of global warming, methane is actually more potent on a
per unit basis (Abrahm, 2011). In Canada, emissions from landfills account for 20% of the countries
methane emissions (Environment Canada, 2013). Increasing levels of methane exacerbates the problem
of global warming.
Global warming is the result of greenhouse gasses, including methane and carbon dioxide,
being released and collecting in the atmosphere. This traps the sun's heat and results in worldwide
changes in climate. Scientific estimates predict that if emissions continue at their current rate, the
temperature in some regions will increase by 3 to 9 degrees by the end of the century (NRDC, 2011).
Global warming not only affects climate, it also will likely impact human health, food, water, and
energy sources. From a health perspective, it is likely that an increase in temperature and increased
occurrence of heat waves will lead to an increase in risk of death and illness. Furthermore, we will see
increased incidences of diseases being transmitted through food, water, and insects. These health issues
affect society disproportionally, increasing the risk to the elderly, children, and/or the poor (NRDC,
2011).
On top of health impacts, Canada will see changes in its diverse geographical landscape. As
temperatures increase, glaciers in Arctic ecosystems will continue to melt at an ever increasing rate. It
is already estimated that in the last century more than fifty percent of B.C's Glacier National Park have
melted. Not only are Arctic ecosystems impacted by climate change, but surrounding communities are
affected as well. As glaciers decrease, the annual water flows produced in the summer months also
diminish. In the last fifty years the summer flows of the Mistaya River have already decreased by thirty
nine percent (David Suzuki Foundation). Many communities depend on these water flows each year.
Other water supply sources, such as lakes and streams, are also impacted by climate change. Changes
in rainfall and evaporation will cause lake levels to fall, and a decreased ability to recharge ground
water will lead to smaller steams drying up as well (David Suzuki Foundation). The impacts of climate
change are varied and pose a serious issue; recycling programs can be utilized to help limit the
contribution of landfill emissions.
Recycling programs can address the negative impacts associated with landfills and the resulting
methane emissions, and in addition, recycling programs are also linked to a variety of other benefits.
Recycling conserves natural resources, reduces energy, and can improve the economy. As we do not
have an endless supply of natural resources, recycling can help to conserve these resources by reusing
collected materials. On top of this, using recycled materials in the manufacturing process requires much
less energy than using raw materials. Studies have indicated that systems using recycled materials in
the production process can save up to 94 percent of the energy required when compared with systems
that manufacture from raw materials (United States Environmental Protection Agency, 1998). These
systems are also characterized by reductions in 10 major categories of air pollutants, 8 major categories
of water quality indicators and pollutants, and solid waste (United States Environmental Protection
Agency, 1998). Recycling also yields economic benefits. Implementing effective recycling programs
and infrastructure will lead to decreased costs associated with landfill operation and waste collection.
Expansion of the recycling sector generates new jobs in the areas of collection, brokering,
manufacturing, and distribution of recycled and reproduced materials. Studies conducted in North
Carolina, where the recycling sector supports around 9,000 state wide jobs, found that for every 100
jobs created in the recycling sector only 13 jobs were lost in the solid waste and raw material collection
sectors (United States Environmental Protection Agency, 1998). Municipalities can also generate
additional revenue through the sale of recycled materials to manufacturing companies (Leigh). The
role of recycling goes beyond reducing waste, it also offers opportunities for economic growth.
A major factor determining the level of waste in a province and the effectiveness of recycling
programs is the environmental values and social norms of the province. These values are going to
impact the disposal decisions of the individual. Such values and norms will differ significantly across
provinces. To illustrate this we can compare British Columbia with Alberta. B.C., especially the city of
Vancouver, places a high value on the environment and sustainable behaviours, and as such is
considered to be a very green province. Currently the city of Vancouver has initiatives in place to
become the 'greenest city' by 2020; this will be accomplished by targeting carbon and waste reduction.
(City of Vancouver, 2013). As these attitudes have become the expected norm of the province, we
assume those who violate this norm are subject to social stigma and are looked down upon by society.
Looking at Alberta in comparison, social pressures associated with recycling are not evident. A major
difference between B.C and Alberta is the presence of the oil sands. The Alberta oil sands represent the
third largest proven crude oil reserve in the world and the largest growing source of greenhouse gas
emissions in Canada (Best & Hoberg , 2008). Instead of setting limits on total emissions, the Alberta
government aims to reduce emissions per unit of production (Best & Hoberg , 2008), which is expected
to reach 3 million barrels of crude oil a day by 2018 (Alberta Energy). If we assume the approach
Alberta's government is taking towards the oil sands is representative of the province's attitudes
towards sustainability, we see there is not the same social pressures to be 'green' as there are in B.C. If
both B.C and Alberta were to implement the same high quality recycling program, we may see an
increase in the ratio of recycling to waste in both provinces, however the program is likely to be more
effective in Alberta and result in larger gains. If residents of B.C. already face very strong social
pressures to engage in recycling and sustainable behaviours, these social norms will drive their
behaviour. Thus, an increase in the recycling-waste ratio cannot be solely attributed to the recycling
program . However, in Alberta, if these social norms and attitudes are absent, the increase in the
recycling to waste ratio is driven by the implementation of a sophisticated recycling program.
Therefore we find such programs will be more effective in provinces like Alberta than provinces that
already place a high valuation on the environment
This idea of social norms can also be examined in other provinces as well. We can compare
larger provinces with higher GDPs like Ontario and Quebec with smaller provinces like Prince Edward
Island and New Brunswick. In such cases, the priorities and values of the provinces may differ when it
comes to recycling and the environment. A province with a high GDP is able to focus on environmental
sustainability; whereas smaller provinces are more likely to focus their efforts on economic growth.
Environmental values and social norms differ across provinces; these values play a critical role in the
disposal and recycling decisions of the individual. We will demonstrate the importance of these values
in our regression and illustrate how high quality recycling programs work to increase the environmental
value and awareness in society.
Previous research done in the area of waste and recycling can offer valuable insight into the
factors motivating the individual’s disposal decision. Studies done by Biswas, Licata, McKee, Pullig
and Daughtridge find that only 27 percent of consumption waste is recycled with the remaining waste
being thrown out (Biswas, Licata, McKee, Pullig & Daughtridge, 2000). The authors suggest that it is
not only the attitudes toward an action that influence individual behaviour, but also what they believe
others think of that behaviour as well (Biswas, Licata, McKee, Pullig & Daughtridge, 2000). In such a
case, if you were indifferent towards recycling but knew your peers held very strong positive views
about recycling, you will be more inclined to recycle than you would be if you believed your peer
group was indifferent. In their paper the authors suggest using promotional content and
communication to affect and shape attitudes towards recycling in a positive way. Advertisements about
recycling will be most effective when they incorporate and highlight aspects of personal intrinsic
rewards and approval of others (Biswas, Licata, McKee, Pullig & Daughtridge, 2000). Further studies
conclude similar results, illustrating the importance of intrinsic motivation. Authors Ferrara and
Missios find there is a positive correlation between environmental concern and waste prevention
(Ferrara & Missios, 2012). They find that individuals who recognize environmental degradation and
exhibit a strong level of environmental concern are more likely to adjust not only their recycling
behaviours but also purchasing and consumption behaviours. On the other hand, individuals who are
only concerned about increasing waste are less likely to adjust their behaviours (Ferrara & Missios,
2012). The findings of these two studies support our claim that high quality recycling programs need to
take on an educational aspect which makes consumers aware of the impacts of their disposal decisions.
