**Energy Consumption Inequality and Human Development**

Qiaosheng Wu1, Svetlana Maslyuk2 and Valerie Clulow3 *1School of Economics and Management, China University of Geosciences, Wuhan 2School of Business and Economics, Monash University, Victoria, 3College of Business, RMIT University, Melbourne, 1China 2,3Australia*

## **1. Introduction**

100 Energy Efficiency – A Bridge to Low Carbon Economy

g. Ståhl, B.: *Exploring Software Resilience*. PhL Thesis, Licentiate Dissertation Series No.

h. Ådahl K.: *On Decision Support in Participatory Medicine supporting Health Care Empowerment.* PhD Thesis, Dissertation Series No. 2011:01, Blekinge Institute of

i. Hussain, S: *Coordination and Monitoring Servises Based on Service Level Agreements in Smart Grid*s. PhL Thesis, Licentiate Dissertation Series No. 2012:01, Blekinge Institute of

2011:05, Blekinge Institute of Technology, ISBN 978-91-7295-206-5.

Technology. ISBN 978-91-7295-221-8

Technology, ISBN 978-91-7295-224-9.

Empirical evidence shows that growing energy consumption leads to a rapid increase in global greenhouse gases emissions (henceforth GHG). As the largest market failure ever experienced, diffusion of GHG in the global atmosphere happens quickly, regardless ofwhere the GHG is emitted (Sinn, 2007). Evidently, by century's end, energy-related carbon dioxide emissions would, at current rates, more than double, putting the world onto a potentially catastrophic trajectory, which could lead to warming of 5℃ or more compared with preindustrial times (IEA, 2009). The existing energy system with most of the energy consumed by the developed nations, has underpinned and constructed deeply unequal social relations, as well as imbalanced nature-society relations. At present given current resource constraints, developing nations cannot follow the path previously chosen by the developed nations to achieve economic growth.

Following Jacobson et al. (2005), the distribution of and access to energy resources may result in significant social, environmental and economic inequalities. To date, inequality in energy consumption across countries has received very limited analytical attention. In the recent literature devoted to climate change, there have been several attempts to use the tools of conventional income distribution analysis to measure inequality in carbon dioxide (CO2) emissions across countries and changes in inequality over time (see Heil & Wodon, 1997, 2000; Hedenus & Azar, 2005; Duro & Padilla, 2006; Padilla & Serrana, 2006; Groot, 2010). Yet, very few studies in the energy literature apart from Jacmart et al. (1979), Jaconson et al. (2005) and Rosas-Flores et al. (2010) have analysed inequality in energy consumption for a large sample of countries.

One of the first to notice the correlation between per capita energy consumption, standard of living and the degree of a country's development and to use the Lorenz curve to measure energy consumption inequality for 1950, 1969 and 1975 was Jacmart et al. (1979). They proposed that changes in the distribution of energy among countries provides another measure of trends in world's inequality and reported a decline in energy consumption inequality over time. In the analysis of the distribution of residential energy consumption in Norway, USA, El Salvador, Thailand and Kenya, Jacobson et al. (2005) found dramatic

Energy Consumption Inequality and Human Development 103

Country HD

Albania M Gabon M Nigeria L Algeria M Georgia M Norway H Angola L Germany H Oman M Argentina H Ghana L Pakistan L Armenia M Greece H Panama M Australia H Guatemala M Paraguay M Austria H Haiti L Peru M Azerbaijan M Honduras M Philippines M Bahrain H Hungary H Poland H Bangladesh L Iceland H Portugal H Belarus M India M Qatar H Belgium H Indonesia M Romania M

Bolivia M Ireland H Saudi Arabia M Bosnia and Herzegovina M Israel H Senegal L Botswana M Italy H Singapore H Brazil M Jamaica M Slovakia H Brunei Darussalam H Japan H Slovenia H Bulgaria M Jordan M South Africa M Cambodia M Kazakhstan M Spain H Cameroon L Kenya L Sri Lanka M Canada H Korea H Sudan L Chile H Kuwait H Sweden H China M Kyrgyzstan M Switzerland H

Benin L Iran M Russian

Colombia M Latvia H Syrian Arab

L Libyan Arab

Congo L Lebanon M Tajikistan M

Costa Rica H Lithuania H Thailand M Côte d'Ivoire L Luxembourg H Togo L Croatia H Macedonia M Trinidad and

Cuba H Malaysia M Tunisia M Cyprus H Malta H Turkey M Czech Republic H Mexico M Turkmenistan M Denmark H Moldova M Ukraine M

Ecuador M Morocco M United Kingdom H Egypt M Mozambique L United States H El Salvador M Myanmar M Uruguay H Eritrea L Namibia M Uzbekistan M Estonia H Nepal L Venezuela M Ethiopia L Netherlands H Viet Nam M Finland H New Zealand H Yemen L France H Nicaragua M Zambia L

Note: The grouping of the countries is based by the 2009 UNDP Human Development Report. H—high

human development countries, M --medium human development countries, L --low human

Jamahiriya

Dominican Republic M Mongolia M United Arab

Category

Country HD

Federation

Republic

M Tanzania L

Tobago

Emirates

Category

M

M

M

H

Country HD

Congo(Democratic Republic )

development countries.

Table 1. Countries included in the sample.

Category

differences between energy use of developed and developing nations with Kenya, El Salvador and Thailand having the highest inequality in energy consumption respectively. These differences can be explained by the differences in a nation's wealth, income distribution and government infrastructure as well as climatic conditions, energy efficiency measures and size and geographic distribution of the rural population. In the analysis of inequality in the distribution of expenses associated with main energy fuels in Mexico, Rosas-Flores et al. (2010) found that natural gas, electricity and gasoline were consumed mainly by the higher income earners, while firewood and kerosene were the main fuels for the lower income consumers.

In the past, the improvements in the human quality of life meant greater use of energy, however it is no longer possible under the current supply contraints and climate change conditions. In fact the literature shows that good quality of life can be achieved on much lower energy consumption levels (Pasternak, 2000, Pachari and Spreng, 2003, Spreng, 2005). According to the United Nations (UN) 2007/2008 Human Development Report, under the energy supply constraints and the constant necessity to improve energy efficiency, when energy use is associated with human development, it is possible to find opportunities for the synergetic development of energy and society, by shifting the focus of the economy to satisfying basic human needs. It is possible to introduce a sufficientarian 'development threshold' attributed to global energy consumption, by the use of the nationally-weighted human development indicators such as the United Nations Development Program (UNDP) Human Development Index (HDI).

The purpose of the study reported in this chapter, is to measure energy consumption inequality by using the standard tools of economic analysis - the Lorenz curve and Gini coefficient. These inequality measures also provide critical insights into the temporal evolution of energy management in different states and nations, and allow us to visualise the impact of factors such as new technologies, government policies, etc (Jacobson et al., 2005). In this chapter, four Lorenz curves were generated based on the four equity criterions namely production-based, energy consumption-based, human development and economic activity equity criterions.

The list of 129 countreis analyzed in this study is given in Table 1 below. To calculate energy consumption inequality measures we use UNDP HDI and the International Energy Agency (IEA) data on per capita energy consumption. HDI is composed of three elements including longevity (L), as proxied by the life expectancy at birth, education index (E, a combination of adult literacy and gross enrollment indeces) and income as measured by the GDP per capita PPP USD index. Because they are equally important, HDI components are weighted equally. The following equations represent how the HDI components are calculated:

$$L = \frac{\text{Life Expectance} - 25}{85 - 25} \tag{1a}$$

$$E = \frac{2}{3} \ast Adult \text{ Literacy Index} + \frac{1}{3} \ast Gross \text{ Enrollment Index} \tag{1b}$$

$$GDP = \frac{\log(GDP \text{ per capita}) - \log(100)}{\log(40000) - \log(100)} \tag{1c}$$

The 2009 UNDP Human Development Report divided nations into three groups based on their HDI level. High human development economies (HHD) have HDI≥0.85, medium

differences between energy use of developed and developing nations with Kenya, El Salvador and Thailand having the highest inequality in energy consumption respectively. These differences can be explained by the differences in a nation's wealth, income distribution and government infrastructure as well as climatic conditions, energy efficiency measures and size and geographic distribution of the rural population. In the analysis of inequality in the distribution of expenses associated with main energy fuels in Mexico, Rosas-Flores et al. (2010) found that natural gas, electricity and gasoline were consumed mainly by the higher income earners, while firewood and kerosene were the main fuels for

In the past, the improvements in the human quality of life meant greater use of energy, however it is no longer possible under the current supply contraints and climate change conditions. In fact the literature shows that good quality of life can be achieved on much lower energy consumption levels (Pasternak, 2000, Pachari and Spreng, 2003, Spreng, 2005). According to the United Nations (UN) 2007/2008 Human Development Report, under the energy supply constraints and the constant necessity to improve energy efficiency, when energy use is associated with human development, it is possible to find opportunities for the synergetic development of energy and society, by shifting the focus of the economy to satisfying basic human needs. It is possible to introduce a sufficientarian 'development threshold' attributed to global energy consumption, by the use of the nationally-weighted human development indicators such as the United Nations Development Program (UNDP)

The purpose of the study reported in this chapter, is to measure energy consumption inequality by using the standard tools of economic analysis - the Lorenz curve and Gini coefficient. These inequality measures also provide critical insights into the temporal evolution of energy management in different states and nations, and allow us to visualise the impact of factors such as new technologies, government policies, etc (Jacobson et al., 2005). In this chapter, four Lorenz curves were generated based on the four equity criterions namely production-based, energy consumption-based, human development and economic

The list of 129 countreis analyzed in this study is given in Table 1 below. To calculate energy consumption inequality measures we use UNDP HDI and the International Energy Agency (IEA) data on per capita energy consumption. HDI is composed of three elements including longevity (L), as proxied by the life expectancy at birth, education index (E, a combination of adult literacy and gross enrollment indeces) and income as measured by the GDP per capita PPP USD index. Because they are equally important, HDI components are weighted

equally. The following equations represent how the HDI components are calculated:

� � ����� �������� ����� � �

� � ���� �������������

��� � ���(��� ��� ������)����(���)

The 2009 UNDP Human Development Report divided nations into three groups based on their HDI level. High human development economies (HHD) have HDI≥0.85, medium

�

����� (1a)

���(�����)����(���) (1c)

� ����� ���������� ����� (1b)

the lower income consumers.

Human Development Index (HDI).

� � �

activity equity criterions.

Note: The grouping of the countries is based by the 2009 UNDP Human Development Report. H—high human development countries, M --medium human development countries, L --low human development countries.

Table 1. Countries included in the sample.

Energy Consumption Inequality and Human Development 105

Although the majority of these nations are developed economies, the list also contains resource-rich developing nations such as Qatar and Oman. The United States with high human development level (HDI is 0.953 in 2007) was the largest energy consumer in the world, consuming 20 percent of the world's total energy. Other nations with relatively high levels of energy use are Qatar, Iceland, United Arab Emirates, Bahrain, Trinidad and Tobago, Kuwait, Luxembourg and Canada. Norway has the highest human development

In this study we found that inequality of energy consumption has been decreasing over the entire time period of analysis. This can be attributed to several factors including globalization and improved access to energy and infrastructure in some developed countries (e.g. China and India). We suggest that concerns to do with inequality of energy consumption must be incorporated and integrated into the development strategies for all

The chapter is structured as follows. Section 2 describes inequality measures used in this chapter. Section 3 discusses energy consumption inequality using four equaity criteria. Section 4 provides an overview of inequality in time from 1998 to 2007 and Section 5

In order to visualize HHD‐MHD/-LHD energy consumption inequality between countries this chapter uses the Lorenz curve and the Gini coefficient. In traditional economics, the Lorenz curve shows what percentage of the total income is held by the corresponding percentage of households, where households are ranked by level of income. Applying the Lorenz curve in the context of energy consumption, means replacing households by countries, and ranking by income is replaced by ranking by energy consumption per capita across countries. Doing so results in a Lorenz curve that depicts distribution of cumulative percentage of world population on the abscissa axis versus the cumulative percentage of the

where *p* is the cumulative population share of persons earning income equal to or below income level *x* , *y* is the cumulative income share of population subgroup *p* . Any Lorenz

2 0, 0, (0) 0, (1) 1 *dy d y y y dp dp*

Applying the Lorenz curve in the context of energy consumption, means replacing households by countries, and ranking by income is replaced by ranking by energy consumption per capita across countries. Doing so results in a Lorenz curve that depicts

2

*y f p*( ) , (2a)

, (2b)

level due to the highest HDI value.

countries irrespective of their human development level.

**2. Measuring energy consumption inequality**

energy consumption distributed along the ordinate axis.

Mathematically Lorenz curve can be represented as

curve must have the following properties,

and is defined on the domain 0 1 *p* .

concludes the chapter by analysing policy implications of our findings.

human development economies (MHD) have 0.6≤HDI<0.85 and low human development economies (LHD) have HDI<0.6. In 2007, 47 economies corresponded to HHD, 60 to MHD and 22 to LHD nations respectively. The period 1998 to 2007 was chosen for this analysis because it corresponds to comparable metgodology of the HDI calculation used by the UNDP allowing us to compare the inequality measures across a common time period.

Table 2 contains total primary energy supply (TPES) per capita, GDP, population and HDI values for 30 countries with the largest per capita energy consumption in the world.

Table 2. Top 30 energy consumers.

human development economies (MHD) have 0.6≤HDI<0.85 and low human development economies (LHD) have HDI<0.6. In 2007, 47 economies corresponded to HHD, 60 to MHD and 22 to LHD nations respectively. The period 1998 to 2007 was chosen for this analysis because it corresponds to comparable metgodology of the HDI calculation used by the UNDP allowing us to compare the inequality measures across a common time period.

Table 2 contains total primary energy supply (TPES) per capita, GDP, population and HDI

Qatar 26.5392 29.02 0.047 34548 0.84 0.013 0.901 Iceland 15.7377 10.83 0.018 34935 0.31 0.005 0.968

Bahrain 11.6523 16.12 0.026 21493 0.75 0.011 0.878

Kuwait 9.4631 70.73 0.115 26590 2.66 0.04 0.893 Luxembourg 8.7901 31.2 0.051 65000 0.48 0.007 0.96 Canada 8.1686 1046.87 1.704 31743 32.98 0.499 0.959 United States 7.7459 11468 18.669 37962 302.09 4.571 0.953

Finland 6.8962 164.81 0.268 31155 5.29 0.08 0.953 Saudi Arabia 6.2128 360.74 0.587 14907 24.2 0.366 0.819 Oman 5.9536 44.73 0.073 17204 2.6 0.039 0.83 Australia 5.8703 666.78 1.085 31541 21.14 0.32 0.965 Singapore 5.83 135.88 0.221 29603 4.59 0.069 0.928 Norway 5.7075 190.75 0.311 40499 4.71 0.071 0.971 Sweden 5.5118 298.31 0.486 32602 9.15 0.138 0.957 Belgium 5.3683 323.58 0.527 30469 10.62 0.161 0.946 Netherlands 4.9107 534.06 0.869 32604 16.38 0.248 0.955

Korea 4.5855 1065.75 1.735 21992 48.46 0.733 0.931 Czech Republic 4.4324 209.12 0.34 20264 10.32 0.156 0.893 Kazakhstan 4.2931 127.68 0.208 8248 15.48 0.234 0.788 Estonia 4.1972 22.03 0.036 16440 1.34 0.02 0.872 France 4.1483 1737.96 2.829 27339 63.57 0.962 0.949 Germany 4.0268 2315.34 3.769 28147 82.26 1.245 0.936 Japan 4.0195 3620.16 5.893 28336 127.76 1.933 0.951 New Zealand 4.0075 101.07 0.165 24122 4.19 0.063 0.942 Austria 3.99 266.51 0.434 32032 8.32 0.126 0.946 Turkmenistan 3.6416 38.18 0.062 7698 4.96 0.075 0.764 Other countries 1.154 34787.8 56.6 6150 5656.1 85.6 - World 1.82 61428.02 100 9294 6609.27 100 -

**population toe/capita Billion 2000\$/capita, Million HDI**

11.8296 113.85 0.185 26053 4.37 0.066 0.879

11.4646 20.35 0.033 15301 1.33 0.02 0.813

7.114 6.03 0.01 15462 0.39 0.006 0.866

4.7455 1603.73 2.611 11323 141.64 2.143 0.803

**GDP/pop, Population, % of World total**

values for 30 countries with the largest per capita energy consumption in the world.

**total GDP**

**TPES/pop, GDP, % of World**

**2000\$,PPP PPP**

**Country**

United Arab Emirates

Trinidad and Tobago

Brunei Darussalam

Russian Federation

Table 2. Top 30 energy consumers.

Although the majority of these nations are developed economies, the list also contains resource-rich developing nations such as Qatar and Oman. The United States with high human development level (HDI is 0.953 in 2007) was the largest energy consumer in the world, consuming 20 percent of the world's total energy. Other nations with relatively high levels of energy use are Qatar, Iceland, United Arab Emirates, Bahrain, Trinidad and Tobago, Kuwait, Luxembourg and Canada. Norway has the highest human development level due to the highest HDI value.

In this study we found that inequality of energy consumption has been decreasing over the entire time period of analysis. This can be attributed to several factors including globalization and improved access to energy and infrastructure in some developed countries (e.g. China and India). We suggest that concerns to do with inequality of energy consumption must be incorporated and integrated into the development strategies for all countries irrespective of their human development level.

The chapter is structured as follows. Section 2 describes inequality measures used in this chapter. Section 3 discusses energy consumption inequality using four equaity criteria. Section 4 provides an overview of inequality in time from 1998 to 2007 and Section 5 concludes the chapter by analysing policy implications of our findings.

#### **2. Measuring energy consumption inequality**

In order to visualize HHD‐MHD/-LHD energy consumption inequality between countries this chapter uses the Lorenz curve and the Gini coefficient. In traditional economics, the Lorenz curve shows what percentage of the total income is held by the corresponding percentage of households, where households are ranked by level of income. Applying the Lorenz curve in the context of energy consumption, means replacing households by countries, and ranking by income is replaced by ranking by energy consumption per capita across countries. Doing so results in a Lorenz curve that depicts distribution of cumulative percentage of world population on the abscissa axis versus the cumulative percentage of the energy consumption distributed along the ordinate axis.

Mathematically Lorenz curve can be represented as

$$y = f(p) \, , \tag{2a}$$

where *p* is the cumulative population share of persons earning income equal to or below income level *x* , *y* is the cumulative income share of population subgroup *p* . Any Lorenz curve must have the following properties,

$$\frac{dy}{dp} > 0, \frac{d^2y}{dp^2} > 0, y(0) = 0, y(1) = 1 \,\text{\AA} \tag{2b}$$

and is defined on the domain 0 1 *p* .

Applying the Lorenz curve in the context of energy consumption, means replacing households by countries, and ranking by income is replaced by ranking by energy consumption per capita across countries. Doing so results in a Lorenz curve that depicts

Energy Consumption Inequality and Human Development 107

A potentially more intuitive way to interpret Figure 1 is by using GDP of US\$ 10000 PPP as a divider between lower and higher income countries. Then, in 2007, 75% of the world's population with per capita GDP of less than US\$10000 accounted for 40% of global energy consumption. The remaining 25% of population with GDP PPP per capita of more than

By ranking countries in a different way it is possible to construct a different Lorenz curve, and it will be shown that the criterion to rank countries is fully determined by the variables used on the coordinate axes in a Lorenz diagram (Groot, 2010). In this chapter we generate four Lorenz curves based on four equity criterions. The first is an energy consumptionbased equity criterion which is predicated on the rationale that all countries should have an equal right to use energy for its social and economic development. In this case the Lorenz curve is constructed by plotting per capita energy consumption shares in the cumulative world energy consumption on the vertical axis, and cumulative world population shares (%) on the horizontal axis. Second is an energy production-based sovereignity equity criterion which is connected to a country's capabilities to produce and consume its own energy. In this case, the horizontal axis of the Lorenz curve is found by sorting cumulative world population shares (%) by per capita energy production. Third is an economic activity equity criterion. In this study we use energy intensity or the number of energy units used in the production of a nation's GDP as the proxy for economic activity. High/low energy intensity represents high/low cost of converting energy into GDP. The Lorenz curve is sorted by energy intensity, where cumulative world GDP shares (%) ranked by energy intensity is on the horizontal and cumulative world energy consumption shares (%) are on the vertical axes. Last is a human development equity criterion which is based on the HDI. In this case cumulative world energy consumption shares (%) are on the vertical axis and cumulative world population shares (%) ranked by the HDI are on the horizontal axis. According to the conventional welfare theories, to achieve higher human development, each individual should enjoy development rights, including social, economic, political, as well as the basic survival needs and the provision of non-material services based upon demand for natural resources. Therefore, the concept of human development is important because it is not only concerned with the current state of the human well-being but also with the realization of human potential. This criterion implies that each member of the society is entitled to realize their basic human right to development potential given constrained natural resources.

Figure 2 shows the distribution of 2007 energy consumption under energy consumption-based equity criterion. Based on this criteria, the Gini coefficient was 0.50. Top 10 countries in terms of energy cosumption include: Qatar, Iceland, United Arab Emirates, Bahrain, Trinidad and Tobago, Kuwait, Luxembourg, Canada, United States and Brunei Darussalam. These countries

Figure 3 shows the distribution of 2007 energy consumption under energy production equity criterion. In 2007 the Gini coefficient was 0.39. Per capita energy production in the top 10 countries include: Qatar, Kuwait, Brunei Darussalam, Norway, United Arab Emirates, Trinidad and Tobago, Oman, Saudi Arabia, Bahrain and Libyan Arab Jamahiriya. These nations harbor 0.77 % of the population, and produce 12.23 % of the world's energy,

harbour 5.52 % of the world's population, and use 24.06 % of the world's energy.

but consume 2.95 % of the world's energy.

US\$10000 accounted for 60% of global energy consumption.

**3. Energy consumption inequality criterions**

distribution of cumulative percentage of world population on the abscissa axis versus the cumulative percentage of the energy consumption distributed along the ordinate axis (Jacobson et al., 2005). In fact, the criterion to rank countries is fully determined by the variables used on the coordinate axes in a Lorenz diagram (Groot, 2010). Therefore, one can also construct a Lorenz curve where the horizontal axis measures cumulative world GDP shares instead of cumulative world population shares (Groot, 2010).

Figure 1 shows an energy consumption Lorenz curve in 2007 for countries sorted by per capita GDP PPP. The 45 degree line represents the line of perfect equality, where national energy consumption is equalized globally on a per capita basis. The area between the perfect equity line and the actual distribution (Lorenz) curve is given by the Gini coefficient wich is calculated as

2 1 1 2 0 100 [( )( )] 100 *n i ii i i P PE E Gini* , (3)

where *Pi* is the population share of country *i* and *Ei* is its energy consumption share in world population and in total world energy consumption respectively. In this case the Gini coefficient indicates the degree of global inequality in per capita energy consumption. A Gini coefficient of zero corresponds to perfect equality in per capita energy consumption among all countries in the sample (every country consumes the same amount of energy and the Lorenz curve corresponds to the 45-degree line), while a Gini coefficient of one would indicate perfect inequality in energy consumption, arising due to all the world's energy being consumed by one nation. For the year 2007, Gini coefficient corresponding to Lorenz curve shown on Figure 1 is 0.47, implying that distribution of energy consumption in 2007 between the richest and the poorest nations that was not equal.

Fig. 1. The Lorenz curve for energy consumption in 2007 for countries sorted by per capita GDP PPP.

distribution of cumulative percentage of world population on the abscissa axis versus the cumulative percentage of the energy consumption distributed along the ordinate axis (Jacobson et al., 2005). In fact, the criterion to rank countries is fully determined by the variables used on the coordinate axes in a Lorenz diagram (Groot, 2010). Therefore, one can also construct a Lorenz curve where the horizontal axis measures cumulative world GDP

Figure 1 shows an energy consumption Lorenz curve in 2007 for countries sorted by per capita GDP PPP. The 45 degree line represents the line of perfect equality, where national energy consumption is equalized globally on a per capita basis. The area between the perfect equity line and the actual distribution (Lorenz) curve is given by the Gini coefficient wich is

> 1 1 2

, (3)

100 [( )( )] 100 *n i ii i*

2

*P PE E Gini*

where *Pi* is the population share of country *i* and *Ei* is its energy consumption share in world population and in total world energy consumption respectively. In this case the Gini coefficient indicates the degree of global inequality in per capita energy consumption. A Gini coefficient of zero corresponds to perfect equality in per capita energy consumption among all countries in the sample (every country consumes the same amount of energy and the Lorenz curve corresponds to the 45-degree line), while a Gini coefficient of one would indicate perfect inequality in energy consumption, arising due to all the world's energy being consumed by one nation. For the year 2007, Gini coefficient corresponding to Lorenz curve shown on Figure 1 is 0.47, implying that distribution of energy consumption in 2007

Fig. 1. The Lorenz curve for energy consumption in 2007 for countries sorted by per capita

0

*i*

between the richest and the poorest nations that was not equal.

shares instead of cumulative world population shares (Groot, 2010).

calculated as

GDP PPP.

A potentially more intuitive way to interpret Figure 1 is by using GDP of US\$ 10000 PPP as a divider between lower and higher income countries. Then, in 2007, 75% of the world's population with per capita GDP of less than US\$10000 accounted for 40% of global energy consumption. The remaining 25% of population with GDP PPP per capita of more than US\$10000 accounted for 60% of global energy consumption.

#### **3. Energy consumption inequality criterions**

By ranking countries in a different way it is possible to construct a different Lorenz curve, and it will be shown that the criterion to rank countries is fully determined by the variables used on the coordinate axes in a Lorenz diagram (Groot, 2010). In this chapter we generate four Lorenz curves based on four equity criterions. The first is an energy consumptionbased equity criterion which is predicated on the rationale that all countries should have an equal right to use energy for its social and economic development. In this case the Lorenz curve is constructed by plotting per capita energy consumption shares in the cumulative world energy consumption on the vertical axis, and cumulative world population shares (%) on the horizontal axis. Second is an energy production-based sovereignity equity criterion which is connected to a country's capabilities to produce and consume its own energy. In this case, the horizontal axis of the Lorenz curve is found by sorting cumulative world population shares (%) by per capita energy production. Third is an economic activity equity criterion. In this study we use energy intensity or the number of energy units used in the production of a nation's GDP as the proxy for economic activity. High/low energy intensity represents high/low cost of converting energy into GDP. The Lorenz curve is sorted by energy intensity, where cumulative world GDP shares (%) ranked by energy intensity is on the horizontal and cumulative world energy consumption shares (%) are on the vertical axes. Last is a human development equity criterion which is based on the HDI. In this case cumulative world energy consumption shares (%) are on the vertical axis and cumulative world population shares (%) ranked by the HDI are on the horizontal axis. According to the conventional welfare theories, to achieve higher human development, each individual should enjoy development rights, including social, economic, political, as well as the basic survival needs and the provision of non-material services based upon demand for natural resources. Therefore, the concept of human development is important because it is not only concerned with the current state of the human well-being but also with the realization of human potential. This criterion implies that each member of the society is entitled to realize their basic human right to development potential given constrained natural resources.

Figure 2 shows the distribution of 2007 energy consumption under energy consumption-based equity criterion. Based on this criteria, the Gini coefficient was 0.50. Top 10 countries in terms of energy cosumption include: Qatar, Iceland, United Arab Emirates, Bahrain, Trinidad and Tobago, Kuwait, Luxembourg, Canada, United States and Brunei Darussalam. These countries harbour 5.52 % of the world's population, and use 24.06 % of the world's energy.

