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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01bv73c2764
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dc.contributor.advisorOppenheimer, Michael-
dc.contributor.authorKreutter, Rebecca-
dc.date.accessioned2015-07-16T15:20:38Z-
dc.date.available2015-07-16T15:20:38Z-
dc.date.created2015-04-08-
dc.date.issued2015-07-16-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01bv73c2764-
dc.description.abstractInternational funding institutions acknowledge that curbing deforestation can be one of the cheapest methods for climate change mitigation. To be as cost effective as possible, international institutions need ways to identify areas with high rates of deforestation and with a proven record for using funds effectively. Using global, open-­‐ source satellite data on forest change, donors can now track deforestation alerts on a monthly basis and analyze annual trends in forest loss. Besides Indonesia and Brazil, however, there has been little research that uses satellite data to investigate national drivers of forest change. In this thesis, I investigate forest change in India. A country with increasing international clout and greenhouse gas impacts, India is seen as a success story for community forestry. Government sources attribute increasing forest cover to the devolution of power from state to community hands; independently collected sources disagree, finding that India’s forests are decreasing. I compared government-­‐collected data with independently-­‐sourced data from Hansen et al. (Chapter 2). The two datasets agree on which states have the most and least forest cover, but disagree on the scale of forest cover in each state. Hansen et al. find that total forest has decreased since 2002, while government data assert forest cover has risen. Both datasets agree that India’s densest forests have experienced degradation from 2002-­‐2011. In Chapter 3, I constructed four econometric models to tease apart the drivers of forest change. I found forests that were not legally protected suffered from more forest loss as did states with higher forest revenues per hectare of forest and lower levels of expenditures per hectare of forest. Agriculture and livestock pressures were not as significant as expected, though they did show more significance over longer timescales. Chapter 4 looked at India’s northeastern region, which experiences 2/3rds of the nation’s forest loss by hectare though it covers just 8% of its land area. I found that the northeast’s physical isolation has protected its forest resources. Tribes govern the region and communities manage the majority of forest resources, decreasing the effectiveness of central-­‐led forestry policy. India has poured money into the northeast to stimulate economic development without success, leaving local communities with few options but to extract forest resources for survival. Finally, I offer several recommendations to national policymakers in India, to northeastern policymakers and to international actors. In brief, the country would benefit from a better understanding of its forest resources, including demarcated maps showing forest ownership and complete data on non-­‐timber forest product (NTFP) extraction. The northeast should increase community empowerment, legally protect more land and increase the sustainability of traditional livelihoods. International actors could better leverage open-­‐source satellite data to identify deforestation hotpots, verify country reports, prioritize funding and build technical capacity in countries.en_US
dc.format.extent121 pages*
dc.language.isoen_USen_US
dc.titleEconometric Drivers of Forest Change in Indiaen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2015en_US
pu.departmentSpanish and Portuguese Languages and Culturesen_US
pu.pdf.coverpageSeniorThesisCoverPage-
Appears in Collections:Princeton School of Public and International Affairs, 1929-2020

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