PRESENTATION AND REGRESSION ANALYSIS OF POPULATION,

ECONOMIC AND ENVIRONMENTAL DATA

Rajat Nag (15202684)

UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4,

Ireland.

Abstract

In this report the population, GDP, energy consumption, and land surface area of 23 countries have

been presented and analyzed through regression method. The countries selected cover all continents

and include both developed and developing countries. This report gives a comprehensive overview of

the different countries in the world based on energy consumption, future energy requirements of

developing and developed countries and changes in population.

Terminology and brief introduction

Population density is midyear population divided by land area in square kilometers. Population is

based on the de facto definition of population [The de facto population is a concept under which

individuals (or vital events) are recorded (or are attributed) to the geographical area where they were

present (or occurred) at a specified time]. It counts all residents regardless of legal status or

citizenship--except for refugees not permanently settled in the country of asylum, who are generally

considered part of the population of their country of origin. Land area is a country's total area,

excluding area under inland water bodies, national claims to continental shelf, and exclusive

economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.

Gross domestic product (GDP) is a measure of the size of an economy. It is defined as "an aggregate

measure of production equal to the sum of the gross values added of all resident, institutional units

engaged in production (plus any taxes, and minus any subsidies, on products not included in the value

of their outputs)" by the Organization for Economic Co-operation and Development (OECD).

The GDP is one of the primary indicators used to gauge the health of a country's economy. It

represents the total dollar value of all goods and services produced over a specific time period; you

can think of it as the size of the economy. Measuring GDP is complicated (which is why we leave it to

the economists), but at its most basic, the calculation can be done in one of two ways: either by adding

up what everyone earned in a year (income approach), or by adding up what everyone spent

(expenditure method). Logically, both measures should arrive at roughly the same total.

Energy use refers to use of primary energy before transformation to other end-use fuels, which is

equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to

ships and aircraft engaged in international transport. World energy consumption refers to the total

energy used by all of human civilization. Typically measured per year, it involves all energy

harnessed from every energy source applied towards humanity's endeavors across every single

industrial and technological sector, across every country. Being the power source metric of

civilization, World Energy Consumption has deep implications for humanity's social-economic-

political sphere. The kg of oil equivalent is a unit of energy defined as the amount of energy released

by burning one kg of crude oil. It is approximately 42 mega joules.

a) Source of data

Institutions such as the World Bank, the International Energy Agency (IEA), the U.S. Energy Information Administration (EIA), and the European

Environment Agency record and publish energy data periodically. The data for this study has been taken from World Bank annual report on all the countries

of the world. The data available from World Bank is reliable and trustworthy. The source of the data can be accessed through the following link:

http://data.worldbank.org/indicator/SP.POP.TOTL/countries?display=default

However in the World Bank data source energy use of Fiji for 2012 is not mentioned so I am referring International Energy Statistics data source, World

Bank data used for other categories for Fiji. The source of the data can be accessed through the following link:

http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=44&pid=44&aid=2

Table 1: Raw Data from sources

Sl.

No

.

Name of the

continent

Name of the

country Income group Condition Population GDP (USD)

Energy

consumption

(kg of oil

equivalent per

capita)

Land

surface

area (km2)

1 Asia Japan High income: OECD Developed 127561489 5.954E+12 3545.596 364560

2 Asia Pakistan Lower middle income Developing 177392252 2.246E+11 483.438 770880

3 Asia India Lower middle income Developing 1263589639 1.832E+12 623.720 2973190

4 Asia China Upper middle income Developing 1350695000 8.462E+12 2142.812 9388211

5 Asia Russian Federation High income: nonOECD Developing 143201676 2.016E+12 5283.410 16376870

6 Asia Indonesia Lower middle income Developing 248037853 9.179E+11 861.106 1811570

7 Africa South Africa Upper middle income Developing 52341695 3.974E+11 2674.818 1213090

8 Africa Libya Upper middle income Developing 6283403 8.191E+10 2728.673 1759540

9 Europe United Kingdom High income: OECD Developed 63700300 2.615E+12 3017.734 241930

10 Europe France High income: OECD Developed 65639975 2.681E+12 3844.155 547561

11 Europe Germany High income: OECD Developed 80425823 3.533E+12 3885.880 348540

12 Europe Italy High income: OECD Developed 59539717 2.075E+12 2667.128 294140

Sl.