We will create a model that illustrates the link between the quality of recycling programs and
waste reduction. Expanding on this, we examine in depth the factors which contribute to increasing the
recycling to waste ratio. From these results, we are able to suggest appropriate actions and policies to
be undertaken by the government to design a high quality and effective recycling program.
Regression 1
We first look to establish a link between high quality recycling programs and decreases in
waste. This acts as a foundation from which we can further investigate the impacts of various factors on
waste reduction. Our first regression model is as follows,
Waste = ß0 + ß1inc + ß2age + ß3quality + ß4govspend + δiyeari + ∂jprovj
Waste is our dependent variable and it represents the total amount of non-hazardous waste
disposed in public and private waste disposal facilities. Waste is measured in tonnes per capita and our
data was obtained through the Statistics Canada survey Waste Management Industry Survey: Businesses
and Government Sectors. In order to utilize the data, units were converted from kilograms to tonnes
and then the data was divided by population estimates for the provinces to obtain a per capita measure.
Data was not available for Newfoundland or Nunavut for any of the years included and data is also
missing for both the Yukon and the Northwest Territories for every year expect 2004 and 2006.
Income is measured in millions of dollars per capita. To construct this measure, data on the
Gross Domestic Product, calculated using the expenditure approach for each province, was obtained
through Statistics Canada. This data was then divided by population estimates for each province to
obtain income per capita. Data is missing for the Northwest Territories and Nunavut for the years 1996
and 1998. The expectation is that as income per capita rises, waste disposed will decrease and the
amount of recycling increases.
Age represents the median age of the province. It is the central age of the population in each
province, as the median is the value that lies at the midpoint of a frequency distribution. Data was
retrieved from Statistics Canada. The expectation is that as age increases, waste will also increase. This
is based on the assumption that the younger generation will be more willing to adopt new norms
centred around environmental sustainability, whereas the older generation is slower to adopt these
changes.
Government spending captures the amount governments spend on the operation of recycling
facilities. This is measured in thousands of dollars and is obtained through Statistics Canada. Data was
not available for Newfoundland, Prince Edward Island, The Northwest Territories, The Yukon, or
Nunavut for any of the years and data was missing for 1996 and 1998 in Alberta. We expect that as
government spending increases, waste will decrease. As government spending increases, we expect that
recycling facilities become more efficient and thus able to handle and process higher levels of
materials.
The variable of interest in this case is quality, as we aim to establish a relationship between high
quality recycling programs and waste reduction. We measure quality as the ratio between recycling and
waste. The measures for recycling and waste were obtained from the Statistics Canada report Waste
Management Industry Survey: Businesses and Government Sectors. The recycling portion of this ratio
represents all residential and non-residential materials prepared for recycling. The residential sources
include all non-hazardous recyclable materials either picked up by the municipality or taken by
residents to depot or transfer stations. Non-residential sources include solid non-hazardous recyclable
materials produced by commercial, institutional, construction, renovation, and demolition sectors. The
waste portion represents the total amount of non-hazardous waste disposed in both private and public
waste disposal facilities. This also includes all waste exported out of the source province or country for
disposal. Both recycling and waste are measured in tonnes per capita. The recycling and waste
measures were then divided to create the ratio between the two. Data was not available for Prince
Edward Island, the Yukon, Northwest Territories, or Nunavut over the entire time period, and the
observation for Newfoundland in 2008 is also missing. We expect that as the quality of programs
increases, total waste will decrease.
We assume that social norms and values of a province stay constant over time. As beliefs are
adopted at a young age and learned through family ties and peer groups, we believe they are slow to
change. This then allows us to use panel data and fixed effects model with province and year dummies
to capture variation that we are unable to directly account for in the regression. Each year and province
is represented in the model with a dummy variable.
Running the regression we obtain the following results:
Table 1: Regression 1
Variable
Parameter Estimate
Standard Error
Income
6.25 **
0.56
Age
0.01
2.14
Government Spending
0.00007
0.00046
Quality
-0.31 ***
0.07
There were 104 observations in the data and 47 were deleted due to incomplete information.
The residual standard error on 38 degrees of freedom is 0.046 and the adjusted R2 is 0.92. Coefficients
are significant are the following levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.
The age and government variables were both statistically insignificant. Income was statistically
significant, however when interpreting the results, we see it will take an increase of one million dollars
to lead to 6.25 tonne decrease in waste. When looking at the units we see an increase in income
actually has a rather small effect.
Our quality measure is significant, indicating that an increase in quality will result in a decrease
in waste disposed. Sensitivity analysis indicates that these results are still significant when differing the
data by factors of -10%, -5%, +5%, and +10%, which demonstrates the robustness of the variable.
Clearly, we can see there is a link between high quality recycling programs and waste reduction.
Endogeneity Problem
While we are able to demonstrate there is a link between high quality recycling programs and
waste reduction, our previous model is subject to endogeneity problems. We are faced with two issues:
an omitted variable, and a systematic relationship between the dependent variable, waste, and the
independent variable, quality.
A major factor influencing the individual’s disposal decision is both their individual values and
the social norms of the communities. These social norms and values are a driving motivator of the
individual’s actions. These values are not represented in our previous regression. As mentioned earlier,
environmental values can vary from province to province for a variety of reasons. Not only do social
norms affect the disposal decision of the individual, they can also influence the overall effectiveness of
recycling programs. In our previous regression, it was assumed that attitudes of a province are constant
over time and thus we were able to utilize panel data and the fixed effects model to address this issue.
However, if we want to analyze the impacts of social norms on recycling behaviours, we need to
capture the environmental values or “greenness” of a province through the use of an instrument.
The second issue with our previous model is the link between the dependent variable and the
quality measure. Quality is represented as the ratio between recycling and waste. As such, we see waste
appear in both our variables and on both sides of our regression equation. This means if waste were to
exogenously increase, we will see a change both in the dependent variable and in our independent
quality variable. This leads to a larger endogeneity issue as these two variables are connected in this
unintentional way.
