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Rpubs link: http://rpubs.com/jpotulic/823249

Introduction

BACKGROUND Waste represents a broad challenge affecting human health, livelihoods, the environment, and prosperity (WorldBank 2021a). Current estimates indicate that the world generates over 2 billion tonnes of municipal solid waste each year and this is projected to grow to 3.4 billions tonnes of waste each year by 2050; a staggering increase of 70 percent over the next 30 years (WorldBank 2021a).

The main drivers of waste generation are rapid urbanization, population growth and economic development with increased prosperity linked to increases in per capita generation of waste (WorldBank 2021a). Waste generation is generally increasing at a faster rate for countries at lower income levels than at high income levels; daily per capita waste generation in high income countries is projected to increase by 19 percent by 2050, compared to 40 percent for low- and middle-income countries (Kaza et al 2018).

By 2050 it is estimated that global waste generation will more than double population growth over the same period (Kaza et al 2018).

Introduction Cont.

IMPORTANT TERMS

OECD: The Organisation for Economic Co-operation and Development (OECD) is an international organisation with 38 member countries and is one of the world’s largest and most trusted sources of comparative socio-economic data (OECD 2021). The OECD works together with governments, policy makers and citizens to find solutions to a range of social, economic and environmental challenges (OECD 2021).

World Bank: The World Bank is a global partnership, consisting of 189 member countries, that works toward sustainable solutions to reduce poverty and build shared prosperity in developing countries (World Bank 2021b)

select OECD Countries: consistent data on household waste generation was available for the following OECD countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Japan, Korea, Lithuania, Luxembourg, Mexico, Netherlands, Norway, Poland, Slovak Republic, Slovenia, Switzerland, United Kingdom, and the United States.

Problem Statement

Problem: With global waste generation projected to increase significantly over the next 30 years this investigation aims to understand:

changes to the amount of household waste generate across different countries over recent years,

whether there are correlations between the amount of household waste generated, other household indicators such as spending and population parameters, and

whether there are significant national or regional differences relating to household waste generation.

Objectives: The purpose of this statistical investigation is to determine if and where there are significant national or regional differences in the amount of waste generated by households across member countries of the OECD. Specifically this investigation will consider whether:

regional averages for household waste generation per capita are comparable across different regions.

Application: Statistics will be used to:

evaluate, describe and summarise key variables,

explore relationships and correlations between key variables, and

test for significant differences across regional averages for household waste generation.

Data

The data for this investigation is from the OECD and World Bank and Our World in Data. A total of five data sets are used for this investigation; references, links and a list of the important variables in each data set follow.

Data Set I: OECD Data, Environment Database - Municipal waste, Generation and Treatment Link: https://stats.oecd.org/viewhtml.aspx?datasetcode=MUNW&lang=en (full indicator data downloaded) This data set presents trends in amounts of municipal, including household waste, and the treatment and disposal method used as reported by member OECD countries.

Data Set II: OECD Data, Economy, Household Accounts, Household Spending Link: https://data.oecd.org/hha/household-spending.htm (full indicator data downloaded) Household spending is the amount of final consumption expenditure made by resident households to meet their everyday needs. It relates to gross domestic product (GDP) and is an essential variable for economic analysis of demand.

Data Set III: OECD Data, Society, Demography, Population Link: https://data.oecd.org/pop/population.htm (full population indicator data downloaded) Population shows the number of people that usually live in a country.

Data Set IV: World Bank, Country classifications by income level, Historical classification by income. Link: http://databank.worldbank.org/data/download/site-content/OGHIST.xlsx (The data needed for this investigation is in Tab 2: Country Analytical History and contains data from 1987 to 2021) The world bank assigns the world’s economies to four income groups and classifications are updated each year and based on Gross National Income (GNI) per capita in current US Dollars (USD) (Hamadeh, Van Rompaey & Metreau 2021).

Data Set V: World Bank, World Region Classification https://ourworldindata.org/world-region-map-definitions (full indicator data downloaded) The World Bank’s classifies countries into seven different regions.These regional definitions have remained constant over time (Beltekian 2021).

