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139 results for “carbon emissions”
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row: </p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. </p> <p>--- </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</p> <p><strong>References </strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> </p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/ </a></p>
Plant management but not fertilization mediates soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
<p><span>The diversity of </span><span>plant functional group</span><span>s</span><span> in plantations affects soil carbon, but we have limited understanding of the underlying mechanisms for how plant management affects soil carbon dynamics. Here, we conducted a 3-year manipulation experiment of plant functional groups that included understory removal, tree root trenching, and fertilization treatments in 2-year-old and 6-year-old <em>Eucalyptus</em> plantations in the subtropical region. The results showed that soil respiration was significantly suppressed by understory removal (-38%), tree root trenching (-41%), and their interactions (-54%), but that fertilization alone and in interactions had no significant effect. The Chao1 indices for soil bacterial and fungal diversity significantly decreased with understory removal in the 2-year-old plantation and with tree root trenching in the 6-year-old plantation. Soil bacterial and fungal communities were also affected by understory removal and tree root trenching. Soil respiration, physicochemical characteristics, microbial diversity, and community composition were significantly affected by plantation age. Reductions in soil carbon emissions were associated with reductions in plant functional groups and soil microbial groups, while increases in soil respiration were associated with soil physicochemical factors, soil temperature, and plantation age. Our findings highlight that plant managements are of great significance to the soil carbon emission processes in afforested plantations.</span></p>
Data repository - Land use change and carbon emissions of a transformation to timber cities
<p>Data and model source code for the publication:</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)<br> DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.txt for further details.</p>
Data from: Rates and drivers of carbon emissions from hydropower reservoirs in the southeastern United States
<p>Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. We compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (<span>CO<sub>2</sub></span>) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (<span>CH<sub>4</sub></span>) diffusion or <span>CH<sub>4</sub></span> ebullition dominated. <span>CH<sub>4</sub></span> diffusion rates were positively associated with water temperature. However, we found no clear predictors of <span>CH<sub>4</sub></span> ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m<sup>-2</sup> day<sup>-1</sup>. For <span>CO<sub>2</sub></span> diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted <span>CO<sub>2</sub></span> in summer 2012, but every station sequestered <span>CO<sub>2</sub></span> in summer 2022. Here, indicators of greater algal production were associated with <span>CO<sub>2</sub></span> sequestration, including surface chlorophyll-<em>a</em> concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of <span>CO<sub>2</sub></span> diffusive fluxes, which shifted to positive <span>CO<sub>2</sub></span> fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing <span>CO<sub>2</sub></span> sequestration in field and modelling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability is vital to reducing uncertainties in global upscaling efforts.</p>
Fig. 1 in Microbial Respiration of Organic Carbon in Freshwater Microcosms: The Potential for Improved Estimation of Microbial CO Emission from Organically Enriched Freshwater Ecosystems
Fig. 1. Densities (ordinate) of ciliates (N × 104 L–1) black bars, and densities of bacteria (N × 109 ml–1) grey bars; for Experiment One and Experiment Two with carbon-enriched (E) and control (C) preparations (abscissa).
Supplementary Information for: Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions
<p>These files comprise the Supplementary Information for the paper "Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions" submitted to Climate Policy.</p>
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
DeDuCE: Deforestation and carbon emissions due to agriculture and forestry activities from 2001-2022
<h2>Overview</h2> <p>This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across <strong>179 countries and 184 commodities</strong> within the specified period, presenting an unmatched scope and granularity of data.</p> <h2>Documentation</h2> <p>The manuscript detailing the dataset is currently archived at EarthArXiV: <strong><em>Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions</em></strong>. <a href="https://doi.org/10.31223/X5T69B" target="_blank" rel="noopener">https://doi.org/10.31223/X5T69B</a></p> <p>The insights from this dataset can also be viewed at: <strong><a href="https://www.deforestationfootprint.earth" target="_blank" rel="noopener">https://www.deforestationfootprint.earth</a></strong></p> <h2>Repository contents</h2> <p>The input and output/data generated by the model are archived here at <strong>Zenodo, </strong>and their description is available in <strong>'README (files in the directory).txt'</strong>.</p> <p>The columns of the (final) dataset '<em>DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx</em>' in the folder <em><strong>'Final Attribution Results'</strong></em> represent the following:</p> <ul> <li><strong>Continent/Country group: </strong>All countries are divided into 8 geographical regions</li> <li><strong>ISO: </strong>Three-letter country codes defined by ISO</li> <li><strong>Producer country: </strong>Country of deforestation</li> <li><strong>Year: </strong>Year of deforestation, ranges from 2001-2022 </li> <li><strong>Commodity group: </strong>All commodities are divided into 11 commodity groups</li> <li><strong>Commodity: </strong>Name of commodity aligning with FAOSTAT</li> <li><strong>Deforestation attribution, unamortized (ha): </strong>Annual deforestation estimates</li> <li><strong>Deforestation risk, amortized (ha): </strong>5-year amortised deforestation estimates</li> <li><strong>Deforestation emissions excl. peat drainage, unamortized (MtCO2): </strong>Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)</li> <li><strong>Deforestation emissions excl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Peatland drainage emissions (MtCO2): </strong>Annual estimates of carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Deforestation emissions incl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, including emissions from peatland drainage</li> <li><strong>Quality Index: </strong>Flagging deforestation estimates </li> </ul> <h2>Contact</h2> <p>If you have any questions, you can contact us at: </p> <p> Chandrakant Singh and U. Martin Persson <br> <em><strong><a href="mailto:chandrakant.singh@chalmers.se;martin.persson@chalmers.se">Email</a></strong>: chandrakant.singh@chalmers.se and martin.persson@chalmers.se </em><br> Physical Resource Theory, Department of Space, Earth & Environment, <br> Chalmers University of Technology, Gothenburg, Sweden</p>
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>
Supplementary data for "Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant"
<p>A preliminary analysis of the data already has been discussed in <a href="https://dx.doi.org/10.2139/ssrn.3812299">10.2139/ssrn.3812299</a>.</p> <p><strong>Raw data</strong></p> <p>Raw measurement data is in the Excel files `day*_raw.xlsx`.</p> <p><strong>Model</strong></p> <p>Covariate and label scaler objects are serialized in joblib format in the following files:</p> <ul> <li>20210812_y_transformer_co2_ammonia_reduced_feature_set</li> <li>20210812_y_transformer__reduced_feature_set</li> <li>20210812_x_scaler_reduced_feature_set</li> </ul> <p>Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is:</p> <pre><code class="language-python">from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts')</code></pre> <p>which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.</p> <p> </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>
Saving carbon emissions through online learning for overseas students [Data]
<p>We estimated the savings in CO<sub>2</sub> emissions by a cohort of master’s students who studied fully online from their home countries, rather than traveling to the UK and living there while attending university.</p> <p>Data come from International Civil Aviation Organization (ICAO) carbon emissions calculator <a href="https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx">https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx</a>; and CO₂ and Greenhouse Gas Emissions <a href="https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions">https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions</a></p>
Webinar: Reducing Industrial Carbon Emissions. Carbon capture, utilisation, and storage technologies
<p>The EU recently set unprecedented goals in terms of reducing emissions (-55% by 2030, climate neutrality by 2050). Solutions such as carbon capture, utilisation and storage, or “CCUS”, involve capturing CO2 from industrial plants or installations, transporting it to designated sites, and injecting it into geological formations, making it extremely relevant to face this ambitious, but necessary challenge.</p> <p>This webinar shared some the latest advances in CCUS. The research project cluster on <strong>Better Carbon Capture for Industrial Emissions</strong> is formed by three EU-funded projects: <strong><a href="http://www.cleanker.eu/">CLEANKER</a></strong>, <strong><a href="http://www.realiseccus.eu/">REALISE CCUS</a></strong>, and <strong><a href="http://www.c4u-project.eu/">C4U</a></strong>, and are advancing the state of the art and reducing emissions from industrial plants directly focussing on three different sectors: cement, refineries and steel.</p> <p>Experts from these EU-backed projects showcased the technological approaches, their financial feasibility, and how their approaches in implementing CCUS have shown up to 90% reduction in CO2 emissions in certain plants. Policy aspects that have an effect on the implementation of CCUS were discussed.</p>
Estimation of biomass combustion carbon emissions data for 2016 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2016, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2015 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2015, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
<p><strong> Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p> </p> <p> </p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> <p>Values of tree species or group correspond to:</p> <p>1 Scots pine</p> <p>2 Norway spruce</p> <p>3 Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of ground correspond to litter deposition environment:</p> <p>above Above-ground litter</p> <p>below Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>
Data from: Differential pulse sensitivity of nitric and nitrous oxide emissions to temperature, carbon, and nitrogen following wetting of desert soils
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Plant management but not fertilization mediates soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
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Carbon-sink potential of continuous alfalfa agriculture lowered by short-term nitrous oxide emission events
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Seasonality Drives Carbon Emissions along a Stream Network
Headwater stream networks contribute a significant amount to the global carbon dioxide terrestrial flux because of high turbulence and coupling with terrestrial environments. Heterogeneity within headwater stream networks, both spatially and temporally, makes measuring and upscaling these emissions challenging because measurements of carbon dioxide in streams are often limited to a few monitoring points. We modified a stream network model to reflect real measurements made under base flow and high flow conditions at Martha Creek in Stabler, WA in the US Pacific Northwest. We found that under high flow conditions, the stream network had much greater total carbon emissions than during low flow conditions (1.22 Mg C day-1 vs. 0.034 Mg C day-1). We attribute this increase to a larger overall stream network area (0.04 km2 vs 0.01 km2) and discharge (1.9 m3/s vs. 0.005 m3/s) in November versus August. Our results demonstrate the need to understand the nonperennial nature of streams when calculating carbon emissions. We compared the stream network emissions with the terrestrial net ecosystem exchange (NEE) estimated by local eddy covariance measurements per area of the watershed (-5.5 Mg C day-1 in August and -2.2 Mg C day-1 in November). Daily stream emissions in November accounted for a much larger percentage of NEE in August (54% vs. 0.62%). We concluded that the stream network can emit a large percentage of the forest NEE in the winter months, and annual estimates of stream network emissions must consider the flow regime throughout the year.
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