No

.

Name of the

continent

Name of the

country Income group Condition Population GDP (USD)

Energy

consumption

(kg of oil

equivalent per

capita)

Land

surface

area (km2)

13 Europe Spain High income: OECD Developed 46773055 1.356E+12 2671.805 500210

14 Europe Ukraine Lower middle income Developing 45593300 1.758E+11 2690.324 579320

15 North America Canada High income: OECD Developed 34754312 1.833E+12 7225.682 9093510

16 North America United States High income: OECD Developed 314112078 1.616E+13 6814.823 9147420

17 North America Mexico Upper middle income Developing 122070963 1.187E+12 1543.293 1943950

18 South America Argentina High income: nonOECD Developing 42095224 6.077E+11 1906.055 2736690

19 South America Colombia Upper middle income Developing 46881018 3.697E+11 673.829 1109500

20 South America Brazil Upper middle income Developing 202401584 2.413E+12 1391.902 8358140

21 Oceania New Zealand High income: OECD Developed 4408100 1.744E+11 4301.383 263310

22 Oceania Australia High income: OECD Developed 22728254 1.534E+12 5643.824 7682300

23 Oceania Fiji Upper middle income Developed 874158 3.850E+09 749.509 18270

Note: In the World Bank data source energy use of Fiji for 2012 is not mentioned so I am referring International Energy Statistics data source. However

World Bank data is used for other categories for Fiji.

(http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=44&pid=44&aid=2)

Unit for Energy consumption is considered as kg of oil equivalent per capita. Conversion: 1 Quadrillion Btu=10^15 Btu=10^15*0.0000251996 =

2.51996*10^10 kg of oil equivalent.

b) From the raw data I have gathered, a table of the countries along with their GDP per capita,

energy use per capita, and population density in persons per square kilometre is created.

Table 2: Analysis of data

Sl.

No

.

Na

me

of

the

con

tin

ent

Na

me

of

the

cou

ntr

y

GD

P (

US

D)

per

ca

pit

a

En

erg

y

con

sum

pti

on

(kg

of

oil

equ

ivale

nt

per

ca

pit

a)

Po

pu

lati

on

den

sity

(per

son

s/

km

2)

1 Asia Japan 46679 3545.596 350

2 Asia Pakistan 1266 483.438 230

3 Asia India 1450 623.720 425

4 Asia China 6265 2142.812 144

5 Asia Russian Federation 14079 5283.410 9

6 Asia Indonesia 3701 861.106 137

7 Africa South Africa 7592 2674.818 43

8 Africa Libya 13035 2728.673 4

9 Europe United Kingdom 41051 3017.734 263

10 Europe France 40850 3844.155 120

11 Europe Germany 43932 3885.880 231

12 Europe Italy 34854 2667.128 202

13 Europe Spain 28985 2671.805 94

14 Europe Ukraine 3855 2690.324 79

15 North America Canada 52733 7225.682 4

16 North America United States 51457 6814.823 34

17 North America Mexico 9721 1543.293 63

18 South America Argentina 14437 1906.055 15

19 South America Colombia 7885 673.829 42

20 South America Brazil 11923 1391.902 24

21 Oceania New Zealand 39574 4301.383 17

22 Oceania Australia 67512 5643.824 3

23 Oceania Fiji 4404 749.509 48

c) In part a) did my data source allow me to include figures for all three measures for all the

major countries of the world? If not what type of countries was it not possible to include, and

why I suppose this might be the case.

Yes World Bank data source allows me to access the data for all three measures for the developing

and developed countries of the all continents. However the energy data is not updated till 2014 as

there might be some policy of governments which restricts the availability of energy data due to cross

boundary obligations.

In some small countries of any continent (mostly Oceania) the data for energy is not available at all.

