Climate Change Economics

We calculate there are 8.1% more houses in Allegheny County, PA (Pittsburgh) due to flood insurance subsidies. Conversely, if/when National Flood Insurance Program (NFIP) premiums rise by 50% to equal expected damages, property values will decrease by 8.8% in the short-term, with about half of that recuperated in the long run (4.7%) as quality-adjusted housing stocks contract by 7.5% over decades. This analysis informs community planning and current NFIP revisions that strive to balance solvency and social consequences. Furthermore, our extension of Poterba’s (1984) dynamic user-cost of housing model can be used in integrated assessment models of climate change adaptation.

Using the MIT Economic Projection and Policy Analysis (EPPA) model, we explore factors influencing carbon capture and storage (CCS) deployment in power generation and its role in mitigating carbon emissions. We find that in the 2^∘C scenario with EPPA’s base-case technology cost and performance assumptions, CCS plays an important role in the second half of the century: by 2100 CCS is applied to almost 40% of world electricity production, with a third coming from coal with CCS and the other two-thirds from gas with CCS. Results on CCS deployment depend on the assumed fraction of carbon captured in CCS power plants, as emissions constraints get tighter and the carbon price rises. Adding options for higher capture fractions or offsetting uncaptured emissions leads to greater deployment of CCS than in the 2^∘C base case. We provide a sensitivity analysis by making favorable assumptions for CCS, nuclear and renewables. We also explore regional differences in the deployment of CCS. We find that US and Europe mostly rely on gas CCS, whereas China relies on coal CCS and India pursues both options. We also assess how these projections align with assessment of CO₂ storage potential, and find that storage potential is larger than storage demand at both global and regional scales. Ultimately, we find that under stringent mitigation scenarios, the power sector relies on a mix of technological options, and the conditions that favor a particular mix of technologies differ by region.

Many studies of climate change adaptation have relied on farmers’ perceptions of climate change to explain why farmers are adopting new farming methods, and to advise adaptation policy framework that justifies Climate Smart Agriculture (CSA) especially in Africa. These studies have rarely verified whether farmers’ perceptions are consistent with observed changes in meteorological conditions to establish sufficient premise. This study compares farmers’ perceptions of changes in precipitation and temperature in a rainfed agriculture region of Ghana against objective measurements made in nearby weather stations in the region. The study finds that farmers correctly perceived the increase in temperature over time but incorrectly perceived a reduction in precipitation, while objective data showed high fluctuations with no clear trend. It is possible that farmers mistakenly assumed reduction in soil moisture meant to support crop growth requirements was caused by less rainfall when in fact it was caused by higher temperature.

We estimate how climate amenities influence where households decide to reside in the United States with two main objectives in mind: (i) to produce estimates with sufficient demographic detail to inform demographic population projections for use in climate impact analysis; (ii) to study the robustness of estimates from the existing literature. With respect to the former goal, we find important differences in job-related migration motives by age group and in the overall propensity to migrate among households with children. With respect to the latter aim, our framework shares a common methodological approach with other, recent attempts to recover climate preferences, allowing us to explore the consequences of a number of key assumptions in a systematic manner. Consistent with the existing literature, we find relatively robust estimates of the impact of the frequency of extreme heat days on household location decisions. The impacts of other common measures of climate, including the frequency of extreme cold days, average summer and winter temperatures, annual precipitation, humidity and frequency of sunshine, do not show a strong enough signal in the data to be estimated with precision.

Observational evidence shows marine species are shifting their geographic distribution in response to warming ocean temperatures. These shifts have implications for the US fisheries and seafood consumers. The analysis presented here employs a two-stage inverse demand model to estimate the consumer welfare impacts of projected increases or decreases in commercial landings for 16 US fisheries from 2021 to 2100, based on the predicted changes in thermally available habitat. The fisheries analyzed together account for 56% of the current US commercial fishing revenues. The analysis compares welfare impacts under two climate scenarios: a high emissions case that assumes limited efforts to reduce atmospheric greenhouse gas and a low emissions case that assumes more stringent mitigation. The present value of consumer surplus impacts when discounted at 3% is a net loss of $2.1 billion (2018 US$) in the low emissions case and $4.2 billion in the high emissions scenario. Projected annual losses reach $278–901 million by 2100.

We measure the amenity values of climate and air quality in Taiwan using the two-step hedonic price method (HPM). Separate hedonic housing price equations, one for each of three metropolitan areas that represent respective housing markets in Northern, Central, and Southern Taiwan, are estimated. We find evidence of a significant impact of climate and air quality on housing prices. As expected, residents in Taiwan prefer a warmer winter, cooler summer, lower humidity, and better air quality. Based on the results of the housing price regression, we compute implicit prices for environmental amenities, and then pool observations from all three metropolitan areas to estimate the demand for environmental amenities. The estimated demand functions for environmental amenities are used to evaluate the benefits and losses from changes in climate and air quality in Taiwan. Our results show that, compared to the benefits of a higher temperature in January, the losses from the rise in temperature in July are much higher in such a way that global warming is resulting in significant net losses in Taiwan. The area-specific welfare analysis in this study will help in informing climate change policy formulation and decision-making in Taiwan.