Quantifying factors giving rise to temporal variation in forest fires is important for advancing scientific understanding and improving fire prevention. We demonstrate that eighty percent of the large year-to-year variation in forest area burned in California can be accounted for by variation in...

Papers on optimal harvesting regimes for maximizing land expectation value (LEV) that compare different logging practices often ignore differences in variable costs and in damages on the residual stand between logging practices. We use data on a multi-age, multi-species forest in East-Kalimantan...

Motivated by tropical deforestation, we analyze (i) a novel theory of resource extraction, (ii) the optimal conservation contract, (iii) when the donor prefers contracting with central rather than local governments, and (iv) how the donor's presence may induce institutional change. Deforestation...

This paper presents a new approach to study the optimal rotation policy with amenity valuation under uncertainty. We first postulate the stochastic forest value and assume plausibly that monetary value of amenities is a continuous and non-negative function of forest value thus presenting the...

This paper analyzes socially optimal forest taxation when the government has a binding tax revenue requirement. In the Faustmann model the optimal design of forest taxation consists of non-distortionary taxes, such as site productivity tax, site value tax or profit tax. A combination of...

This paper extends the Hartman model to study the optimal rotation age of two interdependent stands when the stream of amenities produces from the two stands may be complements or substitutes, both in space and over time. In the presence of stand interdependence both the ownership structure and...

Fuelwood collection is often cited as the most important cause of deforestation in developing countries. Use of fuelwood in cooking is a leading cause of indoor air pollution. Using household data from India, we show that households located farther away from the forest spend more time...

This paper develops a machine-learning method that allows researchers to estimate heterogeneous treatment effects with panel data in a setting with many covariates. Our method, which we name the dynamic causal forest (DCF) method, extends the causal-forest method of Wager and Athey (2018) by...