The small amount of emissions associated with energy may be the reason of this kind of imperfection.

d) Using a spreadsheet a graph of the energy consumption per capita as a function of GDP per capita is created as below. A best fit line is added for the

values and the R2 value for the analysis is reported in Graph 1.

Graph 1: the energy consumption per capita vs GDP per capita

The coefficient of determination (R2) value of 0.709 (=~71%) means that almost 71% of the variation in energy consumption is explained by the GDP which

is quite good to explain the interdependency between GDP and energy use.

Japan

Pakistan

India

China

Russian Federation

Indonesia

South Africa

LibyaUnited Kingdom

FranceGermany

ItalySpain

Ukraine

Canada

United States

MexicoArgentina

Colombia

Brazil

New Zealand

Australia

Fiji

y = 10.593x0.5615

R² = 0.7092

0.000

1000.000

2000.000

3000.000

4000.000

5000.000

6000.000

7000.000

8000.000

0 10000 20000 30000 40000 50000 60000 70000 80000

Ene

rgy

con

sum

pti

on

(kg

of

oil

eq

uiv

ale

nt

pe

r ca

pit

a)

GDP per capita

Graph of the energy consumption per capita as a function of GDP per capita

e) A graph analysis of energy consumption per capita as a function of population density has been carried out. A best fit line is added for the values

and the R2 value for the analysis has been reported in Graph 2.

Graph 2: the energy consumption per capita vs population density

The Regression analysis (R2) value for energy consumption per capita Vs population density is only 0.243. It means there is there is only 24% probability that

energy consumption prediction based on the population density will be correct or even less.

Japan

Pakistan India

China

Russian Federation

Indonesia

South Africa

LibyaUnited Kingdom

France Germany

ItalySpain

Ukraine

Canada

United States

MexicoArgentina

Colombia

Brazil

New Zealand

Australia

Fiji

y = -637.5ln(x) + 5449

R² = 0.2433

0.000

1000.000

2000.000

3000.000

4000.000

5000.000

6000.000

7000.000

8000.000

0 50 100 150 200 250 300 350 400 450

Ene

rgy

con

sum

pti

on

(kg

of

oil

eq

uiv

ale

nt

pe

r ca

pit

a)

Population density (persons/km2)

Graph of the energy consumption per capita as a function of population density

f) Using the values from c) and d) discuss how well GDP per capita and population density

predict energy consumption is mentioned below. Brief explanation what other independent

variables might improve the model is also presented

The role of GDP

There is a straight forward relationship in between GDP per capita with energy consumption per

capita in most of the countries. The economic growth of a country is the main driver for the energy

consumption per capita. Higher economic growth of the country requires higher energy use due to

increase in demand. Countries devote more resources and invest in the power sector development to

meet its future demand. However there is a crisis of power in developing countries like India, Pakistan

so the countries are not able to optimize their energy use for entire assessment period.

There is a significant difference in the energy consumption in developing and developed countries.

Developed Countries usually have very high energy consumption due to their lifestyle and developing

countries have lesser energy consumption. This trend is supported from the graph between GDP per

capita and energy consumption per capita.

The role of Population density

In case of developed countries, increase in the population generally increases the energy consumption.

But, this trend simply reverses in case of poor or developing countries. The increase in population

declines the energy consumption per capita if there is no new power infrastructure has been

developed.

Table 3: Sorting for bar chart: GDP from min to max

Rank Country GDP (USD)

per capita

Population

density

(persons/ km2)

Energy consumption

(kg of oil equivalent per

capita)

1 Pakistan 1266 483 483.4

2 India 1450 624 623.7

3 Indonesia 3701 861 861.1

4 Ukraine 3855 2690 2690.3

5 Fiji 4404 750 749.5

6 China 6265 2143 2142.8

7 South Africa 7592 2675 2674.8

8 Colombia 7885 674 673.8

9 Mexico 9721 1543 1543.3

10 Brazil 11923 1392 1391.9

11 Libya 13035 2729 2728.7

12 Russian Federation 14079 5283 5283.4

13 Argentina 14437 1906 1906.1

14 Spain 28985 2672 2671.8

15 Italy 34854 2667 2667.1

16 New Zealand 39574 4301 4301.4

17 France 40850 3844 3844.2

18 United Kingdom 41051 3018 3017.7

19 Germany 43932 3886 3885.9

Rank Country GDP (USD)

per capita

Population

density

(persons/ km2)

