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Household Energy Consumption:

Community Context and the Fuelwood Transition

Cynthia Macht

William G. Axinn

Dirgha Ghimire

Population Studies CenterUniversity of Michigan

Population Studies Center Research Report 07-629

October 2007

Household Energy Consumption 2

ABSTRACT

We examine the impact of community context on households’ use of fuels other than wood forcooking. Our theoretical framework emphasizes the importance of local community contextualcharacteristics as determinants of the transition from use of fuelwood to use of alternative fuels.We use longitudinal multilevel data on household fuel choice and community context from theNepalese Himalayas to provide empirical estimates of our theoretical model. The results of thisempirical investigation reveal that increased exposure to nonfamily organizations in the localcommunity increases the use of alternative fuels. These findings should motivate greaterincorporation of social changes and sociological ideas into research on environmentalconsumption.


INTRODUCTION

Degradation of the natural environment has become the subject of increasingly intense research overrecent decades. This is just as true in the social sciences as in the natural, biological and physicalsciences. The social sciences have been particularly concerned with the consequences of socialorganization and social actions on levels of environmental degradation. Human consumption ofnatural resources is generally identified as the key link between human behavior and degradation ofthe natural environment (Stern et al. 1997). Though social research has primarily focused on thetotal volume of human consumption, classical sociology points toward the importance of transitionsin the nature of consumption as a fundamental change in the way people relate to their environment(Foster 1999). In this study we investigate a key transition in the total environmental footprint ofhuman consumption (York, Rosa and Dietz 2003), the transition away from biomass fuel towardmore highly processes alternative energy source.

Firewood gathered from forested commons is an important source of domestic energy in ruralareas of many poor countries (Cecelski, Dunkerley and Ramsay 1979; Heltberg, Arndt and Sekhar2000). It has been estimated that more than 2.4 billion people rely directly on traditional biomassfuels for their cooking and heating, and in poor countries biomass use represents over half ofresidential energy consumption (International Energy Agency 2005). Demands for fuelwood bysubsistence agricultural households may be the leading cause of world deforestation (Amacher, Hydeand Joshee 1993; Amacher, Hyde and Kanel 1996). According to recent data, the global rate ofdeforestation continues to be alarmingly high – about 13 million hectares per year (Food andAgriculture Organization of the United Nations 2005). In recent decades deforestation has come to beperceived as a global problem, because of the perception that the earth’s resources are reaching thelimits for supporting the world’s population and economic systems (Schmink 1994). Deforestationhas created a situation of fuelwood scarcity to the point that an impending “fuelwood crisis” looms inmany settings (Dewees 1989; Heltberg et al. 2000).

In addition to the scarcity of fuelwood as a crisis per se, deforestation has numerous otherharmful consequences such as loss of biodiversity and soil erosion (Heltberg et al. 2000; Palloni1994). Also, because women are the main gatherers of fuelwood, the depletion of forests forceswomen to spend more time looking for wood to gather and search farther from their homes,lengthening their working day (Agarwal 1994). Finally, deforestation exacerbates conditions thatcould produce global warming, such as the release of carbon dioxide into the atmosphere (Heltberg etal. 2000; Palloni 1994). Therefore the transition from fuelwood to alternative sources of energy is akey transition in how humans affect the environment. Substitution of fuelwood with alternative fuelscan reduce pressure on natural forests. This is because alternative sources of rural domestic energy(such as crop residues, animal dung, wood from trees on the farm, biogas, kerosene, sun and windpower) do not cause forest degradation (Heltberg et al. 2000).

Our study takes an explicitly longitudinal and multilevel approach to understanding thisfundamental energy use transition. We formulate a theoretical framework linking macro-levelcontextual changes in social and economic organization to micro-level household decisions regardingenergy use. This framework incorporates both sociological research on mechanisms linking


contextual change to individual and household behaviors and environmental research on energy usetransitions. This framework also produces hypotheses linking specific dimensions of contextualchange to the transition from fuelwood to alternative energy sources. Longitudinal multilevel datafrom the foothills of the Nepalese Himalayas enable us to document the impact of communitycontext on household fuel substitution. Longitudinal designs facilitate the study of cause andconsequence. Because of the great potential for reciprocal causal relationships among associatedfactors, measurement of temporal ordering of events is needed to estimate the direction of causality(Axinn and Pearce 2006; Campbell and Stanley 1967). Moreover, the assumption of change overtime is implicit in the concept of energy transition (Leach and Mearns 1988). Multilevel designs arenecessary to establish the relationships across levels of analysis, such as the national level, thecommunity level, the household level, or the individual level (Entwisle and Mason 1985;Raudenbush and Bryk 2002). Thus a longitudinal multilevel research design provides the best meanswith which to assess the impact of community context on household fuel transition.

Our study of this important transition examines the Himalayan foothills of Nepal. Nepal iswidely known as one of the world’s most diverse ecological settings, but also as a setting on thebrink of serious environmental degradation (Blaike and Brookfield 1987; Eckholm 1976). The fragileHimalayan environment is suffering rapid deforestation and soil erosion, which threaten the region’sbiodiversity in terms of both flora and fauna. Rural Nepal is an ideal setting to examine communitycontext and fuel substitution because local communities have changed rapidly over the lifetimes ofits current residents. Thus measures from this setting provide an opportunity to test key hypothesesderived from our theoretical framework. The results give us valuable new insights into forces drivingthe energy transition.

