Artificial afforestation programs are emerging as important policy interventions globally to increase carbon sequestration, yet there has been little systematic study of the impacts of afforestation programs on the livelihoods of forest dependent people. Afforestation projects do not simply improve ecosystem service provision, as widely assumed; they replace other land-cover types such as grasslands, savannas, or degraded forests thus changing the mix of goods provided by these ecosystems. Depending on the species planted and the success of the plantation, afforestation may increase the availability of timber and fuelwood while decreasing availability of fodder and some non-timber forest products. Livelihood impacts will depend on the importance of these goods and services to different households, the availability of alternatives, and the capacity of households to respond. As a result, plantations may improve the livelihoods of some households while hurting others, particularly those dependent on non-forest resources produced on lands converted to plantation. Better understanding of the effects of plantations on livelihoods is crucial for designing policies that maximize the positive benefits and mitigate negative impacts of afforestation. We propose to study the impact of afforestation programs in India, a country in which afforestation efforts are extensive, on the livelihoods of the rural poor. We will do so by combining recent government data on afforestation with long-term estimates of afforestation based on NASA satellite data and household surveys in 140 villages with varying levels of exposure to afforestation. In April of 2016, we obtained government records for all 2252 plantations made by the state forest department in the Kangra district of the Western Himalayan state of Himachal Pradesh between 2005-2015. We will combine this data with ground-truthing in a subset of these plantations.

We will conduct land-cover/land-use change (LCLUC) analysis based upon use of an advanced image endmember-estimation algorithm and spectral unmixing/endmember mapping. This will allow us to detect and differentiate different types of small plantations using historical Landsat data. We will conduct household livelihood surveys in a sample of 140 villages which have been exposed to different types of plantations. Combining estimates of afforestation activities with household livelihood data will allow us to estimate, using regression and propensity score matching techniques, the impacts of afforestation on the livelihoods of households with different characteristics. These analyses will allow us to develop guidelines, which will help Indian policy-makers develop and implement plantation programs that align the imperative for carbon sequestration with the needs and interests of the poor.