Energy consumption

(kg of oil equivalent per

capita)

20 Japan 46679 3546 3545.6

21 United States 51457 6815 6814.8

22 Canada 52733 7226 7225.7

23 Australia 67512 5644 5643.8

So if we draw a bar graph (Graph 3) developing countries will follow a pattern from 1 to 13 and the

rest is developed countries.

Note: The vertical scale (y axis) is presented in log 10 scale.

Graph3: Plot for population density and energy consumption with respect to GDP in ascending order

Another important factor which is true for both developing and developed countries is that whether

this new population has the purchasing power to afford the cost of power. So, increase in population

doesn’t mean that it will increase the energy consumption at all.

These countries are having very high energy consumption and GDP per capita. In hot climate

countries, they have higher power consumption due to use of air-conditioner. Another reason for

higher power consumption is due to excessive use of power in high tech industries, production of high

value products and misuse of power. USA, Canada and Australia are having huge natural resources

which are further boosting their economies. Australia is one of the biggest exporters of uranium, coal,

and iron ore in the world.

1

10

100

1000

10000

100000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Bar chart for population density and energy consumption with respect to increasing GDP per capita

GDP per capita Population density Energy consumption

The energy consumption varies from one country to another country based on its need, climate,

housing, and nature of industry. For example, UK and Ireland having lesser energy consumption than

other nations due to nations economy more inclined to services sector. For USA and Canada, weather

conditions like hot climate and harsh winter conditions and manufacturing sector driving the high

energy consumption. Germany, France, Japan are almost having similar conditions and lie in the same

range.

Independent factors which might improve the model for energy consumption per capita

True interpretation of data: Sometimes the data calculated based on some facts which is not

appropriate measure of economy. For example China invested a lots of fund in real estate

built there is very few people who can afford them. Some of China's most rapidly developing

cities are virtually unheard of in the West; but for every overnight economic success story,

there seems to be a hidden swathe of near misses, dead ends and bankruptcies. Out of all these

phantoms however, nothing compares to the strangeness of China's 'Ghost City': Ordos.

Weather: Cold climate countries like Russia used to have a relatively higher use of energy.

These countries generally devote more resources for heating and power which causes more

energy consumption. Similarly, Very hot climate countries, there is more power consumption

in the summer months due to use of air-conditioner.

Energy waste: It has been observed that there is misuse of energy in some countries where

production of energy is very cheap.

Subsidies: In some developing countries there is subsidy provided for energy production,

however it is good for renewable energy not for the thermal power plants because it causes

greenhouse gas emissions.

Biomass burning: Biomass is used in the form of wood, agricultural waste or as biogas for

cooking and heating. So, better data collection about biomass uses and information about

biomass replacement with other sources of power will further improve the model. Some of the

countries produces electricity with waste, so if population increases the generation of waste

increases which leads to increase in production of electricity. Again the more is the

population the more will be electricity consumption. Hence there might not be a straight

forward relation in between population density with respect to energy consumption per capita.

g) Given the global nature of the world economy what are some of the possible flaws in using

energy consumption figures broken down by country to make statements about relative energy

consumption per capita of different countries?

Today in the era of globalization the economy is spreading country borders. For example to optimize

a production of a product some parts are manufactured in different countries and assembled together

in somewhere else. And the intended population has an indirect contribution of global warming. We

are assigning the energy consumption to the population of a particular country like China but in

reality the energy is consumed in the production of materials or product for consumption in some

other country like USA because China has a low labor cost and ease of business. So our calculation

for the energy consumption per capita for on a country basis is flawed.