THEORETICAL FRAMEWORK

At the foundation of our framework are two ideas borrowed from classical sociological theory. Oneis Durkheim’s idea of the relationship between social change (such as improved communication andtransportation, monetization, and population density) and the division of labor in society (Durkheim1984). The other idea is Marx’s metabolic rift (Marx [1867] 1976, [1863-65] 1981). As argued byFoster (1999), these classical sociological ideas have much to offer contemporary thinking abouthuman relationships with the environment. These ideas suggest a relationship between socialorganization and the basic ways in which humans use their environment. For micro-level energyconsumption, this framework points toward social organization as a key engine of a fundamentaltransition from direct to indirect consumption of the environment (Axinn, Barber and Biddlecom2007). Use of firewood and other biomass fuels involve direct consumption of resources byhousehold members, whereas the shift to use of kerosene, natural gas, and electricity involve indirectconsumption, in which household member obtain these fuels from the market.

To build a framework linking the classical sociology foundations to recent environmentalresearch on energy consumption, we begin by outlining current models of energy transition andmicro-level energy use choices. We then integrate these models into recent sociological research onthe links between macro-level contextual change and micro-level behaviors.


Household Energy Transition

The “energy ladder” is a commonly used concept in models of domestic fuel choices in poorcountries (Alam, Sathaye and Barnes 1998; Campbell et al. 2003; Davis 1998; Hosier and Dowd1987; Leach 1992). The principal notion underlying this concept is that households face a range ofenergy supply choices, which can be ordered from least to most technologically sophisticated. Atransition from biomass fuels to more sophisticated alternatives occurs as part of the process ofeconomic growth. Most empirical studies that have been done on the determinants of fuel transitionshave linked factors such as income, access to electricity, and forest scarcity to fuel substitution (Alamet al. 1998; Campbell et al. 2003; Davis 1998; Heltberg et al. 2000; Madubansi and Shackleton 2007;Ouedraogo 2006).

Although there have been few studies of changing household fuel choices, evidence of theenergy transition is mounting. Household energy surveys have found income to be a majordeterminant of the energy transition (Alam et al. 1998; Campbell et al. 2003; Davis 1998; Ouedraogo2006). For example, Campbell et al (2003) found that in the four largest cities in Zimbabwe higherincome households were less likely to use wood as their primary cooking fuel, switching to keroseneand electricity. Ouedraogo (2006) found that households’ firewood utilization rate decreased withincreasing household income in the capital city of Burkina Faso. Access to electricity has been foundto be another important determinant of the energy transition (Campbell et al. 2003; Davis 1998;Ouedraogo 2006). However, Madubansi and Shackleton (2007) found that the introduction ofelectricity into a rural region of South Africa had little impact on fuelwood consumption. Otherfactors associated with reduced consumption of fuelwood and instead use of alternative fuels areforest scarcity and increased fuelwood collection time (Heltberg et al. 2000) and household size(Alam et al. 1998; Ouedraogo 2006).

More nuanced formulations of energy transition theory suggest that the scenario ofhouseholds switching from exclusive use of one type of fuel to exclusive use of another is overlysimplistic. More accurately, steps on the energy ladder include households using variouscombinations of fuels. Although the use of biomass fuels may gradually decline from exclusive useby many households towards less use by fewer households, the use of biomass fuels may not beentirely abandoned as households climb the energy ladder (Campbell et al. 2003; Hosier and Dowd1987). For example, in his analysis of multiple fuel use patterns in South Africa, Davis (1998) foundthat changes in fuel choice were not a smooth transition from biomass to commercial fuels, but acontinual switching between different combinations. He found that while high-income electrifiedhouseholds were more likely to rely on only electricity, low-income households were more likely torely on four or more fuels even if they were electrified. Therefore, we develop hypotheses regardingthe inclusion of non-wood fuels in households’ fuel selection, and not the complete substitution ofnon-wood for wood.


Macro-Level Social Organization and Household Energy Transition

Most economic and sociological studies of energy consumption emphasize factors such as income,regional electrification, and household size as key determinants of the transition from wood-energyto alternative fuels. However, in his innovative theoretical work on environmental sociology, Foster(1999) expands the focus from factors that influence the total volume of consumption to incorporatefactors that change the nature of consumption as important determinants of environmental quality.Drawing on classical social theory, he identifies the social organization of daily life as potentiallycritical determinants of the nature of consumption. Our theoretical framework links these variationsin social organization to fuel use choices.

Many frameworks for conceptualizing social organization focus exclusively on the mode ofproduction, emphasizing transitions from the family organized mode of production to industrialproduction, or production organized by corporate entities (Coleman 1990; Durkheim 1984; Marx[1863-65] 1981, [1867] 1976; Thornton and Lin 1994). We build on this conceptualization, butbroaden it, to give many different social activities the same analytic status as production. Toaccomplish this we use the modes of social organization approach (Thornton and Fricke 1987;Thornton and Lin 1994). This approach gives consumption, residence, recreation, protection,socialization, and procreation all the same analytic status as production (Axinn and Yabiku 2001;Coleman 1990).

Historically, most of these domains were organized within the family (Ogburn and Nimkoff[1955] 1976; Thornton and Fricke 1987). Technological and organizational changes in the socialcontext alter the extent to which these social activities are organized within family and kinship units(Thornton and Fricke 1987; Thornton and Lin 1994). As new nonfamily organizations and servicesspread at the macro-level, the social activities of daily life are reorganized at the micro- level so thatthey increasingly take place outside of the home and family (Axinn and Yabiku 2001; Coleman1990). Changes in the extent to which social activities are organized within the family can havebroad and dramatic consequences (Coleman 1990; Durkheim 1984; Marx [1863-65] 1981, [1867]1976; Thornton and Lin 1994). Previous research has demonstrated the consequences of change inthe mode of social organization for fertility behavior (Axinn and Yabiku 2001), consumption patterns(Axinn et al. 2007; Axinn and Ghimire 2007). As we argue below, changes in the mode of socialorganization can have dramatic consequences for the nature of fuel choices as well.