The regression results found by our original model show there is a link between high quality
recycling programs and waste reduction. In order to address the endogeneity problems encountered in
the original model and look more in depth at the various factors influencing recycling behaviours, we
introduce a new model. Our new regression model takes the quality measure, the ratio of recycling to
waste, and makes this the dependent variable. We also introduce an instrument to capture the
environmental attitudes of each province. Such a model allows us to look more closely at what
specifically impacts the ratio of recycling to waste. By looking at how different variables effect the
recycling to waste ratio, we can determine the critical components of a high quality recycling program.
We begin by establishing the links between consumption, waste, and recycling, and then introduce our
new regression model.
Model
As we experience continued growth and expansion worldwide, we also see increased demand
and consumption. This increase in consumption leads to a decision at the individual level on how to
dispose of these units of consumption. The individual is now faced with a tradeoff between recycling or
simply throwing these items in the trash. This decision is highly influenced by the both the individual’s
personal preferences and the social norms of society. By modelling the relationship between
consumption, recycling, and waste, we can illustrate the links between these behavioural choices and
the environmental values held by the individual.
This relationship can be modelled as follows:
C = w/(1-ß) + r/ß + E
C represents consumption, w represents waste, and r represents recycling. E is an error term
which captures products in which there is not an option to recycle and also durable goods that are long
lasting and not consumed and recycled in one period. ß is a preference parameter capturing the
individual’s preferences towards recycling and the environment. This can be thought of as the social
norms of society towards recycling previously discussed. This function symbolizes the consumer’s
tradeoff and preferences towards recycling or simply disposing a unit of consumption.
We can solve this model for the ratio of recycling and waste:
r/w = ßC/w – ß/(1-ß) – ßE/w
From here we can see that when ß increases, r/w increases. If the individual’s preferences
towards the environment and recycling suddenly increase, they will be inclined to recycle more out of
their consumption, everything else remaining constant. Changes in C will have an ambiguous effect
depending on the ß parameter. If the individual naturally places a high value on the environment, and
consequently has a high ß, then when the total amount consumed increases, they will recycle a higher
proportion of this increased consumption. On the other hand, if the individual has a low ß and places a
low value on recycling, an increase in consumption results in increased waste, thus lowering the ratio
of recycling to waste.
Clearly ß plays an important role in influencing the individual’s behaviours. We extrapolate the
ideas evident in this model and apply them to our regression model in order to further examine the
impacts of these variables, in particular ß, on the ratio of recycling and waste.
Regression 2
The regression model used is as follows,
R/W = ß0 + ß1inc + ß2age + ß3consumption + ß4ageorg + ß5gov + ß6log(access) + ß7log(green)
+ δiyeari + ∂jprovj
The dependent variable in this model is the ratio of recycling and waste, the quality variable in
the previous regression. The recycling component represents both residential and non-residential
materials prepared for recycling. The waste component contains the total amount of public and private
waste disposed. Recycling was divided by waste to create the ratio between the two components. In
contrast to before, where we established a causal relationship between the quality of a recycling
program and waste reduction, we are now interested in examining the effects of various factors linked
to people’s recycling behaviour on the ratio of recycling to waste.
Income is the same measure used in our original regression and represents income per capita
measured in millions of dollars. We expect that as income rises, the ratio between recycling and waste
will decrease.
Median age is the same data used in the previous regression. This represents the central age of
the province. We expect as age increases, this ratio will decrease. This is based on the belief that the
younger generation is more environmentally conscious and has been exposed to the social norms
surrounding recycling at a younger age. The older generation, on the other hand, will be slower to
change previously learned behaviours and adopt new beliefs.
Consumption represents consumer spending on goods. This data is collected from the
expenditure based Gross Domestic Product, and includes all spending on durable, semi-durable, and
non-durable goods. The data is collected through Statistics Canada. This is measured in millions of
dollars and is chained to 2007 dollars. Data is missing in the years 1996 and 1998 for the Northwest
Territories and Nunavut. Consumption is believed to have an ambiguous effect on the ratio of recycling
and waste. Relating back to the previous model linking composition, waste, and recycling, if there is a
high value placed on the environmentally friendly actions, or a high ß, an increase in consumption
leads to an increase in recycling. This is due to the fact that more of what the consumer purchases and
consumes is being recycled as opposed to thrown in the waste. Conversely, if the ß or the
environmental values of the consumer is low, we expect an increase in consumption to lead to a
decrease in the recycling-waste ratio. The consumer is purchasing and consuming more, however, due
to the low value placed on the environment, these additional units of consumption are being thrown
out. The effect of consumption is then linked with the environmental values of the population.
Age of organization captures how long recycling and sustainability driven councils and
organizations have been present in each province. This measure is developed by calculating the age of
these organizations from when they were first implemented. All provinces with the exception of
Nunavut, Newfoundland and the Yukon, have recycling and sustainability organizations. An increase
in the age of the organization is expected to increase the recycling-waste ratio. An older organization is
more established and experienced within its community, and likely more influential.
Government represents whether the environmental organization is government based or nonprofit and non-political. This measure is represented by a dummy variable indicating whether the
recycling organizations in each province are government run or non-profit. The dummy is 1 if the
organization is government based and 0 if it is a non-profit, non-political organization. Organizations in
Prince Edward Island, New Brunswick, Quebec, Manitoba, and Northwest Territories are all
government organizations. On the other hand Nova Scotia, Ontario, Saskatchewan, Alberta, and British
Columbia's organizations are non-profit or non-political. Newfoundland, Nunavut, and the Yukon do
not have any of these organizations. This captures the role for government in implementing recycling
organizations in the province. The expectation is that government organizations will increase the ratio
due to the fact they likely are more prominent and have a wider access to funding and resources.
Access represents the percentage of all households that had access to recycling programs. Data
was available for 1994 and 2006. A linear approximation connecting the data points was used to
estimate the remaining years. The log of this data is used in the regression. Data is missing for
Nunavut, Northwest Territories, and the Yukon. The expectation is that an increase in access will lead
to an increase in the ratio of recycling to waste. Increasing the access to recycling programs makes the
act of recycling easier and more attractive to consumers. Increasing access to recycling programs leads
to increased efficiency for both households and the institutions that collect and handle recycled
materials. This should result in an overall decrease in waste and increase in recycling.
The green measure is an instrument used to capture the norms and values towards recycling in
the province. The instrument used is percentage of households that purchase environmentally friendly
or “green” cleaning products. This data is split into five different categories; always, often, sometimes,
rarely, and never purchase these products. The green measure was constructed by adding together the
percentage of households that indicated they always or often purchased green cleaning products. The
log of this data is then used in the regression. Data was not available for the Yukon, Nunavut, or the
Northwest Territories. An increase in the values of society, shifting towards a more environmentally
friendly position, will result in an increase in the recycling-waste ratio. If the population becomes more
environmentally conscious, both their disposal and consumption behaviours change. As the population
places an increased importance on being green, they will make more of an effort to recycle as opposed
to disposing items in the garbage and landfills. Additionally, as the environmental values of the
population change, we believe consumption behaviours will change as well. We believe the population
will begin opting for products they know to be recyclable. Both these effects will lead to an increase in
recycling and a decrease in waste.
