Tradable Credit Markets for Intensity Standards

Many environmental standards are expressed in terms of intensity rather than absolute levels. In some cases, intensity standards are associated with tradable credit markets to mitigate the firms’ compliance costs. I develop a jurisdictional model of credit trading under an intensity standard, framed in terms of a Renewable Portfolio Standard for electric utilities. I find that jurisdictions of firms with high costs of compliance may actually be better off by not allowing inter-jurisdictional credit trading. Counterintuitively, increasing the stringency of the intensity standard under credit trading can have the opposite of the intended effect and decrease renewable electricity generation

External Impacts of Local Energy Policy: The Case of Renewable Portfolio Standards

Renewable portfolio standards (RPSs) are state level policies that require in-state electricity providers to procure a minimum percentage of electricity sales from renewable sources. Using theoretical and empirical models, we show how RPSs induce out-of-state emissions reductions through inter-state trade of the credits used for RPS compliance. When one state passes an RPS, it increases demand for credits sold by firms in other (potentially non-RPS) states. We find evidence that increasing a state’s RPS decreases coal generation and increases wind generation in outside states through this tradable credit channel. We perform a welfare simulation to evaluate the aggregate benefits of the reductions in local coal-fired pollutants induced by RPSs. Our estimates suggest that a 1 percentage point increase a state’s RPS results in up to $100 million in gross benefits towards the United States as a whole. However, there is substantial heterogeneity in the total benefits caused by increases in different states’ RPSs

Steering the Climate System: Using Inertia to Lower the Cost of Policy

Conventional wisdom holds that the efficient way to limit warming to a chosen level is to price carbon emissions at a rate that increases exponentially. We show that this “Hotelling” tax on carbon emissions is actually inefficient. The least-cost policy path takes advantage of the climate system’s inertia by growing more slowly than exponentially. Carbon dioxide temporarily overshoots the steady-state level consistent with the temperature limit, and the efficient carbon tax follows an inverse-U-shaped path. Economic models that assume exponentially increasing carbon taxes are overestimating the minimum cost of limiting warming, overestimating the efficient near-term carbon tax, and overvaluing technologies that mature sooner

Efficient Environmental Regulation in the Unconventional Oil Industry

US OIL production has skyrocketed since 2007. Technological advances in oil and gas drilling (commonly referred to as ‘fracking’) have allowed producers to access vast petroleum reserves that were previously too costly to recover. The growth in oil and gas production from unconventional sources has been tremendous, so that unconventional sources now make up more than 50 percent of total US petroleum production (EIA 2015). While this represents a boost to job growth and the broader economy, growth in the oil industry comes with its fair share of problems. Academics and news agencies have documented a host of costs associated with new oil and gas production— groundwater pollution, oil spills, large “man camps” and increased crime, and even increases in traffic accidents and exploding train cars. Some of these costs were seen in Iowa with the contentious nature of right-of- Efficient Environmental Regulation in the Unconventional Oil Industry way issues associated with building out the Dakota Access pipeline across the state. Farmers and environmentalists alike are bound together in their concern for right-of-way, human rights concerns, and environmental issues

Costs of Inefficient Regulation: Evidence from the Bakken

Efficient pollution regulation equalizes marginal abatement costs across sources. Here we study a new flaring regulation in North Dakota\u27s oil and gas industry and document its efficiency. Exploiting detailed well-level data, we find that the regulation reduced flaring 4 to 7 percentage points and accounts for up to half of the observed flaring reductions since 2015. We construct firm-level marginal flaring abatement cost curves and find that the observed flaring reductions could have been achieved at 20%lower cost by imposing a tax on flared gas equal to current public lands royalty rates instead of using firm-specific flaring requirements

Air pollution and visitation at U.S. national parks

Hundreds of millions of visitors travel to U.S. national parks every year to visit America’s iconic landscapes. Concerns about air quality in these areas have led to strict, yet controversial pollution control policies. We document pollution trends in U.S. national parks and estimate the relationship between pollution and park visitation. From 1990 to 2014, average ozone concentrations in national parks were statistically indistinguishable from the 20 largest U.S. metropolitan areas. Further, relative to U.S. cities, national parks have seen only modest reductions in days with ozone concentrations exceeding levels deemed unhealthy by the U.S. Environmental Protection Agency. We find a robust, negative relationship between in-park ozone concentrations and park visitation. Still, 35% of all national park visits occur when ozone levels are elevated

Ozone pollution in US national parks is nearly the same as in large cities

Most Americans associate U.S. national parks with pristine environments that represent the very best of nature. In the 1916 law that established the National Park Service, Congress directed the new agency to “conserve the scenery and the natural and historic objects and the wild life therein and to provide for the enjoyment of the same in such manner and by such means as will leave them unimpaired for the enjoyment of future generations.

