Assessment of access to electricity and the socio-economic impacts in rural areas of developing countries

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Energy Policy 36 (2008) 2016–2029 www.elsevier.com/locate/enpol

Assessment of access to electricity and the socio-economic impacts in rural areas of developing countries Makoto Kanagawa, Toshihiko Nakata Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Aoba-Yama 6-6-11-815, Sendai 980-8579, Japan Received 10 April 2007; accepted 30 January 2008 Available online 18 April 2008

Abstract The purpose of this study is to reveal relations between access to electricity and advancement in a socio-economic condition in rural areas of developing countries. Recently, multi-dimensional aspects of poverty, for example, economy, education, and health, has been increasingly focused on, and access to modern energy such as electricity is one possible solution. As a case study, we have analyzed unelectrified rural areas in Assam state, India. We have developed an energy-economic model in order to analyze the possibility of electrification through dissemination of electric lighting appliances as well as applied multiple regression analysis to estimate the socioeconomic condition, a literacy rate above 6 years old, in the areas. As a result of the case study, the household electrification rate, the 1000 km2 road density, and sex ratio have been chosen as the explanatory variables of the literacy rate. Moreover, the model analysis shows that complete household electrification will be achieved by the year 2012. In combination with the multiple regression and model analysis, the literacy rate in Assam may increase to 74.4% from 63.3%. r 2008 Elsevier Ltd. All rights reserved. Keywords: Energy access; Energy poverty; Rural electrification

1. Introduction Poverty is a major obstacle for sustainable development of not only developing countries but also the entire world. It has been the main objective of the bilateral and multilateral donors, together with economic growth. Nowadays, poverty is defined as low attainment of social condition, for example, education, health, and nutrition in addition to economic deprivation. One way to cope with this multidimensional aspects of poverty is to promote opportunity (World Bank, 2001), and one of the opportunities is access to modern energy such as electricity. In many literatures related to condition of energy consumption in rural areas of developing countries, the term ‘‘energy access’’ is used to refer to the situation where people can secure the modern energy, which is commonly consumed in developed countries, at affordable prices (Bhattacharyya, 2006, in press; Spalding-Fecher et al., 2005). The definition of the term ‘‘energy poverty’’ is, then, the situation in which Corresponding author. Tel./fax: +81 22 795 7004.

E-mail address: [email protected] (T. Nakata). 0301-4215/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2008.01.041

energy access is not established yet (Pachauri et al., 2004; Sagar, 2005). Works dealing with developmental issues from the field of energy can be mainly divided into three categories, and their characteristics and references are the following:





Descriptive study (Aggarwal and Chandel, 2004; Bastakoti, 2003; Dung et al., 2003; Gangopadhyay et al., 2005; Rehman et al., 2005)—It describes current situations of energy demand or consumption as well as policy and program in developing countries. It also investigates critical components of the policy and programs, and evaluates the outcomes. Although it includes various aspects of the policy and programs such as legal, social, and fiscal, most of the study is qualitative evaluation, which is highly case-oriented, and it is difficult to obtain ideas applicable to other areas. Experimental study (Bhattacharya et al., 2002; Chakrabarti and Chakrabarti, 2002; Masera et al., 2000; Wijayatunga and Attalage, 2002)—It tests technological

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or economic efficiency of devices or appliances in order to compare technologies adopted by rural households. It measures not only the data of energy demand, consumption, and expenditure but also emissions of hazardous pollutants, which cause indoor pollution. Although it contains highly disaggregated or highly precise data, policy implication to promote these technologies is not sufficiently discussed based on the results. Analytical study (Bailis et al., 2005; Biswas et al., 2001; Howells et al., 2005; Mathur et al., 2003; Pachauri et al., 2004; Parikh and Ramanathan, 1999)—It analyzes energy demand or consumption structure of a developing country, and applies an analytical tool to energy demand and supply structure at village, regional, and national level, taking into account economic and technological parameters. It contains model analysis, which is divided into top-down and bottom-up modeling approaches. Moreover, it can incorporate emissions associated with energy consumption such as greenhouse gas emissions and government policies, for example, environmental tax.

There are a large number of literatures for the descriptive and experimental studies. In contrast, there are a limited number of researches categorized as the analytical study. In particular, few researches estimate socio-economic effects of results of analyses. Given that nowadays poverty is regarded as a lack of socio-economic welfare, it is unavoidable to consider socio-economic impacts of transition or improvement of energy sources consumed in developing countries. With respect to this point, the model analysis with a bottom-up modeling approach, in combination with the estimation of socio-economic aspects, has a Health - Using modern energy reduces exposure to hazardous pollutants. - Avoiding drudgery such as collecting fuelwood improves health condition of, in particular, women and children. - Access to electricity enables vaccination and medicine storage by a refrigerator.

potential to reveal the links between energy access improvement and poverty eradication as shown in the previous work of the authors (Kanagawa and Nakata, 2007). Therefore, we have developed an energy-economic model with bottom-up modeling approach and applied it to rural areas of developing countries in order to clarify the possibility of energy access improvement. Furthermore, socio-economic impacts are incorporated into the analysis. 2. Energy and poverty 2.1. Energy and Human Development Index (HDI) Energy influences socio-economic condition of developing countries as shown in Fig. 1. In particular, access to modern energy like electricity will drastically improve the quality of life of those who do not have yet. There has also been increasing attention on poverty reduction through energy access improvement among international organizations in the energy field. For example, recently the International Energy Agency (IEA) has been focusing on the topic through the improvement of energy demand and supply situations in developing countries, devoting a chapter to explain the roles of energy for the development in its World Energy Outlook 2002 (IEA, 2002). It mentions that some 2.4 billion people depend on traditional biomass such as wood, agricultural residues, and dung for their cooking and heating demand and that there is one fourth of the world’s population, about 1.6 billion people, who does not have access to electricity. Furthermore, most of them are in rural areas. It is estimated that 2.6 billion people will not improve their energy situation for cooking and heating and 1.4 billion people will not have electricity access by 2030. The lack of energy access also causes Education - Lighting appliances enables to study at night. - Utilization of modern energy results in freeing up from drudgery and creating time for study. - Electricity helps narrow the digital divide through Information Communication Technologies (ICTs).

