Net anthropogenic CO 2 emissions must approach zero by mid-century to stabilize global mean temperature at the levels targeted by international efforts 1 – 5 . Yet continued expansion of fossil fuel energy infrastructure implies already ‘committed’ future CO 2 emissions 6 – 13 . Here we use detailed datasets of current fossil fuel-burning energy infrastructure in 2018 to estimate regional and sectoral patterns of “committed” CO 2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of associated infrastructure. We estimate that, if operated as historically, existing infrastructure will emit ~658 Gt CO 2 (ranging from 226 to 1479 Gt CO 2 depending on assumed lifetimes and utilization rates). More than half of these emissions are projected to come from the electricity sector, and infrastructure in China, the U.S.A., and the EU28 represent ~41%, ~9% and ~7% of the total, respectively. If built, proposed power plants (planned, permitted, or under construction) would emit an additional ~188 (37–427) Gt CO 2 . Committed emissions from existing and proposed energy infrastructure (~846 Gt CO 2 ) thus represent more than the entire carbon budget to limit mean warming to 1.5 °C with 50–66% probability (420–580 Gt CO 2 ) 5 , and perhaps two-thirds of the budget required to similarly limit warming to below 2 °C (1170–1500 Gt CO 2 ) 5 . The remaining carbon budget estimates are varied and nuanced 14 , 15 , depending on the climate target and the availability of large-scale negative emissions 16 , Nevertheless, our emission estimates suggest that little or no additional CO 2 -emitting infrastructure can be commissioned, and that earlier than historical infrastructure retirements (or retrofits with carbon capture and storage technology) may be necessary, in order meet Paris climate agreement goals 17 . Based on asset value per ton of committed emissions, we estimate that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternative technologies are available and affordable 4 , 18 .

Quantitative assessment of the spatial patterns of all human uses of the oceans and their cumulative effects is needed for implementing ecosystem-based management, marine protected areas, and ocean zoning. Here, we apply methods developed to map cumulative impacts globally to the California Current using more comprehensive and higher-quality data for 25 human activities and 19 marine ecosystems. This analysis indicates where protection and threat mitigation are most needed in the California Current and reveals that coastal ecosystems near high human population density and the continental shelves off Oregon and Washington are the most heavily impacted, climate change is the top threat, and impacts from multiple threats are ubiquitous. Remarkably, these results were highly spatially correlated with the global results for this region (R 2 = 0.92), suggesting that the global model provides guidance to areas without local data or resources to conduct similar regional-scale analyses.

No abstract

A rapid transition away from unabated coal use is essential to fulfilling the Paris climate goals. However, many countries are actively building and operating coal power plants. Here we use plant-level data to specify alternative trajectories for coal technologies in an integrated assessment model. We then quantify cost-effective retirement pathways for global and country-level coal fleets to limit long-term temperature change. We present our results using a decision-relevant metric: the operational lifetime limit. Even if no new plants are built, the lifetimes of existing units are reduced to approximately 35 years in a well-below 2 °C scenario or 20 years in a 1.5 °C scenario. The risk of continued coal expansion, including the near-term growth permitted in some Nationally Determined Contributions (NDCs), is large. The lifetime limits for both 2 °C and 1.5 °C are reduced by 5 years if plants under construction come online and 10 years if all proposed projects are built.

As resource management and conservation efforts move toward multi-sector, ecosystem-based approaches, we need methods for comparing the varying responses of ecosystems to the impacts of human activities in order to prioritize management efforts, allocate limited resources, and understand cumulative effects. Given the number and variety of human activities affecting ecosystems, relatively few empirical studies are adequately comprehensive to inform these decisions. Consequently, management often turns to expert judgment for information. Drawing on methods from decision science, we offer a method for eliciting expert judgment to (1) quantitatively estimate the relative vulnerability of ecosystems to stressors, (2) help prioritize the management of stressors across multiple ecosystems, (3) evaluate how experts give weight to different criteria to characterize vulnerability of ecosystems to anthropogenic stressors, and (4) identify key knowledge gaps. We applied this method to the California Current region in order to evaluate the relative vulnerability of 19 marine ecosystems to 53 stressors associated with human activities, based on surveys from 107 experts. When judging the relative vulnerability of ecosystems to stressors, we found that experts primarily considered two criteria: the ecosystem's resistance to the stressor and the number of species or trophic levels affected. Four intertidal ecosystems (mudflat, beach, salt marsh, and rocky intertidal) were judged most vulnerable to the suite of human activities evaluated here. The highest vulnerability rankings for coastal ecosystems were invasive species, ocean acidification, sea temperature change, sea level rise, and habitat alteration from coastal engineering, while offshore ecosystems were assessed to be most vulnerable to ocean acidification, demersal destructive fishing, and shipwrecks. These results provide a quantitative, transparent, and repeatable assessment of relative vulnerability across ecosystems to any ongoing or emerging human activity. Combining these results with data on the spatial distribution and intensity of human activities provides a systematic foundation for ecosystem-based management.