Nonfamily organizations and services, or what Coleman calls corporate entities, provide themeans to organize consumption outside the family, thus stimulating widespread change in relatedsocial activities (Coleman 1990). One example of this trend is the shift from making clothes in thehome to buying clothes in stores. Another example is the shift from cooking in the home to eating inrestaurants. There are many other examples (Coleman 1990; Ogburn and Tibbits 1934). The keyelement in our conceptualization of social change is the idea that the proliferation of nonfamilyorganizations and services in communities alters the social context so that more daily activitiesbecome increasingly organized outside the family.

These changes in daily life promote changes in patterns of environmental consumption suchthat individuals are more likely to consume things which they themselves did not produce. Marxdescribes this change as a metabolic rift – the creation of a gap between natural resources and the


people consuming those resources, causing humans to interact ever more indirectly with the naturalresources they consume (Foster 1999; Marx [1867] 1976). Axinn, Barber and Biddlecom (2007)describe this change as a shift from direct to indirect consumption of environmental resources.Greater access to nonfamily organizations and services at the community level gives local residentsincreasing opportunity to consume resources indirectly, thereby changing patterns of local landconsumption (Axinn et al. 2007). We hypothesize that access to nonfamily organizations and serviceswill change patterns of fuel use as well.

Applying the Modes of Social Organization Framework

Application of the modes of social organization framework requires knowledge of the starting stateof family versus nonfamily social organization and the outcome of interest (Thornton and Fricke1987; Thornton and Lin 1994). From these initial characteristics, the context-specific consequencesof social change can be predicted. This characteristic of the framework is advantageous compared toexisting frameworks predicting fuel use transitions. For example, most fuel transition theories implythat any economic or organizational changes away from subsistence agriculture will producesubstitution of higher technology fuels for firewood (Amacher et al. 1996; Bluffstone 1995; Leach1992). This unilineal outcome, however, is not necessarily a universal consequence of changes inmodes of social organization. The consequence of reorganizing social activities outside the family islikely to depend on both the social context and the specific social activity being reorganized. Forexample, in some settings the reorganization of social activities outside the family may add new fuelconsumption without leading households to give up fuelwood use. Therefore, before predicting theconsequences of specific social changes using the modes of social organization framework, webriefly describe the setting and the starting state of energy use.

Setting

The setting for this study is the Western Chitwan Valley located in South-Central Nepal. Becausepoverty and topographical barriers have delayed the spread of new nonfamily organizations andinstitutions in Nepal (Blaikie, Cameron and Seddon 1982), the vast majority of social activities wereorganized within the family up until the very recent past (Fricke 1986). Chitwan is a wide flat valleynestled in the Himalayan foothills at approximately 450 feet above sea level, 100 miles southwest ofKathmandu, the country's capital. Until the early 1950s Chitwan was covered by virgin forests,infested with malaria-carrying mosquitos, and inhabited by dangerous fauna ranging from poisonoussnakes to Bengal tigers. Beginning in the mid-1950s, with assistance from the United States, theNepalese government began a program of clearing the forest, eradicating malaria, and distributingland to settlers from the higher Himalayas. Approximately one-third of the original forest waspreserved as Chitwan National Park, which remains home to several endangered species today. Ourstudy examines fuel use patterns in a 100 square mile area of Western Chitwan that was cleared andsettled.


Rich soils, flat terrain, and the promise of new opportunities drew many farmers into the area,but the valley remained a remote, isolated frontier until the late 1970s. The first all weather road intoChitwan was completed by 1979. This road linked Chitwan’s largest town, Narayanghat, to cities inEastern Nepal and India. Other important roads followed, linking Narayanghat to Kathmandu,Nepal’s capital city. Because of Narayanghat’s central location, by the mid-1980s this once isolatedtown was transformed into the transportation hub of the country. This change produced a rapidproliferation of government services, businesses, and wage labor jobs in Narayanghat that spreadthroughout Chitwan (Pokharel and Shivakoti 1986). These changes also continued to stimulate thegovernment’s investments in agriculture in the region, including heavy investments in irrigation,mechanization, improved seeds, pesticides, fertilizer, and new methods of production and marketing(Shivakoti and Pokharel 1989). The population of this valley continued to grow as well, with both in-migration and natural increase significantly contributing to this growth (Central Bureau of Statistics2001; Tuladhar 1989).

Together these forces dramatically altered the social and economic organization of Chitwanwithin the lifetimes of its residents. As of the late 1950s, there were virtually no employmentopportunities, market places, schools, health posts, or transportation services. Bus service through thevalley has given residents access to the wage labor opportunities and commerce of Narayanghat.Commercial enterprises, such as grain mills and new retail outlets, have scattered throughoutChitwan. A wide range of government services, from schools, to health posts, to police posts, havealso sprung up. These changes constitute a significant transformation of the local context for thehundreds of small farming communities in Western Chitwan Valley. It is exactly these changes inaccess to nonfamily organizations that we expect to transform the nature of energy consumption,away from wood for fuel and toward the use of alternative fuels.

Despite this massive transformation of the local context however, most of the valley is stillrural and remains predominantly an agriculture-based society. Eighty-three percent of therespondents of the study reported that they were growing crops at the time of our survey. Rapidpopulation growth and lack of supply of alternative energy resources in Chitwan has caused theextensive use of traditional energy sources like fuelwood and agricultural residues supply. Fuelwoodis currently being consumed at rates higher than the sustainable yield of the forests, causing forestencroachment and local environmental degradation (Metz 1990; Rijal1999; Sharma 1991). Anestimate of supply and demand for Chitwan suggests a net shortage of 326,683 tons of fuelwood forthe year 2007 alone (Rijal 1999). Although Nepal has a large economic potential for hydropowergeneration (42,000 MW), because of the poor economy only about 0.5% of this potential has beenharnessed (Sharma 1991). Thus, in the absence of economically feasible alternative energy sources,residents in Chitwan are likely to continue to rely on traditional energy sources such as fuelwood tomeet their energy demand. This heavy demand for fuelwood and other forest products has alreadyhad high cost in terms of decreasing plant density in the surrounding forest.