Energy

Income - Enterprise development through electrification creates job. - Mechanization in industry achieves higher productivity. - Small-scale energy system in rural areas generates local industry.

2017

Environment - Reduction in use of fuelwood pretends deforestation. - Use of efficient electric appliances saves energy consumption. - Application of renewable energy promotes climate protection.

Fig. 1. Links between energy and other components of poverty.

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105

1

104

0.8

103

0.6 HDI [-]

GDP per capita [US$]

2018

102

0.4

10

0.2

Log(y) = 0.6452 Log(x) + 1.7655

y = 0.2139Log(x) + 0.0688 R2 = 0.8288

R2 = 0.8081 0

1 1

102

103

104

10 Electricity consumption per capita [kWh]

105

1

10

102 103 104 Electricity consumption per capita [kWh]

105

Fig. 2. Relation between electricity consumption per capita and GDP per capita in 2002 (based on IEA, 2005; UNDP, 2004).

Fig. 3. Relation between electricity consumption per capita and HDI in 2002 (based on IEA, 2005; UNDP, 2004).

serious adverse effects on the socio-economic condition of rural population. Therefore, achieving energy access improvement has huge impacts on people’s lives in rural areas of the developing countries. Nowadays, development strategy has been increasingly focusing on microlevel, or software of the developmental issues, in other words, human development. Access to modern energy enables people to not only have economic opportunities for income generation but also save their time from time-consuming drudgery and allocate to more enjoyable or educational activities. Considering electricity as a representative of modern energy, electricity consumption has significant correlation with GDP as well as HDI for 120 countries, and the countries which mark high consumption level of per capita electricity, attain upper rank of both economic activities (GDP per capita) and HDI as shown Figs. 2 and 3, respectively.

and the MDGs, classifying into direct and indirect contributions (DfID, 2002). For one of the MDGs, gender equality and women’s empowerment, energy access improvement directly contributes to freeing up women and girls from time-consuming housework such as laundry, cleaning, etc. by utilization of electricity. In addition, through reduction of time-consuming chores and attainment of energy services, it has indirect contributions for women to have opportunity to attend schools or educational activities as well as take into a part in the labor market or establish small enterprises. As a result, gender equality and empowerment of women are promoted. Of the various socio-economic conditions, education is one of the most indispensable components to be considered in order for developing countries to achieve poverty alleviation.

2.2. Energy and the Millennium Development Goals (MDGs)

Education is also widely recognized as one of the most essential components for poverty reduction according to current discourses of developmental studies, which conclude that inequality of income affects opportunities of education. Moreover, primary education generally shows the highest return to investment. Poor households attain less enrollment and completion of schools because direct and indirect educational expenditures are considerable burdens. This results in a perceptibly lower literacy rate of these households than that of middle or high income households. Such low-level attainment of education causes a lack of employment opportunity for poor households, and, even though there is the opportunity, these poor households cannot earn sufficiently for their basic needs.

The MDGs are the numerical target that should be met by the year 2015 and were adopted at the UN General Assembly in 2000. The MDGs consist of eight goals as shown below (UN, 2006): Eradicate extreme poverty and hunger; achieve universal primary education; promote gender equality and empower women; reduce child mortality; improve maternal health; combat HIV/AIDS, malaria and other diseases; ensure environmental sustainability; and develop a global partnership for development. The Department for International Development (DFID) of the United Kingdom mentions links between energy

2.3. Energy and education

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3.2. Procedure of the analysis

1

The procedure of the analysis of lighting demand is the following:

0.8

Education index [-]

2019

1. Select a socio-economics factor related to energy consumption for lighting by multiple regression analysis; a literacy rate above 6 years old. 2. Design an energy-economic model of analyzed areas in order to reveal the possibility of energy access improvement, that is, dissemination of electric lighting appliances. 3. Estimate the impacts of the energy access improvement on the literacy rate in the areas.

0.6

0.4

0.2 y = 0.1896Log(x) + 0.2173 R2 = 0.6625

3.3. Analyzed areas

0 1

10

103 104 102 Electricity consumption per capita [kWh]

105

Fig. 4. Relation between electricity consumption per capita and the education index in 2002 (based on IEA, 2005; UNDP, 2004).

Along with the effects on economic condition, through educational achievement poor people are able to attain confidence, sociality, concern for society, participation for social activity, resistance for oppression, and involvement in the political processes. Furthermore, education has not only direct effects on those who are educated but also external and indirect effects on their household/family, community, and society toward poverty reduction (Okada, 2004). Energy access improvement, in particular access to electricity, has huge impacts on education. For example, it reduces such drudgery and allows children to expand their opportunity for school attendance and other educational activities. Also, due to electrification, rural households obtain sufficient luminescence for study in a household at night and are able to utilize TV, radio, and Information and Communication Technologies for educational purposes. Thus, access to electricity and other modern energy creates child-friendly educational environment, and, in fact, electricity consumption per capita is related to the education index, a component of HDI, as shown in Fig. 4 (IEA, 2005; UNDP, 2004).

