This essay provides an overview of the major emissions trading programs of the past thirty years on which significant documentation exists, and draws a number of important lessons for future applications of this environmental policy instrument. References to a larger number of other emissions trading programs that have been implemented or proposed are included.
This paper compares internationally-tradeable permits with a uniform carbon price, as seen through the lens of an individual country. To ensure a level playing field, these two approaches are initially calibrated to be welfare-equivalent for the country in a deterministic setting. While both price and quantity instruments have identical consequences under perfect certainty, outcomes differ substantially when uncertainty is introduced. The uncertainty analyzed here takes the reduced form of idiosyncratic country-specific abatement-cost shocks. Then, because of cross-border revenue flows, internationally-tradable permits can expose a country to greater risk than the imposition of a uniform carbon price (whose revenue proceeds are domestically retained). This result is formalized in a very simple model that highlights the core essence of the argument. Some implications are discussed. I suggest that this relative-riskiness result may be a pertinent consideration in choosing between negotiated price-based approaches and negotiated quantity-based approaches for controlling worldwide carbon emissions.
Perception of social rank, or how we perform relative to our peers, can be a powerful motivator. While research exists on the effect of social information on decision making, there is less work on how ranked comparisons with our peers influence our behavior. This paper outlines a field experiment conducted with 5,180 households in Castro Valley, California, which used household mailers with various forms of peer information and social rank messaging to motivate water conservation. The experiment tests the effect of a visible social rank on water use, and how the cooperative and competitive framing of rank information influences behavioral response. Difference-in-difference and matching methods reveal sizable treatment effects of the mailers on household water use (reductions of 13-17 gallons per day, depending on mailer version). However, households with relatively low or high water use in the pre-treatment period responded differently to information framing. We find that neutrally-framed rank information caused a "boomerang effect" (i.e., an increase in average water use) for low water use households, but this effect was eliminated by competitive framing. At the same time, competitively-framed rank information demotivated high water use households, increasing their average water use further. This result is supported by evidence that the competitive frame on rank information increased water use for households who ranked "last" in the peer group - a detrimental "last place effect" from competitive framing.
This paper considers the role of integrated assessment models (IAMs) in the construction of climate policy. We focus on questions involving the role of IAMs in estimating the social cost of carbon (SCC), how best to handle the considerable scientific uncertainty underlying the IAMs from the perspective of estimating the SCC, and whether an IAM‐based SCC should be abandoned and replaced by expert judgment or another substitute. The perspective we adopt in tackling these questions is rooted in the specific needs of the existing U.S. institutions responsible for making and implementing climate policy, specifically regulatory agencies within the Executive Branch and Congress should it choose to take up climate legislation. Our discussion has three premises. First, policy makers need a numerical value and an uncertainty range for the SCC for policy evaluation and implementation. Second, whatever the true value of the SCC is, it is not zero. Third, considerable uncertainty surrounds the current state of scientific knowledge about the current and future costs of climate change. The evolving nature of the science and the ultimate goal of informing first‐best policy suggests to us that the official SCC – the SCC used for regulatory analysis by the U.S. Government – should not be thought of as a single number or even a range of numbers, but more broadly as a process that yields updated estimates of those numbers and ranges. Viewed in this way, the ultimate goal of the process is scientific credibility, public acceptance, and political and legal viability.
Traditional least squares estimates of the responsiveness of gasoline consumption to changes in gasoline prices are biased toward zero, given the endogeneity of gasoline prices. A seemingly natural solution to this problem is to instrument for gasoline prices using gasoline taxes, but this approach tends to yield implausibly large price elasticities. We demonstrate that anticipatory behavior provides an important explanation for this result. We provide evidence that gasoline buyers increase gasoline purchases before tax increases and delay gasoline purchases before tax decreases. This intertemporal substitution renders the tax instrument endogenous, invalidating conventional IV analysis. We show that including suitable leads and lags in the regression restores the validity of the IV estimator, resulting in much lower and more plausible elasticity estimates. Our analysis has implications more broadly for the IV analysis of markets in which buyers may store purchases for future consumption.
Energy-efficient technologies offer considerable promise for reducing the financial costs and environmental damages associated with energy use, but these technologies appear not to be adopted by consumers and businesses to the degree that would apparently be justified, even on a purely financial basis. We present two complementary frameworks for understanding this so-called “energy paradox” or “energy-efficiency gap.” First, we build on the previous literature by dividing potential explanations for the energy-efficiency gap into three categories: market failures, behavioral anomalies, and model and measurement errors. Second, we posit that it is useful to think in terms of the fundamental elements of cost-minimizing energy-efficiency decisions. This provides a decomposition that organizes thinking around four questions. First, are product offerings and pricing economically efficient? Second, are energy operating costs inefficiently priced and/or understood? Third, are product choices cost-minimizing in present value terms? Fourth, do other costs inhibit more energy-efficient decisions? We review empirical evidence on these questions, with an emphasis on recent advances, and offer suggestions for future research.
In collaboration with a state environmental regulator in India, we conducted a field experiment to raise the frequency of environmental inspections to the prescribed minimum for a random set of industrial plants. The treatment was successful when judged by process measures, as treatment plants, relative to the control group, were more than twice as likely to be inspected and to be cited for violating pollution standards. Yet the treatment was weaker for more consequential outcomes: the regulator was no more likely to identify extreme polluters (i.e., plants with emissions five times the regulatory standard or more) or to impose costly penalties in the treatment group. In response to the added scrutiny, treatment plants only marginally increased compliance with standards and did not significantly reduce mean pollution emissions. To explain these results and recover the full costs of environmental regulation,we model the regulatory process as a dynamic discrete game where the regulator chooses whether to penalize and plants choose whether to abate to avoid future sanctions. We estimate this model using original data on 10,000 interactions between plants and the regulator. Our estimates imply that the costs of environmental regulation are largely reserved for extremely polluting plants. Applying the cost estimates to the experimental data, we find the average treatment inspection imposes about half the cost on plants that the average control inspection does, because the randomly assigned inspections in the treatment are less likely than normal discretionary inspections to target such extreme polluters.