Empirical Predictions

In this setting, we expect community-level changes in access to nonfamily organizations andinstitutions, such as markets, schools, health care and transportation to result in the reorganization ofproductive, consumptive, residential, recreational, protective and socialization activities outside thefamily. As these activities of daily living are reorganized outside the family, we expect householdsto increase substitution of energy sources from firewood to alternative sources.

Nonfamily employment. If nonfamily work is available to a community then residentsare more likely to secure nonfamily employment. We hypothesize that when nonfamily work isavailable and individuals participate in this work, fuel substitution will increase. If there are noemployment opportunities then the opportunity costs for collecting fuelwood may be low.Availability of nonfamily employment may increase the opportunity cost of time to collect fuelwood,and may lead to higher income thereby enabling the purchase of alternative fuel. In fact Bluffstone(1995) finds that a large off-farm wage increase causes households to withdraw from fuelwoodconsumption and switch to commercial fuels. Additionally, nonfamily employment may exposeworkers to new ideas encouraging fuel substitution.

Nonfamily consumption. The spread of markets at the community level increasesnonfamily consumption opportunities, which is likely to affect decisions about fuel substitution.Markets could reshape the cost structure of alternative fuels by lowering their price or by makingthem more obtainable. At a marketplace individuals may directly compare the costs of fuelwoodcollection to the prices of goods such as kerosene. Furthermore, at a marketplace individuals mayengage in personal contact with sellers of alternative fuels, which may help to encourage theirpurchasing decisions.

Nonfamily banking. Greater accessibility of banks redistributes financial investmentsoutside the family and promotes calculated decision making within a monetized environment. Ofcourse, calculated decision making is not new to the residents of Chitwan. Chitwan is largely anagricultural subsistence economy. These rural Nepalese farmers must be highly shrewd simply tosurvive–what crops to plant, how much of each, when to harvest. What is new, however, is that theresidents of Chitwan have the opportunity to use these decision-making skills in an ever-expandingmarket, where the new medium of exchange is legal currency. With the opportunity to save money inbank accounts, individuals have the opportunity for their purchasing decisions to stretch over longertime periods than before. This may encourage the acquisition of alternative energy sources.

Nonfamily schooling. Schooling may promote fuel substitution through a variety ofmechanisms. If children who are not in school are participating in fuelwood collection, thenchildren’s attendance at schools may increase the costs of fuelwood collection by reducing theircontributions. This may encourage parents to switch to fuel types that require less collection effort.Schools may also be sources of ideational change, providing new information about alternative fuelconsumption technology. Because they are modeled on the British educational system, schools inNepal may use teaching materials that illustrate consumption options such as alternative fuels. Withthe spread of these ideas, those who have been to school outside the family or simply live near aschool will be more likely to switch to alternative fuels.


Nonfamily health care. As government services have spread through Chitwan, manydifferent health care activities have become organized outside the family. Chitwan now has a varietyof medical facilities that did not exist less than a generation ago: a hospital, many local health postsand clinics, plus numerous pharmacies. New nonfamily health care services may motivateindividuals to use alternative fuels by offering educational materials which inform individuals of therespiratory hazards created by smoke from indoor cooking.

Nonfamily transportation. Even if nonfamily employers, markets, schools or health postsare not geographically nearby, improvements in community access to transportation may stimulatethe reorganization of family life and promote fuel substitution. Availability of transportation affectsemployment opportunities if the transportation network links the community to the employers. Forexample, in Chitwan what might have been a three hour walk to Narayanghat may become a shortbus ride, and commuting to nonfamily wage labor can now be part of the daily routine for residentsof formerly remote communities. Similarly, improved transportation infrastructure allows residentsof formerly remote communities to travel to markets, schools or health posts, each of which mayincrease fuel substitution. Additionally, improved infrastructure may advance the distribution ofalternative fuels.

Nonfamily leisure. Social changes at the community level are also likely to increase leisureactivities outside the home and away from family. Examples include new cinema halls, local marketsthat sell televisions and radios, and new bars and tea stalls introduced in market places. Leisureactivities located outside the family allow new ideas to spread and thus may promote the use ofalternative fuels. For example, a movie or radio broadcast could provide information on the energytransition. Also, new nonfamily leisure activities may change cultural preferences or consumptionaspirations. Because much of the mass media in Nepal originates in high income settings, this mediamay directly spread images publicizing modern fuel technologies, motivating fuel substitution.

DATA AND METHODS

Within the Western Chitwan Valley, we examine community change and energy consumption in 151neighborhoods using measures from the Chitwan Valley Family Study (CVFS). The CVFS defined aneighborhood as a geographic cluster of five to fifteen households. Given the rural setting, theseneighborhoods contain a group of people who know each other and interact personally every day.The CVFS selected an equal probability, systematic sample of neighborhoods in Western Chitwan.The sampling strategy was designed to eliminate national and regional sources of variation byfocusing on a single area, Chitwan Valley, yet the strategy also maximizes neighborhood levelvariations by sampling neighborhoods from a setting with much local variation (Smith 1989).

The CVFS measured neighborhood context with the Neighborhood History Calendar method,which combines archival, ethnographic, and structured interview methods to gather detailedcontinuous measures of neighborhood change (Axinn, Barber and Ghimire 1997). In 1996 the CVFSalso administered a household-level agriculture and consumption survey to every household in thoseneighborhoods, with a response rate of 100 percent. The household interviews documented patternsof household consumption, agricultural practices, wealth, and size. To measure changes in household


agriculture practices and consumption, this survey was repeated in 2001. The 2001 survey achieved aresponse rate of 98 percent, generating 2,035 household interviews.

