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33 results for “Fossil Fuels”
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system
<p><strong>Sector-coupled Euro-Calliope model outputs</strong></p> <p>The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model.</p> <p>The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe.</p> <p>The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> <p>The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <p><code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>).</p> <p>Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update".</p> <p>To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.</p>
Result data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Parameter estimations from the conjoint experiments performed in "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors". Parameter estimations are given for different:</p> <p>* estimands: average marginal component effects (amce) or marginal means,</p> <p>* variables: choice and rating,</p> <p>* sectors: buildings (heat) and transport sector,</p> <p>* subgroups: by-<subgroupname>.</p> <p>Filenames accordingly are: <estimand>-<variable>-<sector>.csv or <estimand>-<variable>-<sector>-by-<subgroup>.csv</p>
Hélène Lepaumier - From Fossil to Renewable Fuels
<p>What is the link between neutral climate and renewable fuels? Are all clean fuels equally beneficial? Find out more about solar fuels & don't miss our interview with Hélène Lepaumier, research engineer at ENGIE Laborelec and a SUNRISE consortium member</p>
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
Source data and code for: Existing fossil fuel extraction would warm the world beyond 1.5°C
<p>Source data and code for the study, "Existing fossil fuel extraction would warm the world beyond 1.5°C." Datasets 1-4 include mine-level data collected for China (Dataset 1), India (Dataset 2), and five other countries (Dataset 3) that are among the world's top nine coal producers - the United States, Indonesia, Australia, South Africa, and Poland. Dataset 4 includes global and country-level output data from the 1,000-run Monte Carlo simulation. <Committed_Reserves_Monte_Carlo_Input_Data.zip> includes data and code to replicate the Monte Carlo simulation.</p>
Code and data for "Current fossil fuel infrastructure does not yet commit us to 1.5°C warming"
<p>This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming".</p> <p>See enclosed README file for dependencies and how to run.</p>
Fossil Fuel CO₂ Emissions for the OCO2 Model Intercomparison Project (MIP)
<p>These are fossil CO<sub>2</sub> fluxes updated through August 2024 for atmospheric CO<sub>2</sub> modeling. They were constructed primarily to be used for the OCO2 Model Intercomparison Project (MIP).</p> <ul> <li>For 2000-2022, they're based on <a href="https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2023.html">ODIAC 2023</a>, which in turn uses BP's energy use statistics for 2021 and 2022.</li> <li>ODIAC monthly emissions have been disaggregated to hourly using the TIMES emission factors for day of week and time of day (<a href="https://urldefense.us/v3/__https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196__;!!PvBDto6Hs4WbVuu7!YsQP_T-Vf3Fv83toql-90HY0NO5e92fR0D9kAi10tTzUd0Ugum9d3CTUMBp22qA0M-vYoU_fvd4$">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196</a>).</li> <li>For 2023 onwards, ODIAC's 2022 emissions have been scaled by the ratio of that month to 2022 emissions reported by <a href="https://www.nature.com/articles/s41597-020-00708-7">Carbon Monitor</a>, downloaded on October 15, 2024 from <a href="https://carbonmonitor.org">https://carbonmonitor.org/</a>. <ul> <li>ODIAC does not have sectoral decomposition to the degree provided by Carbon Monitor, so total ODIAC emissions for each region have been scaled by the total emission change between 2022 and each extended year reported by Carbon Monitor, i.e., power, ground transport, etc. have <strong>not</strong> been separately scaled.</li> <li>Carbon Monitor data are daily, but ODIAC emissions are monthly. So Carbon Monitor data have been aggregated to monthly totals before deriving scaling factors between 2022 and the extended years.</li> <li>Carbon Monitor reports international aviation emissions by country of origin, while ODIAC reports aviation emissions on a grid. Since there is no way to derive the points of emission for Carbon Monitor aviation emissions , all Carbon Monitor international aviation was aggregated to create a single number for each month, then that number was used to scale ODIAC's bunker fuel for each month in 2023-2024.</li> <li>CarbonMonitor data used for deriving 2023 and later emissions are now included in this dataset for convenience as netcdf files (converted from original CSV files).</li> </ul> </li> <li>Hourly global totals are given in the files as a check, in case you want to verify your units and file reading.</li> </ul> <p>These files can be downloaded from the browser, or from the command line following guides such as <a href="https://ict.ipbes.net/ipbes-ict-guide/data-and-knowledge-management/technical-guidelines/zenodo#b.-programmatically-using-r" target="_blank" rel="noopener">this</a>.</p>
