We present a conceptual, but empirically applicable, model for determining the optimal allocation of resources between exclusion and control activities for managing an invasive species with an uncertain discovery time. This model is used to investigate how to allocate limited resources between activities before and after the first discovery of an invasive species and the effects of the characteristics of an invasive species on limited resource allocation. The optimality conditions show that it is economically efficient to spend a larger share of outlays for exclusion activities before, rather than after, a species is first discovered, up to a threshold point. We also find that, after discovery, more exclusionary measures and fewer control measures are optimal, when the pest population is less than a threshold. As the pest population increases beyond this threshold, the exclusionary measures are no longer optimal. Finally, a comparative dynamic analysis indicates that the efficient level of total expenditures on preventive and control measures decreases with the level of the invasive species stock and increases with the intrinsic population growth rate, the rate of additional discoveries avoided, and the maximum possible pest population.

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In 2010, the U.S. Environmental Protection Agency (EPA) released a life-cycle analysis of the greenhouse gas (GHG) emissions associated with the production and combustion of corn ethanol. EPA projected that by 2022, the emissions profile of corn ethanol from a new refinery would be 21% lower than that of an energy equivalent quantity of gasoline. Since 2010, the 21% value has dominated policy discussions and federal regulations related to corn ethanol as a renewable fuel and a GHG mitigation option. It is now 2018 and new data, scientific studies, technical reports, and other information allow us to examine the emissions pathway corn-ethanol has actually followed since 2010. Using this information, we assess corn ethanol's current GHG profile at 39-43% lower than gasoline. We also develop two projected emissions scenarios for corn ethanol in 2022. These scenarios highlight opportunities to produce ethanol with emissions that are 47.0-70.0% lower than gasoline. Many countries are now developing or revising renewable energy policies. Typically, biofuel substitutes for gasoline are required to reduce GHG emissions by more than 21%. Our results could help position U.S. corn ethanol to compete in these new and growing markets.

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This research incorporates the development and adoption of an induced technology under uncertainty into a conceptual dynamic model to more broadly examine efficient policies for mitigating invasive species infestations. We find that under optimal policy, marginal costs of adopting conventional control measures are equal to the sum of the marginal benefits from development and adoption of new technology, as well as the use of conventional control measures. This result implies that a resource allocation designed for controlling invasive species is not adequate when an induced technology is not considered. Our results also reveal that the shadow values associated with the probabilities of developing and then adopting an induced technology increase as the shadow values associated with the stock of an invasive species population increase.

By encouraging or discouraging adaptations to new environmental conditions, farm programs could greatly affect the costs of climate change. On balance, today's programs seem susceptible to climate change driven cost increases. Some policy tools and program changes, however, would facilitate adaptation and so could help lower the costs.

This study presents a cradle-to-grave life cycle analysis (LCA) of the greenhouse gas (GHG) emissions of the electricity generated from forest biomass in different regions of the United States (U.S.), taking into consideration regional variations in biomass availabilities and logistics. The regional biomass supply for a 20 MW bioelectricity facility is estimated using the Land Use and Resource Allocation (LURA) model. Results from LURA and data on regional forest management, harvesting, and processing are incorporated into the GHGs, Regulated Emissions, and Energy Use in Technologies (GREET) model for LCA. The results suggest that GHG emissions of mill residues-based pathways can be 15−52% lower than those of pulpwood-based pathways, with logging residues falling in between. Nonetheless, our analysis suggests that screening bioenergy projects on specific feedstock types alone is not sufficient because GHG emissions of a pulpwood-based pathway in one state can be lower than those of a mill residue-based pathway in another state. Furthermore, the available biomass supply often consists of several woody feedstocks, and its composition is region-dependent. Forest biomass-derived electricity is associated with 86−93% lower life-cycle GHG emissions than the emissions of the average grid electricity in the U.S. Key factors driving bioelectricity GHG emissions include electricity generation efficiency, transportation distance, and energy use for biomass harvesting and processing.