Data Cont.

INVESTIGATION VARIABLES The important variables of interest for this investigation across each data set are:

Data Set I: OECD Data, Environment Database - Municipal waste, Generation and Treatment

VAR / Variable: Variable category, type of waste, treatment and disposal method / Variable reported. The variable of interest for this investigation is HOUSEHOLD / Waste from households. Measured in Thousands Tonnes (Kt)

Data Set II: OECD Data, Economy, Household Accounts, Household Spending

Indicator: Type of indicator where HHEXP = Household expenditure.

Subject: The variable of interest for this investigation is TOT = Total household spending.

Measure: Unit of measure for this investigation is MLN_USD = million USD.

Data Set III: OECD Data, Society, Demography, Population

Indicator: Type of indicator where POP = Population.

Subject: Where TOT = Total population.

Measure: Unit of measure where MLN_PER = Million persons.

Data Set IV: World Bank, Country classifications by income level, Historical classification by income

Income classification: where L = Low income, LM = Lower middle income, UM = Upper middle income, and H = High income.

Data Set V: World Bank, World Region Classification

World Region according to the World Bank: classifications are East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa.

Other important variables common across the data sets are:

COU / Country / Location: Country code / Country Name.

Year / Time: Year variable reported / Year or time period.

Value: Reported value.

DATA PREPROCESSING A number of steps were undertaken to preprocess each data set and combine to form a final data set for the statistical analysis.

A detailed outline of the steps and RMarkdown code for data preprocessing , data summaries and visualisations are provided in a separate report.

MISSING DATA As detailed in the seperate data preprocessing report accompanying this presentation the data available for select OECD countries over the investigation period, 2000 – 2019, was missing 12.2% of values. These values were imputed using a Kalman interpolation and a total 67 out of 500 values were imputed.

The countries that had imputed values are: Australia , Belgium, Canada , Czech Republic, Estonia , Germany , Ireland , Japan , Korea , Mexico , Poland, Slovenia , and United States.

Descriptive Statistics and Visualisation

DATA SUMMARIES The pre-processed data was investigated and summaries prepare as follows

The YEARLY SUMMARY indicates that overall for the select OECD countries over the 20 year period from 200 to 2019 the:

Total household waste generated increased by over 27,000 Kt over this period

Total household waste generated per capita increased by 640 kg over this period

Total household waste generation increased by 7.37% over the 20 year period.

The REGIONAL SUMMARY indicates that:

Countries are mostly high income in the Europe and Central Asia region

North America has the highest total and average values across the variables and followed by Europe and Central Asia, East Asia and Pacific and Latin America and Caribbean.

Data set does not have any countries in the following regions: Middle East and North Africa, South Asia, and Sub-Saharan Africa.

The COUNTRY SUMMARY indicates that for the select OECD countries:

The three countries that generated the most, on average across 2000 – 2019, amount household waste were the United States (136,804 Kt), Germany (41,166 Kt) and Japan (33,478 Kt).

The three countries that generated the least, on average across 2000 – 2019, amount of household waste were Estonia (221 Kt), Luxembourg (288 Kt) and Slovenia (609 Kt).

The HOUSEHOLD WASTE PER CAPITA variable is integral to this investigation as it accounts for different population sizes across the different countries. As the Latin America and Caribbean region only has data for one country (Mexico) it will be excluded for the purposes of this summary.

Summary statistics for this variable indicates that North American households, on average, produce the most waste per capita, followed by households in Europe and Central Asia and then East Asian and Pacific Households.