δiyeari represents the year dummy of the model. Each year from 1996 to 2010 is represented in
the regression. For example, if looking at δ11996, δ is equal to 1 if the year is 1996 and 0 if otherwise.
If looking at δ21998, δ is equal to 1 if the year is 1998 and 0 otherwise. This is done for every year in
the model. Additionally, the province dummy, ∂iprovi , represents each province in the model. If we
have ∂1BC, ∂ is equal to 1 given the province is BC and 0 for every other province. Similarly,
∂2Manitoba, is where ∂ is 1 if the province is Manitoba and 0 otherwise. There is a province dummy for
each province represented in the model.
Below we present a summary of our data to illustrate the range and variation between
provinces. Our government variable has been excluded as it is a dummy variable.
Table 2: Summary Statistics
Variable
Mean
Max
Min
Median
1st Quintile
3rd Quintile
Observations
(minus NA)
Income
0.04
0.12
0.02
0.04
0.03
0.05
4
Age
36.09
43.30
21.80
37.10
34.98
38.92
NA
Consumption
0.006
0.014
0.007
0.010
0.009
0.012
4
Age
Organization
22
40
6
23.5
11
27
24
Access
4.4
4.6
3.5
4.5
4.4
4.5
24
Green
3.3
3.6
2.6
3.3
3.1
3.5
24
We can take a closer look the variables, age of organization and green. There is large variation
in the age of recycling organizations in provinces. British Columbia's recycling organization, the
Recycling Council of BC, was established in 1974, making it the maximum value at 40 years. The
minimum comes from New Brunswick, where the organization, Recycle NB, was established in 2008.
Our green variable uses the percentage of households which responded that they always or often
purchase environmentally friendly cleaning products to capture the environmental values of a province.
The log of this percentage was then taken. British Columbia has the highest value with 37% and the
lowest value comes from Newfoundland at 14%.
Results
When running the regression we obtain the following results:
Table 3: Regression 2
Variable
Parameter Estimate
Standard Error
Income
19.9 **
6.6
Age
0.11 ***
0.02
Consumption
-210.86 *
78.72
Age Organization
0.05 **
0.01
Government
0.08
0.09
Access
0.68 **
0.23
Green
3.35 **
1.02
All variables are found to be significant with the exception of government; this indicates that it
is important to have environmental and recycling based organizations in the province, however it is not
important who runs these organizations. All variables match our predictions with the exception being
age. This could be attributed to the fact that the older generation may have both the time and the
resources to devote to environmentally conscious actions, whereas the younger generation may still be
establishing themselves, and thus, may not view recycling as a high priority. While consumption
appears to have quite a large effect, when examining the units in which it is measured, we see that it
will take a one million dollar increase in consumption to induce a decrease in the recycling to waste
ratio by 210.86. When taking the units into account, the effect of consumption is quite small. Although
the result for income is much smaller, the same issue of units of measurement takes place. A one
million dollar increase in income results in a 19.9 increase in the ratio between recycling and waste.
The variable age of the organization indicates that older organizations are more effective;
increasing the age of the organization by one year, and holding all else constant, results in a 0.05
increase in the ratio of recycling to waste. Long standing organizations show a commitment to
sustainability and recycling initiatives. These results suggest established organizations whose primary
goals focus on environmental issues can have a positive impact on recycling and waste decisions of the
province.
The coefficients on the two variables of interest, access and green, are both expected and
significant. These results indicate that increasing the number of households with access to recycling
programs by 1% will lead to an increase in the ratio of recycling to waste by 0.68, holding all else
constant. Similarly, increasing the environmental valuation of households by 1% leads to an increase in
the ratio of recycling and waste by 3.35, holding all else constant. These results indicate these two
factors play a crucial role in influencing the individual’s recycling and waste behaviours.
There were 104 observations in the data, with 40 deleted due to incomplete information. This
was likely generated by provinces where the data was unavailable for all years, such as the Northwest
Territories or the Yukon. The residual standard error on 45 degrees of freedom is 0.08554 and the
2
adjusted R is 0.7541. Coefficients are significant are the following levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’
0.05 ‘.’ 0.1 ‘ ’ 1.
By completing a sensitivity analysis on the two main variables of interest, we test the robustness
of the variables and their sensitivity to change. The data for both variables has been differed by a
factor of -10%, -5%, +5%, and +10%.
Table 4: Sensitivity Analysis, Green
Variable
Parameter Coefficient
Standard Error
-10%
3.35 **
1.02
-5%
3.35 **
1.02
Green
3.35 **
1.02
+5%
3.35 **
1.02
+10%
3.35 **
1.02
Table 5: Sensitivity Analysis, Access
Variable
Parameter Coefficient
Standard Error
-10%
0.68 **
0.23
-5%
0.68 **
0.23
Access
0.68 **
0.23
+5%
0.67 **
0.25
+10%
0.59 **
0.26
Differing the data for both the access and green variables by various percentages has a very
small effect on the results, and in the case of the green variable, zero effect. This shows the results
obtained are significant and the variables are robust.
Robustness of green variable
The choice of green variable is an important factor in the above regression. This variable works
to capture the underlying values of society, values which motivate recycling behaviours. The green
variable used in our regression was constructed from a Statistics Canada Survey in which Canadian
households were asked how often they purchased environmentally friendly or green cleaning products.
The five possible responses included; always, often, sometimes, rarely, and never. Our green variable
was constructed by adding the percentage of households that responded with always and often.Another
potential variable that captures these social norms is looked at to determine the robustness of the green
variable used. This variable uses the same Statistics Canada data on green cleaning products. The
alternative instrument is constructed by summing the percentage of households who responded they
often and sometimes purchase environmentally friendly or 'green' cleaning products.
Regression1 lists the previous results found using our green variable whereas Regression2
indicates the parameter coefficients obtained from running the same regression with the alternative
green measure.
Table 6: Robustness of green instrument
Variable
Regression1 (Green1)
Regression2 (Green2)
Income
19.9 **
19.9 **
Age
0.11 ***
0.11 ***
Consumption
-210.86 *
-210.86 *
Age Organization
0.05 **
0.34 **
Government
0.08
5.5 **
Access
0.68 **
0.67 **
Green1
3.35 **
Green2
60.07 **
In Regression2 we see that both government and age of environmental organization have larger
impacts on the ratio of recycling and waste. If the population does not already have a high
environmental valuation then recycling and government programs will be much more effective in
influencing individual behaviour. Furthermore, we see the parameters on the green variables
themselves are quite different. In the original regression, the parameter coefficient is 3.34 as opposed to
60.07 in the second regression. Again, if society already has strong established positive recycling and
environmental norms, any shifts in preferences that increase the 'greenness' of the population will have
less of an effect than a population who are more neutral.
