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#GHG emissions as a result of production of infrastructure assets and greenhouses value per emis input proportion#
head(Infrastructure_Economic_Accounts_CAN)ESG consideration for sustainable economic development in Canada
Executive Summary
Economic performance and greenhouse gas emissions are highly related.
Canada has made significant efforts to transition to cleaner energy sources, with a decreasing trend in electricity generation from traditional power generation methods such as coal-fired.
ESG factors can be used to measure economic performance to help make better decisions.
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#PPPGDP in Canada#
head(PPPGDP_CAN_long)Introduction
Canada, a country with a large land area, has a large reserve of natural resources. Canada is located at the very top of North America, north of the United States. It is bordered by the Pacific Ocean on the west and the Atlantic Ocean on the east. It borders the United States in the south and the Arctic Ocean in the north. Canada is about half the size of North America. Canada has a population of 36 million, mostly concentrated on the border between the United States and Canada. Canada is sparsely populated with only 10 people per square mile (4 people per square kilometer). Canada has 10 provinces and 3 territories: Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Nunavut , Ontario, Prince Edward Island, Quebec, Saskatchewan, Northwest Territories and Yukon. Canada’s overall northerly location limits the development of agriculture to a certain extent because the high latitudes have short sunshine hours and low temperatures, which do not meet the growing conditions of most crops. Canada has a reputation for producing crude oil, mainly from the western province of Alberta. In addition, Canada has extensive sedimentary rock areas and has proven natural gas reserves of 280 million cubic meters, about 3% of the world’s reserves.
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# Get the map data for Canada at the province level
canada_provinces <- ne_states(country = "canada", returnclass = "sf")
province_colors <- c("British Columbia" = "#78BE20",
"Alberta" = "#ED8B00",
"Saskatchewan" = "#74ADD1",
"Manitoba" = "#FFC407",
"Ontario" = "#0085CA",
"Quebec" = "#005EB8",
"New Brunswick" = "#53284F",
"Nova Scotia" = "#006E50",
"Prince Edward Island" = "#FFAA1D",
"Newfoundland and Labrador" = "#003F87",
"Yukon" = "#8A8D8F",
"Northwest Territories" = "#72757B",
"Nunavut" = "#00BFFF")
# Plot the map data and add province names as labels
ggplot(canada_provinces) +
geom_sf(aes(fill = name), color = "white") +
geom_sf_text(aes(label = name), size = 2.5, color = "black", fontface = "bold", check_overlap = TRUE, nudge_y = 0.3) +
ggtitle("Table 1: Map of Canada") +
scale_fill_manual(values = province_colors) +
theme_bw() +
theme(legend.text = element_text(size = 6))
Electricity Production and consumption in Canada
There is no doubt that population growth leads to economic prosperity, but it also puts pressure on resource consumption. The estimated total population of Canada as of January 1, 2023 is 39,556,248. From 1990 to 2021, Canada’s population increased by 38%. According to the International Energy Agency’s World Energy Outlook 2021, the average Canadian consumed more than 300 gigajoules of energy per person in 2020, two times higher than the world average, ranking among the highest in the world. By 2030, Canada’s electricity demand is expected to fall below an average of 300 gigajoules per person per year, but it will still be one of the world’s highest energy users. A report released by British Petroleum said that oil and natural gas account for 61% of Canada’s energy supply, hydroelectric power accounts for 25%, nuclear power accounts for 6%, renewable energy sources such as wind and solar power account for 4%, and another 3.7% comes from coal-fired. Canada passed a law that requires net-zero greenhouse gas emissions by 2050, but whether the target can be met is still unknown.
