Publications

Cookstove replacement is the fastest growing project type on the voluntary carbon offset market; yet the additionality of stove interventions remains understudied. Most carbon offset projects that disseminate efficient cookstoves assume that each new stove replaces a less efficient one, ignoring the possibility that some participating households would have acquired efficient stoves even without the offset subsidy. We quantify the rate that households would have procured efficient stoves without the offset program (non-additional stove purchases) by (1) identifying and analyzing existing datasets from past cookstove randomized control trials (RCTs) that assess the impacts of efficient stove interventions compared to control households without a similar intervention and (2) collecting primary data ourselves from neighbors of the participants in an RCT conducted by the first author. Our review finds that the average annual rate of non-additionality from the five published studies we identified and from our primary data collection, weighted for sample size and normalizing for time, is 4.2%. These studies, from sub-Saharan Africa, India, and South America, all took place in rural areas. Our review provides quantitative evidence that cookstove projects in rural areas can be highly additional, particularly in areas without government stove programming.

Recent United Nations policymaking on international emissions trading fails to reconcile longstanding flaws that could jeopardize the integrity of these programmes. We call for urgent action by policymakers to safeguard the future of the Paris Agreement.

Terrestrial ecosystems could contribute to climate mitigation through nature-based climate solutions (NbCS), which aim to reduce ecosystem greenhouse gas emissions and/or increase ecosystem carbon storage. Forests have the largest potential for NbCS, aligned with broader sustainability benefits, but—unfortunately—a broad body of literature has revealed widespread problems in forest NbCS projects and protocols that undermine the climate mitigation of forest carbon credits and hamper efforts to reach global net zero. Therefore, there is a need to bring better science and policy to improve NbCS climate mitigation outcomes going forward. Here we synthesize challenges to crediting forest NbCS and offer guidance and key next steps to make improvements in the implementation of these strategies immediately and in the near-term. We structure our Perspective around four key components of rigorous forest NbCS, illuminating key science and policy considerations and providing solutions to improve rigour. Finally, we outline a ‘contribution approach’ to support rigorous forest NbCS that is an alternative funding mechanism that disallows compensation or offsetting claims.

Distributional weighting to address concerns about diminishing marginal utility of income in benefit-cost analysis has been the topic of increased interest in recent years. Concern has been expressed about the practicability of distributional weighting, given limitations on data and on the analytical capacities of agencies. This paper contributes to a small but growing literature that attempts to provide guidance and real-world examples of distributional weighting. We develop a methodology for calculating what we call “population weights,” which, once computed for a given population by an analyst, can be used by other analysts to implement distributional weighting on similar populations, without those analysts needing information on income distribution or the cost or benefit experienced by households at different income levels within those populations. These population weights can be calculated without knowing the costs or benefits received by households at different income levels, using proxies for cost or benefit that may be observable or about which, in the absence of data, assumptions can be made in some cases. We implement the methodology on an example regulation and present results that we believe provide useful information to decision makers, even in the absence of estimates of unweighted costs and benefits.

Estimates of the parameters of the quasi-hyperbolic discounting model have been used for quantitative purposes in a range of contexts. I show these estimates are sensitive to time horizon (largest value of t in the data) and timescale (months, years, etc.), and argue that when the quantitative value of the parameters matters, estimates should be used only with great caution, if at all. Using online survey data, I show that β is decreasing in both time horizon and timescale, and vice versa for ẟ. The time-horizon effect is clearly at least partly the result of estimating a discontinuous discounting model using data that are generated by a continuous discounting process. The time-scale effect appears to be caused by a genuine difference across scales in how individuals discount. I discuss implications for positive and normative analysis as well as possible approaches to proceeding with quantitative analysis in the face of these challenges.

A commonly expressed concern about distributional weighting in benefit‐cost analysis is that the informational burden is too high and the practical challenges insurmountable. In this paper, we address this concern by conducting distributional weighting on a number of real‐world examples, covering a range of different types of policy impacts. We uncover and explore a number of methodological issues that arise in the process of distributional weighting and provide a simplified set of steps that we believe can be implemented by practitioners with a wide range of expertise. We conduct sensitivity analysis and Monte Carlo simulation to test the robustness of our estimates of weighted net benefits to the various assumptions we make, and find that, in general, distributional weighting is no more vulnerable to modeling assumptions and parameter selection than unweighted benefit‐cost analysis itself. We conclude that the concern about the practicability of distributional weighting is, at least in a range of important cases, unfounded.

I explore how “low-quality evidence” from program performance might still be useful in decision-making. Conceptually, a local government named “Here” is motivated to consider a program from “Elsewhere” that seems to show year-over-year exemplary performance. Here must manage five sources of uncertainty about whether and how to extrapolate from Elsewhere: chance in assessing Elsewhere’s performance; illusion due to confounding variables; estimating the several powers of the program’s components; substitutions in the design process made by Elsewhere and contemplated by Here; and estimating whether in the final analysis Here can meet its own breakeven criterion for going ahead. Here can begin with Elsewhere’s experience, but it still must do much thinking and information-collecting on its own.