Find research datasets worth reusing

opencc-by-4.0Apr 2018View details →

Additional greenhouse gas emissions under different scenarios of permafrost melt'

This dataset contains the underlying data for the following publication Significant implications of permafrost thawing for climate change control, Climatic Change, DOI: 10.1007/s10584-016-1666-5.  This data set contains the permafrost emissions used as inputs for the DICE model. These are estimates of the emissions release from permafrost under the RCP 2.6 scenario (GtCO 2 -eq y −1. Three inputs were used: the median, 16th percentile and 84th percentile pathway.

opencc-by-4.0Dec 2020View details →

Climate Watch Historical Country Greenhouse Gas Emissions Data (1990-2018)

Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. See <a href="http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf">http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf</a> for details regarding data source and methodology.   Climate Watch Historical GHG Emissions. 2021. Washington, DC: World Resources Institute. Available online at: <a href="https://www.climatewatchdata.org/ghg-emissions">https://www.climatewatchdata.org/ghg-emissions</a>

opencc-by-4.0Aug 2021View details →

Canadian fossil fuel production, greenhouse gas emissions, emissions targets and carbon budgets

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.

opencc-by-4.0Apr 2023View details →

Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions

Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>. Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.

opencc-by-4.0Sep 2023View details →

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Introduction 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 CO2 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 CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data 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 (Pinus sylvestris 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). 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). 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. 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 CO2 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. The pre-prepared input data are given in files, see Table 1 for descriptions.     Table 1. Description of input data files. <table> <tbody> <tr> <td> File </td> <td> Description of data </td> </tr> <tr> <td> basal.areas.csv </td> <td> Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   Values of tree species or group correspond to: 1  Scots pine 2  Norway spruce 3  Broadleaved species </td> </tr> <tr> <td> biomass.csv </td> <td> Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> dead_litter.csv </td> <td> Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> ghgi_litter.csv </td> <td> 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-1) 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.   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   Values of ground correspond to litter deposition environment: above  Above-ground litter below  Below-ground litter </td> </tr> <tr> <td> lognat_decomp.csv </td> <td> Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> logyasso_weather_data.csv </td> <td> 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).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   </td> </tr> <tr> <td> total_area.csv </td> <td> Areas (ha) of drained peatland forests remaining forest land by region and peat_type.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> weather_data.csv </td> <td> 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).   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> </tbody> </table>   The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References 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. LUKE Statistics <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> Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). 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. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. 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.

opencc-by-4.0Dec 2022View details →

Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity

Gridded global potentially affected fraction of species (PAF) in 2050 and 2100 per kg GHG for 3 RCPs (2.6, 4.5,8.5) averaged over all species groups.  Full method description is available in the article: <a href="https://www.sciencedirect.com/science/article/pii/S092134492300294X">Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity - ScienceDirect</a>

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Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system

Open the record for dataset details and reuse information.

publicDec 2023View details →

opencc-by-4.0Jan 2016View details →

Dataset: Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles

This dataset contains the data underlying all figures of the paper entitled 'Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles'.

opencc-zeroNov 2023View details →

Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands

Drained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one-third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gas fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N2O) and carbon dioxide (CO2) fluxes were more evenly distributed across space than methane (CH4). In the corn system, hot moments of CH4 flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N2O fluxes in the annual corn agroecosystem but was less informative for perennial alfalfa N2O fluxes or CH4 fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO2, CH4, and N2O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.

opencc-by-4.0Nov 2023View details →

Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.Data This dataset includes the following data:<ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.Further information This dataset complements the following scientific article:Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". Journal of Cleaner Production, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.Funding This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

opencc-by-4.0Apr 2024View details →

Diel greenhouse gas emissions demonstrate a strong response to vegetation patch types in a freshwater wetland

Data supporting submitted research paper. "All_flux_variables.csv" includes all plot data and is organized by the date/time of sample, campaign number, and sample location in rows and data collected as headers in the columns. "Flux_tower_data.csv" are meterological variables include in data analysis collected by a flux tower on sight. All other files are time series data for water pH (n = 2), water temperature (n = 3-5). Refer to the "metadata.csv" for units and descriptions of data in files.

opencc-by-4.0Nov 2021View details →

Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

Power Generation Data Set This data set contains imaging data acquired by ESA's Sentinel-2 Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates   provided by the European Pollutant Release and Transfer Register [2]. The images contain scenes of power stations, some of which are actively emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. Description   Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground) . This repository contains a total of 2131 images. This repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. Content The following files are contained in this repository: <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul>      Acknowledgement If you use this data set, please cite our publication:     Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,     "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",     Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.   Author Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch References   [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2 [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial [3]: https://labelstud.io/ [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show [5]: https://doi.org/10.5281/zenodo.3574566

opencc-by-4.0Mar 2022View details →

Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"

