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As the world moves away from fossil fuels, there is growing recognition of the need for a just transition of those working in carbon-intensive industries and for policy to support this transition. While recent policies such as the U.S. Inflation Reduction Act (IRA) have begun to incorporate support for energy-intensive regions, little work has thoroughly investigated which communities are most vulnerable to economic disruption in the energy transition and therefore require policy support. This paper analyzes the distribution of employment vulnerability in the U.S. by calculating the average "employment carbon footprint" of close-to every job in the U.S. economy at high geographic and sectoral granularity. The measure considers employment vulnerability across the entire economy and captures both fossil fuel consumption and production effects, with the sectors covered in our analysis accounting for 86% of total U.S. employment and 94% of U.S. carbon emissions outside of the transportation sector. We find that existing efforts to identify at-risk communities both in the literature and the IRA exclude regions of high employment vulnerability, and thereby risk leaving these communities behind in the energy transition. This work underscores the importance of proactive and continuous measures of employment vulnerability, presents policymakers with much-needed data to incorporate such measures into just transition policy and makes the case for place-based policy approaches when considering how best to support communities through the energy transition. [ABSTRACT FROM AUTHOR]

Marginal emissions of CO2 from the electricity sector are critical for evaluating climate policies that rely on shifts in electricity demand or supply. This paper provides estimates of marginal CO2 emissions from US electricity generation using the most recently available and comprehensive data. The estimates vary by region, hour of the day, and year to year over the last decade. We identify an important and somewhat counterintuitive finding: While average emissions have decreased substantially over the last decade (28% nationally), marginal emissions have increased (7% nationally). We show that underlying these trends is primarily a shift toward greater reliance on coal to satisfy marginal electricity use. We apply our estimates to an analysis of the Biden administration’s target of having electric vehicles (EVs) make up 50% of new vehicle purchases by 2030. We find that, without significant and concurrent changes to the electricity sector, the increase in electricity emissions is likely to offset more than half of the emission reductions from having fewer gasoline-powered vehicles on the road. Moreover, using average rather than marginal emissions to predict the impacts significantly overestimates the emission benefits. Overall, we find that the promise of EVs for reducing emissions depends, to a large degree, on complementary policies that decarbonize both average and marginal emissions in the electricity sector. [ABSTRACT FROM AUTHOR]

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research. [ABSTRACT FROM AUTHOR]

International cooperation on the reduction of greenhouse gas emissions, disarmament, or free trade needs to be negotiated. The success of such negotiations depends on how they are designed. In the context of international climate change policy, it has been proposed [e.g., M. L. Weitzman J. Assoc. Environ. Resour. Econ. 1, 29-49 (2014)] that shifting the negotiation focus to a uniform common commitment (such as a uniform minimum carbon price) would lead to more ambitious cooperation. Yet, a proof-of-concept for this important claim is lacking. Based on game theoretical analyses, we present experimental evidence that strongly supports this conjecture. In our study, human subjects negotiate contributions to a public good. Subjects differ in their benefits and costs of cooperation. Participation in the negotiations and all commitments are voluntary. We consider treatments in which agreements are enforceable, and treatments in which they have to be self-enforcing. In both situations, negotiating a uniform common commitment is more successful in promoting cooperation than negotiating individual commitments (as in the Paris Agreement) and complex common commitments that tailor the commitment to the specific situation of each party (as attempted with the Kyoto Protocol). Furthermore, as suggested by our model, a uniform common commitment benefits most from being enforced. [ABSTRACT FROM AUTHOR]

Reducing emissions from deforestation and forest degradation (REDD+) is a climate change mitigation policy in which rich countries provide payments to developing countries for protecting their forests. In 2009, the countries of Norway and Guyana entered into one of the first bilateral REDD+ programs, with Norway offering to pay US$250 million to Guyana if annual deforestation rates remained below 0.056% from 2010 to 2015. To quantify the impact of this national REDD+ program, we construct a counterfactual times-series trajectory of annual tree cover loss using synthetic matching. This analytical approach allows us to quantify tree cover loss that would have occurred in the absence of the Norway-Guyana REDD+ program. We found that the Norway-Guyana REDD+ program reduced tree cover loss by 35% during the implementation period (2010 to 2015), equivalent to 12.8 million tons of avoided CO2 emissions. Our analysis indicates that national REDD+ payments attenuated the effect of increases in gold prices, an internationally traded commodity that is the primary deforestation driver in Guyana. Overall, we found strong evidence that the program met the additionality criteria of REDD+. However, we found that tree cover loss increased after the payments ended, and therefore, our results suggest that without continued payments, forest protection is not guaranteed. On the issue of leakage, which is complex and difficult to quantify, a multinational REDD+ program for a region could address leakage that results from differences in forest policies between neighboring countries [ABSTRACT FROM AUTHOR]

Capture and permanent geologic sequestration of biogenic CO2 emissions may provide critical flexibility in ambitious climate change mitigation. However, most bioenergy with carbon capture and sequestration (BECCS) technologies are technically immature or commercially unavailable. Here, we evaluate low-cost, commercially ready CO2 capture opportunities for existing ethanol biorefineries in the United States. The analysis combines process engineering, spatial optimization, and lifecycle assessment to consider the technical, economic, and institutional feasibility of near-term carbon capture and sequestration (CCS). Our modeling framework evaluates least cost source-sink relationships and aggregation opportunities for pipeline transport, which can cost-effectively transport small CO2 volumes to suitable sequestration sites; 216 existing US biorefineries emit 45 Mt CO2 annually from fermentation, of which 60% could be captured and compressed for pipeline transport for under $25/tCO2. A sequestration credit, analogous to existing CCS tax credits, of $60/tCO2 could incent 30 Mt of sequestration and 6,900 km of pipeline infrastructure across the United States. Similarly, a carbon abatement credit, analogous to existing tradeable CO2 credits, of $90/tCO2 can incent 38 Mt of abatement. Aggregation of CO2 sources enables cost-effective long-distance pipeline transport to distant sequestration sites. Financial incentives under the low-carbon fuel standard in California and recent revisions to existing federal tax credits suggest a substantial near-term opportunity to permanently sequester biogenic CO2. This financial opportunity could catalyze the growth of carbon capture, transport, and sequestration; improve the lifecycle impacts of conventional biofuels; support development of carbon-negative fuels; and help fulfill the mandates of low-carbon fuel policies across the United States. [ABSTRACT FROM AUTHOR]

Benefit-cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs.We show that its model of long-run economic growth--one of its most important economic components--had questionable predictive power over the 20th century. [ABSTRACT FROM AUTHOR]