# SUMMARY 1: BY YEAR
OECD_Waste %>% group_by(Year) %>%
  summarise(Tot.HHWASTE=sum(HH_WASTE_Kt), Ave.HHWASTE=mean(HH_WASTE_Kt),
            Tot.HHWASTE.CAP=sum(WASTE_CAP), Ave.HHWASTE.CAP=mean(WASTE_CAP))  -> table1
knitr::kable(table1)

# SUMMARY 2: BY REGION
Region_COU <- OECD_Waste %>% group_by(Region) %>% count() # find No. Countries per region
Region_COU$n <- Region_COU$n/20 # 20 observation per country
Region_SUM <- OECD_Waste %>% group_by(Region) %>%
  summarise(Ave.Inc.Lvl=mfv(INC_LVL), Tot.Pop=sum(TOT_POP_MLN),
            Tot.HHWASTE=sum(HH_WASTE_Kt),
            Ave.HHWASTE=mean(HH_WASTE_Kt), Ave.HHEXP=mean(HH_EXP_MLNUSD),
            Ave.HHWASTE.CAP=mean(WASTE_CAP), Ave.HHEXP.CAP=mean(EXP_CAP))
Summary_REG <- left_join(Region_COU, Region_SUM, by=c("Region"="Region"))
Summary_REG<- rename(Summary_REG, No.Cou=n)# rename column
Summary_REG  -> table2
knitr::kable(table2)

# SUMMARY 3: BY COUNTRY
# Find average Household Waste Generate, Expenditure, and Population for each country (2000-2019)
Country_SUM <- OECD_Waste %>% group_by(Country) %>%
  summarise(Region=mfv(Region), Ave.Inc_Lvl=mfv(INC_LVL), Ave.HHWASTE=mean(HH_WASTE_Kt),
            Ave.HHEXP=mean(HH_EXP_MLNUSD), Ave.Pop=mean(TOT_POP_MLN),
            Ave.HHWASTE.CAP=mean(WASTE_CAP), Ave.HHEXP.CAP=mean(EXP_CAP))
Country_SUM_EXTRACT <- Country_SUM[ ,c(1:2,4:6)] %>% arrange((Ave.HHWASTE), (Ave.HHEXP))
Country_SUM_EXTRACT[c(1:3, 23:25),] -> table3  # Print top and bottom three countries
knitr::kable(table3)

# SUMMARY 4: HOUSEHOLD WASTE GENERATED PER CAPITA ACROSS REGIONS
# Investigate key descriptive statistics over WASTE_CAP variable across regions excluding latin america
OECD_Waste %>% filter(Region!="Latin America and Caribbean") %>% group_by(Region) %>%
  summarise(Min=min(WASTE_CAP), Q1=quantile(WASTE_CAP,probs=.25),
            Median=median(WASTE_CAP), Q3=quantile(WASTE_CAP,probs=.75),
            Max=max(WASTE_CAP), Mean=mean(WASTE_CAP), SD=sd(WASTE_CAP),
            Range=max(WASTE_CAP)-min(WASTE_CAP), IQR=IQR(WASTE_CAP)) -> table4
knitr::kable(table4)

# Calculate 95% confidence interval of mean values of WASTE_CAP across regions
OECD_Waste %>% filter(Region!="Latin America and Caribbean") %>% group_by(Region) %>%
  summarise(Mean=round(mean(WASTE_CAP),2),SD=round(sd(WASTE_CAP,3)),
            n=n(), tcrit=round(qt(p=0.975, df=n-1),3),SE=round(SD/sqrt(n),3),
            `95% CI Lower Bound`=round(Mean-tcrit*SE,2),
            `95% CI Upper Bound`=round(Mean + tcrit * SE,2)) -> table5
knitr::kable(table5)

Decsriptive Statistics Cont.

DATA VISUALISATIONS Changes in Household Waste Generation over time While the yearly summary indicates that households in the OECD countries part of this investigation have, overall, generated more waste over the 20 year period from 2000 to 2019. Figure 1 indicates that some countries have decreased the amount of household waste generated over that time period.

Total Household Waste Generated per Country As the total amount of waste generated varies significantly across each countries Figure 2 examines the relationship between household waste generated, household expenditure and population for the top ten household waste producing countries. The visualisation uses the summary or average data across 2000-2019.

The visualisation shows there is a relationship between household waste generated, household expenditure and population. Generally, countries with large populations generate more waste. Although some countries with larger average populations, such as Mexico and Japan, produce less waste than countries with smaller populations such as Germany. Similarly Poland has a larger population than Australia but produces, on average, less waste. The difference could be due to economic factors or other policy settings which need to be further investigated.