Measures of Changing Energy Consumption

The key dependent variable of interest is use of any fuel other than wood to cook with in 2001. Thisis constructed from the question: “What heating sources do you use in your house for cooking?”followed by a series of yes/no items asking about wood, electricity, gas, biogas, sawdust, kerosene,or other fuel. If the household reported using any fuel other than wood as a heating source forcooking, our measure is coded 1; if they use only wood, it is coded 0.

We use a longitudinal research design to control pre-existing energy consumption in eachhousehold. We include in our model a measure of use of any fuel other than wood in 1996 withquestions from the baseline agriculture and consumption survey that are identical to those asked in2001. This strategy of controlling for previous energy consumption allows us to focus on change inenergy consumption over the five years between interviews. Because the dependent variable in ourmodel is use of any fuel other than wood in 2001, including this control for use of any fuel other thanwood in 1996 transforms other measures in the model to predictors of change in fuel wood usebetween 1996 and 2001.

Measures of Changing Community Context

We use information from the Neighborhood History Calendar to construct dichotomous measures ofwhether each neighborhood has an employer, market, bank, school, health post, bus stop, or movietheater within a fifteen minute walk. These measures of access to seven key nonfamily organizationsare constructed from the neighborhood’s distance to the nearest service.1 For example, respondentswere asked whether the nearest employer is within 15 minutes walk of the neighborhood. “Yes” iscoded 1; “no” is coded 0. The other measures of community context are coded similarly. Tosummarize these seven measures of access to nonfamily services, we construct a composite measureby adding responses to the seven questions. This yields a variable ranging from 0 to 7 which countsthe total number of nonfamily services within a fifteen minute walk of the neighborhood.2

1 An employment opportunity is the nearest employer who employs ten or more individuals for pay. Amarket place is the nearest location of two or more contiguous shops where goods and services are soldfor money, including tea shops. A bank is any institution that has the legal right to operate a financialtransaction such as maintaining an account, issuing a check and running cash transaction. A school is thenearest location of nonfamily instruction and socialization aimed at children and youth. This includesreligious schools and schools without a physical building. A health post is any facility that providesmedical services or supplies, including hospitals, family planning clinics, and pharmacies. Bus service isthe nearest location where a resident could board a public motorized vehicle and ride for a fee (includingtractors and jeeps). A movie theater is any place that shows big screen movies for business on a regularbasis.2 Varying these distance thresholds produces no substantive changes in the interpretation of our results.Lower thresholds of five or ten minutes generally produce slightly weaker effects for most nonfamilyservices. However, the effects of markets and bus stops increase when we use lower thresholds.


Household Electric Appliances

The number of electric appliances owned by the household may be an endogenous interveningmechanism linking nonfamily services to use of alternative fuel. We measure household possessionof electric appliances in 2001 by constructing a count with a series of dichotomous variables. Forexample, respondents were asked “Do you have a radio in your house?” This measure is coded 1 ifthe respondent answered yes and 0 if the respondent answered no. Similar questions were askedabout a television, a telephone, a sewing machine, a VCR, a computer, a refrigerator, an electric ricecooker, an electric fan, and an iron. We construct a count of the number of electric appliances byadding responses to these questions, and code the variable so it ranges from 0 to 6 or more.

Controls

In order to properly specify our models, we control for various factors that may be confoundersbetween community context and the likelihood of use of alternative fuel. We control for incomebecause wealthier households are less likely to be constrained by the usually higher costs of non-biomass fuels and therefore more inclined to make the transition to more sophisticated alternatives(Campbell et al. 2003). We construct an ordinal income scale coded from 0 to 5, derived fromresponses to the question “Thinking about your total household income from all sources, includingwages, salaries, pensions, income from selling crops, animals, or goods, income from renting houses,land or equipment, business income, or income from gifts or other payments. Since…(month)…lastyear, would you say that the total income you received from all sources was 50,000 rupees or less, ormore than 50,000 rupees?” After that opening question, respondents were asked follow-up questionsto narrow down their total income based on their previous answers. For example, respondents whoanswered “50,000 rupees or less” to the opening question were then asked “Since…(month)…lastyear, would you say that the total income you received from all sources was 25,000 rupees or less, ormore than 25,000 rupees?” From these responses we construct an ordinal scale with 0 indicating noincome, 1 indicating income 10,000 rupees or less, 2 indicating income between 10,000 and 25,000rupees, 3 indicating income between 25,000 and 50,000 rupees, 4 indicating income between 50,000and 100,000 rupees, and 5 indicating income of more than 100,000 rupees.

Because much of the Nepalese economy is not monetized, we include another measure ofaffluence focusing on house plot ownership. We consider this an indicator of wealth because it canbe a source of long-term wealth; ownership of a house plot gives residents the opportunity to growfruits and vegetables for home use and to conduct businesses (such as a small store) that wouldotherwise require rental property. We measure house plot ownership with a dichotomous variablecoded 1 if the respondent reports that the household owns the land on which their home is built and 0otherwise.3

Generally household size is expected to increase fuelwood consumption because of increasedenergy demand and increased laborers available for fuelwood collection. The impact of household

3 We also estimated the impact of other potential indicators of wealth, including a measure of the numberof stories in the house and the household number of livestock. Neither of these measures had a significantimpact.


size on use of alternative fuels is ambiguous, because greater household size means increaseddemand for energy but also increased possibility of fuel substitution. We control for household sizewith a measure of the number of residents of the household age fifteen or older. A resident of ahousehold is defined as having eaten or slept in the household for at least three of the past six monthsat the time of the study.

We control for whether electricity is available to the neighborhood, because electrificationstatus among towns has been found to promote fuel substitution from wood to alternative sources(Campbell et al. 2003). Our measure of electrification comes from the Neighborhood HistoryCalendar. A dichotomous variable is coded 1 if the neighborhood has electricity and 0 otherwise.