Our alternative variable produces fairly consistent results, indicating the green measure used in
our regression to represent society’s preferences is both robust and appropriate. The alternative variable
does produce much stronger results. This is likely because it covers a larger percentage of the
population. There is a loss of variation in the data though; and as such, we are unable to properly
isolate the true impacts and benefits of our variables. This variation will also be important when
conducting further counter factual analysis.
Counter Factual Analysis
We can use the results generated in the model to illustrate the potential gains from improving
and influencing the environmental values of the province. When looking at our green variable, which is
an instrument that captures the environmental attitudes or 'greenness' of a province, we can compare a
province with a high value, like B.C., to a province with a lower valuation, like New Brunswick. We
discussed earlier why values may differ between two provinces; in this case B.C is not only known for
being very environmentally conscious, it is also a larger province in terms of GDP and population.
These factors allow B.C. to make the environment a much higher priority, leading to strong
environmental values. The same is likely not true for New Brunswick, who will devote its efforts to
building and expanding their economy, potentially leading to a lesser focus on environmental issues.
When we look at our green variable, the percentage of households that indicated they purchased
environmentally friendly cleaning products always or often, the percentage in B.C. is 37% compared
with 22% in New Brunswick.
Through the parameter coefficients found in our regression, we estimate the recycling to waste
ratio for New Brunswick in 2010 given the available data. The steps are illustrated in the appendix.
We find the estimated recycling to waste ratio for New Brunswick is 0.68.
To find the benefit of increasing the 'greenness' of a province we first assume that our regression
model is correct and also that all other factors do not change. We then estimate the recycling to waste
ratio in the same way as before, but this time assume New Brunswick has as the same environmental
valuation as B.C. In this case, we find the estimated recycling to waste ratio for New Brunswick is
2.43. By keeping all else constant and assigning New Brunswick the same level of environmental
consciousness as a high valuation province, there is a 1.75 increase in the recycling-waste ratio. In such
a case, New Brunswick is going from producing more waste than recycling, as the recycling to waste
ratio is less than one, to producing more than two times the amount recycling to waste. We can see
increasing the environmental valuation and 'greenness' of a province can lead to gains in terms of
amount recycled and disposed.
We can graph the relationship between the
ratio of recycling to waste and our green variable.
Ignoring any outliers we can see that as the
'greenness' of a province increases, the ratio of
recycling to waste is increasing as well. This provides
further evidence that there are benefits to targeting
the environmental attitudes of a province in an effort
to increase the ratio of recycling to waste.
We can conduct the same experiment when looking into the age of environmental organizations
in a province. In this case, we will compare B.C. with Quebec. Both provinces are fairly similar in
terms of income per capita, and as such they will have access to relatively similar resources. B.C.'s
major recycling organization is the Recycling Council of BC, which is 40 years old. In Quebec, the
major organization is the crown corporation Recyc-Quebec, which is 24 years old.
Again we start by estimating the recycling to waste ratio for Quebec in the year 2010 using the
data available and the year of their recycling organization. The estimated ratio of recycling to waste is
0.2. Next, we calculate the same ratio by assuming the age of Quebec's recycling program is the same
as B.C.’s and hold all other factors at their original level. We find that the estimated recycling to waste
ratio is now 1; this is an increase in the recycling-waste ratio by 0.8. By appointing Quebec a higher
organization age, they move from producing more waste than recycling, to an even amount of recycling
and waste. This provides evidence that recycling and environmental organizations should be
implemented earlier as opposed to later. A long standing recycling organization will be recognizable
and credible to the communities, which contributes to increasing the recycling-waste ratio.
We can construct a graph illustrating the
relationship between the ratio of recycling to
waste and the age of a recycling organization. If
we again ignore the outliers we see a positive
relationship between these two variables. This
shows that as the age of a recycling organization
increases, the ratio of recycling to waste will
increase; therefore, it is beneficial to implement
such organizations as early as possible.
Impacts on organic waste
A natural extension of this model is to examine not only the effect of these variables on the
recycling to waste ratio, but also the compost to waste ratio. One of the major impacts associated with
increasing levels of landfill waste is the increased production of methane. Methane is released during
the breakdown of organic materials, adding to greenhouse gasses in the atmosphere. It is useful then to
determine whether these variables have a similar effect when concentrating solely on composting. We
run the previous regression using the same independent variables; however, the dependent variable is
the ratio of compost to waste measured. Compost data was obtained from the same report used to
collect recycling and waste data, Waste Management Industry Survey: Businesses and Government
Sectors, and represents organic materials. Data is missing for Newfoundland, Prince Edward Island,
Yukon, Northwest Territories, and Nunavut in all years. Data is also missing in 1996, 1998, 2000, and
2006 for New Brunswick, in 1998 and 2008 for Manitoba, and in 1998, 2002, 2004, and 2010 in
Saskatchewan. Running the regression with this as the dependent variable gives the following results.
Table 7: Regression 3
Variable
Parameter Estimate
Standard Error
Income
7.92 *
3.63
Age
0.08 ***
0.02
Consumption
-61.63
42.89
Age Organization
0.03 **
0.01
Government
-0.13 *
0.06
Access
0.34 *
0.14
Green
2.15 ***
0.58
All variables are significant with the exception of consumption. Again we see positive and
expected signs on our two main variables of interest, access and green, indicating programs that
influence these variables will result in an increase in the compost to waste ratio. An interesting result of
this regression is the negative government coefficient. This could potentially indicate that, when it
comes to composting, individuals are motivated by intrinsic factors opposed to external factors. As
such, they may be more likely to engage in composting behaviours independent of government
programs. There were 104 observations with 50 deleted due to incomplete information. The residual
standard error is 0.04 on 35 degrees of freedom and the adjusted R2 is 0.81. Coefficients are significant
are the following levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.
We can compare the results obtained by using the ratio of recycling to waste as the dependent
variable in the regression with using the ratio of compost to waste.
Table 8: Comparison of recycling and compost regressions
Variable
Regression -recycling to waste
Regression -compost to waste
Income
19.9 **
7.92 *
Age
0.11 ***
0.08 ***
Consumption
-210.86 *
-61.63
Age Organization
0.05 **
0.03 **
Government
0.08
-0.13 *
Access
0.68 **
0.34 *
Green
3.35 **
2.15 ***
These results are clearly aligned with the results obtained in our original regression with the
ratio between recycling and waste as the dependent variable. Given these results, we can incorporate
similar strategies when addressing both the issues of recycling and composting. Existing recycling
programs should be expanded to include a composting component. The city of Vancouver has adopted
such a strategy by implementing green bin programs that collect and encourage composting and
reduces the amount of organic materials which ends up in landfills (City of Vancouver, 2013). A high
quality recycling program will then incorporate aspects that target not only traditional recyclable
materials, such as cardboard and plastic, but also organic and compostable materials.
