William Shobe

Economy-wide efforts to achieve net-zero emissions offer climate and air quality-related public health benefits from reducing fossil fuel combustion. However, carbon dioxide removal (CDR) may be necessary to meet emissions targets cost-effectively, and relying on CDR would forego some air-quality benefits. Here, we systematically quantify the regional air quality and public health implications of six CDR portfolios for the U.S. using a coupled modeling approach and compare those to a no U.S. climate action scenario. While both high- and low-CDR deployment avoid about $2.5-5.8 trillion USD2020 (or 0.4-0.8% of cumulative GDP (CGDP)) in climate damages, the high-CDR pathway costs $11-13 trillion USD2020 (or 1.8-1.9% of CGDP) by 2050, whereas the low-CDR pathway costs $16-20 trillion USD2020 (or 2.6-2.9% of CGDP) due to deeper near-term fossil fuel reductions. Public health benefits reach $2.8-6.5 trillion USD2020 (or 0.5-0.9% of CGDP) under high-CDR and are $3.5-8 trillion USD2020 (0.6-1.2% of CGDP) under low-CDR, reflecting greater reductions in particulate matter and ozone exposure and preventing approximately 12,600 additional premature deaths by mid-century. However, heavy reliance on CDR technologies could generate $5-6 trillion USD2020 (∼0.8-0.9% of CGDP) in CDR revenues by 2050, exemplifying the trade-offs between public health, economy, and climate.

Deterministic energy transition planning risks uninformed decisions. Yet, the challenge of high-dimensional uncertainty–encompassing various technological, economic, social, and climatic factors–often leads to a deterministic treatment or simplification of uncertainties in planning. Here, we propose a computationally efficient framework that leverages surrogate-based sensitivity analysis to identify the key uncertainty sources driving the cost of different energy transition scenarios. We applied the proposed approach to Puerto Rico as a hurricane-prone power system that lacks efficient management. We find that changes in the frequency of hurricanes and organizational inefficiency are the two primary sources of uncertainty determining the system’s total expected cost. When examining operational costs, different transition scenarios demonstrate unique key uncertainty sources. For example, the price of biofuel would mainly drive the operational cost when transitioning to a fully renewable power system. These findings can help planners by allowing them to focus on a narrower set of uncertainties in planning.

To achieve net zero carbon emissions by mid-century, the United States may need to rely on carbon dioxide removal (CDR) to offset emissions from difficult-to-decarbonize sectors and/or shortfalls in near-term mitigation efforts. CDR can be delivered using many approaches with different requirements for land, water, geologic carbon storage capacity, energy, and other resources. The availability of these resources varies by region in the U.S. suggesting that CDR deployment will be uneven across the country. Using the global change analysis model for the United States (GCAM-USA), we modeled six classes of CDR and explored their potential using four scenarios: a scenario where all the CDR pathways are available (Full Portfolio), a scenario with restricted carbon capture and storage (Low CCS), a scenario where the availability of bio-based CDR options is limited (Low Bio), and a scenario with constraints on enhanced rock weathering (ERW) capabilities (Low ERW). We find that by employing a diverse set of CDR approaches, the U.S. could remove between 1 and 1.9 GtCO₂/yr by midcentury. In the Full Portfolio scenario, direct air carbon capture and storage (DACCS) predominates, delivering approximately 50% of CO₂ removal, with bioenergy with carbon capture and storage contributing 25%, and ERW delivering 11.5%. Texas and the agricultural Midwest lead in CDR deployment due to their abundant agricultural land and geological storage availability. In the Low CCS scenario, reliance on DACCS decreases, easing pressure on energy systems but increasing pressure on the land. In all cases CDR deployment was found to drive important impacts on energy, land, or materials supply chains (to supply ERW, for example) and these effects were generally more pronounced when fewer CDR technologies were available.

: Net-zero greenhouse gas emission targets are central to current international efforts to stabilize global climate, and many of these plans rely on carbon dioxide removal (CDR) to meet mid-century goals. CDR can be performed via nature-based approaches, such as afforestation, or engineered approaches, such as direct air capture. Both will have large impacts in the regions where they are sited. We used the Global Change Analysis Model for the United States to analyze how regional resources will influence and be influenced by CDR deployment in service of United States national net-zero targets. Our modeling suggests that CDR will be deployed extensively, but unevenly, across the country. A number of US states have the resources, such as geologic carbon storage capacity and agricultural land, needed to become net exporters of negative emissions. But this will require reallocation of resources, such as natural gas and electricity, and dramatically increase water and fertilizer use in many places. Modeling these kinds of regional or sub-national impacts associated with CDR, as intrinsically uncertain as it is at this time, is critical for understanding its true potential in meeting decarbonization commitments.

Price floors are common policies in markets for storable goods such as commodities, bankable emissions permits, and currencies. Hard price floors are implemented as unlimited governmental buybacks and prevent the price from falling below the floor; soft floors, including those implemented as auction reserve prices in emission permit auctions, allow the market price to fall below the floor. We specify and then test in the laboratory a two-period model with the same properties as our infinite-horizon model, Salant et al. (2022). Most of our experimental findings are consistent with theoretical predictions: a seemingly nonbinding floor can cause the price and carryover to jump up, the jump is higher with a hard floor than a soft one, and when the floor is so low that the theory predicts no effect, none is observed.