We expect fuel substitution to be a rational response to increased resource scarcity andincreased wood collection time. It has been found that when forests are scarce and collection time forfuelwood is increased, households respond by substituting fuels from private sources for fuelwood(Heltberg et al. 2000). We measure scarcity of nearby forests indirectly, with a retrospective questionabout wood collection time. At the baseline survey respondents were asked “Three years ago, howlong did it take to travel to the place where the firewood was, collect it, and then bring it home (inminutes)?” We divide responses by 60 so that the variable is coded in hours.

We control for ethnicity because Nepalese ethnic groups are related to key cultural factorsaffecting the relationship between individuals and their natural environment. This approach isconsistent with previous research linking ethnicity and cultural differences to consumption patterns(Lutzenhiser 1993; Lutzenhiser and Hackett 1993; Lutzenhiser, Harris and Olson 2001). Ethnicity inNepal is complex, multifaceted, and interrelated with religion. A full description of the ethnic groupsresiding in this setting is beyond the scope of this paper (for detailed descriptions of these groups seeAcharya and Bennett 1981, Fricke 1986, Gellner and Quigley 1995, Gurung 1980, Guneratne 1994,Pearce 2000). We use five dichotomous indicators of ethnicity: Upper Caste Hindu, Lower CasteHindu, Newar, Hill Tibeto-Burmese, and Terai Tibeto-Burmese. Upper Caste Hindus are an elitegroup in Nepalese society which has historically had the most access to opportunity, includingnonfamily opportunities (Acharya and Bennett 1981). Lower Caste Hindus identify with the samereligious background; both groups’ ancestors originate from India, and both groups practiceHinduism. However, they have not benefited from as many opportunities as Upper Caste Hindus.Newars are of Tibetan origin and practice a mixture of Buddhism and Hinduism (Acharya andBennett 1981). They are also an elite group in Nepal, and average education among Newars rivalsthat of Upper Caste Hindus. Hill Tibeto-Burmese are also of Tibetan origin but tend to practiceBuddhism. This group includes people such as the Tamang, the Gurung, and the Magar (Fricke 1986).Finally, the Terai Tibeto-Burmese, which includes the Tharu, the Derai and the Kumal, are theoriginal inhabitants of the Chitwan Valley. They are indigenous jungle-dwellers who adoptedsedentary agriculture in the 1950s when the valley was cleared and converted to farmland. Thesepeople have been much less able to take advantage of the social changes occurring around them thanother groups. In our multivariate models Upper Caste Hindu status is the omitted category; effects ofbelonging to the other ethnic groups are relative to this group. The means and standard deviations ofthese variables are presented in Table 1.

(Table 1, about here)


Estimation Technique

We use logistic regression procedures to estimate multivariate models of use of alternative fuel.Logistic regression is an appropriate statistical technique for analyzing models of dichotomousdependent variables (Kmenta 1986). We report parameters from logistic regression equations in theform

ln[p/(1-p)] = α+ Σ(βk)(Xk),where p is the probability that a household uses fuel other than wood to cook with, p/(1-p) is the oddsthat a household uses fuel other than wood to cook with, αis a constant term, βk represents the effectsparameters of the explanatory variables, and Xk represents the explanatory variables in the model.Coefficients in a logit model give the change in the log-odds of using alternative fuel for a unitchange in the explanatory variables. To facilitate interpretation of the coefficients, we report theexponentiated log-odds coefficients, or the odds ratios, which are interpreted as the amount by whichthe odds of using alternative fuel are multiplied for a unit change in the explanatory variable. Thusodds ratios equal to 1 represent no effect, odds ratios greater than 1 represent positive effects, andodds ratios less than 1 represent negative effects.

Finally, because our models of community context are hierarchically clustered, with severalindividuals living in the same neighborhood, we use a multilevel logit model. Recent researchdemonstrates that this modeling strategy is suitable to these data because it accounts for theirhierarchical structure (Barber et al. 2000). Our estimates are calculated using the GLIMMIX macrofor SAS according to the approach described by Barber et al. 2000. Thus our results are properlyspecified for the multilevel nature of these data.

RESULTS

In Table 2 we present models of the overall relationship between community context and use ofalternative fuel. Each context measure indicates whether the respondent’s neighborhood has thatservice within a fifteen minute walk. The estimates in this table consistently show that changingcommunity context increases the odds of using fuel that is not wood. The pattern is remarkablystrong and consistent. Almost all of these nonfamily organizations and services have a significantpositive effect on use of fuel other than wood: employment opportunities, banks, schools, healthposts, bus stops and movie theaters. For example, the odds ratio of 1.61 for employers means thatrespondents who had a wage labor employer within a fifteen minute walk of their neighborhood atbaseline have 61% higher odds of using fuel other than wood in 2001 than respondents withoutnearby employers. The only services that do not have significant effects are markets, although theireffects are in the hypothesized direction.4

(Table 2, about here)In the last column we present our composite measure, which is simply a sum of the previous sevendichotomous measures. As expected it too has a significant positive effect on use of any fuel besideswood. In fact, because the coefficient is multiplicative, the magnitude of this effect is enormous. For

4 In a separate model we included all seven services. Employment opportunities, schools, health posts andbus stops retained positive independent effects (analyses not shown).


example, respondents with one nonfamily service within a fifteen minute walk have 33% higher oddsof using fuel other than wood than respondents without any nonfamily service nearby. Butrespondents with all seven nonfamily services within a fifteen minute walk have 636% higher odds(1.33 raised to the power of seven) of using fuel other than wood than respondents without any ofthese services nearby. These findings show strong support for our hypothesis that local nonfamilyorganizations increase households’ use of non-forest fuels.