Household Waste Generated and Household Expenditure per Capita for each Country Let’s explore the relationship between Household Waste generated and Household Expenditure taking the population into account. Figure 3 shows the relationship between the average Household Waste generated and Household Expenditure per capita and ranks the select OECD countries by Household Waste generated. The visualisation uses the summary or average data across 2000-2019.

The visualisation shows that the United States, Germany, and Japan do not generate the most waste across the select OECD countries when you take into consideration their respective populations. The top three countries that generated the most, on average across 2000 – 2019, amount household waste per head of population were Denmark, Luxembourg and Australia. Estonia still generates the least amount of household waste even when measured on a per capita basis.

Relationship between Household Waste generate and Household Expenditure The two previous visualisations indicate there may be a correlations between the Household Waste generated and House Expenditure.

Modelling in Figure 4 indicates that there is a positive correlation between the two variables with increased prosperity linked to increases in per capita generation of waste (Kaza et al 2018).

Household Waste Generated per Capita for each Region Figure 5 highlights the regional difference in Household Waste generated per capita and illustrates the 95% confidence interval for the average value for each region.

This visualisation illustrates that the: • East Asian and Pacific region has a smaller sample size and large range of reported values which has resulted in a wider confidence interval for the regional average. • European and Central Asian region has the most number of countries reporting on Household Waste generated and the range of values across the countries is quite large. While the regional average is comparable to the East Asian and Pacific region the confidence interval is narrower due to the larger sample size. • North American region has the highest average value, smallest sample size and narrowest range of reported values.

# CHANGE IN WASTE GENERATED BY COUNTRY BETWEEN 2000 AND 2019
COU_YEAR <- OECD_Waste %>% filter(Year=="2000"|Year=="2019") %>% 
  select(Country, Region, Year, HH_WASTE_Kt)
COU_YEAR2 <-  pivot_wider(COU_YEAR, names_from="Year", values_from="HH_WASTE_Kt")
COU_YEAR2 <- COU_YEAR2 %>% mutate(DIFF=`2019`-`2000`, CHG=(DIFF/`2000`)*100) %>% 
  arrange(desc(CHG))
COU_YEAR2$Rank <- factor(c(1:25), labels=c(COU_YEAR2$Country), ordered=TRUE)

ggplot(data=COU_YEAR2, aes(x=Rank, y=CHG, fill=Region)) +
  geom_bar(stat="identity") + coord_flip() +
  theme(axis.ticks=element_blank(), axis.title.y=element_blank(), axis.title.x=element_blank()) +
  labs(title="Figure 1: Total Household Waste Generated (Kt)",
       subtitle="Percentage change 2000 to 2019",
       caption="for select OECD countries")

# WASTE GENERATED BY COUNTRY
Country_SUM10 <- Country_SUM_EXTRACT[16:25, ]
Country_SUM10$Rank <- factor(c(1:10), labels=c(Country_SUM10$Country), ordered=TRUE)
Country_SUM10 <- Country_SUM10 %>% select(Rank, Region:Ave.Pop)
Country_TOP10 <- pivot_longer(Country_SUM10, names_to="Ave.Variables", values_to="Values", cols=3:5)
Country_TOP10$Ave.Variables <- factor(c(Country_TOP10$Ave.Variables),
                                         levels=c("Ave.HHWASTE","Ave.HHEXP","Ave.Pop"),
                                         labels=c("Average Household Waste Generated (Kt)",
                                         "Average Household Expenditure (USD million)",
                                         "Average Total Population (millions)"),
                                         ordered=TRUE)

ggplot(data=Country_TOP10, aes(x=Rank, y=Values, fill=Region)) +
  geom_bar(stat="identity") + coord_flip() +
  facet_grid(.~Ave.Variables, scales="free") +
  theme(axis.ticks=element_blank(), axis.title.y=element_blank(), axis.title.x=element_blank()) +
  labs(title="Figure 2: Top 10 ranked OECD countries for waste generation (2000-2019)",
       caption="for select OECD countries")