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Population_CAN_long_line <- ggplot(Population_CAN_long, aes(x=Year, y=Population)) +
geom_line(aes(group=1),show.legend = FALSE) +
geom_point(data = Population_CAN_long[c(1, nrow(Population_CAN_long)),], aes(x=Year, y=Population), size=3) +
labs(title="Table 2: Population in Canada", y="Population") +
theme_minimal() +
scale_y_continuous(labels=scales::comma) +
theme(axis.text.x = element_blank())
Population_CAN_long_plotly <- ggplotly(Population_CAN_long_line, tooltip=c("x", "y")) %>%
layout(title = list(text = "<b>Table 2: Population in Canada</b>", font = list(size = 20)),
yaxis = list(title = "Population"),
margin = list(l = 50, r = 50, t = 70, b = 50),
annotations = list(x = 1, y = 0, showarrow = FALSE, text = "Source: World Bank | Author: Runnan Cao", xref='paper', yref='paper', font = list(size = 12)))
Population_CAN_long_plotlyBack in 1995, burning coal was the major way how Canada generate electricity for total industry and utilities use. After 2000, natural gas gradually took the stage and became the second major contributor to electricity generation. In 2015, natural gas usage for electricity first surpassed coal and became a major resource and this trend continues to improve as less and less coal is burnt. It can be said that Canada put emphasis on climate change issues and made efforts on energy transitions. In addition, dependence on heavy fuel oil had been reduced by a large extent to almost not even 1% in 2021 (table 3).
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Electricity_generated_filtered <- Electricity_generated %>%
filter(Fuel.type != "Total all fuels")
pp <- ggplot(data = Electricity_generated_filtered[Electricity_generated_filtered$GEO == "Canada" & Electricity_generated_filtered$Source == "Total industry and utilities",],
aes(x = Year, y = VALUE, fill = Fuel.type)) +
geom_col() +
labs(title = "Table 3: Electricity generated based on fuel type", caption = "Source: Statistic Canada | Author: Runnan Cao") +
scale_y_continuous(name = "Gigawatt hours") +
theme(legend.position = "none")
Electricity_plotly <- ggplotly(pp)
Electricity_plotly %>%
layout(
annotations = list(
x = 1,
y = -0.11,
xref = "paper",
yref = "paper",
text = "Source: Statistic Canada | Author: Runnan Cao",
showarrow = FALSE,
font = list(size = 10) # font size set to 10
)
)The overall electricity generated in Canada saw a declining trend (table 4). This could be explained by several reasons. Over the past three years, due to Covid-19, industrial and commercial electricity demand and activities fall, resulting in reduced electricity production. It could be because the reason government adopts measures to encourage energy conservation is to promote the development and use of renewable energy. Meanwhile, with the development of science and technology, new clean energy technologies (such as wind energy, solar energy, etc.) are being used more and more widely, which may lead to a decline in the market share of traditional power generation methods such as coal-fired, thereby reducing electricity production.
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# Create a subset of the data frame with the desired variables
df_Canada <- Electricity_generated[Electricity_generated$GEO == "Canada" & Electricity_generated$Source == "Total industry and utilities" & Electricity_generated$Fuel.type == "Total all fuels", c("Year", "VALUE")]
ss<-ggplot(df_Canada, aes(x = Year, y = VALUE)) +
geom_col()+
labs(title="Table 4: Electricity generated in total",caption = "Source: Statistic Canada | Author: Runnan Cao")+
scale_y_continuous(name = "Gigawatt hours")+
theme_bw(11)
sector_plotly <- ggplotly(ss)
sector_plotly %>%
layout(
annotations = list(
x = 1,
y = -0.11,
xref = "paper",
yref = "paper",
text = "Source: Statistic Canada | Author: Runnan Cao",
showarrow = FALSE,
font = list(size = 10) # font size set to 10
)
)Except the electricity, energy can also be used in multiple ways. The goods and services that use electricity the most in Canada are environmental goods, clean electricity, clean technology services. There are some other usage can be told from table 5. Electricity could be used in biofuels and primary goods. For example,e electricity can be used to power a biofuel machinery such as electric motors which used power pumps. Electricity is also needed for the production for various primary goods, such as building bricks, concrete, metals, chemicals. Some environmental products, for instance, recycling equipment such as compactors use electricity to process waste materials and turn them into new products also needs electricity.
