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Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set.

Programs to reduce traffic congestion and air pollution by restricting use of motor vehicles on working days have generally not met with success, given existing studies of such programs. We conduct the first study of Quito, Ecuador's four-year-old Pico y Placa program and find that it has reduced ambient concentrations of carbon monoxide (CO), a pollutant primarily emitted by vehicles, by 9% to 11% during peak traffic hours. Given that ambient concentrations of CO generally track the spatial and temporal distributions of traffic, these reductions in pollution suggest similar reductions in vehicle flows. We find no significant evidence that traffic has shifted to other times of the day or week, or to other locations. Les programmes de réduction du trafic automobile et de la pollution de l'air via des restrictions sur l'usage des automobiles dans les jours ouvrables ont généralement échoué, selon les études disponibles. Les auteurs présentent la première étude du programme Pico y Placa de Quito en Equateur - un programme mis en place au cours des dernières années. On montre qu'il a réduit la concentration ambiante de monoxide de carbone (un polluant émis surtout par les véhicules) de 9 à 11% durant les heures de trafic le plus intensif. Comme les mesures de concentrations ambiantes de monoxide de carbone sont reliées aux coordonnées spatiales et temporelles du trafic, ces réductions de pollution suggèrent des réductions correspondantes de flux de véhicules. Les auteurs n'ont aucune indication que le trafic a été déplacé vers d'autres plages horaires dans la journée ou la semaine ou vers d'autres lieux.

I present a model that explains two common features of regulatory enforcement: selective forgiveness of noncompliance, and the collection of information on a firm’s compliance activities and not just its compliance status. I show that forgiving noncompliance is optimal if the information on a firm’s compliance activities constitutes sufficiently strong evidence of the firm having exerted a high level of compliance effort. The key benefit of forgiving noncompliance is a reduction in the probability with which the firm needs to be monitored.

We extend existing stand-level models of forest landowner behavior in the presence of fire risk to include the level and timing of fuel management activities. These activities reduce losses if a stand ignites. Based on simulations, we find the standard result that fire risk reduces the optimal rotation age does not hold when landowners use fuel management. Instead, the optimal rotation age rises as fire risk increases. The optimal planting density decreases. The level of intermediate fuel treatment, but not its timing, is sensitive to the magnitude of fire risk. Cost-sharing is shown to be an effective instrument for encouraging fuel treatment. (JEL Q23)

We put in perspective the papers in this special issue by characterizing different forms of adaptation to climate change and discussing the role of adaptation in a developing country context. We highlight adaptation decision-making under uncertainty, empirics of autonomous adaptation, and data and methodological challenges. We identify unresolved questions, emphasizing interactions between autonomous and planned adaptation, adaptation externalities, and the relationship between adaptation and conflict.

I develop a principal-agent model of environmental regulation in which the regulator can acquire two costly signals of the firm's abatement effort. Acquisition of the second signal is conditioned on the observed value of the first, emissions signal. The optimal contract takes the form of an emissions standard when only the emissions signal is acquired, and a set of contingent emissions standards when both signals are acquired; the standards are coupled with uniform, maximal penalties for noncompliance. Acquisition of the second signal may be optimal when intermediate values of the first signal are observed but not when extreme values are observed. [PUBLICATION ABSTRACT]

We evaluate two financial incentives to encourage nonindustrial forest landowners to undertake activities that mitigate fire losses: sharing of fire suppression costs by the landowner and sharing of fuel reduction costs by the government. First and second best outcomes are identified and compared to assess the effectiveness of these incentives in reducing social losses and fire suppression costs, under various assumptions about landowner behavior and information. We find that while cost sharing of fire suppression by the landowner invariably reduces social losses, this is not always true for government cost sharing of landowner fuel reduction. However, cost sharing of fuel reduction can yield larger reductions in social losses when fire risk is high. Both policies tend to induce larger reductions in both social losses and fire suppression costs when landowners engage in fuel reduction. We find that improving a landowner’s information about fire risk and fuel reduction can yield substantial reductions in social losses. (JEL Q23, Q54)

We estimate the value of three types of information about fire risk to a nonindustrial forest landowner: the relationship between fire arrival rates and stand age, the magnitude of fire arrival rates, and the efficacy of fuel reduction treatment. Our model incorporates planting density and the level and timing of fuel reduction treatment as landowner decisions. These factors affect, among other things, the loss a landowner incurs should fire arrive before harvesting. The value of information depends on the nature and combination of mistakes a landowner makes, the relationship between fire arrival and stand age, and on whether the landowner undertakes fuel treatment and values nontimber benefits. Information of various types is of most value to a landowner who does not undertake fuel treatment. The value of information about the magnitude of fire risk is also more than twice as high when the landowner underestimates fire risk, rather than overestimating it. For a landowner who undertakes fuel treatment but makes multiple mistakes, the asymmetry between overestimating and underestimating fire risk and efficacy of fuel reduction is even more pronounced.

I examine a model of optimum enforcement in which offenders can engage in activities that reduce the probability of being caught and fined. The costs associated with these avoidance activities imply that it is not necessarily optimal to set fines for offenses as high as possible. These avoidance costs also provide an incentive to screen individuals in settings where it is not optimal to deter all offenses.