# HOUSEHOLD WASTE AND EXPENDITURE PER CAPITA FOR EACH COUNTRY
Country_CAP <- Country_SUM[ ,c(1:2,7:8)] %>% arrange((Ave.HHWASTE.CAP), (Ave.HHEXP.CAP))
Country_CAP$Rank <- factor(c(1:25), labels=c(Country_CAP$Country), ordered=TRUE)
Country_CAP <- Country_CAP %>% select(Rank, Region:Ave.HHEXP.CAP)
Country_CAP2 <- pivot_longer(Country_CAP, names_to="Ave.Variables", values_to="Values", cols=3:4)
Country_CAP2$Ave.Variables <- factor(c(Country_CAP2$Ave.Variables), levels=c("Ave.HHWASTE.CAP","Ave.HHEXP.CAP"), labels=c("Average Household Waste Generated per capita (kg)", "Average Household Expenditure per capita (USD)"), ordered=TRUE)
                                     
ggplot(data=Country_CAP2, aes(x=Rank, y=Values, fill=Region)) +
  geom_bar(stat="identity") + coord_flip() +
  facet_grid(.~Ave.Variables,scales = "free") +
  theme(axis.ticks=element_blank(), axis.title.y=element_blank(), axis.title.x=element_blank()) +
  labs(title="Figure 3: Average Household Waste Generated and Expenditure per Capita (2000-2019)", caption="for select OECD countries")

# CORRELATION BETWEN HH WASTE and HH EXPENDITURE per CAPITA
OECD_FILTER <- OECD_Waste %>% filter(Region!="Latin America and Caribbean") %>% group_by(Region)

ggplot(data=OECD_Waste, aes(y=WASTE_CAP, x=EXP_CAP, colour=Region)) +
  geom_point() +
  theme(axis.ticks=element_blank()) +
  labs(title="Figure 4: Household Waste Generated and Expenditure per Capita (2000-2019)", caption="for select OECD countries",
       x="Household expenditure per capita (USD)",
       y="Household waste generated per capita (kg)")

# HOUSEHOLD WASTE GENERATED PER CAPITA ACROSS REGIONS
ggplot(data=OECD_FILTER, aes(x=Region, y=WASTE_CAP)) +
  geom_dotplot(binaxis="y", stackdir="center", dotsize=1/2, alpha=.25, colour="darkolivegreen3") +
  theme(axis.ticks=element_blank(), axis.title.x=element_blank()) +
    labs(title="Figure 5: Household Waste Generated per Capita (2000-2019) across Regions",caption="for select OECD countries",
       y="Household waste generated per capita (kg)") +  
  stat_summary(fun.y="mean", geom="point", colour="darkred") +
  stat_summary(fun.data="mean_cl_normal", colour="darkred", geom="errorbar",width=.2)

Hypothesis Testing

The summary statistics and boxplot visualisation presented previously highlighted that regional averages for household waste generation per capita between the East Asian and Pacific and European and Central Asian regions are comparable.

The difference between the average values across the East Asian and Pacific region and European and Central Asia region is:

East Asian and Pacific (367.52) - European and Central Asia (369.85) = -2.33

As part of this statistical investigation a hypothesis test will be conducted to determine whether :

East Asia and Pacific region and European and Central Asia region have the same average household waste generation per capita.

HYPOTHESIS TEST The null hypothesis is that the difference between the two independent population means is 0. The alternative hypothesis is that the difference between the two independent population means is not equal to 0.

The hypothesis test is: \[H_0: \mu_1 = \mu_2 \] \[H_A: \mu_1 \ne \mu_2\] A two-sample -test will be conducted to determine whether the difference between the two average values across these regions is statistically significant.

CHECKING ASSUMPTIONS

Assumption 1: Populations being compared are independent of each other. TRUE. The regional definitions used as part of the data have remained constant over time and countries have not been reclassified over the time period investigated.

Assumption 2: Data for both populations have equal variance. TRUE. Equal variance is tested using the Levene’s test which reports a p-value that is compared to the standard 0.05 significance level.

The p-value for the Levene’s test of equal variance for Household Waste per capita between East Asia and Pacific and European and Central Asian was p = 0.2301.