3. Method of the analysis 3.1. Definition of energy access improvement In the study, energy access improvement is defined as ‘‘electrification in analyzed areas through dissemination of electric lighting appliances such as incandescent bulbs, fluorescent tubes, and Compact Fluorescent Lamps (CFL) instead of using kerosene lamps.’’

As a case study, we have chosen rural areas of Assam, India and referred to the data of Sarmah et al. in 2002, such as population, size of households, and so on for the model analysis explained in Section 3.5. There are 1.24 billion people in India, and about 75% of the total population is in rural areas. According to prospects of the UN agency, in spite of rapid urbanization, 818 million people of the total population still live in the rural areas (Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, 2006). With respect to the economy, India is one of the most successful developing countries, and, in fact, its GDP has rapidly risen at an annual growth rate of 4.6% from the year 2001 to 2002. Energy use and electric power consumption in the country as a whole have also increased steadily to 22,540.4 (PJ) and 576.5 billion (kWh) in 2001 at annual growth rates of 3.6% and 6.5% from 1990 to 2001, respectively. However, population living on less than $1 a day and $2 a day are 34.7% and 79.9%, respectively, and thus there is serious inequality in the country (World Bank, 2004). From the viewpoint of energy, is is estimated that there are 585 million people who depend on traditional biomass and the population will increase to 632 million in 2030 (IEA, 2002). For the electricity sector, the Government of India has launched several projects. For power sector development, the Ministry of Power of the Government of India has set ‘‘Mission 2012: Power for All,’’ which includes complete household electrification as well as power supply to achieve continuous economic development of the country (Ministry of Power of the Government of India, 2002). Furthermore, the Rural Electrification Supply Technology Mission was also adopted in 2002, and its purpose is to complete electrification of all villages and households by the year 2012 with renewable energy sources, decentralized technologies, and grid expansion. Fig. 5 shows the relation between electricity consumption per capita and Gross Domestic State Products (GDSP) per capita for 25 states and Union Territories (UTs) in India. The more electricity is consumed in a state,

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2020

100%

Literacy rate above 6 years old [%]

GDSP per capita [US$]

103

102 Assam

10

Log(y) = 0.3633 Log(x) + 1.5453 R2 = 0.4036

1 1

Assam

60%

40%

20%

103 10 102 Electricity consumption per capita [kWh]

Fig. 5. Relation between electricity consumption per capita and GDSP per capita in India in 2001 (based on Central Electricity Authority, 2005; Ministry of Statistics and Programme Implementation, 2006).

y = 0.2764x + 0.511 R2 = 0.3881 0% 0%

20%

40% 60% Electrification rate [%]

80%

100%

Fig. 7. Relation between a household electrification rate and a literacy rate above 6 years old in India in 2001 (based on Banthia, 2003; Office of the Registrar General, 2006).

100

80 Literacy rate above 6 years old [%]

80%

Table 1 Description of the analyzed areas

Assam 60

Population [] Male Female

5958 (54%) (46%)

40

Population growth Energy consumption (GJ) Cooking and water heating (fuelwood) Space heating in winter (fuelwood) Space lighting (kerosene)

1.4% 67,267 (85%) (14%) (1%)

20

Source: CIA (2005), Sarmah et al. (2002).

y = 0.0246Log(x) + 0.2685 R2 = 0.4328 0 1

10 102 Domestic electricity consumption per capita [kWh]

103

Fig. 6. Relation between domestic electricity consumption per capita and a literacy rate above 6 years old in India in 2001 (based on Central Electricity Authority, 2005; Office of the Registrar General, 2006).

the higher per capita economic production, which can be a proxy to the level of a household’s prosperity, is achieved. Also, domestic electricity consumption per capita has correlation with educational attainment, the literacy rate above 6 years old as shown in Fig. 6. Moreover, since domestic electricity consumption per capita is in logarithm in the figure, those households which consume less electricity benefit more due to an additional consumption of electricity. Thus, as shown in Fig. 7, it is rational to consider that electrification for households without electricity access has huge impacts on the literacy rate in a state.

For the analysis, we have targeted villages of the Jorhat district of Assam in India, where fuelwood consists of approximately 85% of total energy consumption and access to electricity is not established (Sarmah et al., 2002). Table 1 summarizes the size and energy consumption patterns of the areas. Although the analyzed areas are strictly specified, the framework of the analysis including concepts of estimation of the educational attainment is applicable to other rural areas in developing countries. 3.4. Literacy rate Educational attainment such as a literacy rate and enrollment in schools is one of the most fundamental elements of economic and social development. High illiterate rates have been the major obstacle of further progress in developing countries, preventing poor population from income-generating activities and attaining empowerment. It is presumed that advancement in the

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3.5. Energy-economic model

Table 2 Possible factors influencing on the literacy rate Factor

Year

Economic GDSPa per capita in logarithm Expenditure on education etc. per capita in logarithm

2000–01 2001–02

Educational Number of teachers in primary schools per 1000 persons Number of primary schools per 1000 persons

2002–03 2002–03

Geographical Railway density per 1000 km2 Road density per 1000 km2

2000–01 1998–99

Social/cultural Household size Sex ratiob Ratio of Scheduled Caste Ratio of Scheduled Tribe Ratio of Hindus Ratio of Christian

2001 2001 2001 2001 2001 2001

Electricity Electricity consumption per capita in logarithm Domestic electricity consumption per capita in logarithm Household electrification rate