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Energy_use_CAN_FLow_filtered <- Energy_use_CAN_FLow %>%
filter(!`Good.and.Services` %in% c("Total, environmental and clean technology products", "Total, environmental products", "Waste management and remediation services", "Total, clean technology products","Waste and scrap goods","Total, clean technology products","Scientific and research and development services"))
ggplot(Energy_use_CAN_FLow_filtered, aes(x = Year, y = VALUE, group = `Good.and.Services`, color = `Good.and.Services`)) +
geom_line() +
facet_wrap(~ `Good.and.Services`, ncol = 3) +
labs(x = "Year", y = "Value", title = "Table 5: Energy Used by Good and Services") +
scale_y_continuous(limits = c(0, 150000), breaks = seq(0, 75000, by = 25000), name = "Terajoules") +
theme_bw() +
theme(strip.text = element_text(size = 7)) +
guides(color = FALSE) +
labs(strip = "Good and Services", caption = "Source: Statistic Canada | Author: Runnan Cao")
Explore the emission impact on economic performance.
Over the past 20 years, the GDP in Canada has grown steadily, although there have been some fluctuations during economic downturns. Canada is also a country that relies on foreign exports, with trade in goods and services accounting for nearly two-thirds of GDP, with imports contributing 33% and exports contributing 32%. In this analysis, an alternative approach involves taking Purchase Power Parity-adjusted GDP will be discussed suggested by PCAF to improve the comparison between the actual economy sizes and attribution of emissions. According to the data from the World Bank, the PPPGDP in Canada was $1.38 trillion in 2002 and grew to $1.96 trillion in 2021, representing a growth of approximately 42% over the 20-year period. It is worth noting that the COVID-19 pandemic has had a significant impact on the Canadian economy, leading to a contraction in the PPPGDP in 2020.
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PPPGDP_CAN_long$Year <- as.integer(PPPGDP_CAN_long$Year)
PPPGDP_CAN_long$PPPGDP <- as.numeric(PPPGDP_CAN_long$PPPGDP)
sss <- subset(PPPGDP_CAN_long, Country.Name == "Canada")
sss <- ggplot(data = sss, aes(x = Year, y = PPPGDP)) +
geom_line(size=1.5) +
transition_reveal(Year) +
labs(title = "Table 6: PPPGDP Changes over years in Canada", caption = "Source: World Bank | Author: Runnan Cao")
animate(sss)
Table 7 illustrates the emissions in Canada from 1990 to 2019 and it can be told that in almost every year, energy occupies the largest portion as the major source of emissions, followed by electricity and transportation. Even exclude the influence by land use change and forestry, the overall GHG emission saw an increasing trend in recent years (Table 8). Given the fact that Canada is committed to achieving net-zero emissions by 2050 to avert the worst impact of climate change, there is still much work to be done.
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Emissions_by_sector_long$Year <- factor(Emissions_by_sector_long$Year)
aaa <- ggplot(Emissions_by_sector_long[Emissions_by_sector_long$Sector != "Total excluding LUCF", ], aes(x = Year, y = Emissions, fill = Sector)) +
geom_bar(stat = "identity") +
labs(title = "Table 7: GHG emissions by sector", caption = "Source: Climate Watch | Author: Runnan Cao") +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none") +
scale_x_discrete(breaks = levels(factor(seq(1990, 2020, 5))))
aaa_plotly <-ggplotly(aaa)
aaa_plotly%>%
layout(
annotations = list(
x = 1,
y = -0.11,
xref = "paper",
yref = "paper",
text = "Source: Climate Watch | Author: Runnan Cao",
showarrow = FALSE,
font = list(size = 10)
)
)Show the code
Emissions_by_sector_long$Year <- factor(Emissions_by_sector_long$Year)
bbb <- ggplot(Emissions_by_sector_long[Emissions_by_sector_long$Sector == "Total excluding LUCF", ], aes(x = Year, y = Emissions)) +
geom_bar(stat = "identity") +
labs(title = "Table 8: GHG emissions",
x = "Year",
y = "Emissions (MtCO2e)",
caption = "Source: Climate Watch | Author: Runnan Cao") +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none") +
scale_x_discrete(breaks = levels(factor(seq(1990, 2020, 5))))
bbb_plotly <- ggplotly(bbb)
bbb_plotly %>%
layout(
annotations = list(
x = 1,
y = -0.11,
xref = "paper",
yref = "paper",
text = "Source: Climate Watch | Author: Runnan Cao",
showarrow = FALSE,
font = list(size = 10)
)
)There is a correlation between PPPGDP and GHG Emissions in Canada (Table 9). This relationship is largely due to the fact that economic growth is associated with an increase in energy consumption, which in turn leads to increased GHG emissions. It is important for Canada to continue to implement policies and initiatives that promote sustainable economic growth and reduce GHG emissions, while also ensuring that vulnerable communities and ecosystems are protected from the impacts of climate change.