As the p-value is > 0.05 we fail to reject that the variance are equal; we can assume that they are equal.

Assumption 3: For small samples, the data for both populations are normally distributed. Not Required, sample size >30. East Asia and Pacific: n = 60, not normally distributed Europe and Central Asia: n=380, normally distributed.

#Create filtered data sets as needed
OECD_Region <- OECD_Waste %>% filter(Region=="East Asia and Pacific"|Region=="Europe and Central Asia") %>%
  select(Country, Region, WASTE_CAP)
Region_EAP <- OECD_Region %>% filter(Region=="East Asia and Pacific") 
Region_ECA <- OECD_Region %>% filter(Region=="Europe and Central Asia")


# Assumption 2: Check for population variance using Levene's test
leveneTest(WASTE_CAP~Region, data=OECD_Region) 

# Assumption 3: Check for data normality across regions
Region_EAP$WASTE_CAP %>% qqPlot(dist="norm") # not normally distributed

## [1] 22 25

Region_ECA$WASTE_CAP %>% qqPlot(dist="norm") # normal distribtion

## [1] 165 146

# Perform a two-sample t-test assuming equal variance and a two-sided hypothesis test
t.test(WASTE_CAP~Region, data=OECD_Region, var.equal=TRUE, alternative="two.sided")

## 
##  Two Sample t-test
## 
## data:  WASTE_CAP by Region
## t = -0.13662, df = 438, p-value = 0.8914
## alternative hypothesis: true difference in means between group East Asia and Pacific and group Europe and Central Asia is not equal to 0
## 95 percent confidence interval:
##  -35.84818  31.18827
## sample estimates:
##   mean in group East Asia and Pacific mean in group Europe and Central Asia 
##                              367.5212                              369.8512

Hypthesis Testing Cont.

HYPOTHESIS TEST RESULTS A two-sample t-test was used to test for a significant difference between the average values of Household Waste generated per capita by countries in the East Asia and Pacific and Europe and Central Asia regions.

The reported values for Household Waste generated per capita across the East Asia and Pacific region exhibited evidence of non-normality, as modelled in the normal Q-Q plot. Due to the large sample size for this group (n=60 > 30) the central limit theorem principles can be applied and the t-test undertaken. The Levene’s test of homogeneity of variance indicated that equal variance could be assumed.

The results of the two-sample t-test assuming equal variance found a statistically significant difference between the mean Household Waste per capita values of the East Asia and Pacific region and Europe and Central Asia region,t(df = 438)=−0.136 p = 0.89, 95% CI [-35.84818, 31.18827].

The results of the investigation suggest that the Europe and Central Asia region has a significantly higher average Household Waste per capita value than the East Asia and Pacific region.

Discussion

KEY FINDINGS With global waste generation projected to increase significantly over the next three decade this investigation examined whether there are significant national or regional differences in the amount of waste generated by households across member countries of the OECD.

The key findings from this investigation are that:

Total household waste generated increased by by 7.37% to over 27,000 Kt and 640kg per capita over the 20 year period investigated. While, overall there was a general increase in the amount of household waste generated, some countries have decreased the amount of household waste generated over the time period.

The North American region has the highest total and average values across the variables investigation and, on average, produce the most waste per capita.

There is a relationship between household waste generated, household expenditure and population. Generally, countries with large populations generate more waste.

Modelling of household waste and expenditure per capita data indicates a positive correlation between the two variables with increased prosperity linked to increases in per capita generation of waste.

Initial modelling of household waste per capita data across the different regions indicated average across two regions may be comparable. Further hypothesis testing suggest that the Europe and Central Asia region has a significantly higher average Household Waste per capita value than the East Asia and Pacific region.

FURTHER INVESTIGATION Waste management data are critical to creating policy and planning across both national and regional contexts.

Identifying countries or regions that are significantly different to their counterparts assists with the next stage of this research which will examine social, economic, and other policy settings that have a positive impact on the amount of waste generated by households.

The aim of this investigation and any future research is to identify ways to reduce global waste generation by 2050so that it’s growth does double population growth over the same period.

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