Policy Implications
These results indicate there are ways in which the ratio between recycling and waste can be
influenced, as such, the amount of recycling can be increased and the amount of waste decreased. We
see that the age of the organization can influence the ratio of recycled materials to waste. Older
organizations have a bigger impact on this ratio as long standing organizations are rooted and
established within the community. Accordingly, older organizations demonstrate a commitment by the
province to continue to implement sustainable practices. If the province doesn’t already have an
organization committed to environmental awareness, one should be established. . In addition, if a
province has multiple, smaller committees, these should be consolidated into one organization. These
environmental organizations can work to increase the ratio of recycling to waste by providing their
communities with all the necessary resources and information needed to effectively recycle.
In order to increase the ratio of recycling to waste and promote sustainable behaviours, the
government should focus on targeting the quality of recycling programs and their accessibility and
ease. This corresponds to the green and access variables from our regression. As shown by the
regression results, a focus on these two aspects will lead to an increase in the recycling to waste ratio.
The government should be looking to implement high quality recycling programs, a key aspect
of which is an educational component. This will target and influence the community’s environmental
values and social norms, the ß in our model. By including an educational component to a recycling
program, the government could increase the awareness of the impacts of recycling and waste.
Providing not only the tools to recycle but also the knowledge as to why it is important will increase
the overall effectiveness of the program. Over time, this will shift the underlying norms and values of
the province, moving towards a more environmentally conscious society. Above influencing the
amount of materials recycled, a high quality recycling program based on education also has the
potential to influence society’s consumption choices as well. When an educational component is
introduced, individuals are made aware of the impacts of their consumption and disposal decisions. As
such, we potentially will see a shift towards the purchase of environmentally friendly products, either
in the form of items made from recycled, as opposed to raw materials, or items the consumer knows to
be recyclable.
The second component of a high quality recycling program works to increase the ease and
accessibility of the program. By making the act of recycling as easy as possible for the individual, there
will be an increase in the amount of people willing to participate. The study by Ferrara and Missios
found that the more accessible recycling services are, the more likely individuals are to engage in
environmentally conscious behaviours. They show that curbside recycling programs in which the
recycled materials are picked up from households are more effective then drop off programs where the
individual is responsible for delivering the recycled materials (Ferrara & Missios, 2012).This can be
accomplished by increasing the current scope of recycling programs and accepting a larger variety of
items. For example, Vancouver currently has initiatives to accept organic food waste, which also targets
the compost to waste ratio. The city should also provide households with all the necessary materials
required, such as bags and bins, and increase the pick-up times for recycled materials. Furthermore, a
focus should be placed on making recycling accessible in public areas. Installing recycling bins
alongside trash cans in public areas, like parks and on the streets, gives the individual options for
disposing of items when outside their homes that previously were not present. These strategies can be
mimicked at the business and institutional level as well.
Providing households and businesses with the education and tools to efficiently recycle will
increase the effectiveness of recycling programs and increase the recycling to waste ratio. When
individuals not only understand the impacts of their disposal decisions but also have all the resources to
properly and easily recycle, the overall effectiveness of recycling programs increases. Moreover, not
only does the overall ratio of recycling to waste increase, but the environmental values and social
norms of the province begin to shift as well. By focusing on these two aspects, quality and accessibility,
the government has the tools to implement very successful recycling programs in their provinces.
Limitations & Extensions
Our model and regression illustrate the link between high quality, accessible recycling programs
and increases in the ratio of recycled materials to waste. However, as with any model, there are
limitations encountered. One major barrier faced in running this regression was the source and quality
of data. All data used in the regressions was obtained from Statistics Canada surveys. While this does
provide a consistent source of data, there is the potential for reporting bias stemming from the actual
reporting agency. Using multiple and diverse sources of data would potentially limit this type of bias,
however at this time such data was unavailable. Furthermore, the number of observations was very
limited, producing a small data set. On top of an initially modest data set, we also encountered years
and provinces with missing observations. This not only impacts the reliability of the results, it also
restricts our ability to conduct additional analysis or include supplementary variables of interest. For
example, the interaction term illustrating the impact of green values, or ß, of a province on
consumption was excluded from the model since a lack of data produced unreliable results. The results
found in our regressions are statistically significant; however, these issues, rooted in the data, are
potential causes of error and limitation.
The issue of recycling and environment is becoming increasingly important and is receiving a
higher level of attention. Therefore, it is an area that can benefit from more in depth research. Our
model provides interesting results and strategies for increasing the ratio of recycling to waste within
each province. This model can also be used not only to examine Canada on a provincial level, but can
also be extended to look at specific municipalities within each province. Each province is very diverse
with its own unique identify, and the same is true for the municipalities within these provinces. For
example, if we were to look at the province of B.C we see that metro areas like the Lower Mainland
and Vancouver are very different than Northern B.C. These regions differ not only in aspects like
population and geography, but also in their values and social norms. Strategies to reduce waste in metro
Vancouver may not be successful in a northern town like Prince George. Using the model to determine
the impacts of our variables on distinct municipalities can help develop specific, specialized recycling
programs to be implemented in different cities versus a general province wide program.
It is also useful to look into the costs of implementing such programs. Extensions of this model
should incorporate the government budget constraints and determine the level of funding needed to
implement the high quality programs proposed. Furthermore, this would provide an interesting
comparison when contrasting provinces with large budgets and resources to those with less funding
available.
Finally, when approaching the issue of recycling and environmental sustainability, it is
important to note that this is not a concern limited to Canada. The impacts of recycling and waste are
experienced on a global level, as illustrated through the effects of global climate change. Therefore, a
relevant extension would be to apply this model to various countries. Just as we see variation from
municipality to municipality and province to province, there are also large differences between
countries. The diversity in population, geography, political atmosphere, and social norms will likely
lead to different results from our model. In particular, if we use the model to compare developed and
developing countries, we will see different variables having larger impacts. For example, income per
capita had a very minor effect in our regression. If we assume that a developed country, like Canada,
operates using a Kuznet's Curve type model in terms of their environmental views and behaviours, we
would expect income to have a low impact. Under a Kuznet's Curve assumption, as a country
develops, their focus and priority is on industrialization and not on sustainability. This translates into
increased waste and environmental degradation. As the country develops, and a higher average income
is reached, we expect to see a shift towards more sustainable behaviours and a decrease in waste
produced. Thus, the expectation is income per capita will have a much larger impact on the recycling to
waste ratio of a developing country than a developed one. Expanding our model to look at different
countries can help to formulate strategies to increase environmental awareness and program
effectiveness in developing countries.
