This paper examines whether offering landless forest-adjacent communities options to grow appropriate food crops inside forest reserves during early stages of reforestation programmes increases incomes of low-income households and conserve forests. We consider the forest cover and household welfare impacts of a unique incentive scheme in Kenya known as the Plantation Establishment and Livelihood Improvement Scheme (PELIS). PELIS seeks to deepen community participation in forestry, and improve the livelihoods of adjacent communities.

It is widely believed that forests help improve water quality by reducing soil erosion (and hence reducing silt) as well as filtering out nutrients and pollutants carried in water, which allows the municipal drinking water supply sector to simplify or expedite many costly water treatment procedures and thereby save on operating costs. This study statistically quantifies such cost savings in the contexts of China’s Sichuan province, by analyzing how drinking water treatment costs change in response to changes in upstream forest cover.

To encourage sustainable use of forests, in 2003 China allowed rural villages to choose among a range of options to manage forests, including individual user rights with joint management. We studied how this individual user rights-based joint management affected forests and households.

Both property rights and voluntary decisions encourage cooperation. This resulted in better forest conservation in these villages in China, with about 10% more forest cover. This is also important because forests store carbon.

The water purification functions of forests represent one of the most frequently invoked examples of non-market ecosystem services that are economically valuable. Yet, there has been a paucity of statistical estimates that robustly quantify such benefits. This study enriches this thin evidence base through valuing forests’ water purification services in the form of the ensuing cost savings of municipal drinking water treatment, using a rich panel dataset from China’s Sichuan province.