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AbstractEnvironmental impact assessment (EIA) procedures required in the United States and many other countries are often highlighted as a major hindrance to timely and efficient deployment of critical infrastructure projects. Under the U.S. National Environmental Policy Act, a more extensive environmental impact statement (EIS) review can take several more years and cost much more than a succinct environmental assessment (EA). This not only affects the project in question, but also likely informs how—or whether—additional projects are pursued. Thus, understanding key predictors of the EA versus EIS choice sheds light on supply‐side considerations affecting infrastructure deficits. Using the case of NEPA reviews conducted for 244 transmission line projects between 2005 and 2018 by two U.S. federal agencies in the western United States, the Bureau of Land Management (BLM) and Department of Energy (DOE), this addresses the following question: What project features most predict whether EA or an EIS is used to assess a transmission line project? Drawing upon NEPA assessment guidance and agency NEPA records, we use a regression classification tree to analyze how protocols and project attributes relate to assessment choice. The result is essentially a null finding: transmission line length is by far the most important predictor of whether a project receives an extensive EIS or a shorter EA, with little predictive value provided by other attributes. While absolute project size undoubtedly influences impacts, the lack of further differentiation in what predicts use of EISs versus EAs suggests assessment does not simply respond to project details but also shapes proposal and design choices beforehand.

AbstractCritical infrastructure systems that provide local public services are owned by a complicated array of public and private entities that are subject to disparate regulatory regimes. Determining whether and why performance differences between public and private service providers emerge in these different contexts is critical for understanding the efficacy of current management efforts and informing policy choices about how to deal with public and private service providers. This paper analyzes a census of all U.S. gas distributors and a more detailed sample of the largest utilities in the United States to model how sector and regional market concentration relate to infrastructure quality and leak volumes. Using safety reports, market data, and yearly infrastructure records submitted by gas distributors from 2010 to 2017, the article uses a series of Bayesian hierarchical models to show that publicly owned utilities report lower quality infrastructure (i.e., operation of pipelines with lower quality materials), but also self‐report significantly lower leak rates, all else equal. The article concludes by discussing potential explanations for this discrepancy, including the capital investment incentives private utilities face under cost‐of‐service pricing regulations and the fact that performance measurement capacity is itself a potential outgrowth of increased investment and expenditures.

AbstractRegulation increasingly mandates collaborative approaches to increase stakeholder input and streamline approval processes. However, understanding how to maintain stakeholder involvement over the course of a long collaborative process is vital to optimize effectiveness. This paper observes more than 700 stakeholders involved in developing and implementing a dam operating license over 16 years. We use text mining and Bayesian hierarchical modeling to observe meeting attendance and recorded actions in meeting minutes. We find that involvement decreased after the initial planning phase, but steadily increased through license development and implementation. After the regulatory mandate to consult with external stakeholders dissolved, overall attendance declined while attendance stability increased, meaning that the non‐mandatory stage involved a smaller cadre of dedicated actors. This indicates that high‐performing mandated stakeholder involvement processes rely on a constrained group of conveners to sustain interaction and have less turnover than what might be expected given existing evidence from grassroots involvement; assumptions about group dynamics based on involvement in grassroots processes may lead to improper predictions about who will participate, and how, in processes where stakeholder involvement is mandated.

Critiques of benefit-cost analysis (BCA) are usually made on theoretical or methodological grounds; however, understanding how BCA is actually used in decision-making processes is critical if BCA is to inform policy-making. Our paper examines how the implementation of BCA within policy decision-making processes can serve to increase, rather than alleviate, controversy. This runs contrary to the standard assumption that BCA improves decision-making by providing objective data that serves as a basis for policy consensus. To frame this issue, we engage the literature on the role of science in policy decisions and the role of bureaucrats in understanding and implementing policy research. We introduce the concept of "Bureaucratic BCA" as a framework for the practical application of BCA; Bureaucratic BCA does not refer to BCA specifically conducted by bureaucrats or a lesser, technically inferior version of BCA, but rather acknowledges that BCA plays an interactive role within bureaucratic decision-making processes rather than simply serving as a sterilized information input. We show how the dynamics of BCA within the policy process can make BCA a source of controversy and waste rather than an aid to policy efficiency. In light of the Bureaucratic BCA framework, we provide recommendations as to how BCA can be implemented more productively.

AbstractIn Pakistan, annual poliovirus investment decisions drive quantities of supplemental immunization campaigns districts receive. In this article, we assess whether increased spending on poliovirus surveillance is associated with greater likelihood of correctly identifying districts at high risk of polio with assignment of an elevated "risk ranking." We reviewed programmatic documents from Pakistan for the period from 2012–2017, recording whether districts had been classified as "high risk" or "low risk" in each year. Through document review, we developed a decision tree to describe the ranking decisions. Then, integrating data from the World Health Organization and Global Polio Eradication Initiative, we constructed a Bayesian decision network reflecting investments in polio surveillance and immunization campaigns, surveillance metrics, disease incidence, immunization rates, and occurrence of polio cases. We test these factors for statistical association with the outcome of interest—a change in risk rank between the beginning and the end of the one‐year time period. We simulate different spending scenarios and predict their impact on district risk ranking in future time periods. We find that per district spending increases are associated with increased identification of cases of acute flaccid paralysis (AFP). However, the low specificity of AFP investment and the largely invariant ranking of district risk means that even large increases in surveillance spending are unlikely to promote major changes in risk rankings at the current stage of the Pakistan polio eradication campaign.

Abstract
Local governments consider a wide range of policies to increase resilience in the face of myriad risks and employ a variety of tactics to communicate about these policies to external actors. An important platform to signal resilience as a policy priority is through the budget process wherein local communities decide "who gets what, when, and how." Using computational text mining techniques, we assess how county governments in California signal efforts toward resilience in their budgets during the 2012–2017 fiscal years, as well as whether and how those signals are received by the capital market. Comparable budget documents are available for 38 counties across the state for a total of 161 county-year observations. To test the relationship between local government resilience signals and capital market outcomes, we focus on county underlying credit ratings issued by counties. Empirical results show that county underlying credit ratings are insensitive to resilience signals in local government budgets. By examining the efficacy of resilience signals and their effects on the capital market, we offer evidence on the link between policy signaling and financial outcomes at the local government level.

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.