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KELSEY K JOHNSON
Post-Doctoral Researcher: Natural Resource and Environmental Economics
Publications
The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.
The escalating pace of climate change and biodiversity loss has energized endeavors to expand protected areas. Recent studies find that agricultural land may play a vital role in tackling climate change and promoting biodiversity. However, most agricultural protection areas (APAs) are implemented based solely on agricultural production characteristics, and there are limited strategies that incorporate other conservation goals. We combined Systematic Conservation Planning (SCP) principles, optimization algorithms, and the Ecosystem Service framework to identify potential APAs and explore the trade-offs in promoting the multifunctionality of agricultural land. We conducted our study in the Treasure Valley, Idaho, where we generated four optimization scenarios. 1. Agricultural Productivity 2. Climate Mitigation 3. Wildlife Habitat 4. Combined Ecosystem Services. We compared the four scenarios based on their a) ability to protect cultivated land, b) potential to contribute to climate mitigation, c) protection of important biodiversity habitat, and d) economic cost. We found that the Climate Mitigation, Wildlife Habitat, and Combined Ecosystem Services scenarios protected a more even distribution of ecosystem services without sacrificing the amount of cultivated land protected. We found that the Agricultural Productivity scenario resulted in the lowest total cost; however, the other scenarios protected a larger area at a lower cost per unit area. The inclusion of multiple objectives showed strong potential to help reach global conservation goals. Our work adds to the body of literature on the role of private land in protecting natural resources and is a starting point for future research to guide agricultural land protection.
As human populations grow, one strategy for meeting housing demand is through the development of agricultural land and other open space, which can generate negative externalities. This may be addressed at local, state, or federal levels with land-use planning, including farmland preservation policies. Efficient land-use planning in the presence of competing land uses requires knowledge of development risk, housing preferences, and the full costs of farmland loss. We conduct a national scale hedonic analysis using the ZTRAX program U.S. housing transaction data to investigate how COVID-19 has affected property prices in suburban and rural areas with farmland at high risk of development, for the purpose of understanding the effects of COVID-19 driven shifts in housing location preferences. Our analysis demonstrates that the pandemic caused differential price impacts across the 33 states that we analyzed. Furthermore, our estimates suggest heterogeneous price effects driven by the characteristics of nearby urban areas, with prices appreciating faster on land at risk located near smaller urban areas than those near larger urban areas. Our analysis finds that the spatial pattern of development pressure on agricultural lands, as measured through transaction prices, changed in the aftermath of the COVID-19 pandemic.