In these same models, household income has a strong impact on use of alternative fuels.Ownership of the house land plot has a positive but not statistically significant relationship to use ofalternative fuels. As expected, respondents who have electricity available to their neighborhoods aremore likely to use fuel other than wood. Also, note that the effect of the control for use of non-woodfuel in 1996 is quite large. This is because 1996 to 2001 is a relatively brief time interval, and thusvariation in alternative fuel use in 1996 explains a great deal of variation in alternative fuel use in2001. However, the significant effects of community context during this brief time interval highlightthe fact that changes in community context are related to changes in energy consumption.

Also note from Table 2 that ethnicity does affect the odds of non-wood fuel use. Lower CasteHindus and Terai Tibeto-Burmese have lower odds of non-wood fuel use than Upper Caste Hindus,even when household income and size are controlled for. A detailed exploration of the nature of therelationship between ethnicity and domestic fuel use is beyond the scope of this paper. Nevertheless,these results suggest that this relationship may be a fruitful avenue for future research. Moreover, thesignificant differences among ethnic groups in fuel use confirm our hypothesis that it is necessary tocontrol for pre-existing cultural differences in patterns of interaction with the environment whenestimating the influence of social changes on fuel choice at the household level.

In Table 3 we test our model with a measure of electric appliances as a possible interveningmechanism. To make the comparison more clear, in Model 1 we re-estimate the model presented inModel 8 of Table 2, which shows that the number of neighborhood services has a large influence onuse of alternative fuel. Next, Model 2 shows that the household number of electric appliances has asignificant positive effect on use of alternative fuel. As with the coefficient for neighborhood services,the coefficient for electric appliances is multiplicative and thus has a huge effect. For example,respondents whose households have one electric appliance have 50% higher odds of using fuel otherthan wood than respondents whose households do not have any electric appliances. But respondentswhose households have three electric appliances have 238% higher odds (1.50 raised to the power ofthree) of using fuel other than wood than respondents whose households do not have any electricappliances.

(Table 3, about here)Furthermore, Model 2 shows that household number of electric appliances seems to play only

a small role in transmitting the effects of nonfamily services. We hypothesized that a reason whynonfamily services increase household use of alternative fuel is because they increase the likelihoodof a household acquiring more electric appliances, by granting the household information about oraccess to these technologies. Including these two measures in the same model slightly reduces themagnitude of the effect of nonfamily services. In Model 1 households with one nonfamily servicewithin a fifteen minute walk have 33 percent higher odds of using alternative fuel. Once electric


appliances are added to the model in Model 2, households with one nonfamily service within afifteen minute walk have 31 percent higher odds of using alternative fuel. This suggests that thenumber of electric appliances explains approximately six percent of the influence of nonfamilyservices. However, the effect of nonfamily services remains large and significant even after electricappliances are controlled. This could be because error in our measurement of electric appliances ispreventing this variable from being able to explain more of the impact of nonfamily services on useof alternative fuel, or it could be that other intervening mechanisms explain more of the impact. Notethat, as one might expect, ownership of electric appliances does seem to be an interveningmechanism explaining the effects of household income on use of alternative fuel. In other words,income impacts use of alternative fuel partially because income is related to how many electricappliances a household owns.

We also tested household ownership of a gas stove or a gobar gas plant as interveningmechanisms, because improved stoves have been found to reduce household fuelwood consumption(Amacher et al. 1993; Amacher et al. 1996). However, we found that adding these variables into themodel creates a tautology; that is, asking if the household owns a modern stove and asking if thehousehold uses any fuel besides wood is redundant. In fact, less than one percent of respondentsreport owning a gas stove and using wood as their only heating source for cooking; these few casesare probably reporting error. Therefore including a measure of whether the household owns either agas stove or a gobar gas plant in the same model with the number of neighborhood services does notyield insight into the mechanisms of why neighborhood services influence the use of alternative fuels.

DISCUSSION

Our results indicate that community context has important consequences for household fuel choicesand suggest that the energy transition is unfolding in rural Nepal. Increased exposure to nonfamilyorganizations in the local community increases households’ use of alternatives to wood fuels. Thesefindings are consistent with theoretical predictions because we expect that the proliferation ofnonfamily organizations and services changes daily life so that more activities are organized outsidethe family, thereby changing patterns of environmental consumption. In fact, this relationshipbetween social change and fuel substitution is net of the conventionally studied relationships amongincome, electrification and fuel substitution.

Although we find important effects of nonfamily organizations on use of alternative fuels,additional research is needed to document the mechanisms producing these effects. Possiblemechanisms, such as knowledge of fuel choices or attitudes toward different types of fuels, may helpto explain the impact of nonfamily services on fuel substitution. Explorations of the roles of variousmechanisms in transmitting the impact of nonfamily organizations are likely to provide fruitfulavenues for future research. Furthermore, a better understanding of the determinants of households’adoption of alternative energy sources is essential for informing forest policies (Heltberg et al. 2000).Our results suggest that policies and projects aimed at preserving remaining forests (e.g. reforestation,stricter rules of access to commons and state forests) might be more effective if implemented inconjunction with policies supporting the spread of nonfamily organizations. Forest policies might


seek to induce substitution away from fuelwood toward alternative fuels by promoting new servicesin the community.

Finally, this study demonstrates the capacity of social science to contribute to environmentalresearch. Sociology has great potential to advance our theoretical understanding of the links amongsocial organization, social actions, consumption, and the environment (Foster 1999). The above casestudy of Nepal illustrates this potential, and yields important insights into the processes linkingcommunity context to household fuel substitution. However, these results are only a starting point forsuch investigations in this relatively new field. For example, these results may suggest new avenuesof inquiry into other environmental consequences of nonfamily organizations. We argue that thetheoretical and methodological tools utilized here will prove to be useful in attaining conclusivemodels of the connections between humans and the environment.