Not only manufacturing sector but also services like Information Technology, communication are

heavily outsourced from one country to another countries. Companies outsourced their work to

another nation to get cheap labor, experts in the particular fields in which they are lacking. The

multinational firms of USA has more employees from Asia compared to its own population.

Agriculture products like milk, pulses and grains are exported from one country to another country

based on the demand. The production usually requires lots of energy in the terms of infrastructure and

maintenance. Even Sweden purchases waste from other countries and 40% of the electricity produced

from the waste.

Developing countries have millions of people who don’t have access to electricity. In this case, the

overall calculation for energy consumption per capita is flawed in itself because we are considering

the whole population in order to calculate energy consumption but millions of population doesn’t

have access to electricity, oil or natural gas.

Assumptions and Limitations

We assume the terms are independent to each other but in actual they are interconnected. For example

greenhouse gas emission is related to energy consumption due to burning of fossil fuel (considering

81.3% of total energy in 2012). However improvement in living standard may give rise to a greater

ability to purchase or develop more efficient and less polluting energy generating technologies that

will decrease the adverse impacts of energy.

I = PAT is the lettering of a formula put forward to describe the impact of human activity on

the environment.

I = P × A × T

Human Impact on the environment equals the product of Population, Affluence, and Technology. This

shows how the population, affluence and technology produce an impact. The equation can aid in

understanding some of the factors affecting human impacts on the environment, but it has also been

cited as one of the primary factors underlying many of the dire environmental predictions. The Kaya

identity is closely related to the I = PAT equation. The I = PAT equation is more general, describing

an abstract "impact". The Kaya identity describes more clearly the impact of human activity

on CO2 emissions. However, the I = PAT equation has been criticized for being too simplistic by

assuming that P, A, and T are independent of each other. In reality, at least 7 interdependencies

between P, A, and T could exist, indicating that it is more correct to rewrite the equation as I =

f(P,A,T). For example, a doubling of technological efficiency, or equivalently a reduction of the T-

factor by 50%, does not necessarily reduce the environmental impact (I) by 50% if efficiency induced

price reductions stimulate additional consumption of the resource that was supposed to be conserved, a phenomenon called the Rebound effect. The rebound effect is generally expressed as a ratio of the

lost benefit compared to the expected environmental benefit when holding consumption constant. For

instance, if a 5% improvement in vehicle fuel efficiency results in only a 2% drop in fuel use, there is

a 60% rebound effect (since (5-2)⁄5 = 60%). The 'missing' 3% might have been consumed by driving

faster or further than before.

Conclusion

From the data analysis we have come to a conclusion that energy use per capita can be predicted in

terms of GDP per capita i.e. the economic backbone of the country. Different independent factors

inclusions in the model certainly help in better prediction. There is a need for the development of

more power infrastructure to meet this growing demand. However at the same time Kaya equation

(Equation 1) gives us the warning to the emission of CO2 associated to energy use. Followings are

some example of emission with respect to GDP/P (refer Table 4). So for example we can easily find

the adverse effect of emission if China gains the GDP of any developed country.

Table 4: Comparison of GDP/P and CO2 emission

Renewable energy is the only option to mitigate this burning problem of entire world. Under the

Kyoto Protocol, Japan is obligated to reduce greenhouse gas (GHG) emissions by 6% between 2008

and 2012 compared to the reference year (1990). To achieve this goal, various policy measures,

including promotion of green innovations, diffusion of renewable energy, promotion of modal shifts

in transportation, development and diffusion of energy-efficient electrical appliances, introduction of

a smart-grid system, and construction of smart communities, have been implemented or planned. The

accident at the Fukushima Dai-ichi nuclear power station highlighted the need for further energy

savings.

The effect of greenhouse gas emission is dynamic. As it causes the increase in temperature the

dissolved CO2 will release from the polar ice caps and cold water. We need to reduce our reliance on

the fossil fuels and generate more power from renewable energy sources like wind energy, solar

energy, fuel cells. At the same time developed countries must harness to their luxurious lifestyle and

developing countries which has population boom must curtail the birthrate in terms of new policies.

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