Taking differences in institutional quality into account in global forest modeling

Johanna Wehkamp, of the Technical University of Berlin and the Mercator Research Institute on Global Commons and Climate Change, examined which countries have the most effective institutions to reduce deforestation.

Johanna Wehkamp

Johanna Wehkamp

Introduction

Deforestation accounts for around 3.0±1.1 Gt CO2 of global greenhouse gas emissions [1], and is a major cause of biodiversity loss, soil erosion, and many other environmental problems. Its primary cause is the conversion of forests for more profitable purposes, mainly agriculture. This idea is captured in IIASA’s global forest model (G4M). G4M is a spatially explicit model, combining a biophysical and a socioeconomic component. It is used to compare the agricultural and forestry net present values of land and model deforestation trends. More recently, the importance of differences in the quality of institutions has been recognized [2][3]. Most authors find that a country is more likely to limit its deforestation if its institutions allow forest protection to be properly enforced.

In this research project, the importance of differences in institutional quality in the context of the G4M was examined. The model predicts land use change based on a purely economic rationale. To match observed deforestation and afforestation patterns, a residual factor is calibrated for each country. We showed that taking differences in institutional quality into account allows a substantial part of this residual to be explained.

Methods

We conducted ordinary least square regressions with 139 indicators on institutional quality and built a composed index from the most significant indicators. Integrating the indicator into the model allowed the model’s residual to be reduced.

Results and conclusion

Adding an institutional quality index to G4M reduces the model’s residual factor and hence improves the model with respect to its net present value-based routines. The results allow a better understanding of possible impacts of differences in institutional quality on forest cover change.

References

[1] Harris NL, Brown S, Hagen SC, Saatchi SS, Petrova S, Salas W, Hansen MC, Potapov PV, Lotsch A (2012) Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576.

[2] Buitenzorgy M, Mol A (2011). Does democracy lead to a better environment? Deforestation and the democratic transition peak. Environmental and Resource Economics 48, 59–70.

[3] Galinato GI, Galinato SP (2012). The effects of corruption control, political stability and economic growth on deforestation-induced carbon dioxide emissions. Environment and Development Economics 17, 67–90.

Supervisors

Florian Kraxner and Stephan Pietsch, Ecosystem Services and Management Program, IIASA

Note

Johanna Wehkamp, of the Technical University of Berlin and the Mercator Research Institute on Global Commons and Climate Change, is a citizen of Germany. She was funded by the IIASA German National Member Organization and worked in the Ecosystem Services and Management Program during the YSSP.

Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.


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Last edited: 02 February 2016

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