Figure 3 shows the distribution of 2007 energy consumption under energy production equity criterion. In 2007 the Gini coefficient was 0.39. Per capita energy production in the top 10 countries include: Qatar, Kuwait, Brunei Darussalam, Norway, United Arab Emirates, Trinidad and Tobago, Oman, Saudi Arabia, Bahrain and Libyan Arab Jamahiriya. These nations harbor 0.77 % of the population, and produce 12.23 % of the world's energy, but consume 2.95 % of the world's energy.

Energy Consumption Inequality and Human Development 109

Fig. 4. The Lorenz curve in 2007 for countries sorted by energy intensity.

Fig. 5. The Lorenz curve in 2007 for countries sorted by HDI.

the world's energy.

Figure 5 shows the Lorenz curve sorted by HDI criterion, where cumulative world energy consumption shares (%) are on the vertical axis and cumulative world population shares (%) are on the horizontal axis. The Gini coefficient in 2007 is 0.46. Top 10 HDI nations are Norway, Iceland, Australia, Ireland, Luxembourg, Canada, Sweden,the Netherlands, Finland, United States. Their total GDP accounts for 23.7 % of the world's GDP, 6.3 % of the world's population, and use 25.9 % of the world's energy. The energy use of HHD countries is 48.5 % of the world's total, their GDP accounts for 52.3 % of the world's total, and they are the home countries of 17.8 % of the world's population. MHD countries use 48.1 % of the world's energy, harbor 67.3 % of the world's population and account for 43.1 % of the world's GDP. LHD countries harbor 14.9 % of the world's population and only use 3.4 % of

Fig. 2. The Lorenz curve in 2007 for countries sorted by per capita energy consumption.

Fig. 3. The Lorenz curve in 2007 for countries sorted by per capita energy production.

Figure 4 shows the distribution of 2007 energy consumption under economic activity equity criterion. In 2007 the Gini coefficient was 0.19. The energy intensity of the top 10 countries namely, Uzbekistan, Qatar, Trinidad and Tobago, Nigeria, Tanzania, Zambia, Bahrain, Kazakhstan, Jamaica and Tajikistan, with GDP of 0.78 % of the 129 countries, indicateduse of 2.65 % of world's energy.

Fig. 2. The Lorenz curve in 2007 for countries sorted by per capita energy consumption.

Fig. 3. The Lorenz curve in 2007 for countries sorted by per capita energy production.

2.65 % of world's energy.

Figure 4 shows the distribution of 2007 energy consumption under economic activity equity criterion. In 2007 the Gini coefficient was 0.19. The energy intensity of the top 10 countries namely, Uzbekistan, Qatar, Trinidad and Tobago, Nigeria, Tanzania, Zambia, Bahrain, Kazakhstan, Jamaica and Tajikistan, with GDP of 0.78 % of the 129 countries, indicateduse of

Fig. 4. The Lorenz curve in 2007 for countries sorted by energy intensity.

Figure 5 shows the Lorenz curve sorted by HDI criterion, where cumulative world energy consumption shares (%) are on the vertical axis and cumulative world population shares (%) are on the horizontal axis. The Gini coefficient in 2007 is 0.46. Top 10 HDI nations are Norway, Iceland, Australia, Ireland, Luxembourg, Canada, Sweden,the Netherlands, Finland, United States. Their total GDP accounts for 23.7 % of the world's GDP, 6.3 % of the world's population, and use 25.9 % of the world's energy. The energy use of HHD countries is 48.5 % of the world's total, their GDP accounts for 52.3 % of the world's total, and they are the home countries of 17.8 % of the world's population. MHD countries use 48.1 % of the world's energy, harbor 67.3 % of the world's population and account for 43.1 % of the world's GDP. LHD countries harbor 14.9 % of the world's population and only use 3.4 % of the world's energy.

Fig. 5. The Lorenz curve in 2007 for countries sorted by HDI.

Energy Consumption Inequality and Human Development 111

One can see that the largest inequality is based on the HDI and energy consumption criterions. This finding can be explained by the continued poor access to energy resources by the developing nations, insufficient and in some cases inadequate infrastructure facilities and the use of energy-inefficient technologies. Although over the time, developed nations have improved access to energy resources, on average they are still consuming much less

The distribution of energy resources may result in significant social, environmental and economic inequalities (Jacobson et al., 2005). A critical issue faced by policy makers across the world is how to distribute the costs and benefits through policies designed to address such problems. This chapter argues that energy consumption has a distinct and critical social dimension. Based on the UN Human Development Index, it analyses the energy consumption equality problem involving the different HDI groups. Although energy consumption inequality has been declining over time, it is not yet on a dissapearing trend. Economic growth, as well other socio-economic factors such as urbanisation and population increases are unbalanced globally, meaning that the contributions of developed and developing countries to climate change are changing. Therefore, compared with developed countries (which typically have high levels of energy consumption and corresponding high HDI and are aiming to keep a high standard of living), developing countries (usually they have lower HDI) have different tasks concerning energy consumption and human development. If the goal of low and medium HDI nation is to achieve improvement in its

In this study, we consider world energy consumption inequality from 1998 to 2007 and found that all of the conventional income inequality approaches can also be applied to the distribution of per capita energy consumption provided appropriate adjustments are made. We have chosen to apply the Lorenz curve and Gini coefficient to examine the inequality of per capita energy consumption across countries under different equality criteria. As stated earlier 1998 to 2007 was chosen as a sample period because it corresponded to the same methodology of HDI calculation used by the UNDP. In 2010 the UNDP has changed the HDI calculation methodology and approach to country classification. Therefore the calculation of inequality measures based on the new HDI definition is left to the future, but

Energy consumption inequality, as measured by the divergence of Lorenz curve from the diagonal and by the Gini coefficient, was found to be different based on different equity criterions. In particular, Gini coefficient was much lower when energy consumption shares are pictured against world GDP shares rather than world population shares. Irrespective of the equity criterion used, energy consumption inequality was found to be diminishing over time. These are the reasons that could have lead to a reduction in energy consumption

a. Globalization or the international integration of markets for goods, services and capital (Brune and Garrett, 2005). Globalization for developing countries often leads to an increase in the energy consumption as developed countries shift production and

energy on a per capita basis as compared to the developed nations.

HDI, the goal of the high HDI nation is its maintenance.

these measures will not be strictly comparable with the past.

technologies to developing countries.

**5. Conclusion**

inequality:

#### **4. Energy consumption inequality from 1998 to 2007**

Table 3 and Figure 6 present calculated Gini coefficients calculated based on the four equity criterions from 1998 to 2007. One can see that although inequality in energy consumption (shown by the difference between the respective Lorenz curve and the diagonal and decline in the Gini coefficient values) has diminished over the time according to all four criterions analysed, it did not disappear completely.

Source: Authors' own calculations based on the UNDP (2000-2009) and IEA (2009).

Table 3. The Gini based on equity criterions from 1998 to 2007.