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Table 1. Means and Standard Deviations of Variables Used in the Analyses (N=1153)

Variables MeanStandard

Deviation Minimum Maximum

Use of Any Fuel besides Wood in 2001 0.39 0.49 0 1

Community Context:Nonfamily Employment 0.52 0.50 0 1Nonfamily Consumption 0.79 0.41 0 1Nonfamily Banking 0.06 0.23 0 1Nonfamily Schooling 0.90 0.30 0 1Nonfamily Health Care 0.52 0.50 0 1Nonfamily Transportation 0.71 0.45 0 1Nonfamily Leisure 0.03 0.18 0 1Number of Nonfamily Services 3.54 1.43 0 7

Household Number of Electric Appliances 1.70 1.61 0 6

Control Variables

Household Income 2.14 1.37 0 5Household Owns Home Land Plot 0.88 0.33 0 1Number of Adults in Household 3.50 1.77 1 15Electricity Available to Neighborhood 0.34 0.47 0 1Time to Collect Wood in 1993 (hours) 5.95 3.54 0.08 17Use of Any Fuel besides Wood in 1996 0.30 0.46 0 1

Ethnic Group:Upper Caste Hindu 0.46 0.50 0 1Lower Caste Hindu 0.13 0.33 0 1Newar 0.05 0.22 0 1Hill Tibeto-Burmese 0.16 0.37 0 1Terai Tibeto-Burmese 0.21 0.41 0 1


Table 2. Multilevel Logistic Regression Estimates: Effects of Neighborhood Services on Use of Any Fuel Besides Wood in 2001

Independent Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Nonfamily Employment 1.61**(2.43)

Nonfamily Consumption 1.35(1.26)

Nonfamily Banking 1.90†(1.57)

Nonfamily Schooling 4.00***(3.59)

Nonfamily Health Care 1.79**(2.98)

Nonfamily Transportation 1.65*(2.25)

Nonfamily Leisure 1.98†(1.38)

Number of Nonfamily Services 1.33***(4.28)

Control Variables

Household Income 1.40*** 1.40*** 1.39*** 1.40*** 1.40*** 1.40*** 1.40*** 1.40***(6.50) (6.50) (6.40) (6.54) (6.43) (6.50) (6.46) (6.52)

Household Owns Home Land Plot 1.30 1.28 1.32 1.28 1.28 1.32 1.30 1.32(1.17) (1.11) (1.23) (1.12) (1.09) (1.24) (1.18) (1.22)

Number of Adults in Household 1.00 1.00 1.00 1.00 1.01 1.00 1.00 1.01(0.04) (0.10) (0.02) (0.11) (0.14) (0.09) (0.09) (0.19)

Electricity Available to Neighborhood 1.32† 1.49* 1.49* 1.57* 1.32† 1.58* 1.43* 1.29†(1.34) (1.97) (1.97) (2.27) (1.37) (2.24) (1.72) (1.31)

Time to Collect Wood in 1993 (hours) 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.01(0.82) (0.81) (0.89) (0.80) (0.73) (0.77) (0.84) (0.71)

Use of Any Fuel besides Wood in 1996 3.86*** 3.93*** 3.91*** 3.90*** 3.84*** 3.72*** 3.87*** 3.69***(8.56) (8.67) (8.64) (8.66) (8.51) (8.25) (8.55) (8.30)

Ethnic Group:a

Lower Caste Hindu 0.52** 0.55** 0.55** 0.56** 0.49** 0.55** 0.55** 0.49**(-2.64) (-2.44) ( -2.44) (-2.44) ( -2.88) (-2.49) ( -2.44) (-2.93)

Newar 1.24 1.26 1.24 1.25 1.10 1.24 1.27 1.12(0.64) (0.68) (0.66) (0.66) (0.28) (0.64) (0.72) (0.35)

Hill Tibeto-Burmese 1.04 1.06 1.00 1.09 0.95 1.02 1.00 0.97(0.18) (0.25) (0.00) (0.40) ( -0.24) (0.07) ( -0.02) (-0.14)

Terai Tibeto-Burmese 0.21*** 0.20*** 0.20*** 0.23*** 0.20*** 0.22*** 0.20*** 0.21***(-6.12) (-6.25) ( -6.28) (-5.71) ( -6.31) (-5.95) ( -6.14) (-6.18)

N 1153 1153 1153 1153 1153 1153 1153 1153Deviance 1097.39 1097.61 1095.00 1094.91 1100.67 1096.05 1095.73 1102.80

Note: Numbers in parentheses are t-ratiosa Reference Group is Upper Caste Hindu†p<.10; *p<.05; **p<.01; ***p<.001 (one-tailed tests)


Table 3. Multilevel Logistic Regression Estimates of the Effects of NeighborhoodServices and Electric Appliances on Use of Any Fuel Besides Wood in 2001

Independent Variables Model 1 Model 2

Household Number of Electric Appliances 1.50***(7.44)

Number of Nonfamily Services 1.33*** 1.31***(4.28) (4.10)

Control Variables

Household Income 1.40*** 1.24***(6.52) (3.97)

Household Owns Home Land Plot 1.32 1.15(1.22) (0.59)

Number of Adults in Household 1.01 0.93*(0.19) (-1.67)

Electricity Available to Neighborhood 1.29† 1.13(1.31) (0.66)

Time to Collect Wood in 1993 (hours) 1.01 1.03†(0.71) (1.37)

Use of Any Fuel besides Wood in 1996 3.69*** 3.12***(8.30) (7.10)

Ethnic Group:a

Lower Caste Hindu 0.49** 0.59*(-2.93) (-2.12)

Newar 1.12 0.87(0.35) (-0.40)

Hill Tibeto-Burmese 0.97 0.94(-0.14) (-0.28)

Terai Tibeto-Burmese 0.21*** 0.27***(-6.18) (-5.32)

N 1153 1153Deviance 1102.80 1068.79

Note: Numbers in parentheses are t-ratiosa Reference Group is Upper Caste Hindu†p<.10; *p<.05; **p<.01; ***p<.001 (one-tailed tests)

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