Zero-deforestation commitments are a type of voluntary sustainability initiative that companies adopt to signal their intention to reduce or eliminate deforestation associated with commodities that they produce, trade, and/or sell. Because each company defines its own zero-deforestation commitment goals and implementation mechanisms, commitment content varies widely. This creates challenges for the assessment of commitment implementation or effectiveness.

Most of the literature on the causes of tropical deforestation has focused on the proximate and distal causes. However, research exploring the psychological drivers of deforestation, i.e., motivations, is still scant despite being crucial to understand the processes of land-use change and individual decision making within social-ecological systems. We studied the combined effect of structural and individual causes of deforestation, with particular emphasis on motivations, for a sample of rural households in Colombia’s foremost tropical deforestation frontier.

During the mid-1990s, one of the most ambitious land reforms in recent decades took place in Colombia. The reform recognized collective land rights of 5 million hectares by Afro-Colombian groups, with the dual goals of improving livelihoods and preserving valuable ecosystems. We estimate the impact of this collective land titling program on forest cover using panel data and a difference-in-difference empirical strategy. We find that overall, collective titling significantly reduces deforestation rates, but the effect varies substantially by sub-region.

We study the impact of Ecuador’s national forest conservation incentives program
on reported land conflicts. Data come from a survey of >900 households located

within 49 indigenous and Afro-Ecuadorian communities holding communal conser-
vation contracts. We use quasi-experimental methods to test for relationships between

Empirical assessment of relationships between land use and land cover (LULC) and drinking water chemical treatment cost is lacking in developing countries. This study is conducted to assess the impact of forest and non-forest land cover on water purification chemical costs using panel data collected from eight drinking water treatment plants in Ethiopia for the period of 2002-2014. Forest cover and LULC data were extracted from Global Forest Change and GlobeLand30 datasets, respectively.

This paper examines technical efficiency in farming activities and its implication on forest conservation in Kilosa District. The empirical analysis is based on data collected from 301 households selected randomly from five villages in Kilosa district, of which three villages were under the REDD+ project. Two empirical models were estimated: stochastic frontier Translog production function, and forest resources extraction model.