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Abstract Carbon dioxide removal (CDR) technologies and international emissions trading are both widely represented in climate change mitigation scenarios, but the interplay among them has not been closely examined. By systematically varying key policy and technology assumptions in a global energy-economic model, we find that CDR and international emissions trading are mutually reinforcing in deep decarbonization scenarios. This occurs because CDR potential is not evenly distributed geographically, allowing trade to unlock this potential, and because trading in a net-zero emissions world requires negative emissions, allowing CDR to enable trade. Since carbon prices change in the opposite direction as the quantity of permits traded and CDR deployed, we find that the total amount spent on emissions trading and the revenue received by CDR producers do not vary strongly with constraints on emissions trading or CDR. However, spending is more efficient and GDP is higher when both CDR and trading are available.

Abstract Energy transition scenarios are characterized by increasing electrification and improving efficiency of energy end uses, rapid decarbonization of the electric power sector, and deployment of carbon dioxide removal (CDR) technologies to offset remaining emissions. Although hydrocarbon fuels typically decline in such scenarios, significant volumes remain in many scenarios even at the time of net-zero emissions. While scenarios rely on different approaches for decarbonizing remaining fuels, the underlying drivers for these differences are unclear. Here we develop several illustrative net-zero systems in a simple structural energy model and show that, for a given set of final energy demands, assumptions about the use of biomass and CO2 sequestration drive key differences in how emissions from remaining fuels are mitigated. Limiting one resource may increase reliance on another, implying that decisions about using or restricting resources in pursuit of net-zero objectives could have significant tradeoffs that will need to be evaluated and managed.

To limit global warming to well below 2°C, immediate emissions reductions must be coupled with active removal of greenhouse gases from the atmosphere. “Natural Climate Solutions” (NCS) achieve atmospheric CO2 reduction through the conservation, restoration, or altered management of natural ecosystems, with enormous potential to deliver “win-win-win” outcomes for climate, nature and society. Yet the supply of high-quality NCS projects does not meet market demand, and projects already underway often fail to deliver their promised benefits, due to a complex set of interacting ecological, social, and financial constraints. How can these cross-sectoral challenges be surmounted? Here we draw from expert elicitation surveys and workshops with professionals across the ecological, sociological, and economic sciences, evaluating differing perspectives on NCS, and suggesting how these might be integrated to address urgent environmental challenges. We demonstrate that funders” perceptions of operational, political, and regulatory risk strongly shape the kinds of NCS projects that are implemented, and the locations where they occur. Because of this, greenhouse gas removal through NCS may fall far short of technical potential. Moreover, socioecological co-benefits of NCS are unlikely to be realized unless the local communities engaged with these projects are granted ownership over implementation and outcomes.

Physical and societal risks across the natural, managed, and built environments are becoming increasingly complex, multi-faceted, and compounding. Such risks stem from socio-economic and environmental stresses that co-evolve and force tipping points and instabilities. Robust decision-making necessitates extensive analyses and model assessments for insights toward solutions. However, these exercises are consumptive in terms of computational and investigative resources. In practical terms, such exercises cannot be performed extensively—but selectively in terms of priority and scale. Therefore, an efficient analysis platform is needed through which the variety of multi-systems/sector observational and simulated data can be readily incorporated, combined, diagnosed, visualized, and in doing so, identifies “hotspots” of salient compounding threats. In view of this, we have constructed a “triage-based” visualization and data-sharing platform—the System for the Triage of Risks from Environmental and Socio-Economic Stressors (STRESS)—that brings together data across socio-environmental systems, economics, demographics, health, biodiversity, and infrastructure. Through the STRESS website, users can display risk indices that result from weighted combinations of risk metrics they can select. Currently, these risk metrics include land-, water-, and energy systems, biodiversity, as well as demographics, environmental equity, and transportation networks. We highlight the utility of the STRESS platform through several demonstrative analyses over the United States from the national to county level. The STRESS is an open-science tool and available to the community-at-large. We will continue to develop it with an open, accessible, and interactive approach, including academics, researchers, industry, and the general public.

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

Climate impact assessments usually rely on modeling approaches that are either comprehensive but inflexible and inefficient, or lacking sufficient detail. Here the authors describe a framework to assess multi-sectoral climate impacts and show its capability and timely responsiveness to new policies.

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

State and local policy-makers in the US have shown interest in transitioning electricity systems toward renewable energy sources and in mitigating harmful air pollution. However, the extent to which sub-national renewable energy policies can improve air quality remains unclear. To investigate this issue, we develop a systemic modeling framework that combines economic and air pollution models to assess the projected sub-national impacts of Renewable Portfolio Standards (RPSs) on air quality and human health, as well as on the economy and on climate change. We contribute to existing RPS cost-benefit literature by providing a comprehensive assessment of economic costs and estimating economy-wide changes in emissions and their impacts, using a general equilibrium modeling approach. This study is also the first to our knowledge to directly compare the health co-benefits of RPSs to those of carbon pricing. We estimate that existing RPSs in the ‘Rust Belt’ region generate a health co-benefit of $94 per ton CO _2 reduced ($2-477/tCO _2 ) in 2030, or 8¢ for each kWh of renewable energy deployed (0.2–40¢ kWh ^−1 ) in 2015 dollars. Our central estimate is 34% larger than total policy costs. We estimate that the central marginal benefit of raising renewable energy requirements exceeds the marginal cost, suggesting that strengthening RPSs increases net societal benefits. We also calculate that carbon pricing delivers health co-benefits of $211/tCO _2 in 2030, 63% greater than the health co-benefit of reducing the same amount of CO _2 through an RPS approach.

Estimates of greenhouse gas (GHG) emissions from shale gas production and use are controversial. Here we assess the level of GHG emissions from shale gas well hydraulic fracturing operations in the United States during 2010. Data from each of the approximately 4000 horizontal shale gas wells brought online that year are used to show that about 900 Gg CH _4 of potential fugitive emissions were generated by these operations, or 228 Mg CH _4 per well—a figure inappropriately used in analyses of the GHG impact of shale gas. In fact, along with simply venting gas produced during the completion of shale gas wells, two additional techniques are widely used to handle these potential emissions: gas flaring and reduced emission ‘green’ completions. The use of flaring and reduced emission completions reduce the levels of actual fugitive emissions from shale well completion operations to about 216 Gg CH _4 , or 50 Mg CH _4 per well, a release substantially lower than several widely quoted estimates. Although fugitive emissions from the overall natural gas sector are a proper concern, it is incorrect to suggest that shale gas-related hydraulic fracturing has substantially altered the overall GHG intensity of natural gas production.