As Canada and the rest of the world’s population continues to grow and expand, both
production and consumption will increase. This translates into an increase in the waste by-product
produced. While landfill waste is associated with smaller impacts like increased odour, noise, and loss
of aesthetics, there are also much more serious impacts. Landfill waste can potentially lead to climate
change through increased methane levels resulting from the breakdown of organic materials in
landfills. Recycling provides not only the opportunity to reduce and mitigate the negative impacts
associated with increased waste levels, but also has its own inherently positive impacts. Using recycled
materials in the production process conserves natural resources and energy required for production.
Furthermore, expansion of the recycling sector creates additional jobs and revenue for the municipality
through the sale of recycled materials.
A multitude of factors influence the provinces’ disposal decisions, and can play an important
role in increasing the ratio of recycling to waste. Our model identifies key variables that are crucial
when designing recycling programs with the overall goal of increasing recycling and reducing waste.
Both access to recycling programs and the green value and norms of society are linked with increases
in the recycling to waste ratio. Governments can utilize these findings when designing and
implementing recycling programs. High quality recycling programs should improve the accessibility
and ease of recycling by increasing the scope of programs and variety of items accepted. Furthermore,
a high quality program will have an educational aspect which makes consumers aware of the impacts of
their disposal decisions. This educational component influences not only disposal behaviour but also
potentially consumption behaviour. By implementing high quality recycling programs, governments
will be able to successfully and effectively limit the amount of waste and negative effects produced
while providing benefits to society and the environment.
References:
Abhijit Biswas, Jane W. Licata, Daryl McKee, Chris Pullig and Christopher Daughtridge. Journal of
Public Policy & Marketing , Vol. 19, No. 1, Privacy and Ethical Issues in Database/Interactive
Marketing and Public Policy (Spring, 2000), pp. 93-105
Abrahm , L. (2011, Jan 25). Climate benefits of natural gas may be overstated. Retrieved from
http://www.propublica.org/article/natural-gas-and-coal-pollution-gap-in-doubt
Alberta Energy. (n.d.). Oil sands. Retrieved from
http://www.energy.alberta.ca/OurBusiness/oilsands.asp
Best, J., & Hoberg , G. (2008, Jun 18). Alberta’s oil sands: Key issues and impacts. Retrieved from
http://mapleleafweb.com/features/alberta-s-oil-sands-key-issues-and-impacts
Biswas, A., Licata, J. W., McKee, D., Pullig, C., & Daughtridge, C. (2000). The recycling cycle: An
empirical examination of consumer waste recycling and recycling shopping behaviors. Journal of
Public Policy & Marketing, 19(1), 93-105. Retrieved from
http://www.jstor.org.proxy.lib.sfu.ca/stable/30000490
City of Vancouver. (2013, Nov 21). Zero Waste. Retrieved from http://vancouver.ca/greenvancouver/zero-waste.aspx
Clean Nova Scotia. (n.d.). About us. Retrieved from http://clean.ns.ca/about-us/
David Suzuki Foundation. (n.d.). Water impacts. Retrieved from http://davidsuzuki.org/issues/climatechange/science/impacts/water-impacts/
Environment Victoria. (n.d.). The problem with landfill. Retrieved from
http://environmentvictoria.org.au/content/problem-landfill.
Ferrara I, Missios P. Land Economics, Vol. 88, No. 4, A Cross-Country Study of Household Waste
Prevention and Recycling: Assessing the Effectiveness of Policy Instruments. (November 2012),
pp. 710-744.
Green Manitoba. (n.d.). Background. Retrieved from http://greenmanitoba.ca/home/
Howard, A. (2009, 8 6).Landfills and the environmental effects. Retrieved from
http://livelifegreen.com/landfills- and-th-environmental-effects/
I Care NWT (n.d.). About us. Retrieved from http://icarenwt.ca/about-us
Island Waste Management Corporation. (2014). Retrieved from http://www.iwmc.pe.ca/
Leigh, E. (n.d.). Effects of recycling on humans. Retrieved from http://homeguides.sfgate.com/effects-
recycling-humans-79735.html
Levinson , A. (n.d.). Environmental kuznets curve. Retrieved from
http://www9.georgetown.edu/faculty/aml6/pdfs&zips/PalgraveEKC.pdf
NRDC. (2011, 11 8). An introduction to climate change. Retrieved from
http://www.nrdc.org/globalwarming/climatebasics.asp
R-bloggers. (2012, April 5). Melt. Retrieved from http://www.r-bloggers.com/melt/
Recyc-Quebec. (n.d.). Frequently asked questions. Retrieved from http://www.recycquebec.gouv.qc.ca/client/fr/rubriques/questions.asp?idCat=15
Recycle NB. (n.d.). About us. Retrieved from http://www.recyclenb.com/en/about_us/
Recycling Council of Alberta. (n.d.). About the RCA. Retrieved from https://www.recycle.ab.ca/
Recycling Council of Ontario. (n.d.). History. Retrieved from https://www.rco.on.ca/history
Recycling Council of British Columbia. (n.d.). What is RCBC and what do we do?. Retrieved from
http://www.rcbc.ca/
Saskatchewan Waste Reduction Council. (2011). SWRC - the first 10 years. Retrieved from
http://www.saskwastereduction.ca/about/
United States Environmental Protection Agency. (1998, Jan).Puzzled about recycling’s value? look
beyond the bin. Retrieved from http://www.epa.gov/osw/conserve/downloads/benefits.pdf
Data References
Statistics Canada. (1999, Oct ). Waste management industry survey: Business and government sectors
1996. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x1996001- eng.pdf
Statistics Canada. (2000, Oct 12). Waste management industry survey: Business and government
sectors 1998. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x1998001- eng.pdf
Statistics Canada. (2003, Mar 14). Waste management industry survey: Business and government
sectors 2000. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x2000001- eng.pdf
Statistics Canada. (2004, Sep 24). Waste management industry survey: Business and government
sectors 2002. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x2002001-eng.pdf
Statistics Canada. (2007, Feb 5). Waste management industry survey: Business and government Sectors
2004. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x200400 eng.pdf
Statistics Canada. (2008, Jun 23). Waste management industry survey: Business and government
sectors 2006. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x200600 eng.pdf
Statistics Canada. (2010, Dec 22). Waste management industry survey: Business and government
sectors 2008. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x2010001- eng.pdf
Statistics Canada. (2012, Dec 19). Households that had access to and used recycling programs in
Canada, by province, 1994 and 2007. Retrieved from http://www.statcan.gc.ca/pub/16-001m/2010013/t001-eng.htm
Statistics Canada. (2012, Sept). Households and the environment. Retrieved from
http://www.statcan.gc.ca/pub/11-526-x/11-526-x2011001-eng.pdf
Statistics Canada. (2013, Aug 21). Waste management industry survey: Business and government
sectors 2010. Retrieved from http://www.statcan.gc.ca/pub/16f0023x/16f0023x2013001- eng.pdf
Statistics Canada. (2013, Nov 8). Gross domestic product, expenditure-based, by province and t
erritory. Retrieved from http://www.statcan.gc.ca/tables-tableaux/sum- som/l01/cst01/econ15eng.htm
Statistics Canada. (2013, Nov 25). Population by year, by province and territory (number). Retrieved
from http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo02a eng.htm
Statistics Canada. (2013, Nov 25). Estimates of population, by age group and sex for july 1, Canada,
provinces and territories. Retrieved from http://www5.statcan.gc.ca/cansim/a26?
lang=eng&retrLang=eng&id=0510001&paSer=&pattern=&stByVal=1&p1=1&p2=1&tabMode=dataTable&csid=
Statistics Canada. (2014, Feb 28). Gross domestic product, expenditure-based. Table 380-0064 .
