Post developed by Katie Brown and Arun Agrawal.

475547743

Photo credit: Thinkstock

Center for Political Studies (CPS) faculty associate and Professor in the School of Natural Resources and the Environment (SNRE) Arun Agrawal studies environmental policy.

Professor Agrawal and the International Forestry Resources and Institutions (IFRI) program he coordinates just received a grant of 1.9 million pounds (about $3.2 million) over four years from the United Kingdom’s Department for International Development (DFID).

Agrawal’s proposed research seeks to improve measurement of the outcomes of forest investments. Forest investments try to minimize the negative impacts of forest-related changes: by influencing land use, agriculture, institutions, migration, and commodity production. Substantial forest investments have occurred in the past few years, driven in part by concerns about climate change and the role of land changes in contributing to global emissions. Assessing the impacts of these programs, therefore, and improving their effectiveness is urgent. As Agrawal explains, “there is a lack of rigorous and generalizable empirical analyses of the effectiveness of past forest investments. Existing knowledge is often anecdotal, based on non-systematically selected indicators, usually supported only by information from a small number of unrepresentative cases, and through studies without rigorous counterfactual analysis.”

With this grant, Agrawal and his collaborators will methodically study the impact of forest investments with empirical data and quantitative analysis. In particular, Agrawal will seek answers to three key questions:

  1. What is the impact of specific types of forest interventions across different policy and governance contexts?
  2. Where and under what conditions do forest interventions deliver positive impacts?
  3. Which forest interventions have resulted in more positive impacts and why?

The research will focus on Brazil, Ethiopia, Ghana, Indonesia, and Nepal, using these countries to generate rigorous, valid, reliable, and generalizable findings.