Header menu link for other important links
What drives forest degradation in the central Himalayas? Understanding the feedback dynamics between participatory forest management institutions and the species composition of forests
Published in Elsevier B.V.
Volume: 95
Pages: 85 - 101
Human populations and their socio-economic conditions, such as road networks and poverty, are thought to be the main drivers of deforestation. However, a high deforestation rate can also alter the species composition of forests, providing further feedback to the socio-economic drivers of deforestation as well as weakening the community forest management institutions. In this paper, we model the feedback linkages associated with the degradation of forests and the weakening of the local institutions to understand how they impact the long-term sustainability of these linked socio-economic-ecological systems. In particular, we explore the impact of excessive harvesting of forests for fuelwood and fodder on a shift in the species composition from oak to pine trees in the central Himalayan region of India. This shift provides adverse feedback to the communities’ livelihoods and erodes the quality of their participatory management institutions. A change in the species composition also increases forest fire risks, which further exacerbates the ecological as well as socio-economic feedback effects. We develop and apply a dynamic optimization model of community forest management where, through optimally controlling harvesting efforts over time, a weighted sum of community and environmental objectives is maximized. Findings indicate that factors such as population size, the extent of dependence of the community on fuelwood, the strength of community institutions, and the degree of feedback effects, affect the long-term sustainability of forests. When faced with forest fire risks, there is a discounting effect present which increases deforestation and institutional entropy. © 2018 Elsevier B.V.
About the journal
Published in Elsevier B.V.
Open Access
Impact factor