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Política Ambiental/economia , Política Ambiental/legislação & jurisprudência , Aquecimento Global/economia , Aquecimento Global/legislação & jurisprudência , Modelos Econômicos , Política , Aquecimento Global/prevenção & controle , Objetivos , Efeito Estufa/economia , Efeito Estufa/prevenção & controle , Efeito Estufa/estatística & dados numéricos , Motivação , Participação dos InteressadosRESUMO
In passing the Bipartisan Budget Act of 2018, Congress reformed and strengthened a section of the tax code, 45Q, which provides tax credits of up to $35/ton CO2 for the capture and utilization of CO2 in qualifying applications such as enhanced oil recovery (EOR) and up to $50/ton CO2 for CO2 that is captured and permanently stored in a geologic repository. Earlier versions of the tax credit with lower credit values generated limited interest. This change to the tax code could potentially alter U.S. energy systems. This paper examines the effect of the increased 45Q credits on CO2 capture, utilization and storage (CCUS) deployment in the United States and on petroleum and power production. A range of potential outcomes is explored using five modeling tools. The paper goes on to explore the potential impact of possible modifications of the current tax credit including extension of its availability in time, the period over which 45Q tax credits can be utilized for any given asset and increases in the value of the credit as well as interactions with technology availability and carbon taxation. The paper concludes that 45Q tax credits could stimulate additional CCUS beyond that which is already underway.
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Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial-temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a "fossil-fueled development" scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8-2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.
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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.
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Land-use change to meet 21st-century demands for food, fuel, and fiber will depend on many interactive factors, including global policies limiting anthropogenic climate change and realized improvements in agricultural productivity. Climate-change mitigation policies will alter the decision-making environment for land management, and changes in agricultural productivity will influence cultivated land expansion. We explore to what extent future increases in agricultural productivity might offset conversion of tropical forest lands to crop lands under a climate mitigation policy and a contrasting no-policy scenario in a global integrated assessment model. The Global Change Assessment Model is applied here to simulate a mitigation policy that stabilizes radiative forcing at 4.5 W m(-2) (approximately 526 ppm CO(2)) in the year 2100 by introducing a price for all greenhouse gas emissions, including those from land use. These scenarios are simulated with several cases of future agricultural productivity growth rates and the results downscaled to produce gridded maps of potential land-use change. We find that tropical forests are preserved near their present-day extent, and bioenergy crops emerge as an effective mitigation option, only in cases in which a climate mitigation policy that includes an economic price for land-use emissions is in place, and in which agricultural productivity growth continues throughout the century. We find that idealized land-use emissions price assumptions are most effective at limiting deforestation, even when cropland area must increase to meet future food demand. These findings emphasize the importance of accounting for feedbacks from land-use change emissions in global climate change mitigation strategies.
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Agricultura/tendências , Mudança Climática , Conservação de Recursos Energéticos/métodos , Conservação de Recursos Energéticos/tendências , Clima Tropical , Biocombustíveis/análise , Dióxido de Carbono/análise , Modelos Teóricos , Zea mays/economiaRESUMO
As countries take stock of progress made in accomplishing their climate goals ahead of COP28 this year, it is increasingly apparent that countries must ratchet ambition in policy areas such as non-CO2 gases and carbon dioxide removal, while halting deforestation to lead the globe on a path consistent with the goals of the Paris Agreement.