2000–01 2000–01 2001

Source: Banthia (2003), CMIE (2004), Ministry of Human Resource Development of the Government of India (2005), Ministry of Statistics and Programme Implementation (2006), Office of the Registrar General (2006), RBI (2002). a GDSP: gross domestic state products. b Sex ratio ¼ number of female/number of male.

literacy rate is affected by five aspects: economic, educational, geographical, and social/cultural aspects as well as an aspect of electricity. In order to specify factors quantitatively, which influence the literacy rate, multiple regression analysis has been applied. First, based on literature surveys, possible factors of 19 states and UTs in India have been listed up as shown in Table 2. Second, applying a stepwise method, we have selected explanatory variables, which explain a literacy rate above 6 years old in India with statistical significance at 0.5 in p-value. Utilizing this method, we have been able to determine a regression equation, which explains the relation between explained and explanatory variables among possible factors. The possible factors considered in the regression analysis are justified by the following explanation: GDSP per capita, Expenditure on education, etc. per capita, Number of primary schools per 1000 persons, Number of teachers in primary schools per 1000 persons, Road density per 1000 km2, Rail density per 1000 km2, Household size, Sex ratio, Ratio of Scheduled Caste, Ratio of Scheduled Tribe, Ratio of Hindus, Ratio of Christian, Electricity consumption per capita, Domestic electricity consumption per capita and Household electrification rate. These factors are explained in more detailed in the Appendix of this study.

3.5.1. Method of the analysis For the analysis, we have developed an energy-economic model of rural areas in India, the energy access model, based on both economic and technological parameters of energy conversion processes, and adopted a nonlinear optimization tool. The energy access model is shown in Fig. 8. The model consists of 40 nodes: 5 end-use nodes (cooking demand, lighting demand, etc.), 28 technological conversion nodes (traditional wood stove, improved wood stove, gas stove, etc.) including seven market nodes (heat market for cooking, electricity market, etc.), and seven resource nodes (fuelwood, LPG, etc.). We have applied the META  Net economic modeling system being developed at Tohoku University and the Lawrence Livermore National Laboratory, as an analysis tool (Lamont, 1994). META  Net is a partial equilibrium modeling system that allows for explicit price competition between technologies (in this study, lighting devices and generating technologies) at the market nodes, in a network of mainly four types of node explained above. It finds the multiperiod equilibrium prices and quantities of the network and the solution includes the prices and quantities of each resource, technology and device along with the capacity additions for each conversion process (in this study, selection of a lighting device, for example). This analysis tool has been already used to analyze energy systems of national and regional levels, and is also compatible with a local level without any particular modification. Further explanation of the details is available in the previous studies (Kanagawa and Nakata, 2006; Nakata, 2004). The periods of the analysis are from the year 2004 to 2012. In this type of analyses, there are several key assumptions for simplification. It is assumed that total energy demand in the areas increases linearly during the analysis periods according to population growth of the areas referred to the annual growth rate of India, 1.4% (CIA, 2005). There is another assumption that other demographic conditions, for example, average number of people in a household and outflow of the population to urban cities, are constant. Cost parameters for lighting devices, generating technologies and resources are provided in Tables 3–5, respectively. As for the generating technology, only community based electrification and grid electricity have been considered, in terms of sustainable utilization of electricity, although Solar Home System is one of the useful options for electrification in rural areas. It is assumed that the costs of the devices, technologies and price escalations of the resources are constant during the period of the analysis. For kerosene and LPG, currently the Government of India subsidizes the prices and will reduce the subsidy, and there is the assumption that their prices will be at the international level. According to the initial size and growth rate of the market, we have had assumption about market of electric lighting appliances, that is, an incandescent bulb, fluorescent tube, and CFL (Kumar et al., 2003; Ramaswamy, 2004).

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2022 Space heating

Gas. Ht

Cooking

Lighting

TV&Radio

SH. Dmd

Cook. Dmd

Light. Dmd

SH. Mkt

Cook. Mkt

Light. Mkt

Imprv. Wood. Ht

Trad. Wood .Ht

Trad. Wood .Stv

Imprv. Wood. Stv

Gas. Stv

Inc. Blb

Krs. Lmp

Electricity Wood. Mkt

Gas. Mkt

Flr. Tub

CFL

Radio. Dmd

TV

Radio

Accum. Elect Mkt. Elect

Dsl. Elect

Wood. Clct

Wood. Rsc

TV. Dmd

Krs. Rsc

LPG. Rsc

Abbreviation: SH: Space heating Cook: Cooking Imprv: Improved Stv: Stove Tub: Tube CFL: Compact Krs: Kerosene Dsl: Diesel

Dsl .Rsc

Light: Lighting Dmd: Demand Lmp: Lamp Inc: Incandescent Fluorescent Lamp Ivr: Inverter Clct: Collection Cnct: Connection

PV. Ivr

Wind. Ivr

PV. Btr

Wind. Btr

PV. Elect

Wind. Elect

Solar .Rsc

Wind .Rsc

Grid. Cnct

Grid .Rsc

Mkt: Market Trad: Traditional Blb: Bulb Flr: Fluorescent Btr: Battery Elect: Electricity Rsc: Resource

Fig. 8. Energy access model. Table 3 Costs of lighting devices

Kerosene lamp Incandescent bulb Fluorescent tube CFLc

Equipment costa (US$)

Unit costa (US$)

Life (h)

0.602 0.736

0 0.241

5b 1000

0.400 0.060

6.024

1.205

10,000

0.040

0.736

4.819

10,000

0.011

Energy consumption per unit-hour (kWh)

Source: Jana and Chattopadhyay (2004), Kumar et al. (2003). a US$1 ¼ 41.5 Rs. (World Bank, 2004). b Life for kerosene lamp is in years. c CFL: compact fluorescent lamp.

