A Model For The Quality Of Life As A Function Of Electrical Energy Consumption

INTRODUCTION
Only about one-quarter of more than 4 billion people on this planet live in countries where the average food consumption is well above physiological needs, where infant mortality is relatively low (typically below 25 per 1000 live births), life expectancy is high (around 70 years), and literacy approaches 100%. These are the world's most developed nations: one-quarter of mankind consuming four-fifths of the commercial energy consumed annually and enjoying a quality of life unsurpassed in history.

For the remaining three-quarters of the human population, conditions are painfully different. The overwhelming majority of these people are illiterate or semi-literate poor villagers surviving on less than adequate diets, whose infant mortality is an order of magnitude higher than in the developed world and whose life expectancy is as much as three decades shorter. The difficult present and less than promising future of this developing world, or as some prefer, the less developed countries (LDC) or underdeveloped Third World is, to a very large extent, the result of relatively low consumption of commercial energy.¹

In developing countries, agriculture is the main source of biomass fuel, as well as one or the main energy-consuming sectors. The energy captured through agriculture in crops and crop residues provides food for people and fodder for draft animals, Dung and crop residues are used for cooking and heating.^2,3 During the past two decades, these traditional energy sources have been supplemented by the use of coal, oil and electricity in agriculture, transport, industry, and the domestic sectors. The most striking feature of energy use in the Third World is that the amount of useful work which the poor obtain from the energy they use is relatively small.⁴ When the inputs to agriculture (including directly applied energy) are increased properly, the energy outputs per worker and per unit of land increase. Energy obtained from the consumption and sale of crops is, in turn, needed to increase the input to agriculture to raise crop yields, extend irrigated land, increase multicropping, mechanize construction and repairs of water projects, build modern roads, and, in general, improve the quality of life of the peasants. The rate with which the developing countries move toward the distant goal of rural modernization is largely determined by the direct and indirect energy flows into agriculture, which may be expected to make up a larger fraction of energy consumption in the future than at present.

The standard of living or quality of life achieved in any community and for any group of people may be measured, for practical purposes, by the quantity of total energy used per capita.⁵ It has been widely recognized that the preceding statement is more appropriate for societies in which the production and distribution of energy is secure and widely spread than for LDCs.

Several studies have been reported on increasing energy requirements for economic development and enhancing the quality of life, Smil⁶ reports that economic growth and energy consumption are closely related. Smil and Knowland⁷ state that energy is the prime mover for economic growth and development. Revelle⁸ comments that men and women in rural areas are tied to poverty and misery because they use too little energy and use it inefficiently. In addition, Rahman and Huq⁹ claimed that there was a distinct correlation between energy consumption and the overall economic conditions in any country. Brisco and Bari¹⁰ pointed out that the socio-economic position of rural people will deteriorate rapidly unless increased inputs of technology and energy are made into agriculture. Friedlander¹¹ noted that in order to achieve an Improved standard of living, developed countries have encouraged the consumption of energy at a very fast rate Dalal⁵ emphasized that electrical energy consumption is an indicator of economic condition. Hoque et al¹² state that the total energy produced and obtained from all possible sources is an index of the physical quality of life.

Forrester¹³ estimated the physical quality of life by using pollution, population density, food consumption, and resources as indicators. Islam¹⁴ quantified the quality of life by using 28 variables. Alam et al¹⁵ reported that the physical quality of life in developing countries depends on fundamental needs and energy consumption in various forms and quantified it in terms of cooking energy (in the form of bio-gas) and population density. Martin¹ calculated the physical quality of life index for different countries by using an average life expectancy (LE) at age 1 year, infant mortality (IM), and literacy rates (LR).

In this paper, we present an empirical equation showing the relation between the physical quality of life and the per capita electrical energy consumption in kilowatt hours.

Fig. 1. Comparison of reported ( + ) and correlated values ( — ) of LE vs ln(kWh/cap).

Fig. 2. Comparison of reported ( + ) and correlated values ( — ) of LR vs ln(kWh/cap).

In Fig. 1, we show the correlation (line) together with actual data for LE as a function of per capita energy consumption and the regression equation is:

Fig. 3. Comparison of reported ( + ) and correlated values ( — ) of IM vs ln(kWh/cap).

Fig. 4. Comparison of reported ( + ) and correlated values ( — ) of PQLI vs LE.

Fig. 5. Comparison of reported ( + ) and correlated values ( — ) of PQLI vs LR.

Fig. 6. Comparison of reported ( + ) and correlated values ( — ) of PQLI vs IM.

In Figs. 2 and 3, we also show the correlation (line) together with actual data for each or the variables LR and IM as a function of per capita energy consumption respectively and the corresponding regression equations are:

Again, we show the correlation (line) together with actual data for physical quality of life as a function of each of the variables LE, LR and IM in Figs. 4, 5 and 6 respectively and the corresponding regression equations are:

Martin's estimation and examination of Figs. 1 6 and of Eqs. (1) (6) suggests the following functional relations:

where PQLI = physical quality of life. Equations (7) and (8) suggest the relation

where A may be taken as a technological constant. This technological constant depends on the form and mode of energy use and on energy-use efficiency. The technological constant will increase in value with technology advancement. The parameter A may be estimated by regression analysis. We find:

Fig. 7. Comparison of reported ( + ) and correlated values ( — ) of PQLI vs kWh/cap.

Other measures of the utility of our model are the coefficient of correlation r and rank correlation coefficient r(rank), where r = 0.89 and r(rank) -0.98. These large values confirm the conclusion that PQLI and ln(kWh/cap) are highly correlated.

Figure 7 shows the correlation between PQLI and kWh/cap. The agreement is fairly good. Figure 8 shows the residuals (PQLI_i - PQL1_i) vs ln(kWh/cap). Since the residuals are randomly scattered about the reference tine, we have additional evidence that our regression model correlates the data well.

Fig. 8. Residuals (PQLI_i - PQL1_i) from Eq. (10) and random scatter.

1. M. Martin, "Economic and Social Indicators of Development, by Income Groups and by Region," in Energy in the Developing World, pp. 372-386, Oxford University Press. Oxford (1980).
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