Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 750

The DOI matches its Crossref record.

Economic models of tropical deforestation: a review

Open access
D., Kaimowitz, A., Angelsen·1998·Center for International Forestry Research (CIFOR) eBooks

Abstract

As international concern over tropical deforestation has grown over the last ten years, researchers have sought to understand the causes of deforestation and possible solutions using quantitative economic models. This book reviews the results and methodology of over 150 of these models and synthesizes the main lessons that can be learned from them. Higher agricultural prices, lower wages, less off-farm employment, and more roads generally lead to more deforestation. Major doubts remain on the impact of technological change, agricultural input prices, household incomes, and tenure security. The role of macro level factors such as population growth, poverty reduction, national income levels, economic growth and foreign debt is still largely uncertain. While the boom in deforestation modeling has yielded new insights, many model results should be regarded with caution because of poor data quality and methodological weaknesses. In particular, the book finds most multi country regression models to be of limited value. It recommends a shift in future research towards household and regional level studies, instead of the current emphasis on national and multi-country studies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.17528/cifor/000341

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 750

Metadata

Authors
D., K., & A., A.
Venue
Center for International Forestry Research (CIFOR) eBooks
Published
1998
OpenAlex
W1567187307
Cited by
750
References
0
FWCI
48.35
Type
book
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Economic models of tropical deforestation: a review. https://app.cochat.ai/papers/10.17528/cifor/000341
The paper
D., K., & A., A. (1998). Economic models of tropical deforestation: a review. Center for International Forestry Research (CIFOR) eBooks. https://doi.org/10.17528/cifor/000341

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.