3.5.2. META  Net Economic Modeling System The META  Net Economic Modeling System has been developed jointly by Nakata laboratory at Tohoku University and Lawrence Livermore National Laboratory, USA. In this sub-section, the analysis tool used in this study is explained according to the user’s guide (Lamont,

1994). It is categorized as a bottom-up model, which deals with disaggregate process of energy production and conversion process by network of nodes. In addition, it is a partial equilibrium model, based on technological and economic characteristics of technologies, for example, electric power generation in an electricity sector or heating and electric equipments in an industry sector. The modeling approach is characterized as the network modeling approach. It represents the economy as a network of nodes, and each node models actual actors in the economy, for example, end-users, conversion technologies, and resources. Among these entities, signals of price and quantity are sent and received. Furthermore, it is mentioned that the META  Net can take into account constraints on prices and quantities as well as various taxes and constraints on environmental emissions affected by government policies. 4. Results of the analysis 4.1. Multiple regression analysis As a result of the multiple regression analysis, the following three explanatory variables are selected to

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Table 4 Cost of power-generating technologies

Diesel generator PV Wind Grid electricityc Batteryd Inverterd

Capital costa (US$/kW)

Fixed O&Mb (US$/kW)

Variable O&M (mills/kWh)

Life (year)

Efficiency (–)

289 7229 1479 919 1318 591

7.229 180.72 36.97 29.87 0 0

6.024 0 0 1.246 0 0

10 25 20 30 3 6

0.278 1 1 0.234 1.250 1.111

Source: Banerjee (2006), Central Electricity Authority (2005), Chakrabarti and Chakrabarti (2002), Gullberg et al. (2005), TERI et al. (1999), Tongia and Banerjee (1998). a US$1 ¼ 41.5 Rs. (World Bank, 2004). b Fixed O&M is 2.5% of capital cost except grid electricity. c Gas steam power plant. d Battery and inverter are considered for PV and wind. Table 5 Price and price escalation of resources

Kerosene LPG Diesel Natural gas

Table 6 Result of the multiple regression analysis

Price (US$/kWh)

Price escalation

Literacy rate above 6 years old

0.022 0.031 0.030 0.011

0.079 0.087 0.039 0.112

Partial regression coefficient

*

US$1 ¼ 41.5 Rs. (World Bank, 2004). Source: Banerjee (2006), Chakrabarti and Chakrabarti (2002), IEA (2005), UNDP/ESMAP (2003), US DOE, EIA (2002).

estimate a literacy rate above 6 years old in India: household electrification rate, road density per 1000 km2 in logarithm, and sex ratio. This result is shown in Table 6. From 19 observations including 18 states and one UT, the regression equation attains the adjusted coefficient of determination of 0.743. All of the explanatory variables affect the literacy rate positively, and it is compatible with their simple correlation coefficients. All variables, the electrification rate, road density in logarithm, and sex ratio, indicate 1% level of statistical significance. Of the three variables, the household electrification rate is the third significant variable, according to standardized partial regression coefficient; the road density is the first and the sex ratio the second. The partial regression coefficient of the electrification rate means that a percentage point increase in households electrified might increase the literacy rate by 0.17 percentage point. Combined with the result of the model analysis described in the next sub-section, the coefficient is used to estimate the literacy rate achieved by energy access improvement. 4.2. Model analysis Fig. 9 shows changes in lighting demand supplied by lighting devices from the year 2004 to 2012. As the result of the model analysis, it is revealed that complete household electrification, which the Government of India has been targeting, will be achieved by 2012. Thus, the electric lighting appliances are widely adopted by the rural

Constant 0.489 (0.040)a Electrification 0.166 (0.004)b rate Sex ratio 0.683 (0.003)b Log (road 0.142 (o0.001)b density) Adjusted R2 0.743 Observation 19

Standardized partial regression coefficient

0.421 0.425 0.684

Note: Value in the parenthesis is p-value. a 5% significant. b 1% significant.

households in the analyzed areas. On the contrary, a kerosene lamp is replaced although it shows relatively lower cost for initial installation. It is explained that, although the cost and lifetime are competitive to electric lighting appliances, the total lifetime cost of a kerosene lamp for an hour of use is much higher than that of the electric appliances, partly due to its low efficiency. In addition, as the Government of India has decided to phase out subsidies on fossil fuels, the cost of kerosene is anticipated to increase. Of the three electric lighting appliances, a fluorescent tube acquires the largest share in the analyzed areas in 2012, reaching to more than 70% of the number of the households. Although the equipment for fitting the tube in a house is high, the long life and high efficiency result in the wide dissemination. On the other hand, the incandescent bulb obtain approximately 25% of the share because of the low unit cost, and CFL is hardly used by the rural households due to the high unit cost even though it lasts for 10,000 on average and is highly efficient in terms of energy consumption. As for the supply side, the result of the analysis is shown in Fig. 10. Of all technologies, the diesel generator will

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Lighting energy consumption by devices [unit-hr]

4.E+05

3.E+05

2.E+05

1.E+05

CFL Fluorescent tube Incandescent bulb Kerosene lamp

0.E+00 2004

2006

2008 Year

2010

2012

Fig. 9. Changes in energy demand supplied by lighting devices.

