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- Masnadi, Mohammad S., et al.
(författare)
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Global carbon intensity of crude oil production
- 2018
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 361:6405, s. 851-853
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Tidskriftsartikel (refereegranskat)abstract
- Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ∼15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.
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- Akbilgic, O., et al.
(författare)
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A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates
- 2015
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 159, s. 11-18
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Tidskriftsartikel (refereegranskat)abstract
- The estimated cost of reducing carbon emissions through the deployment of carbon capture and storage (CCS) in power systems vary by a factor of five or more across studies published over the past 8 years. The objective of this paper is to understand the contribution of techno-economic variables and modeling assumptions to explain the large variability in the published international literature on cost of avoided CO2 (CACO2) using statistical methods. We carry out a meta-analysis of the variations in reported CACO2 for coal and natural gas power plants with CCS. We use regression and correlation analysis to explain the variation in reported CACO2. The regression models built in our analysis have strong predictive power (R2 > 0.90) for all power plant types. We find that the parameters that have high variability and large influence on the value of CACO2 estimated are levelized cost of electricity (LCOE) penalty, capital cost of CCS, and efficiency penalty. In addition, the selection of baseline technologies and more attention and transparency around the calculation of capital costs will reduce the variability across studies to better reflect technology uncertainty and improve comparability across studies.
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- DeCarolis, Joseph, et al.
(författare)
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Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts
- 2020
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Ingår i: Joule. - : Elsevier BV. - 2542-4351. ; 4:12, s. 2523-2526
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. DeCarolis et al. articulate the benefits of forming collaborative teams with a wide array of disciplinary and domain expertise to conduct analysis with macro-energy system models. Open-source models, tools, and datasets underpin such efforts by enabling transparency, accessibility, and replicability among team members and with the broader modeling community.
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