Satellite-based image classification facilitates low-cost measurement of the Earth’s surface composition. However, misclassified imagery can lead to misleading conclusions about transition processes. We propose a correction for transition rate estimates based on the econometric measurement error literature to extract the signal (truth) from its noisy measurement (satellite-based classifications). No ground-truth data are required in the implementation. Our proposed correction produces consistent estimates of transition rates, confirmed by longitudinal validation data, while transition rates without correction are severely biased. Using our approach, we show how eliminating deforestation in Brazil’s Atlantic forest region through 2040 could save $100 billion in CO2 emissions.

We develop a model of competition in prices and infrastructure among mobile network operators. Although consolidation increases market power, it can lead to more efficient data transmission due to economies of scale, which we derive from physical principles. After estimating our model with French consumer and infrastructure data, equilibrium simulations reveal that while prices decrease with the number of firms, so do download speeds. Our framework also allows us to quantify the impact of spectrum allocation. The marginal social value of spectrum exceeds firms’ willingness to pay in our model as well as observed prices in spectrum auctions.