the Census of India, 2001 (Banthia, 2003). Combining the result of the model analysis with that of the multiple regression analysis in Section 4.1, we have also estimated the potential of increase in literacy rate above 6 years old in the state of Assam. Assuming that the same results might be achieved in the rest of unelectrified rural areas of Assam, 96.3% of households in the entire state attain electricity access through the dissemination of the electric lighting appliances. When the value of the partial regression coefficient of the household electrification rate is applied to the consequent electrification rate, it is expected that the literacy rate above 6 years old in Assam will reach to 74.4% from 63.3%, increase by 11.9% compared to the literacy rate before energy access improvement, according to the following equation. Literacy rate above 6 years old ¼  0:489 þ 0:166  Household electrification rate þ 0:143  Log ðRoad densityÞ þ 0:683  Sex ration:

PV 0%

¼ 0:489 þ 0:166  0:963 þ 0:143  3:039 þ 0:683  0:935

(4.1) Thus, the impact of electrification on the educational outcome is huge even when only the direct link is considered. It also has large ripple effects on other components. For example, electrification will stimulate small industries in the areas, improve productivity, and, consequently, generate income. Therefore, energy access improvement will alleviate poverty in combination with the increase in the educational level and ripple effects.

Grid 30%

Wind 0% Diesel engine 70%

Yr.2012

Fig. 10. Electricity supply structure by generating technologies in 2012.

share the highest percentage of the electricity supply in the area. It is because the diesel generator is the cheapest option though the price of diesel will increase considerably during the analysis period and, as a decentralized energy supply technology, it needs neither battery nor inverter on the contrary of PV system and wind turbines. PV and wind turbines are recognized as possible electrification options in rural areas and, in the case of wind, its capital cost has been more competitive recently. However, as it needs ancillary such as a battery and inverter, PV and wind turbine system as a whole becomes uncompetitive to other generating technologies.

5. Discussion The model analysis we have conducted is based on reliable electricity supply and secured availability of lighting devices on the supply side as well as decision making by the rural household, which takes present value of the devices into account, on the demand side. However, in reality, lack of infrastructure and capacity of supply prevents rural areas from achieving energy access improvement. Moreover, electric lighting appliances are not adopted because of their higher initial investment in spite of lower life-time cost than a kerosene lamp. In this section, first, we discuss issues for the supply side, infrastructure and capacity of supply. Then, for the demand side, international assistance such as Official Development Assistance (ODA) and government policies are illustrated for activating private investment. Finally, ripple effects on other socio-economic aspects are argued in order to provide implication to the results of the analysis. 5.1. Supply side—infrastructure and capacity of supply

4.3. Estimation of literacy rate increase Stated as a whole, there are only 24.9% of households electrified in Assam and also 83.5% of rural households who do not have electric lighting appliance according to

The result of the model analysis shows that households in the rural areas of India will select the electric lighting appliances. Infrastructures such as road, transmission line, and distribution line, are one of the fundamental components

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to ensure accessibility to market for not only electric appliances but also distributed generators or grid electricity, which are necessary to acquire reliable electricity. In terms of road infrastructures, Assam is a relatively advanced state compared to the other states, reaching to 1000 km2 road density of 1093.58 km, one tenth of 25 Indian states. In particular, there are 90.92% of villages, which have the connection to so-called pucca roads in Jorhat district, to which the analyzed areas belong (National Commission on Population of the Government of India, 2001). Assam is also ranked at 21st of 29 states and UT, for transmission and distribution line per area, indicating 1.00 (circuit km/km2), given that the average of the states and UTs is 1.88 (Central Electricity Authority, 2005). In addition to the infrastructures, it is also indispensable to discuss the supply side of electricity. For rural electrification, the Government of India has launched ‘‘Mission 2012: Power for All’’ to achieve complete household electrification in the country, focusing on socially weaker groups (Ministry of Power of the Government of India, 2002). Moreover, due to the high rate, more than 30%, of the transmission loss, rural electrification is promoted with renewables such as solar PV, small hydro, biomass, and wind in disconnected, remote regions since the year 2001 (Ministry of Non-Conventional Energy Sources of the Government of India, 2004). On the other hand, the country as a whole, India has been increasing its installed capacity of captive electricity generation from 587.85 (MW) in 1950 to 18,740.31 (MW) in 2004. In the process of the electricity sector reform, the government has opened the electricity market for the generation sector to private companies in 1991 as well as for the transmission sector in 1998. Since then, although no entity has entered into the transmission sector, participation of private companies into the generation sector has been steadily expanding and it gains 10% share of the total generation in 2004 (Central Electricity Authority, 2005). Thus, it is promoted to utilize private investment in the electricity sector, and it is also necessary to attract the investment to electrify rural areas, considering the government’s limited financial source.

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large initial investment on the electric lighting appliances and generating technologies prevents the households from improving the energy access. 5.3. Implication to the study As a result of the analysis, it is revealed that rural areas of developing countries, rural Assam in India in this study, have potential to attain access to electricity. In addition, the results imply how and where government policies or international aids can contribute to the promotion of electricity access and consequent advancement in socioeconomic condition. For the supply side, private sectors must be fully incorporated into the countries’ development strategies. Financial sources of governments of developing countries are limited and huge investment, US$ 197–395 billion, is needed to provide energy access as shown in Table 7, according to the estimation by Spalding-Fecher et al. (2005). Public expenditure of the government is to be spent on constitution of a market performing efficiently and establishment of institutions. Here, the institution is one of the basic concepts to succeed in assistant programs, and fundamental and indispensable structure of society toward development (Akiyama et al., 2003). Therefore, government expenditure promotes the private investment, working as a catalyst. ODA has so-called ‘‘catalytic effects’’ to activate penetration of private entities, which are expected to become the main driving force of economic growth of developing countries. The idea of the catalytic effects is that ODA should be expended on encouraging the private sector to invest their funds as well as demonstrate the initiatives, through establishment of soft infrastructures. Soft infrastructures mean institutional condition, and it is important to establish the institutions in order for a market mechanism to function effectively (Watanabe and Miura, 2003). For energy access issues, as a key instrument for financing the access, a new Global Energy Access Fund is proposed (South Africa, 2002). This new financing mechanism uses public and private financial resources to

