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Household dynamics and fuelwood consumption in developing countries: a cross-national analysis


Previous research has suggested a link between household dynamics (i.e., average household size and number of households) and environmental impacts at the national level. Building on this work, we empirically test the relationship between household dynamics and fuelwood consumption, which has been implicated in anthropogenic threats to biodiversity. We focus our analysis on developing countries (where fuelwood is an important energy source). Our results show that nations with smaller average households consume more fuelwood per capita. This finding indicates that the household economies of scale are, indeed, associated with the consumption of fuelwood. In addition, we found that number of households is a better predictor of total fuelwood consumption than average household size suggesting a greater relative contribution to consumption levels. Thus, insofar as declining average household sizes result in increased number of households and higher per capita consumption, this trend may be a signal of serious threats to biodiversity and resource conservation. We also found further support for the “energy ladder” hypothesis that economic development reduces demand for traditional fuels.

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  1. Research indicates that three factors explain the majority of cross-national variation in average household size among developing countries: level of fertility, mean age at marriage, and the level of marital disruption (i.e., divorce rates) (Bongaarts 2001). Furthermore, the decline in average household size has been attributed to a number of factors, such as increased standards of living, cultural and social change (e.g., gender norms), population aging, and declining fertility (Keilman 2003).

  2. The term “driving forces” or “drivers,” familiar to several areas of research in the physical sciences such as plate tectonics and statistical thermodynamics, is new to the social sciences. More importantly, it has been universally adopted by natural scientists studying global environmental change to refer to what are presumed to be the most important factors producing environmental change. In the more generic language of science they are independent variables, particularly ones shown to have environmental effects.

  3. The household is both the physical structure and the locus of the majority of the decisions individuals make about environmentally significant consumption: about housing type, size, and location, types of temperature control and patterns of use, levels of energy efficiency in behavior or infrastructure, and households pattern a broad similarity in occupant lifestyles.

  4. Hotspots are “areas featuring exceptional concentrations of endemic species and experiencing exceptional loss of habitat” (Meyers et al. 2000:853).

  5. Fuelwood footprint data were provided by Brad Ewing of the Global Footprint Network, to whom we are grateful.

  6. We measure forest area in 1990 because past forest endowment is likely to be just as or more important in explaining fuelwood consumption patterns as present endowment. We thank an anonymous reviewer for this suggestion.

  7. A condition that violates the assumption of OLS estimation that the error term has a constant variance.

  8. All other coefficients were not substantially affected by excluding urbanization.

  9. The data on the overall change in household size among developing countries differ from those offered by Keilman (2003) above because of the composition of our sample; we do not include data for all developing countries in our analysis due to data limitations.

  10. Note that each of our three model specifications are calculated using both ordinary least squares and robust regression. In order to make this discussion more readable, we refer to each pair of models in the singular (e.g., Model 1). Where important differences in estimates occur, we refer to the specific model (e.g., Model 1.2).

  11. Following the thoughtful suggestion of one anonymous reviewer, we also estimated models including the percent change in GDP per capita 1970–2000 and Gini coefficient (alternatively measured in 1990, 1995, and 2000). Neither variable was found to be significant in any model.

  12. Results are substantively similar when forest area is measured in 1995. When measured in 2000, forest area is significant and positive, though with an extremely small coefficient, in Models 1 and 2, but not in Model 3 in which per capita consumption is the dependent variable.


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Correspondence to Kyle W. Knight.

Appendix: List of countries (n = 87)

Appendix: List of countries (n = 87)

  • Albania

  • Algeria

  • Argentina

  • Bangladesh

  • Benin

  • Bolivia

  • Botswana

  • Brazil

  • Bulgaria

  • Burkina Faso

  • Burundi

  • Cambodia

  • Cameroon

  • Central African Republic

  • Chad

  • Chile

  • China

  • Colombia

  • Congo

  • Congo, Dem. Rep.

  • Costa Rica

  • Djibouti

  • Dominican Republic

  • Ecuador

  • Egypt

  • El Salvador

  • Fiji

  • Gabon

  • Gambia

  • Ghana

  • Guatemala

  • Guinea

  • Guinea-Bissau

  • Haiti

  • Honduras

  • Hungary

  • India

  • Indonesia

  • Iran

  • Iraq

  • Ivory Coast

  • Jordan

  • Kenya

  • Laos

  • Liberia

  • Libya

  • Madagascar

  • Malawi

  • Mali

  • Mauritania

  • Mexico

  • Mongolia

  • Morocco

  • Mozambique

  • Nepal

  • Nicaragua

  • Niger

  • Nigeria

  • Oman

  • Pakistan

  • Panama

  • Papua New Guinea

  • Paraguay

  • Peru

  • Philippines

  • Poland

  • Romania

  • Rwanda

  • Saudi Arabia

  • Senegal

  • Solomon Islands

  • South Korea

  • Sri Lanka

  • Sudan

  • Syria

  • Tanzania

  • Thailand

  • Togo

  • Tunisia

  • Turkey

  • Uganda

  • Uruguay

  • Venezuela

  • Viet Nam

  • Yemen

  • Zambia

  • Zimbabwe

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Knight, K.W., Rosa, E.A. Household dynamics and fuelwood consumption in developing countries: a cross-national analysis. Popul Environ 33, 365–378 (2012).

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  • Households
  • Fuelwood
  • Ecological footprint