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Purpose: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis. Methods: We used Illinois Eye and Ear Infirmary fundus data set as an instance of RWD in addition to six publicly available fundus data sets. We compared the performance of DL-trained models on public data and RWD for glaucoma classification and optic disc (OD) segmentation tasks. For each task, we created models trained on each data set, respectively, and each model was tested on both data sets. We further examined each model's decision-making process and learned embeddings for the glaucoma classification task. Results: Using public data for the test set, public-trained models outperformed RWD-trained models in OD segmentation and glaucoma classification with a mean intersection over union of 96.3% and mean area under the receiver operating characteristic curve of 95.0%, respectively. Using the RWD test set, the performance of public models decreased by 8.0% and 18.4% to 85.6% and 76.6% for OD segmentation and glaucoma classification tasks, respectively. RWD models outperformed public models on RWD test sets by 2.0% and 9.5%, respectively, in OD segmentation and glaucoma classification tasks. Conclusions: DL models trained on commonly used public data have limited ability to generalize to RWD for classifying glaucoma. They perform similarly to RWD models for OD segmentation. Translational Relevance: RWD is a potential solution for improving generalizability of DL models and enabling clinical translations in the care of prevalent blinding ophthalmic conditions, such as glaucoma.
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Aprendizado Profundo , Glaucoma , Disco Óptico , Humanos , Inteligência Artificial , Disco Óptico/diagnóstico por imagem , Glaucoma/diagnóstico , Fundo de OlhoRESUMO
Current climate pledges are insufficient to achieve the aspirational goal of limiting global warming to 1.5°C. Here we discuss the critical role that non-CO2 greenhouse gas emissions might play in global climate change stabilization, and challenges and opportunities to pivot research and policy focus towards accelerated reductions of non-CO2 gases.
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The new and updated emission reduction pledges submitted by countries ahead of COP26 represent a meaningful strengthening of global ambition compared to the 2015 Paris pledges1,2. Yet, limiting global warming below 1.5°C this century will require countries to ratchet ambition for 2030 and beyond2-6. We explore a suite of emissions pathways in which countries ratchet and achieve ambition through a combination of increasing near-term ambition through 2030, accelerating post-2030 decarbonization, and advancing the dates for national net-zero pledges. We show that ratcheting near-term ambition through 2030 will be crucial to limiting peak temperature changes. Delaying ratcheting ambition to beyond 2030 could still deliver end-of-century temperature change of less than 1.5°C, but that would result in higher temperature overshoot over many decades with the potential for adverse consequences. Ratcheting near-term ambition would also deliver benefits from enhanced non-CO2 mitigation and facilitate faster transitions to net-zero emissions systems in major economies.
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Given the increasing interest in keeping global warming below 1.5°C, a key question is what this would mean for China's emission pathway, energy restructuring, and decarbonization. By conducting a multimodel study, we find that the 1.5°C-consistent goal would require China to reduce its carbon emissions and energy consumption by more than 90 and 39%, respectively, compared with the "no policy" case. Negative emission technologies play an important role in achieving near-zero emissions, with captured carbon accounting on average for 20% of the total reductions in 2050. Our multimodel comparisons reveal large differences in necessary emission reductions across sectors, whereas what is consistent is that the power sector is required to achieve full decarbonization by 2050. The cross-model averages indicate that China's accumulated policy costs may amount to 2.8 to 5.7% of its gross domestic product by 2050, given the 1.5°C warming limit.
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Agricultural crop yields are susceptible to changes in future temperature, precipitation, and other Earth system factors. Future changes to these physical Earth system attributes and their effects on agricultural crop yields are highly uncertain. United States agricultural producers will be affected by such changes whether they occur domestically or internationally through international commodity markets. Here we present a replication study of previous investigations (with different models) showing that potential direct domestic climate effects on crop yields in the U.S. have financial consequences for U.S. producers on the same order of magnitude but opposite in sign to indirect financial impacts on U.S. producers from climate effects on crop yields elsewhere in the world. We conclude that the analysis of country-specific financial climate impacts cannot ignore indirect effects arising through international markets. We find our results to be robust across a wide range of potential future crop yield impacts analyzed in the multi-sector dynamic global model GCAM.