Note: HDI – Human development equality criterion, ENERGY PRODUCTION—Energy productionbased equality criterion, ENERGY CONSUMPTION—Energy consumption-based equality criterion, ECONOMIC ACTIVITY—Economic activity equality criterion.

Fig. 6. The Lorenz curve in 1998 and 2007 for different equality criterions.

One can see that the largest inequality is based on the HDI and energy consumption criterions. This finding can be explained by the continued poor access to energy resources by the developing nations, insufficient and in some cases inadequate infrastructure facilities and the use of energy-inefficient technologies. Although over the time, developed nations have improved access to energy resources, on average they are still consuming much less energy on a per capita basis as compared to the developed nations.

#### **5. Conclusion**

110 Energy Efficiency – A Bridge to Low Carbon Economy

Table 3 and Figure 6 present calculated Gini coefficients calculated based on the four equity criterions from 1998 to 2007. One can see that although inequality in energy consumption (shown by the difference between the respective Lorenz curve and the diagonal and decline in the Gini coefficient values) has diminished over the time according to all four criterions

**Gini coefficient**

**HDI criterion**  **Economic activity criterion**

**Energy consumptionbased criterion**

1998 0.4273 0.5365 0.5052 0.2082 1999 0.4237 0.5356 0.5013 0.2025 2000 0.4262 0.5384 0.5059 0.2018 2001 0.4240 0.5364 0.4956 0.1990 2002 0.4206 0.5323 0.4965 0.1971 2003 0.4129 0.5258 0.4876 0.1939 2004 0.4043 0.5172 0.4781 0.1899 2005 0.3996 0.5125 0.4746 0.1882 2006 0.3951 0.5054 0.4656 0.1876 2007 0.3890 0.5000 0.4572 0.1870

Source: Authors' own calculations based on the UNDP (2000-2009) and IEA (2009).

1998 2007

ENERGY PRODUCTION ECONOMIC ACTIVITY

Fig. 6. The Lorenz curve in 1998 and 2007 for different equality criterions.

ECONOMIC ACTIVITY—Economic activity equality criterion.

0 10 20 30 40 50 60 70 80 90 100

Note: HDI – Human development equality criterion, ENERGY PRODUCTION—Energy productionbased equality criterion, ENERGY CONSUMPTION—Energy consumption-based equality criterion,

0

HDI

ENERGY CONSUMPTION

0 10 20 30 40 50 60 70 80 90 100

ENERGY PRODUCTION ECONOMIC ACTIVITY

20

40

60

80

100

Table 3. The Gini based on equity criterions from 1998 to 2007.

**4. Energy consumption inequality from 1998 to 2007**

analysed, it did not disappear completely.

**Energy productionbased criterion**

**Year**

0

HDI

ENERGY CONSUMPTION

20

40

60

80

100

The distribution of energy resources may result in significant social, environmental and economic inequalities (Jacobson et al., 2005). A critical issue faced by policy makers across the world is how to distribute the costs and benefits through policies designed to address such problems. This chapter argues that energy consumption has a distinct and critical social dimension. Based on the UN Human Development Index, it analyses the energy consumption equality problem involving the different HDI groups. Although energy consumption inequality has been declining over time, it is not yet on a dissapearing trend. Economic growth, as well other socio-economic factors such as urbanisation and population increases are unbalanced globally, meaning that the contributions of developed and developing countries to climate change are changing. Therefore, compared with developed countries (which typically have high levels of energy consumption and corresponding high HDI and are aiming to keep a high standard of living), developing countries (usually they have lower HDI) have different tasks concerning energy consumption and human development. If the goal of low and medium HDI nation is to achieve improvement in its HDI, the goal of the high HDI nation is its maintenance.

In this study, we consider world energy consumption inequality from 1998 to 2007 and found that all of the conventional income inequality approaches can also be applied to the distribution of per capita energy consumption provided appropriate adjustments are made. We have chosen to apply the Lorenz curve and Gini coefficient to examine the inequality of per capita energy consumption across countries under different equality criteria. As stated earlier 1998 to 2007 was chosen as a sample period because it corresponded to the same methodology of HDI calculation used by the UNDP. In 2010 the UNDP has changed the HDI calculation methodology and approach to country classification. Therefore the calculation of inequality measures based on the new HDI definition is left to the future, but these measures will not be strictly comparable with the past.

Energy consumption inequality, as measured by the divergence of Lorenz curve from the diagonal and by the Gini coefficient, was found to be different based on different equity criterions. In particular, Gini coefficient was much lower when energy consumption shares are pictured against world GDP shares rather than world population shares. Irrespective of the equity criterion used, energy consumption inequality was found to be diminishing over time. These are the reasons that could have lead to a reduction in energy consumption inequality:

a. Globalization or the international integration of markets for goods, services and capital (Brune and Garrett, 2005). Globalization for developing countries often leads to an increase in the energy consumption as developed countries shift production and technologies to developing countries.

Energy Consumption Inequality and Human Development 113

modern energy, electrification and creation of essential infrastrucuture are more likely to achieve improvement in HDI. However, global efforts together with inidividual low HDI country efforts might be necessary in order to achieve improvement in human development. For example, lets consider Australia and Kenya as HHD (HDI = 0.970) and MHD (HDI = 0.541) nations in 2007 respectively. It should be noted that in the beginning of the sample, Kenya, which is now largest economy in East Africa, had lower HDI value. However in a less than decade government policies on improving human development with the help of international organizations (for example, UN World Food Programm since 2004 was installing energy-efficient stoves in Kenyan schools) have been relatively successful, although a lot of challeneges still remain. While primary energy sources in Australia are brown and black coal and natural gas, Kenya is largely dependent on biomass (wood), imported crude oil and electricity with respective shares 70 per cent, 21 per cent, and 9 per cent of total energy use (UNEP, 2006). While in Australia, major electricity source is coal, in Kenya major sources of electricity are hydro, geothermal and thermal power (UNEP, 2006). Governments of these two countries face different challenges, namely maintaining already high HDI (Australia) and achieving improvement in HDI (Kenya). In both cases, this would require efficient use of energy resources, but for Australia this would also mean significant climate change mitigation policy constraints. For example, Australia has pledged to reduce its GHG emissions (the primary means of achieving is goal is transitional carbon tax on producers and introduction of a national mandatory emissions trading scheme in 2015) and increase investment in alternative energy such as direct geothermal and wave energy. For Kenya, where 80% of population depends on biomass as the primary source of energy, the challenges lie in improving electricity generation and distribution, creating essential transmission and distribution infrastructure, reducing the cost of electricity, reducing its dependence on crude oil imports and investing in green energy sources (UNEP, 2006). However, the poverty still remains acute in Kenya due to high income inequality, disproportionate access to essential resources including land, susceptibility to natural disasters such as floods and still inadequate access to basic social services including

Fig. 7. Energy consumption per capita and the HDI (2007).

education (Hendriks, 2010, p.99).