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PPPGDP_CAN_long_ccc <- subset(PPPGDP_CAN_long, Country.Name == "Canada")
Emissions_by_country_ccc <- subset(Emissions_by_sector_long, Sector == "Total excluding LUCF")
PPPGDP_CAN_long_ccc$PPPGDP <- as.numeric(PPPGDP_CAN_long_ccc$PPPGDP)
merged_data_ccc <- merge(PPPGDP_CAN_long_ccc, Emissions_by_country_ccc, by = c("Year"))
correlation <- cor(merged_data_ccc$PPPGDP, merged_data_ccc$Emissions)
ggplot(merged_data_ccc, aes(x = PPPGDP, y = Emissions)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Table 9: Correlation between PPPGDP and GHG Emissions in Canada",
x = "PPPGDP (USD)",
y = "GHG Emissions (MtCO2e)", caption ="Source: Climate Watch | Author: Runnan Cao" ) +
annotate("text", x = max(merged_data_ccc$PPPGDP) - 500, y = max(merged_data_ccc$Emissions) - 10,
label = paste0("Correlation: ", round(correlation, 2)))
ESG consideration
In order to better measure how environmental, social and governance factors being included in Canada, next part, a regression analysis will be made to explore a potential ESG measurement on economy. In this study, I investigate the relationship between PPPGDP and several other independent variables. PPPGDP is the dependent variable. and other independent variables we use in this study are listed below.
Environment
GHG emissions is the major concern for environment, thus, it will be added into the regression as an independent variable.
Governance
Union coverge is the third independent variable. Union coverage, which refers to the extent to which employees are represented by a labor union. It protect worker’s rights and improve employee engagement. Canada is also doing a great job in promoting employee’s job qualities (Table 11)
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year <- 1997:2022
Union_coverage<-c(3834.9, 3841.6, 3862.6, 4004.6, 4093.5, 4175.6, 4268.5, 4272.0, 4360.7, 4390.7, 4442.3, 4453.6, 4440.9, 4498.2, 4502.1, 4598.0, 4648.7, 4562.4, 4629.3, 4608.3, 4712.4, 4722.6, 4861.8, 4749.2, 4998.7, 5159.7)
uc<-data.frame(year=as.character(year),`Union_coverage`=Union_coverage)Show the code
# Convert year to a factor variable
uc$year <- factor(uc$year)
# Create ggplot object
uc_plot <- ggplot(uc, aes(x=year, y=Union_coverage)) +
geom_line() +
geom_point() +
labs(title="Table 11: Union Coverage over Time",
x="Year",
y="Union Coverage")+
scale_x_discrete(breaks = levels(factor(seq(1990, 2020, 5))))
uc_plotly <- ggplotly(uc_plot, tooltip=c("x","y"))
uc_plotly %>%
layout(
annotations = list(
x = 1,
y = -0.11,
xref = "paper",
yref = "paper",
text = "Source: Statistic Canada | Author: Runnan Cao",
showarrow = FALSE,
font = list(size = 10) # font size set to 10
)
)Regression
The first step is to check whether the dependent variable meet the regression assumption. Table 12 demonstrates that all of the variables follow a normal distribution that obey the rules of normality. Table 13 demonstrates that the relationship between the independent and dependent variables are linear, testifying by scatter plots. Table 14 demonstrates that the relationship between independent variables. The darker the color, the higher the correlation. Our data aren’t too highly correlated at this stage. After finish these steps, we will run the regression.