Retrieved from http://www5.statcan.gc.ca/cansim/a26
Appendix
Regressions
Regressions with intercept, year, and province factors included.
Regression 1
Variable
Parameter Estimate
Standard Error
Intercept
0.4
0.56
Income
6.25 **
0.56
Age
0.01
2.14
Government Spending
0.00007
0.00046
Quality
-0.31 ***
0.07
British Columbia
-0.2 **
0.06
Manitoba
-0.09
0.06
New Brunswick
-0.25 **
0.08
Nova Scotia
-0.34 ***
0.07
Ontario
-0.18 **
0.05
Quebec
-0.05
0.07
Saskatchewan
-0.1 *
0.04
1998
0.006
0.03
2000
-0.06
0.04
2002
-0.05
0.05
2004
-0.06
0.07
2006
-0.07
0.08
2008
-0.1
0.1
2010
-0.1
0.1
Regression 2
Variable
Parameter Estimate
Standard Error
Intercept
-18.42 ***
4.05
Income
19.9 **
6.6
Age
0.11 ***
0.02
Consumption
-210.86 *
78.72
Age Organization
0.05 **
0.01
Government
0.08
0.09
Access
0.68 **
0.23
Green
3.35 **
1.02
British Columbia
-1.26**
0.42
Manitoba
1.9 **
0.57
New Brunswick
2.14 **
0.67
Nova Scotia
1.11 ***
0.28
Ontario
NA
NA
Quebec
NA
NA
Saskatchewan
NA
NA
1998
0.072
0.06
2000
-0.04
0.08
2002
-0.05
0.11
2004
-0.16
0.14
2006
-0.14
0.17
2008
-0.18
0.2
2010
-0.22
0.22
Regression 3: Compost to waste ratio
Variable
Parameter Estimate
Standard Error
Intercept
-11.98 ***
2.35
Income
7.92 *
3.63
Age
0.08 ***
0.02
Consumption
-61.63
42.89
Age Organization
0.03 **
0.01
Government
-0.13 *
0.06
Access
0.34 *
0.14
Green
2.15 ***
0.58
British Columbia
-0.92 ***
0.24
Manitoba
1.33 ***
0.33
New Brunswick
1.49 ***
0.38
Nova Scotia
0.61 ***
0.16
Ontario
NA
NA
Quebec
NA
NA
Saskatchewan
NA
NA
1998
-0.07 *
0.03
2000
-0.12 **
0.04
2002
-0.17 **
0.06
2004
-0.22 **
0.07
2006
-0.26 **
0.09
2008
-0.28 *
0.11
2010
-0.31 **
0.11
Regression 4: Robustness Testing, Green alternative
Variable
Parameter Estimate
Standard Error
Intercept
-255.6 **
75.85
Income
19.9 **
6.6
Age
0.11 ***
0.03
Consumption
-210.86 *
78.72
Age Organization
0.34 **
0.11
Government
5.5 **
1.63
Access
0.67 **
0.23
Green
60.07 **
18.24
British Columbia
-2.41 **
0.77
Manitoba
5.5 **
1.67
New Brunswick
3.9 **
1.2
Nova Scotia
-0.64 *
0.29
Ontario
NA
NA
Quebec
NA
NA
Saskatchewan
NA
NA
1998
0.07
0.06
2000
-0.04
0.08
2002
-0.05
0.11
2004
-0.16
0.14
2006
-0.14
0.17
2008
-0.18
0.2
2010
-0.21
0.21
Counter Factual Analysis Calculation
1. Green variable
Model with estimated parameter coefficients.
R/What = -18.42 + 19.9(inc) + 0.11(age) -210.87(consumption) + 0.08(gov) +0.05(ageorg)
+0.68(access) +3.35(green) -0.21(year) + 2.14(prov)
i. New Brunswick: Original data
GreenNew Brunswick = 22%
Log(22) = 3.09
R/What = -18.42 + 19.9(0.04) + 0.11(42.7) -210.87(0.01) + 0.08(1) +0.05(6) +0.68(4.48)
+ 3.35(3.09) -0.21 + 2.14
= -18.42+ 0.8 + 4.7 -2.11 + 0.08 + 0.3 + 3.05 + 10.35 – 0.21 + 2.14
= 0.68
ii. New Brunswick: New green data
Replace New Brunswick's green data with British Columbia's green data
GreenBritish Columba = 37%
Log(37) = 3.61
R/What = -18.42 + 19.9(0.04) + 0.11(42.7) -210.87(0.01) + 0.08(1) +0.05(6) +0.68(4.48)
+ 3.35(3.61) -0.21(year) + 2.14(prov)
= -18.42 + 0.8 + 4.7 -2.11 + 0.08 + 0.3 + 3.05 + 12.1 – 0.21 + 2.14
= 2.43
2. Age of Organization
Model with estimated parameter coefficients
R/What = -18.42 + 19.9(inc) + 0.11(age) -210.87(consumption) + 0.08(gov) +0.05(ageorg)
+0.68(accesslong) +3.35(greenlog) -0.21(year)
i. Quebec: Original data
Age of OrganizationQuebec = 24
R/What = -18.42 + 19.9(0.04) + 0.11(41.2) -210.87(0.012) + 0.08(1) +0.05(24) +0.68(4.61)
+3.35(3.47) -0.21
= -18.42 + 0.8 + 4.53 – 2.53 + 0.08 + 1.2 + 3.13 + 11.62 – 0.21
= 0.2
ii. Quebec: New age of organization
Replace age of Quebec's recycling organization with age of British Columbia's organization.
Age of OrganizationBritish Columbia = 40
R/What = -18.42 + 19.9(0.04) + 0.11(41.2) - 210.87(0.012) + 0.08(1) + 0.05(40) + 0.68(4.61)
+ 3.35(3.47) - 0.21(year)
= -18.42 + 0.8 + 4.53 – 2.53 + 0.08 + 2 + 3.13 + 11.62 – 0.21
=1