Figure 7 below shows that relationship between the HDI and energy consumption per capita (in tonnes of oil equivalent) is not linear. This means that at low human development levels, increase in energy consumption will lead to large increases in a country's HDI. This is supported by Martinez and Ebenhack (2008), who calculated that addition of 400 kg of oilequivalent per capita in the poorest nations with HDI values less than 0.4 will support a doubling of their HDI. However, as a country develops, the importance of energy in establishing higher HDI diminishes. Therefore for high and medium human development levels, simply increasing energy consumption is not enough to maintain its human development progress. In this case, a combination of factors such as more efficient energy use, development of energy-saving technologies, establishing appropriate social welfare systems and others are necessary to achieve and maintain high HDI.

Maintenance of high HDI would require policies targeting efficient energy use both on personal and company-based level and promoting energy-efficient technologies. Such policies should be country-specific and reflect current energy mix, industrial structure, potential fossill fuel and alternative energy resources, exisiting climate change mitigation policies (e.g. environmental taxes, subsidies for clean energy initiatives, creating a market for pollution, etc) and global action in climate change mitigation. For example, Canada and Germany are the world leaders in terms of direct geothermal energy and solar power respectively.

At the same time, low HDI countries should reduce energy poverty by creating essential infrastrure, changing their energy consumption mix and establishing access to modern energy sources. For instance, low HDI nations such as Nairobi and Gabon are largely dependent on biomass (firewood, charcoal or dung) as the primary energy source, which is not efficient energy source and highly GHG pollusive. Effors targeting establishing access to

b. Creation of essential infrastructure and establishing access to electricity in developing countries. In 2009 the number of people without access to electricity was 1.3 billion or almost 20% of the world's population (IEA, 2011). The speed of electrification in

c. Changes in the energy consumption mix towards more efficient energy use and a shift towards alternative energy in developed countries and some developing. For example, in 2009 more than 84% of energy produced in Brazil was due to alternative energy sources, the largest of which was sugar cane ethanol. Although the shift towards alternative energy resources is still in the introductory stages, there is a lot of research underway in terms of solar energy, algae and wave energy. At the same time, technology for some energy sources, such as direct geothermal, has been already

d. Introduction of the climate change mitigation policies in both developed and developing nations in order to prevent dangerous anthropogenic interference with the climate system. Such policies target reduction in GHG gases, which can be achieved due to a reduction in energy consumption and more efficient energy use. Examples of such policies are carbon taxes and emissions trading schemes (ETS). While ETS are more recent instrument (e.g. ETS to control GHG in European Union have been operational since 2005), carbon taxes have been used since 1990s. ETS have been proposed to be introduced in Australia, Japan, US, Canada, Korea, India and China in

Figure 7 below shows that relationship between the HDI and energy consumption per capita (in tonnes of oil equivalent) is not linear. This means that at low human development levels, increase in energy consumption will lead to large increases in a country's HDI. This is supported by Martinez and Ebenhack (2008), who calculated that addition of 400 kg of oilequivalent per capita in the poorest nations with HDI values less than 0.4 will support a doubling of their HDI. However, as a country develops, the importance of energy in establishing higher HDI diminishes. Therefore for high and medium human development levels, simply increasing energy consumption is not enough to maintain its human development progress. In this case, a combination of factors such as more efficient energy use, development of energy-saving technologies, establishing appropriate social welfare

Maintenance of high HDI would require policies targeting efficient energy use both on personal and company-based level and promoting energy-efficient technologies. Such policies should be country-specific and reflect current energy mix, industrial structure, potential fossill fuel and alternative energy resources, exisiting climate change mitigation policies (e.g. environmental taxes, subsidies for clean energy initiatives, creating a market for pollution, etc) and global action in climate change mitigation. For example, Canada and Germany are the world leaders in terms of direct geothermal energy and solar power

At the same time, low HDI countries should reduce energy poverty by creating essential infrastrure, changing their energy consumption mix and establishing access to modern energy sources. For instance, low HDI nations such as Nairobi and Gabon are largely dependent on biomass (firewood, charcoal or dung) as the primary energy source, which is not efficient energy source and highly GHG pollusive. Effors targeting establishing access to

systems and others are necessary to achieve and maintain high HDI.

developing countries is still relatively slow, but it is happaning.

established.

the near future.

respectively.

Fig. 7. Energy consumption per capita and the HDI (2007).

modern energy, electrification and creation of essential infrastrucuture are more likely to achieve improvement in HDI. However, global efforts together with inidividual low HDI country efforts might be necessary in order to achieve improvement in human development.

For example, lets consider Australia and Kenya as HHD (HDI = 0.970) and MHD (HDI = 0.541) nations in 2007 respectively. It should be noted that in the beginning of the sample, Kenya, which is now largest economy in East Africa, had lower HDI value. However in a less than decade government policies on improving human development with the help of international organizations (for example, UN World Food Programm since 2004 was installing energy-efficient stoves in Kenyan schools) have been relatively successful, although a lot of challeneges still remain. While primary energy sources in Australia are brown and black coal and natural gas, Kenya is largely dependent on biomass (wood), imported crude oil and electricity with respective shares 70 per cent, 21 per cent, and 9 per cent of total energy use (UNEP, 2006). While in Australia, major electricity source is coal, in Kenya major sources of electricity are hydro, geothermal and thermal power (UNEP, 2006). Governments of these two countries face different challenges, namely maintaining already high HDI (Australia) and achieving improvement in HDI (Kenya). In both cases, this would require efficient use of energy resources, but for Australia this would also mean significant climate change mitigation policy constraints. For example, Australia has pledged to reduce its GHG emissions (the primary means of achieving is goal is transitional carbon tax on producers and introduction of a national mandatory emissions trading scheme in 2015) and increase investment in alternative energy such as direct geothermal and wave energy. For Kenya, where 80% of population depends on biomass as the primary source of energy, the challenges lie in improving electricity generation and distribution, creating essential transmission and distribution infrastructure, reducing the cost of electricity, reducing its dependence on crude oil imports and investing in green energy sources (UNEP, 2006). However, the poverty still remains acute in Kenya due to high income inequality, disproportionate access to essential resources including land, susceptibility to natural disasters such as floods and still inadequate access to basic social services including education (Hendriks, 2010, p.99).

Energy Consumption Inequality and Human Development 115

Druckman A, Jackson T., 2008. Measuring resource inequalities: the concepts and methodology for an area-based Gini coefficient. Ecological Economics, 65, 242–52. Duro, J.A., Padilla, E., 2006. International inequalities in per capita CO2 emissions: a decomposition methodology by Kaya factors. Energy Economics, 28, 170–187. Foster, J.E., 1985. Inequality measurement. Proceedings of Symposia in Applied

Foster, J.E., Shorrocks, A.F., 1988. Inequality and poverty orderings. European Economic

Giannini Pereira, M., Vasconcelos Freitas, M. A., Fidelis da Silva, N., 2011. The challenge of

Hedenus, F., Azar , C., 2005. Estimates of trends in global income and resource inequalities.

Heil, M.T., Wodon , Q.T., 1997. Inequality in CO2 emissions between poor and rich

Heil, M.T., Wodon ,Q.T., 2000, Future inequality in CO2 emissions and the impact of abatement proposals. Environmental and Resource Economics, 17, 163–181. Hendriks, B., 2010, Urban livelihoods, institutions and inclusive governance in Nairobi.