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Agricultura , Clima , Internacionalidade , Modelos Teóricos , Produtos Agrícolas/crescimento & desenvolvimento , Estados UnidosRESUMO
Water stressed regions rely heavily on the import of water-intensive goods to offset insufficient food production driven by socioeconomic and environmental factors. The water embedded in these traded commodities, virtual water, has received increasing interest in the scientific community. However, comprehensive future projections of virtual water trading remain absent. Here we show, for the first time, changes over the 21st century in the amount of various water types required to meet international agricultural demands. Accounting for evolution in socioeconomic and climatic conditions, we estimate future interregional virtual water trading and find trading of renewable water sources may triple by 2100 while nonrenewable groundwater trading may at least double. Basins in North America, and the La Plata and Nile Rivers are found to contribute extensively to virtual water exports, while much of Africa, India, and the Middle East relies heavily on virtual water imports by the end of the century.
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In the 2015 Paris Agreement, nations worldwide pledged emissions reductions (Nationally Determined Contributions-NDCs) to avert the threat of climate change, and agreed to periodically review these pledges to strengthen their level of ambition. Previous studies have analyzed NDCs largely in terms of their implied contribution to limit global warming, their implications on the energy sector or on mitigation costs. Nevertheless, a gap in the literature exists regarding the understanding of implications of the NDCs on countries' Energy-Water-Land nexus resource systems. The present paper explores this angle within the regional context of Latin America by employing the Global Change Assessment Model, a state-of-the-art integrated assessment model capable of representing key system-wide interactions among nexus sectors and mitigation policies. By focusing on Brazil, Mexico, Argentina and Colombia, we stress potential implications on national-level water demands depending on countries' strategies to enforce energy-related emissions reductions and their interplays with the land sector. Despite the differential implications of the Paris pledges on each country, increased water demands for crop and biomass irrigation and for electricity generation stand out as potential trade-offs that may emerge under the NDC policy. Hence, this study underscores the need of considering a nexus resource planning framework (known as "Nexus Approach") in the forthcoming NDCs updating cycles as a mean to contribute toward sustainable development.
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Política Ambiental , Aquecimento Global , Efeito Estufa , Recursos Hídricos , Argentina , Brasil , Colômbia , Gases de Efeito Estufa , América Latina , MéxicoRESUMO
A growing consensus in social cognitive neuroscience holds that large portions of the primate visual brain are dedicated to the processing of social information, i.e., to those aspects of stimuli that are usually encountered in social interactions such as others' facial expressions, actions, and symbols. Yet, studies of social perception have mostly employed simple pictorial representations of conspecifics. These stimuli are social only in the restricted sense that they physically resemble objects with which the observer would typically interact. In an equally important sense, however, these stimuli might be regarded as "non-social": the observer knows that they are viewing pictures and might therefore not attribute current mental states to the stimuli or might do so in a qualitatively different way than in a real social interaction. Recent studies have demonstrated the importance of such higher-order conceptualization of the stimulus for social perceptual processing. Here, we assess the similarity between the various types of stimuli used in the laboratory and object classes encountered in real social interactions. We distinguish two different levels at which experimental stimuli can match social stimuli as encountered in everyday social settings: (1) the extent to which a stimulus' physical properties resemble those typically encountered in social interactions and (2) the higher-level conceptualization of the stimulus as indicating another person's mental states. We illustrate the significance of this distinction for social perception research and report new empirical evidence further highlighting the importance of mental state attribution for perceptual processing. Finally, we discuss the potential of this approach to inform studies of clinical conditions such as autism.
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Limiting atmospheric carbon dioxide (CO2) concentrations to low levels requires strategies to manage anthropogenic carbon emissions from terrestrial systems as well as fossil fuel and industrial sources. We explore the implications of fully integrating terrestrial systems and the energy system into a comprehensive mitigation regime that limits atmospheric CO2 concentrations. We find that this comprehensive approach lowers the cost of meeting environmental goals but also carries with it profound implications for agriculture: Unmanaged ecosystems and forests expand, and food crop and livestock prices rise. Finally, we find that future improvement in food crop productivity directly affects land-use change emissions, making the technology for growing crops potentially important for limiting atmospheric CO2 concentrations.