5.2 Demand side—initial investment As a result of the model analysis, it is revealed that electric lighting appliances such as an incandescent bulb, fluorescent tube, and CFL are widely adopted by the rural households of Assam, India. This means that, even including costs of electricity-generating technologies and fuels, the cost of the lighting appliances attains costeffectiveness in the rural areas, where presently the households are completely dependent on kerosene for lighting and there is no facility or equipment to generate electricity. In fact, it is reported that the real cost of energy, which poor rural households expend, is higher than that of electricity (ESMAP, 2000; IEA, 2003). However, in reality,

Table 7 Cumulative and annual investment needed to provide access to modern energy services to 1 billion people

Cumulative investment (2002–2015) Electricity supply Grid connections LPG Total Annual investment (2002–2015)

Low (US$ billion)

High (US$ billion)

128 50 19 197 14

208 150 37 395 28

Source: Spalding-Fecher et al. (2005).

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catalyze private-sector investment flows. It includes distribution investment, and related capacity building; grant funding for feasibility studies and capacity building; seed funding for innovative delivery models; and microfinancing for entrepreneurs, particularly for women (SpaldingFecher et al., 2005). It is also argued that the fund and establishment of the institutions are primarily supported by redirecting current financial flows through international donors and export credit agencies (ECA). Here, an ECA provides exporters of capital goods and services with finance and insurance to assist them in wining overseas orders as well as to insure businesses, which invest abroad against the risk of loss on their investment arising from political risks (ECGD, 2006). It plays, in particular, an important role because each dollar of support from ECAs catalyses another dollar and a half of private-sector investment, and, therefore, only by shifting ECA funding priorities can a significant change in private-sector investment patterns be attained. For the demand side, as for government policies, financial support systems play a critical role to create favorable situations for rural areas in developing countries to adopt advanced technologies. It is a well-known myth that the poor cannot pay for energy services even though many of poor households pay more now because of inefficiency of traditional energy and devices (Energy and Mining Sector Board, 2005). However, comparatively higher initial costs of advanced technologies prevent rural households from participating in a market mechanism. Therefore, for the households, financial support aiming to reduce initial costs of the technologies is needed. These include subsidies and loans with a considerably low interest rate for rural households. Microcredit is programs extending small loans, and other financial services such as savings, to very poor people for self-employment projects that generate income, allowing them to care for themselves and their families (Microcredit Summit Campaign, 2006). In combination with government policies and microcredit, poor rural households, which hardly obtain financial sources, are able to pay for initial costs of advanced technologies. There is an example of an electrification

project with solar panels in Nepal; approximately one half of the installation cost is covered by a government subsidy and the rest of the payment is supported by the microcredit scheme, in which households repay with sales of handmade bags sold via internet (The GEF Small Grants Programme, 2006). Finally, in terms of the environmental issue, developing countries will be major actors in post-Kyoto phase, and, given India’s population, high birth rate without any birth control policy and strong economic growth, India will play an influential role in this challenge face by the international society. Thus, for India it is sought the compatible strategy for economic development against environmental deterioration. Although in the process of energy access improvement, it might be inevitable to consume more fossil fuels, proper policy implementation or international assistance, as discussed in this section, will lead the country to approach energy access issues with environmentally friendly manner. In this sense, the results of the study illustrate the way to improve the quality of life as well as minimizing harmful effects on the environment. As described above, with the supports by governments of developing countries or international donor community including Japan, rural households are able to select technologies based on cost-effectiveness for a unit of energy demanded. Then, as the results of the study have illustrated, energy access improvement will be achieved through self-help efforts by rural households in developing countries, which is the major objective of Japan’s ODA Charter (Ministry of Foreign Affair of Japan, 2004). It will improve socio-economic condition there and, moreover, developing countries will take the first step toward selfindependent and sustainable development. In conclusion, the results of the entire study conducted are summarized in Fig. 11. 6. Conclusion In the study, it is aimed to reveal relations quantitatively between access to electricity and advancements in socioeconomic condition in rural areas of developing countries.

Energy access improvement

Advancement in socio-economic factors - Infrastructure - Gender equality

- Infrastructure - Capacity of supply - Governmental policy - International cooperation

Increase in educational level Access to electricity Ripple effect on other factors

Fig. 11. Summarized figure of the study.