Research Needs and Challenges in the FEW System: Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems

On October 12–13, a workshop funded by the National Science Foundation was held at Iowa State University in Ames, Iowa with a goal of identifying research needs related to coupled economic and biophysical models within the FEW system. Approximately 80 people attended the workshop with about half representing the social sciences (primarily economics) and the rest from the physical and natural sciences. The focus and attendees were chosen so that findings would be particularly relevant to SBE research needs while taking into account the critical connectivity needed between social sciences and other disciplines. We have identified several major gaps in existing scientific knowledge that present substantial impediments to understanding the FEW system. We especially recommend research in these areas as a priority for future funding: 1. Economic models of decision-making in coupled systems Deliberate human activity has been the dominant factor driving environmental and land-use changes for hundreds of years. While economists have made great strides in modeling and understanding these choices, the coupled systems modeling literature, with some important exceptions, has not reflected these contributions. Several paths forward seem fruitful. First, baseline economic models that assume rationality can be used much more widely than they are currently. Moreover, the current generation of IAMs that include rational agents have emphasized partial equilibrium studies appropriate for smaller systems. To allow this approach to be used to study larger systems, the potential for (and consequences of) general equilibrium effects should be studied as well. Second, it is important to address shortcomings in these models of economic decision-making. Valuable improvements could be gained from developing coupled models that draw insights from behavioral economics. Many decision-makers deviate systematically from actions that would be predicted by strict rationality, but very few IAMs incorporate this behavior, potentially leading to inaccurate predictions about the effects of policies and regulations. Improved models of human adaptation and induced technological change can also be incorporated into coupled models. Particularly for medium to long-run models, decisions about adaptation and technological change will have substantial effects on the conclusions and policy implications, but more compelling methods for incorporating these changes into modeling are sorely needed. In addition, some economic decisions are intrinsically dynamic yet few coupled models explicitly incorporate dynamic models. Economic models that address uncertainty in decision making are also underutilized in coupled models of the FEW system. 2. Coupling models across disciplines Despite much recent progress, established models for one component of the FEW system often cannot currently produce outcomes that can be used as inputs for models of other components. This misalignment makes integrated modeling difficult and is especially apparent in linking models of natural phenomena with models of economic decision-making. Economic agents typically act to maximize a form of utility or welfare that is not directly linked to physical processes, and they typically require probabilistic forecasts as an input to their decision-making that many models in the natural sciences cannot directly produce. We believe that an especially promising approach is the development of “bridge” models that convert outputs from one model into inputs for another. Such models can be viewed as application-specific, reduced-form distillations of a richer and more realistic underlying model. Ideally, these bridge models would be developed in collaborative research projects involving economists, statisticians, and disciplinary specialists, and would contribute to improved understanding in the scientific discipline as well. 3. Model validation and comparison There is little clarity on how models should be evaluated and compared to each other, both within individual disciplines and as components of larger IAMs. This challenge makes larger integrated modeling exercises extremely difficult. Some potential ways to advance are by developing statistical criteria that measure model performance along the dimensions suitable for inclusion in an IAM as well as infrastructure and procedures to facilitate model comparisons. Focusing on the models’ out-of-sample distributional forecasting performance, as well as that of the IAM overall, is especially promising and of particular importance. Moreover, applications of IAMs tend to estimate the effect of hypothetical future policy actions, but there have been very few studies that have used these models to estimate the effect of past policy actions. These exercises should be encouraged. They offer a well-understood test bed for the IAMs, and also contribute to fundamental scientific knowledge through better understanding of the episode in question. The retrospective nature of this form of analysis also presents the opportunity to combine reduced-form estimation strategies with the IAMs as an additional method of validation

Opportunities for advances in climate change economics

There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric