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9 results for “carbon accounting”
Dataset for the article 'Estimating countries' additional carbon accountability for closing the mitigation gap based on past and future emissions'.
<p>Dataset for the article 'Estimating countries’ additional carbon accountability for closing the mitigation gap based on past and future emissions', published in Nature Communications. DOI: <a href="https://doi.org/10.1038/s41467-024-54039-x">10.1038/s41467-024-54039-x</a></p> <p>TablesInManuscriptandCalculations.xlsx includes a calculations sheet where the main results can be estimated using only Excel, and each respective table found in the article.</p> <p>PlannedEmissions.xlsx includes estimated pathways for all analyzed countries during 2023-2070. Results are given in million tonnes of carbon dioxide (MtCO₂).</p> <p>DataForSensitivityAnalysis.xlsx is a full database with all the results used in the article, both the main approach and sensitivity cases.</p> <p>These files are generated using R-code available at:</p> <p><a href="https://github.com/morfeldt/AdditionalCarbonAccountability">https://github.com/morfeldt/AdditionalCarbonAccountability</a></p> <p> </p>
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.
Consistent LULUCF Carbon Accounting
<p>These data are assembled from 1750-2020 timeseries Global Carbon Budget data and data from Houghton and Castanho 2023:</p> <p><span>Friedlingstein P, O’Sullivan M, Jones MW, Andrew RM, Bakker DCE, Hauck J, et al. Global Carbon Budget 2023. Earth System Science Data. 2023 Dec 5;15(12):5301–69. </span></p> <p><span>Houghton RA, Castanho A. Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020. Earth System Science Data. 2023 May 23;15(5):2025–54.</span></p> <p><span>Two timeseries datasets are assembled, 1) Conventional GCB data with H&C <strong>net</strong> LULUCF and 2) Consistent gross accounting data with GCB data with H&C <strong>gross</strong> LULUCF </span></p> <p> </p>
Global carbon uptake of cement carbonization accounts 1930-2021
<p>This dataset is associated with the publication on ESSD, with the same title.</p> <p>It includes four sections including the model inputs of the estimation models, the simulation results of global CO<sub>2</sub> emission and uptake by year, the variables and ranges considered in the uncertainty analyses of the uptake estimation and the corresponding confidence intervals from the analyses. </p> <p>For detailed explanations of these datasets, one should should refer to the original manuscript.</p>
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
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Data from: The importance of accounting method and sampling depth to estimate changes in soil carbon stocks
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Accounting Measurement of Carbon Credits in Brazil, China, and India
<p>its population is characterized as Brazilian, Chinese, and Indian companies that presented financial information to external users through securities markets’ regulatory agencies in Brazil, China, and India and that implemented CDM projects during the 2005–2012 period, ranking in the “registered” status on the UNFCCC website.</p> <p>Quantitative data were obtaining to test the statistical hypothesis proposed in the study from information referring to the companies and CDM projects that made up the sample as follows: (i) the financial information referring to the equity (E) of companies that have their shares listed in the capital markets of Brazil, China, and India; and (ii) the emission reduction estimates of CDM projects, available from the UNFCCC website.</p> <p>The data collection, referring to the financial information of the companies that have made themselves available via regulatory bodies in the securities markets of the countries under study, was carried out through Thomson Reuters Eikon’s Electronic and Financial Database on July 30, 2013. Thus, when the data collection was carried out, financial information was obtained and converted into euros, referring to the equity (E) of 380 Brazilian companies, 2,584 Chinese companies, and 4,219 Indian companies, for the period under review.</p> <p>The collection of data concerning CDM projects with the status “registered” on the UNFCCC site, on the other hand, was carried out using the Bloomberg Economic and Financial Database on July 29, 2013, at which time a total of 289 projects registered by the Brazilian DNA, 3,651 projects registered by the Chinese DNA, and 1,296 projects registered by the Indian DNA were available for analysis for the 2005–2012 period. On November 18, 2004, just one project was registered by the Brazilian DNA, entitled “Brazil NovaGerar Landfill Gas to Energy Project” (UNFCCC, 2014). This project was eliminated from the research because of its set limits defined between 2005 and 2012, the first stage of the Kyoto Protocol.</p> <p>However, it was necessary to carry out new searches directly on the UNFCCC site for supplementary information that was crucial to implementing the research, given the fact that it did not include full descriptions concerning the names of the receiving agencies in each country (host party), in the Bloomberg Economic and Financial database, on the date mentioned above, information that was characterized as the only link between the CDM project database (Bloomberg) and the financial information database (Thomson Reuters Eikon). These searches were carried during the October 2013–May 2014 period.</p> <p>Subsequently, on September 1, 2014, new searches were carried out on the UNFCCC website to update the information referring to CDM projects registered by the agency during the 2005–2012 period.</p> <p>Thus, this research was carried out based on CDM projects located in the “registered” status section of the UNFCCC site over the 2005–2012 period, the records of which were finalized by the body prior to September 1, 2014, containing 299 projects registered by the DNA of Brazil, 3,682 projects registered by the DNA of China, and 1,371 projects registered by the DNA of India, adding up to 5,353 projects, that is, 74.69% of the total implemented projects in all the developing countries that ratified the Kyoto Protocol.</p> <p>To allow the measurement to be applied to the fair value of estimates of project emission reduction approved by the companies that make up the research sample, we obtained the interest rate EURIBOR – Euro Interbank Offered Rate (average annual rates) from the Bloomberg Financial and Economic Database on July 29, 2013 to adjust the future flows of economic benefits of CER estimates to the present value.</p>
Variation in trends of consumption based carbon accounts
<p>In this work we present results of all the major global models and normalise the model results by looking at changes over time relative to a common base year value. <br> We give an analysis of the variability across the models, both before and after normalisation in order to give insights into variance at national and regional level. <br> A dataset of harmonised results (based on means) and measures of dispersion is presented, providing a baseline dataset for CBCA validation and analysis.</p> <p>The dataset is intended as a goto dataset for country and regional results of consumption and production based accounts. The normalised mean for each country/region is the principle result that can be used to assess the magnitude and trend in the emission accounts. However, an additional key element of the dataset are the measures of robustness and spread of the results across the source models. These metrics give insight into the amount of trust should be placed in the individual country/region results.</p> <p>Code at https://doi.org/10.5281/zenodo.3181930</p>
Consequential Carbon Accounting Scenarios: CCUS Mexico
<p>This dataset presents the models for a consequential carbon accounting method as a time series for evaluating a hypothetical Carbon Capture, Utilisation and Storage (CCUS) project in the Southeast Region of México. The CO2 source is the Dos Bocas Refinery; CO2 utilisation for EOR considers two injection strategies (Water Alternating Gas and Continous Gas Injection); two different scenarios for the energy mix for Mexico´s context were considered (National Strategy on Climate Change and Sustainability):</p> <p>Scenario 1: WAG+NSCC</p> <p>Scenario 2: CGI + Sustainability</p> <p>Scenario 3: WAG+Sustainability</p> <p>Scenario 4: CGI+NSCC</p> <p> </p>
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