International Energy Agency (IEA), 2011. Access to electricity. Available online at:

Jacobson, A., Milman, A. D., Kammen, D. M., 2005. Letting the (energy) Gini out of the

Lambert, P.J., 1993. The Distribution and redistribution of income: a mathematical analysis.

Moriarty, P., Honney, D., 2010. A Human needs approach to reducing atmospheric carbon.

Opschoor, H., 2009. Sustainable development and a dwindling carbon space. Environmental

Pachauri, S., Spreng, D., 2003. Energy use and energy access in relation to poverty. Centre

Padilla, E., Serrano, A., 2006. Inequality in CO2 emissions across countries and its

Pasternak, A. D., 2000. Global energy futures and human development: a framework for

Rosas-Flores, J.A., Galvez, D. M., 2010. What goes up: Recent trends in Mexican residential

for Energy Policy and Economics—Swiss Federal Institutes of Technology,

relationship with income inequality: a distributive approach. Energy Policy, 34,

analysis. US Department of Energy Report UCRL-ID- 140773, Lawrence Livermore

metrics of energy distribution and equity. Energy Policy, 33, 1825–1832. Jacmart, M. C., Arditi, M., Arditi, I., 1979. The world distribution of commercial energy

Kolm, S.-C., 1976a. Unequal inequalities I. Journal of Economic Theory, 12, 416–442. Kolm, S.-C., 1976b. Unequal inequalities II. Journal of Economic Theory, 13, 82–111.

'Spaces' and their impacts on quality of life, influence and political rights.

bottle: Lorenz curves of cumulative electricity consumption and Gini coefficients as

energy poverty: Brazilian case study. Energy Policy, 39, 167–175. Groot L., 2010. Carbon Lorenz curves. Resource and Energy Economics, 32, 45–64.

countries. Journal of Environment and Development, 6, 426–452.

Mathematics, 33, 31–68.

Ecological Economics, 55, 351–364.

Amsterdam University Press.

Energy Policy, 38, 695-700.

1762–1772.

http://www.iea.org/weo/electricity.asp

consumption. Energy Policy, 7, 199–207.

Working Paper No. 25, Zurich, Switserland.

National Laboratory, Livermore, CA.

energy use. Energy, 35, 2596-2602.

Manchester Univ. Press, Manchester and New York.

and Resources Economics, DOI 10.1007/s10640-009-9332-2.

Review, 32, 654–662.

Since HDI is composed of three elements (longevity as proxied by the life expectancy at birth, education as proxied by the gross enrollment and adult litarcy indeces, standard of living as proxied by the GDP per capita), its improvement or maitenance would require achieving progress in all of them. For instance, achieving higher economic growth is no possible without energy use, but the strategies will be different for HHD, MHD and LHD because of different energy mix, different demographic characteristics and different techological levels (more pollusive in developing nations). Due to existing infrastrucuture faciliting as well as technologies, high HDI countries should take a leadership role on reducing energy consumption, reducing emissions and improving energy efficiency measures that could help fostering replicable models of the good quality life that are based on much lower energy consumption levels.

Potential direction for future research would be analysis of causality between energy consumption and human development. Causal relationship (i.e. lead-lag relationsip) between energy consumption and GDP has been examined in the literature at the countryspecific level, as well as based on the panel data analysis. In summary, the findings of these research efforts are mixed and largely depend on the time period of analysis, energy mix and level of country's economic development. However, current literature gap lies in stydying causality between energy consumption, including different enenrgy sources, and development indicators other than GDP, such as for example Human Development Index.

#### **6. References**

www.treasury.gov.au/carbonpricemodelling/content/report.asp

Since HDI is composed of three elements (longevity as proxied by the life expectancy at birth, education as proxied by the gross enrollment and adult litarcy indeces, standard of living as proxied by the GDP per capita), its improvement or maitenance would require achieving progress in all of them. For instance, achieving higher economic growth is no possible without energy use, but the strategies will be different for HHD, MHD and LHD because of different energy mix, different demographic characteristics and different techological levels (more pollusive in developing nations). Due to existing infrastrucuture faciliting as well as technologies, high HDI countries should take a leadership role on reducing energy consumption, reducing emissions and improving energy efficiency measures that could help fostering replicable models of the good quality life that are based

Potential direction for future research would be analysis of causality between energy consumption and human development. Causal relationship (i.e. lead-lag relationsip) between energy consumption and GDP has been examined in the literature at the countryspecific level, as well as based on the panel data analysis. In summary, the findings of these research efforts are mixed and largely depend on the time period of analysis, energy mix and level of country's economic development. However, current literature gap lies in stydying causality between energy consumption, including different enenrgy sources, and development indicators other than GDP, such as for example Human Development Index.

Atkinson, A.B., 1970. On the measurement of inequality. Journal of Economic Theory, 2,

Australian Government, 2011. Strong Growth, Low pollution- modelling a carbon price.

Banerjee, A., Yakovenko, V. M., 2010. Universal patterns on inequality, New Journal of

Birol, F., 2007. Energy economics: a place for energy poverty in the agenda?. The Energy

Blackorby, C., Donaldson, D., 1978. Measures of relative equality and their meaning in terms

Brune N., Garrett G., 2005. The globalization Rorschach test: international economic

Chakravarty, S.R., 1990. Ethical social index numbers. Springer-Verlag, Heidelberg, New

Champernowne, D.G., Cowell, F.A., 1998. Economic inequality and income distribution.

Cowell, F.A., 1995. Measuring income inequality (2nd ed.), Harvester Wheatsheaf, Hemel

Dalton, H., 1920. The measurement of the inequality of incomes. Economic Journal, 30, 348–

Dasgupta, P., Sen, A., Starrett, D., 1973. Notes on the measurement of inequality. Journal of

integration, inequality, and the role of government. Annual Review of Political

www.treasury.gov.au/carbonpricemodelling/content/report.asp

of social welfare*.* Journal of Economic Theory, 18, 59–80.

York,London, Paris, Tokyo, Hong Kong.

Cambridge Univ. Press, United Kingdom.

Economic Theory, 6, 180–187.

Available online at:

on much lower energy consumption levels.

**6. References**

244–263.

Physics, 12, 075032.

Journal, 28, 01–06.

Science*,* 8, 399–423.

Hempstead.

361.

**Part 2**

**Energy Efficiency on Demand Side**


**Nonlinear Analysis and Optimization with Applications**

Editors

**Wei-Shih Du Liang-Ju Chu Fei He Radu Precup**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Wei-Shih Du Department of Mathematics, National Kaohsiung Normal University Taiwan Radu Precup Department of Mathematics, Babes¸-Bolyai University Romania

Liang-Ju Chu Department of Mathematics, National Taiwan Normal University Taiwan

Fei He School of Mathematical Sciences, Inner Mongolia University China

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Axioms* (ISSN 2075-1680) (available at: https://www.mdpi.com/journal/axioms/special issues/nonlinear analysis).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-2045-2 (Hbk) ISBN 978-3-0365-2046-9 (PDF)**

© 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.