The rise of “e-rulemaking” has opened the door to mass comment campaigns that inundate agencies with thousands—and sometimes millions—of public comments on proposed rules. Artificial intelligence technology, and natural language processing programs in particular, promise a powerful solution for agencies looking to respond more efficiently and effectively to mass participation in notice-and-comment procedure, but these technologies also pose risks and challenges that may undermine the purposes of public comment. This article identifies four functions of the notice-and-comment procedures of APA § 553—accuracy, accountability and judicial review, democratic legitimacy, and “the right to be taken seriously”—and evaluates the effect of A.I. processing of public comments on each of those functions. The article then compares natural language processing to cost-benefit analysis, demonstrating that the two tools present similar trade-offs between the instrumental functions of § 553 on one hand, and the democratic legitimacy of notice-and-comment procedures on the other. Having situated A.I. processing and cost-benefit analysis as two steps in the progressive instrumentalization of agency rulemaking, the article argues that the best practices developed by scholars and officials to preserve transparency and accountability in agencies’ use of cost-benefit analysis should also be applied to their use of A.I. to process public comments. By recognizing the applicability of these lessons from the rise of cost-benefit analysis in rulemaking, agencies can take fuller advantage of the benefits of A.I. and avoid undermining the democratic legitimacy of notice-and-comment procedures.

Coastal communities are increasingly experiencing climate change-induced coastal disasters and chronic flooding and erosion. Decision makers and the public alike are struggling to reconcile the lack of "fit" between a rapidly changing environment and relatively rigid governance structures. In efforts to bridge this environment-governance gap in Tillamook County, Oregon, stakeholders formed a knowledge-to-action network (KTAN). The KTAN examined alternative future coastal policy and climate scenarios through extensive stakeholder engagement and the spatially explicit agent-based modeling framework Envision. The KTAN's results were further evaluated through a two-step mixed methods approach. First, KTAN-identified metrics were quantitatively assessed and compared under present-day vs. alternative policy scenarios. Second, the feasibility of implementing these policy scenarios was qualitatively evaluated through a review of governmental regulations and semistructured interviews. The findings show that alternative policy scenarios ranged from significantly beneficial to extremely harmful to coastal buildings and beach accessibility in comparison to present-day policies, and they were relatively feasible to almost impossible to implement. Beneficial policies that lower impacts of flooding and erosion clearly diverge from the existing regulatory environment, which inhibits their implementation. In response, leadership and cross-sector cooperation and coordination can help to overcome mixed interests and motivations, and increase information exchange between and within the public and government organizations. The combination of stakeholder engagement, an alternative futures modeling framework, and the robust quantitative and qualitative evaluation of policy scenarios provides a powerful model for coastal communities hoping to adapt to climate change along any coastline.

The impact of a tsunami can vary greatly across short distances due to differences in topography, building structures, concentration of economic activities in the inundation zone, and economic linkages beyond the inundation zone. This study takes these factors into account in an analysis of a potential tsunami on the West Coast of the United States. An integrated engineering-economic model is proposed that uses detailed information on spatial heterogeneities in flood depth and economic activity by connecting engineering estimates of tax-lot physical damage with economic activity at the sector level. The performance of this new approach, in terms of estimated total losses and the distribution of impacts across sectors, is compared with two prominent alternatives.

Coupled models of coastal hazards, ecosystems, socioeconomics, and landscape management in conjunction with alternative scenario analysis provide tools that can allow decision-makers to explore effects of policy decisions under uncertain futures. Here, we describe the development and assessment of a set of model-based alternative future scenarios examining climate and population driven landscape dynamics for a coastal region in the U.S. Pacific Northwest. These scenarios incorporated coupled spatiotemporal models of climate and coastal hazards, population and development, and policy and assessed a variety of landscape metrics for each scenario. Coastal flooding and erosion were probabilistically simulated using 99 future 95-year climate scenarios. Five policy scenarios were iteratively co-developed by researchers and stakeholders in Tillamook County, Oregon. Results suggest that both climate change and management decisions have a significant impact across the landscape, and can potentially impact geographic regions at different magnitudes and timescales.

The impact of a tsunami can vary greatly across short distances because of differences in topography, building structures, the concentration of economic activities in the inundation zone, and the economic links beyond the inundation zone. In this study, we take these factors into account in an analysis of a potential tsunami on the west coast of the United States. An integrated engineering-economic model is proposed that uses detailed information on spatial heterogeneities in flood depth and economic activity by connecting engineering estimates of tax-lot physical damages with economic activity at the sector level. The performance of this new approach, in terms of estimated total losses and the distribution of effects across sectors, is compared with two prominent alternatives: engineering-only and economic-only. This study reveals special concerns of overestimation and inaccurate vulnerability assessment by the Federal Emergency Management Agency’s Hazus program, which uses an input-output–based framework and lacks spatially explicit estimates of physical damages and economic losses.

Documented and forecasted trends in rising sea levels and changes in storminess patterns have the potential to increase the frequency, magnitude, and spatial extent of coastal change hazards. To develop realistic adaptation strategies, coastal planners need information about coastal change hazards that recognizes the dynamic temporal and spatial scales of beach morphology, the climate controls on coastal change hazards, and the uncertainties surrounding the drivers and impacts of climate change. We present a probabilistic approach for quantifying and mapping coastal change hazards that incorporates the uncertainty associated with both climate change and morphological variability. To demonstrate the approach, coastal change hazard zones of arbitrary confidence levels are developed for the Tillamook County (State of Oregon, USA) coastline using a suite of simple models and a range of possible climate futures related to wave climate, sea-level rise projections, and the frequency of major El Niño events. Extreme total water levels are more influenced by wave height variability, whereas the magnitude of erosion is more influenced by sea-level rise scenarios. Morphological variability has a stronger influence on the width of coastal hazard zones than the uncertainty associated with the range of climate change scenarios.

An approach is developed for performing probabilistic coastal vulnerability assessments. By exploring a wide range of possible climate futures, coastal change hazard zones of arbitrary confidence level are developed using a suite of simple models. Exposure analyses are performed by superimposing relevant socio-economic data, such as locations of structures and roads, on the hazard zones. The approach allows for quantitative assessments of the impact of climate change uncertainty on possible future coastal configurations. Here we use the simplest coastal change models available, but the approach is developed modularly in that more sophisticated models can replace the simple models when appropriate (e.g., when more detailed results are needed).

Progressive increases in storm intensities and extreme wave heights have been documented along the U.S. West Coast. Paired with global sea level rise and the potential for an increase in El Nino occurrences, these trends have substantial implications for the vulnerability of coastal communities to natural coastal hazards. Community vulnerability to hazards is characterized by the exposure, sensitivity, and adaptive capacity of human-environmental systems that influence potential impacts. To demonstrate how societal vulnerability to coastal hazards varies with both physical and social factors, we compared community exposure and sensitivity to storm-induced coastal change scenarios in Tillamook (Oregon) and Pacific (Washington) Counties. While both are backed by low-lying coastal dunes, communities in these two counties have experienced different shoreline change histories and have chosen to use the adjacent land in different ways. Therefore, community vulnerability varies significantly between the two counties. Identifying the reasons for this variability can help land-use managers make decisions to increase community resilience and reduce vulnerability in spite of a changing climate. (PDF contains 4 pages)