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139 results for “carbon emissions”

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zenodo36/100

Fig. 2 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania

Fig. 2. Plant respiration (Ra) in organic agroecosystems, 2014-2016 (mean±SE).

opencc-by-4.0Dec 2019View details →

zenodo36/100

Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki

This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are: <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>

opencc-by-4.0Feb 2019View details →

zenodo36/100

Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations

The description of the NetCDF files (12×200×350): <ol> <li> The first dimension represents the months, the second represents latitude, and the third represents longitude. </li> <li> The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both. </li> </ol> The units for all files are as follows: <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> File </td> <td> Format </td> <td> Unit </td> </tr> <tr> <td> All emission data.zip </td> <td> netcdf </td> <td> kg·m-2·s-1 </td> </tr> <tr> <td> Emissions in seven regions.csv </td> <td> csv </td> <td> 103 kt </td> </tr> <tr> <td> Simulated CO concentrations.zip </td> <td> txt </td> <td> μg·m-3  </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo36/100

Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'

1. ALK.mat <a name="OLE_LINK86"></a><a name="OLE_LINK87"></a>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m.  Units: meq/m3 Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 1a, b and Fig.2   2. pH.mat Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH.  Units: unitless <a name="OLE_LINK92"></a><a name="OLE_LINK93"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 1c and Fig.3   3. total_DIC_ALK.mat <a name="OLE_LINK88"></a><a name="OLE_LINK89"></a>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory. Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d) Data in this file is used in Fig. 1d and Fig. 4.   4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC.  Unit: mmol/m3 Coordinates: DIC: longitude * latitude* depth* time Data in this file is used in Fig. 5   5. pCO2.mat Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2. Unit: ppmv Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig.6   6. FG_CO2.mat <a name="OLE_LINK94"></a><a name="OLE_LINK95"></a>Description: This file includes three structs, <a name="OLE_LINK90"></a><a name="OLE_LINK91"></a>FG_CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) <a name="OLE_LINK96"></a><a name="OLE_LINK97"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 7   7. NPP.mat <a name="OLE_LINK98"></a><a name="OLE_LINK99"></a>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) <a name="OLE_LINK104"></a><a name="OLE_LINK105"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 8   8. spC.mat, diatC.mat, diazC.mat Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude,  mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean.  Unit: mmol/m3 <a name="OLE_LINK108"></a><a name="OLE_LINK109"></a>Coordinates: Para_a10: longitude * latitude * time <a name="OLE_LINK110"></a><a name="OLE_LINK111"></a>Data in these files are used in Fig. 9   9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux,  para_a10: 10-years average of CaCO3 flux.  Unit: nmol/cm2/s (need to convert unit to mol/m2/yr) Coordinates: Para_a10: longitude * latitude * time <a name="OLE_LINK112"></a><a name="OLE_LINK113"></a>Data in these files are used in Fig. 10   10. POC.mat Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) Coordinates: Para_a10: longitude * latitude * time Data in these files are used in Fig. 11   11. atmoCO2.mat Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO2 variation with the unit ppm. Data in this file is used in Fig.1f

opencc-by-4.0Sep 2024View details →

zenodo36/100

Emissions-weighted Carbon Price

<div>This note describes the data and methods used to calculate the emissions-weighted carbon price (ECP), a sector or economy-wide average price on CO2 emissions. A major benefit is that it provides a methodology to measure sector- or economy-level average prices consistently across jurisdictions. To the best of our knowledge, the ECP data constitute the first centralized and systematic assessment providing a consistent description of carbon prices that simultaneously includes price information disaggregated at the sector(-fuel) level, extends back to 1990 to include price information for the earliest carbon taxpricing policies, and accounts for as many sector(-fuel) exemptions as accurately possible. The methodology and data currently available allow to readily expand the calculation to new national or subnational jurisdictions, should some of their emissions become subject to a carbon pricing mechanism, as well as to new greenhouse gases.</div> <div> </div> <div>It is calculated for 46 national and 31 subnational jurisdictions (13 Canadian provinces and territories, 11 US states, and 7 Chinese provinces) over 1990–2022. The average World CO2 price is also calculated. For national jurisdictions, the emissions-weighted price accounts for the prices arising from carbon pricing instruments introduced in their respective subnational jurisdictions. For instance, the emissions-weighted price for the United States includes the prices arising from state-level carbon pricing mechanisms.</div>

opencc-by-nc-4.0Jun 2024View details →

zenodo36/100

Battery sizing capacity, Carbon emission and NPV data

The dataset contains: The simulation environment and results of the building energy model, including meteorological data, battery capacity sizing data, and battery degradation data. The data set also includes calculations and results based on the net present value and carbon intensity of the systems described above.

opencc-by-4.0Feb 2024View details →

dryad36/100

Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests

The global burned area declined by nearly one-quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, e.g., savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at the Pantropical scale. On average, fires affected 1.4% of the total TDF' area (60,208 km2) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2 and 10.3%) and carbon emissions (42.9 and 10.6%). Socioeconomic indicators such as land management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of "half protected species" in the globe, i.e., high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire management.

opencc-zeroDec 2022View details →

dryad36/100

Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales

Streams and rivers are important sources of nitrous oxide (N2O), a powerful greenhouse gas. Estimating global riverine N2O emissions is critical for the assessment of anthropogenic N2O emission inventories. The indirect N2O emission factor (EF5r) model, one of the bottom-up approaches, adopts a fixed EF5r value to estimate riverine N2O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF5r values. Factors regulating EF5r are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF5r values. Our results show that the EF5r values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF5r value is comparable to the IPCC default value, the global EF5r values are highly skewed with large variations, indicating that adopting region-specific EF5r values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N2O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO3-) and NO3- concentration are identified as the dominant predictors of region-specific EF5r values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N2O production efficiency in rivers. A multiple linear regression model using DOC/NO3- and NO3- is proposed to predict region-specific EF5r values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF5r values at the global scale and provides a pathway to estimate global riverine N2O emissions more accurately based on IPCC methodology. This dataset is a global integrated N2O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N2O concentration, biogeochemical (DOC, NO3-, NH4+, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.

opencc-zeroJan 2023View details →

zenodo36/100

Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1

This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).

opencc-by-4.0Mar 2023View details →

zenodo36/100

A comparison of the climate and carbon cycle effects of carbon removal by Afforestation and Reduction of Fossil fuel emissions

This dataset is for the journal article titled "A comparison of the climate and carbon cycle effects of carbon removal by Afforestation and Reduction of Fossil fuel emissions".

opencc-by-4.0Nov 2022View details →

zenodo36/100

Url of the Fire-induced Carbon Emissions Dataset in Africa 2014-2021. (1.0) [Data set].

Estimated carbon emission data from biomass burning in Africa from 2014 to 2021, based on the GABAM 30m burning area product. This product uses the WGS84 horizontal datum and the 0.00025° (approximately 30 meter) resolution for geographic (latitude/longitude) projection of the EGM96 vertical datum, consisting of 10° x 10° blocks covering the entire African region.

opencc-by-4.0Sep 2023View details →

dryad36/100