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histogram_data <- data.frame(
Union_coverage = ESG_consideration$Union_coverage,
GHG_emissions = ESG_consideration$GHG_emissions,
gender_wage_ratio = ESG_consideration$gender_wage_ratio,
PPPGDP = ESG_consideration$PPPGDP
)
# create a histogram with facets for each variable
ggplot(gather(histogram_data), aes(value)) +
geom_histogram(bins = 5) +
facet_wrap(~ key, scales = "free") +
labs(x = "", y = "Frequency",caption="Source: Statistic Canada & Climate Watch | Author: Runnan Cao") +
ggtitle("Table 12: Histograms of ESG Variables") +
theme_bw() +
theme(plot.title = element_text(hjust = 1))
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linearity <- ggplot(data = ESG_consideration, aes(x = PPPGDP)) +
geom_point(aes(y = Union_coverage), color = "pink") +
geom_smooth(aes(y = Union_coverage), method = "lm", se = FALSE, color = "pink") +
labs(x = "PPPGDP", y = "Union_coverage") +
theme_bw()+
theme(axis.text.x = element_blank())
linearity2 <- ggplot(data = ESG_consideration, aes(x = PPPGDP)) +
geom_point(aes(y = gender_wage_ratio), color = "purple") +
geom_smooth(aes(y = gender_wage_ratio), method = "lm", se = FALSE, color = "purple") +
labs(x = "PPPGDP", y = "gender_wage_ratio") +
theme_bw()+
theme(axis.text.x = element_blank())
linearity3 <- ggplot(data = ESG_consideration, aes(x = PPPGDP)) +
geom_point(aes(y = GHG_emissions), color = "yellow") +
geom_smooth(aes(y = GHG_emissions), method = "lm", se = FALSE, color = "yellow") +
labs(x = "PPPGDP", y = "GHG_emissions") +
theme_bw()+
theme(axis.text.x = element_blank())
# Combine the three plots into a single graph
plot_combined <- plot_grid(linearity, linearity2, linearity3, ncol = 3)
title <- ggdraw() +
draw_label("Table 13: Linearity of ESG variables", size = 15)
# Add a caption
caption <- ggdraw() +
draw_label("Source: Statistic Canada & Climate Watch | Author: Runnan Cao", size = 10)
# Combine the title, plot, and caption into a single graph
plot_final <- plot_grid(title, plot_combined, caption, ncol = 1, rel_heights = c(0.1, 0.8, 0.1))
# Display the plot
plot_final
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corr_matrix <- cor(ESG_consideration[,c("GHG_emissions", "Union_coverage", "gender_wage_ratio")], method = "kendall")
melted_corr <- melt(corr_matrix)
names(melted_corr) <- c("variable1", "variable2", "correlation")
ggplot(melted_corr, aes(variable1, variable2, fill = correlation)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "pink", mid = 1, midpoint = 0) +
theme_minimal() +
labs(title = "Table 14: Correlation Matrix of ESG Variables", fill = "Correlation", caption="Source: Statistic Canada & Climate Watch | Author: Runnan Cao")+
guides(fill=FALSE)
The linear result is: PPPGDP = -8871251570208 + 2346605454GHG_emissions + 202179878Union_coverage + 9126131325729*gender_wage_ratio. This means that one unit increase in GHG_emission will cause PPPGDP to increase by 2,346,605,454. Because the coefficient is too large, we could try to take the log of the variables. Table 15 is the scatter matrix which can help us identify any patterns in the data frame. It indicates that the variables are all positively related.
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ESG_regression <- lm(PPPGDP ~ GHG_emissions + Union_coverage + gender_wage_ratio, data = ESG_consideration)
summary(ESG_regression)
Call:
lm(formula = PPPGDP ~ GHG_emissions + Union_coverage + gender_wage_ratio,
data = ESG_consideration)
Residuals:
Min 1Q Median 3Q Max
-1.254e+11 -4.763e+10 -2.506e+09 4.672e+10 9.791e+10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.871e+12 1.090e+12 -8.137 1.3e-07 ***
GHG_emissions 2.347e+09 1.063e+09 2.207 0.039778 *
Union_coverage 2.022e+08 2.064e+08 0.979 0.339642
gender_wage_ratio 9.126e+12 1.919e+12 4.754 0.000138 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.466e+10 on 19 degrees of freedom
Multiple R-squared: 0.9694, Adjusted R-squared: 0.9646
F-statistic: 200.9 on 3 and 19 DF, p-value: 1.446e-14Show the code
pairs.panels(ESG_consideration[,c("PPPGDP","GHG_emissions","Union_coverage","gender_wage_ratio")],
main="Table 15: Scatterplot Matrix for ESG Considerations")
The result after taking the log: PPPGDP = 8.0246 + 0.7829log(GHG_emissions) + 1.8432log(Union_coverage) + 4.2158*log(gender_wage_ratio). It means that one percentage increase in GHG_Emissions will increase PPPGDP by 0.7829%. To check the accuracy of the model. we create Table 16. From the graph it can be told that no obvious outlines presented. Thus we can say that the model fits well.