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As reported in previous works, energy access improvement including electrification has huge impacts on such factors as health, education, economy, etc. Rural households in developing countries adopt kerosene lamps in order to meet their lighting demand. They do not obtain sufficient luminance for studying in a house at night, and this is one of the obstacles to achieve higher educational attainment. Therefore, it is largely expected that energy access improvement for lighting demand through electrification, which is achieved by the dissemination of electric lighting appliances, creates desirable educational environment for children. Energy access improvement influences significantly socio-economic factors such as health and education as mentioned above. Furthermore, it has substantial ripple effects on other factors, for example, economy, gender equality, environment, etc., in rural areas of developing countries. Together with such socio-economic factors as a whole, it might contribute to poverty eradication and lead developing countries toward sustainable development. Based on the analysis, we can draw the following conclusions:







The multiple regression analysis shows that the literacy rate above 6 years old, a socio-economic factor, is explained by Household electrification rate, Sex ratio, and road density per 1,000 km2. The household electrification rate shows 1% of statistical significance, and 1 point of increase in electrification rate will result in 0.17 point improvement of the literacy rate. Applying panel data to the regression analysis might reveal precise insights of relation between the literacy rate and the electricity consumption. As for the model analysis, it is revealed that household electrification will be completed by the year 2012 due to relatively lower costs of electric lighting appliances in terms of a unit of lighting demand. Electrification will boost the demand of electricity for appliances, which also affect educational level in the rural areas. Therefore, interactive relation between electrification and consequent electricity demand, incorporating incremental demand for these appliances and their influence, should be analyzed as the next study. Assuming that all rural areas in Assam state, India, would be electrified and other factors would be unchanged, it is estimated that the literacy rate could rise to 74.4% from 63.3%. Electrification also has influence on other socio-economic factors with large impacts. Therefore, along with the level of educational attainment, socio-economic condition in rural areas of developing countries might be advanced through electrification.

In terms of technological and economic aspects, the possibility of energy access improvement might be high in rural areas of developing countries. A market economy, infrastructures, and capacity of electricity supply are the

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essential components. Although, in the analyzed areas, road infrastructures are highly established, there are various assumptions for simplification of calculation process, for example, stable supply of electricity and devices and rural households evaluating devices by their present values, in the model analysis. Uncertainties for technological parameters and lives of devices and appliances are also included. Nevertheless, taking into consideration the roles of government policies and international assistance, the results of the study offer insights about links between poverty reduction and energy access improvement in rural areas of developing countries. The roles of a government and a market are complementary and a market economy will function due to the utilization of government policies and ODA as well as financing mechanism by international donor community and ECA. These work as a catalyst in order to promote private investment, and, as a consequence, sustainable development by self-help efforts of developing countries might be achieved through establishment of institutions, which are essential for a market economy. As illustrated in the example of an electrification project utilizing both a governmental subsidy and a microcredit scheme, initial costs of advanced technologies are reduced due to a combination of government policies and a market economy. As a result, poverty reduction through advancement in socio-economic condition might be achieved by energy access improvement. Further research is needed for the following points in order to conduct more practical analysis: acquisition of more field-oriented data, inclusion of other socio-economic impacts of energy access improvement, interactive influence between socio-economic condition and energy access improvement. Appendix Possible factors for the regression analysis:







GDSP per capita—GDSP per capita is regarded as a proxy of economic affluence of a state. Due to higher GDSP per capita, better economic condition, infrastructures, and educational environment are provided by the state. It is rational to presume that inhabitants of the state have more opportunity for education. Expenditure on education, etc. per capita—This represents educational environment in a state more directly than GDSP per capita. Higher amount of the expenditure on education results in providing people in the state to have more opportunity for education. Number of primary schools per 1000 persons—This factor is regarded as a quantitative aspect of educational environment. The more schools are built, the more capacity a state can obtain in order for children to attend a school. However, it should be noted that, with limited educational expenditure, quality of school is not secured.

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Number of teachers in primary schools per 1000 persons—This is also considered as a quantitative aspect of educational environment. Hiring more teachers available in a state allows expanding capacity of schooling. We have considered the number of teachers in primary schools per 1,000 persons as a determining factor of the literacy rate. Road density per 1000 km2—It represents the accessibility of education in terms of infrastructures. Because of a lack of the infrastructures, children in remote regions or geographically disadvantaged areas cannot attend schools. Rail density per 1000 km2—It is a proxy of the accessibility of education in terms of infrastructures along with the road density. Poorer infrastructures in a state hinder children in schooling opportunity. Household size—It explains a situation that a household having more children is unlikely to afford the education under the constraint of low income. It results in low achievement of educational level. Sex ratio—It represents gender equality of a state. The higher the rate is, the more equally male and female are treated. Thus, it is rational to postulate that opportunity of girls for education is equal to that of boys, which results in improvement of the literacy rate. Ratio of Scheduled Caste—It is regarded as a possible explanatory variable, which shows a social equity in a state. Higher percentage of the Scheduled Caste means lower educational attainment of the state as they live in socially and economically disadvantaged condition. Ratio of Scheduled Tribe—It also indicates a social equity as in the case of the ratio of scheduled caste. Since Scheduled Tribes are socially and economically underprivileged, it is likely that lower educational level is attained in the state with the higher ratio. Ratio of Hindus—It is taken into consideration as a factor of religious condition in a state. Hinduism is the most fundamental principle in the Indian society, and it is the basis of the caste system. High percentage of Hindus may represent the conservative aspect of India. Ratio of Christian As well as the ratio of Hindus—It shows religious condition in a state. It is reported that missionaries of Christianity give basic education as a part of the propagandist activities, and it explains the part of the reason the Northeastern region of India attains higher educational achievement despite lower economic condition compared to the other regions (Inoue, 2002). Electricity consumption per capita—Utilization of electricity results in higher productivity in industry and reduction of time-consuming activities in a household, creating preferable educational environment for women and children. Domestic electricity consumption per capita—It indicates the impacts of electricity utilization on educational environment in a household more directly than the electricity consumption per capita. Electricity enables



children to study at night with sufficient luminescence. Household electrification rate—It shows the number of households with electricity for their lighting demand in a state. In contrast to the factors of electricity consumption, it measures the access to electricity explicitly.

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