What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence

What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence

Survey looks at causes of deforestation, including the impact of cultural and socio-political forces

Using the framework of the Land Use and Cover Change (LUCC) Science/Research Plan this study takes 152 studies of deforestation in different regions of varying size from around the tropics and analyses them to assess how important different causes of deforestation really are. It claims to be the first which quantifies the impact of cultural or socio-political driving forces upon deforestation.

The study gives a brief rationale why subnational – and not country-wide – case study evidence constitutes an important tool in LUCC and outlines the analytical concept of proximate and underlying causes of land change used in LUCC research. The authors then attempt to generalize results across regions or broad geographical entities (Africa, Asia, Latin America). Results are presented in three sections:

  • frequency of occurrence of broad clusters of causes and more specific activities (or actor-driven processes) associated with tropical deforestation.
  • patterns of causality (interlinkages) both at the proximate and underlying levels (and between them), in terms of the mode of connection of causes
  • variations of results other than by broad geographical entities – i.e., by forest type, area size, topography, spatial pattern and process at work, deforestation rate, and poverty- versus capital-driven deforestation.

Finally the paper compares its findings against other empirical evidence on tropical deforestation and conclusions are drawn.

Empirical findings versus prevailing explanations

  • Causes and drivers of tropical deforestation cannot be reduced to a single variable, or to a few variables even. Rather, the interplay of several proximate as well as underlying factors drive deforestation in a synergetic way.
  • While the expansion of cropped land and pasture is clearly the most important proximate cause of tropical deforestation, shifting cultivators are not always the key agents
  • Chain-logical causation in the form of simple tandems (or 2-factor chains) underlies about two thirds of the proximate as well as underlying causes. On average, 4 to 5 tandems are associated with each case of tropical deforestation.
  • Rather than single or direct fundamental causes, underlying driver tandems were identified to be most important. These are mainly economy-, policy and institution - and culture - driven tandems impacting upon the proximate level
  • Population pressure in the form of natural increases in number of population due to high fertility is clearly not the major underlying driving force. Rather, inmigration into forested (not natural increase in densely populated) areas plays an important role in cases of frontier colonization. In all cases, however, population does not operate as a single force but is interlinked with other underlying factors.
  • With view on variations of cause frequencies and causality patterns, there is empirical indication that in cases with high rates of annual deforestation pre-disposing biophysical factors are at work or shape the pattern of deforestation.
  • The explanatory power of PAT variables (population, aff luence, technology) is astonishinglypoor. This model obviously fails to consider policy and institutional factors shaping market opportunities as powerful explanatory factors of tropical deforestation.
  • The multiple factors intervening in tropical deforestation also make it particularly difficult to develop generic and widely applicable policies that best attempt to control the process. Many land-use policies are underlain by simplifications on the drivers of change. Such simplification suggest simple technical solutions and sometimes may serve the interests of critical groups (Lambin et al., 2001). From the results of the meta-analysis it is clear that any universal policy or global attempt to control deforestation (e.g., through poverty alleviation) is doomed to failure.

Implications for modelling and controlling deforestation

  • Deforestation is a complex, multiform process which cannot be represented by a mechanistic approach. This has implications for modelling as many of the simulation models of land-use change tend to be mechanistic.
  • The case study evidence examined in this meta-analysis clearly suggests that we still lack an overarching theory to encompass the different factors which intervene in the processes of deforestation.
  • Empirical evidence shows that the complexity and diversity of driving factors of deforestation is reduced when looking at specific processes – e.g., subsistence agriculture, commercial agriculture, colonisation activities or logging activities – and specific geographic situations – i.e., frontier areas, roadside areas, peri-urban areas.
  • The overall dominance of the broad cluster of agricultural expansion is well perceived in the modelling of tropical deforestation. However, this is not always the case when it comes more specific agricultural uses and other than agricultural land uses such as logging.
Implications for future case study comparisons
  • The LUCC research framework proved to be a fruitful platform from which to proceed to develop a general understanding of the drivers of land use and land cover change.
  • Concerning regional representation of case studies in future comprehensive comparisons, the study found that weighting bias in our meta-analysis was low, but also found indication that future work will have to include considerably more African less Asian and about the same number of Latin American cases,
  • Concerning weighting bias in terms of agents involved in the process of deforestation, a better understanding of logging company behaviour and/or industrial forestry plantation activities is required
  • Finally the study found a systematic comparison of local-scale case studies is an extremely productive methodology.


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