For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act" Description: Data/code used in energy-economic impacts and health impacts analysis. Directory contents: Energy Economic Impacts <ul> <li>Code used for producing figures and data tables <ul> <li>'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.)</li> <li>'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper</li> </ul> </li> <li>Table: data tables for figures in the paper and supplementary materials</li> <li>Figure: figures in the paper and supplementary materials</li> <li>Data: USREP-ReEDS results and data from other sources <ul> <li>'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021.</li> <li>'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018.</li> <li>'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G.</li> </ul> </li> </ul> Health Impacts <ul> <li>analysis: <ul> <li>lib: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis</li> <li>data: outputs <ul> <li>inmap: spatial inputs/outputs for inmap</li> <li>working: intermediate procssed output files</li> <li>final: final health impacts results</li> </ul> </li> </ul> </li> <li>data: raw data used in analysis <ul> <li>working: processed intermediate raw data for faster loading in R</li> </ul> </li> </ul>

opencc-by-4.0Oct 2021View details →

A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

opencc-zeroApr 2022View details →

Life-cycle greenhouse gas emissions in power generation using palm kernel shell

Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO2 emissions from PKS combustion were also calculated by assuming that carbon neutrality was lost because of cultivation abandonment. The GHG emissions in one scenario, where the plantations were replanted and continuously managed and no biogas capture implemented in oil extraction, exhibited an average of 0.134 kg-CO2eq/kWh reduction in a plant in Kyushu District, and 0.043 kg-CO2eq/kWh reduction in a plant in Shikoku District for liquid natural gas-fired steam power generation, respectively. More than 65% of life-cycle GHG emissions originate from biogas generated during oil extraction; thus, biogas capture is an effective strategy to reduce current emissions. In contrast, in the case of accompanying land-use change or collapse of carbon neutrality, the emissions considerably exceeded those of fossil fuels. These findings indicated that the FIT fails to consider the risk of increased emissions or further substantial emission reductions. Therefore, the feasibility of FIT application to PKS needs to be re-established by evaluating the entire PKS life-cycle.

opencc-zeroJul 2022View details →

Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada

Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.

opencc-zeroAug 2022View details →

Data for meta-analysis of the soil greenhouse gas emissions

Exploring the responses of greenhouse gases (GHGs) emissions to land use conversion or reversion is significant for taking effective land use measures to alleviate global warming. A global meta-analysis was conducted to analyze the responses of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions to land use conversion or reversion, and determine their temporal evolution, driving factors and potential mechanisms. Our results showed that CH4 and N2O responded positively to land use conversion while CO2 responded negatively to the changes from natural herb and secondary forest to plantation. By comparison, CH4 responded negatively to land use reversion and N2O also showed negative response to the reversion from agricultural land to forest. The conversion of land use weakened the function of natural forest and grassland as CH4 sink and the artificial nitrogen (N) addition for plantation increased N source for N2O release from soil, while the reversion of land use could alleviate them to some degree. Besides, soil carbon would impact CO2 emission for a long time after land use conversion, and secondary forest reached the methane uptake level similar to that of primary forest after over 40 years. N2O responses had negative relationships with time interval under the conversions from forest to plantation, secondary forest and pasture. In addition, meta-regression indicated that CH4 had correlations with several environmental variables, and carbon-nitrogen ratio had contrary relationships with N2O emission responses to land use conversion and reversion. And the importance of driving factors displayed that CO2, CH4 and N2O response to land use conversion and reversion were easily affected by NH4+ and soil moisture, mean annual temperature and NO3-, total nitrogen and mean annual temperature, respectively. This study would provide enlightenment for scientific land management and reducing of GHG emissions.

opencc-zeroApr 2024View details →

Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir

Extreme hydrological and thermal regimes characterize the Mediterranean biome and can significantly impact the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH4 emissions substantially increased during stratification, influenced predominantly by the rise of water temperature and gross primary production and the drop in reservoir mean depth. N2O emissions mirrored CH4's seasonal trend, significantly correlating to water temperature, wind speed, and net primary production. Conversely, CO2 emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature -an unexpected deviation from CH4 and N2O emission patterns- likely associated with calcite formation coupled to photosynthesis. This investigation highlights the need to integrate these idiosyncratic patterns into GHG emissions models, enhancing the prediction of global GHG emissions in the global change era.

opencc-zeroJul 2024View details →

Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.