Canadian fossil fuel production, greenhouse gas emissions, emissions targets and carbon budgets
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. It includes calculations of the corresponding emissions according to a life-cycle analysis. The total greenhouse gas emissions from fossil fuels extracted annually in Canada (including those burned abroad) are computed. McGlade and Ekins (2015) proposed budgets for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0 °C by 2100. The proportion of each budget that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets within the period 2010-2050 already emitted.</p>
China's fossil fuel CO2 emissions estimated using surface observations of co-emitted NO2
<p>We employed an EnKF-based Regional Multi-Air Pollutant Assimilation System (RAPAS) to assimilate <em>in-situ</em> NO<sub>2</sub> observations, allowing us to combine observation-constrained NO<em><sub>x</sub></em> emissions co-emitted with FFCO<sub>2</sub> and grid-specific CO<sub>2</sub>-to-NO<em><sub>x</sub></em> emission ratios for inferring daily <strong>FFCO<sub>2</sub> emissions</strong> over China.</p> <p><strong>cnemc_obs.nc </strong>includes assimilated and verified observations.</p> <p><strong>emission.tar.gz</strong> includes inferred daily posterior NO<em><sub>x</sub></em> and FFCO<sub>2</sub> emissions for the year 2016.</p>
A look back on Saudi fossil fuel subsidy in the last decade
<p>Calculations pertaining to the paper, "A look back on Saudi fossil fuel subsidy in the last decade".</p>
Global, Regional, and National Fossil-Fuel CO2 Emissions: 1751-2017
<p>This data product is a time series of Carbon Dioxide (CO2) emissions from fossil fuel combustion and cement manufacture. Estimates of CO2 emissions are included for the globe and by nation back to 1751, and include emissions from solid fuel consumption, liquid fuel consumption, gas fuel consumption, cement production, and gas flaring. Per capita CO2 emissions and emissions from international trade (bunker fuels) are included as well; bunker fuels are not included in country totals, but are assigned to the country in which loading took place. Estimates are generated using the United Nations Energy Statistics database and the United States Geologic Survey’s cement statistics. Datasets produced from this group at Appalachian State University are located at https://data.ess-dive.lbl.gov/view/doi:10.15485/1712447, and are also located at https://energy.appstate.edu/research/work-areas/cdiac-appstate. Historic CDIAC data from Oak Ridge National Laboratory are located here: https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/00001_V2017. This dataset is the foundational dataset for the annual global carbon budget and other carbon cycle analyses that need relevant fossil fuel CO2 data. Within this data package are spreadsheets (.csv) of global and national estimates of CO2 emissions as well as text files of the ranking of each country’s total CO2 emissions and per capita for that year</p>
Measurement report: quantifying source contribution of fossil fuels and biomass-burning black carbon aerosol in the southeastern margin of the Tibetan Plateau
<p>Anthropogenic emissions of Black carbon (BC) aerosol are transported from Southeast Asia to the southwestern Tibetan Plateau (TP) during the pre-monsoon; however, the quantities of BC from different anthropogenic sources and the transport mechanisms are still not well constrained because there have been no high-time-resolution BC source apportionments. Intensive measurements were taken in a transport channel for pollutants from Southeast Asia to the southeastern margin of TP during the pre-monsoon to investigate the influences of fossil fuels and biomass burning on BC. A receptor model coupled multi-wavelength absorption with aerosol species concentrations was used to retrieve site-specific Ångström exponents (AAE) and mass absorption cross-sections (MAC) for BC. An ‘aethalometer model’ that used those values showed that biomass burning had a larger contribution to BC mass than fossil fuels (BCbiomass = 57% versus BCfossil = 43%). The potential source contribution function indicated that BCbiomass was transported to the site from northeastern India and northern Burma, The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) model indicated that 40% of BCbiomass originated from Southeast Asia, while the high BCfossil was transported from the southwest of sampling site. A radiative transfer model indicated that the average atmospheric direct radiative effects (DRE) of BC was +4.6 ± 2.4 W m<sup>-2</sup> with +2.5 ± 1.8 W m<sup>-2</sup> from BCbiomass and +2.1 ± 0.9 W m<sup>-2</sup> from BCfossil. The DRE of BCbiomass and BCfossil produced heating rates of 0.07 ± 0.05 and 0.06 ± 0.02 K day<sup>-1</sup>, respectively. This study provides insights into sources of BC over a transport channel to the southeastern TP and the influence of the cross-border transportation of biomass burning emissions from Southeast Asia during the pre-monsoon.</p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Dataset for plots and results in manuscript: "Reliance on fossil fuels increases during extreme temperature events in the continental United States"
<p>These are the dataset for plots and results in the manuscript titled "Reliance on fossil fuels increases during extreme temperature events in the continental United States".</p><p>Figure 1,2,3,4,5 are the dataset used for analysis and plotting the figures in the manuscript.</p><p>Other dataset are the 34 years' air temperature and population-weighted air temperature thresholds for detecting extreme temperature events in each U.S. states. For example, Ta_threshold(1990-2023)_99th_percentiles.csv is the 99th percentiles for each U.S. states.</p><p>Any questions and further assitance or collaborations are welcome to contact through: wz2481@columbia.edu, happystillwaterzhao@gmail.com</p><p> </p><p> </p>
Replication Data for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>Model and observation data, used to prepare the figures in the main text, are submitted at this repository.</p>
Emission Scenarios used for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>CH<sub>4</sub> emission scenarios, based on bottom-up emission estimates (Chandra et al., CEE, 2024; Fig 2) for simulating the long-term trends and latitudinal gradients of CH<sub>4</sub> and <em>δ</em><sup>13</sup>C-CH<sub>4</sub>. The details can be found at </p> <p>Chandra, N., Patra, P.K., Fujita, R. <em>et al.</em> Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020. <em>Commun Earth Environ</em> <strong>5</strong>, 147 (2024). https://doi.org/10.1038/s43247-024-01286-x</p>
Fossil Fuel Subsidy Reform - Data and Analysis
<p>This is raw data, the code for cleaning and compiling the data into a panel data, code for mapping and replication of main results for</p> <p>Droste, Chatterton, and Skovgaard (2024). A political economy theory of fossil fuel subsidy reforms in OECD countries. <em>Nature Communications 15</em>: <span>5452</span> </p>
Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model
<p>Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model</p>
Atmospheric oxygen as a tracer for fossil fuel carbon dioxide: a sensitivity study in the UK
<p>Abstract. We investigate the use of oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimising the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which have APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compared three ocean O2 flux estimates, from the NEMO – ERSEM and ECCO-Darwin ocean models, and the Jena Carboscope inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios was investigated through Monte Carlo sampling within literature uncertainty ranges, and by comparing different inventory estimates. Of the factors that could potentially compromise APO-derived fossil fuel CO2 estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable to the contribution to APO of simulated fossil fuel CO2. We find that simulations using different ocean fluxes differ from each other substantially, with no single estimate, or a simulation with zero ocean flux, providing a significantly closer fit to the observations. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources need to be well accounted for, in order to reduce their potential influence on inferred fossil fuel CO2.</p>
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