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# Log-transform the variables
ESG_consideration$log_PPPGDP <- log(ESG_consideration$PPPGDP)
ESG_consideration$log_GHG_emissions <- log(ESG_consideration$GHG_emissions)
ESG_consideration$log_Union_coverage <- log(ESG_consideration$Union_coverage)
ESG_consideration$log_gender_wage_ratio <- log(ESG_consideration$gender_wage_ratio)
# Fit the linear regression model
ESG_regression_log <- lm(log_PPPGDP ~ log_GHG_emissions + log_Union_coverage + log_gender_wage_ratio, data = ESG_consideration)
# View the model summary
summary(ESG_regression_log)
Call:
lm(formula = log_PPPGDP ~ log_GHG_emissions + log_Union_coverage +
log_gender_wage_ratio, data = ESG_consideration)
Residuals:
Min 1Q Median 3Q Max
-0.07075 -0.03241 0.00340 0.03186 0.06455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.0246 3.3928 2.365 0.028814 *
log_GHG_emissions 0.7829 0.4741 1.651 0.115074
log_Union_coverage 1.8432 0.5600 3.291 0.003840 **
log_gender_wage_ratio 4.2158 1.0000 4.216 0.000468 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04235 on 19 degrees of freedom
Multiple R-squared: 0.9798, Adjusted R-squared: 0.9766
F-statistic: 307.5 on 3 and 19 DF, p-value: 2.823e-16Show the code
plot(ESG_regression_log$fitted.values, ESG_consideration$log_PPPGDP,
xlab = "Predicted values", ylab = "Actual values",
main = "Table 16: Scatterplot of Predicted vs. Actual")
abline(0, 1, col = "pink")
mtext("Author: Runnan Cao", side = 1, line = 4,cex=0.8)
Conclusion
ESG factors can positively impact economic performance. Canada is a country with a vast land area and abundant natural resources. The population growth in Canada has put pressure on resource consumption, leading to high energy consumption per capita. The country has made significant efforts to transition to cleaner energy sources, with a decreasing trend in electricity generation from traditional power generation methods such as coal-fired. Despite these efforts, energy and transportation remain the largest sources of greenhouse gas emissions in Canada. The country has committed to achieving net-zero greenhouse gas emissions by 2050, and continued efforts will be necessary to reach this goal.
To assess the relationship between economic performance and ESG factors, three variables have been chosen, namely GHG emissions, union coverage ratio and gender wage ratio to each represent a factor of ESG indicators. Regressions have been run to testify the relationship between PPPGDP and three variable and positive relationship have been demonstrated.
Reference:
Geography. (2016, November 17). The Canada Guide; JJ McCullough. https://thecanadaguide.com/places/geography/
Canada.(n.d.). IEA. Retrieved from https://www.iea.org/countries/canada
Yuetong., H. (n.d.). Development trends of world energy. Diva-portal.org. Retrieved April 25, 2023, from https://www.diva-portal.org/smash/get/diva2:225295/FULLTEXT01.pdf
Partnership for Carbon Accounting Financials. (2021, November). https://carbonaccountingfinancials.com/files/consultation-2021/pcaf-draft-new-methods-public-consultation.pdf
Social
The second independent variable is gender wage ratio. The gender wage ratio refers to the difference in earnings between women and men. It is important because it measures gender inequality in the labor market. Gender wage ratio is a key indicator of a company’s commitment to diversity and inclusion and it can be an important driver of employee engagement and motivation. Canada is doing quite well on improving gender equality as its gender wage ratio keeps climbing (Table 10).
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