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111 results for “CO2 emissions”
Impact of Clean Energy on CO2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries
<p>This study explores the impact of clean energy and non-renewable energy consumption on CO<sub>2</sub> emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO<sub>2</sub> emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.</p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions"
<p>Dataset used for statistical analysis of the manuscript "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Driving and limiting factors of CH<sub>4</sub> and CO<sub>2</sub> emissions from coastal brackish-water wetlands in temperate regions, EGUsphere, https://doi.org/10.5194/egusphere-2023-605, 2023."</p> <p>The dataset include:</p> <ul> <li>CO2 and CH4 fluxes retrived with a portable fluximeter from soils and standing waters</li> <li>environemntal parameters ( T of air and water, Electrical Conductivity (EC), irradiance and water depth </li> </ul> <p>To cite content from this repository: "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions", EGUsphere, 10.5281/zenodo.10390803."</p> <p> </p>
Data used in manuscript Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing
<p>This dataset provides the data used in the manuscript "<em>Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing</em>".</p> <p>The folders are:</p> <p><strong>1. Modelled_CO2_Flux</strong><br> This folder contains a portion of modelled CO2 fluxes generated by SUEWS. Fc is the net CO2 flux, FcPhoto the CO2 uptake by vegetation, FcRespi the CO2 release from soil and vegetation respiration, FcMetab the CO2 emissions from human metabolism, FcBuild the CO2 emissions from the local fuel combustion in buildings. Longitudes and latitudes denote the centroid of grid.<br> <strong>1.1 Fc_annual_2016_g_C_m-2_yr-1.nc</strong> is the annual CO2 fluxes in g C m-2 year-1.<br> <strong> 1.2 Fc_monthly_2016_g_C_m-2_mon-1.nc</strong> is the monthly CO2 fluxes in g C m-2 month-1.<br> <strong>1.3 Fc_annual_2016_g_C_m-2_yr-1.tiff</strong> is the annual Fc (g C m-2 year-1) provided in GeoTiff format.<br> <strong>1.4 6_ring_EPSG4326</strong> contains the ESRI Shapefile defining the study area (with the 6th Ring Road in Beijing as the boundary).</p> <p><strong>2. ModelRun</strong><br> This folder includes SUEWS source code (Järvi et al., 2011; Ward et al., 2016; Järvi et al., 2019) and a model run sample.<br> <strong>2.1 SUEWS_SourceCode</strong> is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/). Enter "make" through the command line and a SUEWS executive will be built under ".../ModelRun/Release".<br> <strong>2.2 EvaluationRun</strong> is a folder including the SUEWS run for model performance evaluation. To conduct a quick model run to reproduce the results demonstrated in the manuscript, use command line "./SUEWS_V2020b". </p> <p><strong>3. Observations</strong><br> The unit for CO2 flux (Fc) is μmol m-2 s-1 under this folder.<br> <strong>3.1 co2_flux_140m_2016_rm_QC.csv</strong> is the Fc observations after quality control and resampled to hourly resolution.<br> <strong>3.2 Fc_gapfilled_with_MeanDC.csv</strong> is the Fc time series for the year 2016 gap-filled with the Mean Diurnal Cycle method on a seasonal basis.</p> <p> </p> <p>Contact information: zhengyingqi@mail.iap.ac.cn</p> <p><br><strong>[References]</strong><br>Järvi, L., Grimmond, C. S. B., & Christen, A. (2011). The surface urban energy and water balance scheme (SUEWS): Evaluation in Los Angeles and Vancouver. Journal of Hydrology, 411(3-4), 219-237.<br>Ward, H. C., Kotthaus, S., Järvi, L., & Grimmond, C. S. B. (2016). Surface Urban Energy and Water Balance Scheme (SUEWS): development and evaluation at two UK sites. Urban Climate, 18, 1-32.<br>Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., ... & Grimmond, C. S. B. (2019). Spatial modeling of local‐scale biogenic and anthropogenic carbon dioxide emissions in Helsinki. Journal of Geophysical Research: Atmospheres, 124(15), 8363-8384.</p>
Wastewater alkalinity addition enhancement for carbon emission reduction and marine CO2 removal
<p>The ROMS_RCA model settings of 2010 runs. The reference date of the 'TIME' variable is 1983-01-01.</p> <p>The files with 'Y2010_' in their names contain the boundary data and initial fileds for the model run. The file "ROMSeutro_1strun.inp" lists all the model parameters, while the files with 'CPB_WWTP_ps' in the names are the settings of the discharges from each WWTP outlet. </p> <p>The data used to generate the figures are provided in the MAT file.</p>
Real time CO2 emissions 2012-2021 at 3 minutes interval
<p>This dataset includes the power generated split by fuel and related CO2 emissions based on production and consumption. </p> <p>Geographical: Finland, Sweden, Norway, Russia, Estonia</p> <p>Time: 01-01-2013 - 01-10-2021</p> <p>time resolution: 3 minutes</p> <p>This repository is only keeping track of historical data and was used in an analysis. Real-time data can be accessed through the API of a the project. </p> <p><a href="https://app.swaggerhub.com/apis-docs/jean-nicolas.louis/emission-and_power_grid_status/1.1.0">API documentation</a></p>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"
<p>Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°) </p>
EV hourly CO2 emission inventory
<p>This is the appendix supporting data for the manuscript entitled "Developing an hourly-resolution well-to-wheel carbon dioxide emission inventory of electric vehicles" submitted to Applied Energy. </p>
Cars Technical Parameters and CO2 Emissions in Germany France Italy Spain Portugal 2017 - 2022
<p><a href="https://www.eea.europa.eu/data-and-maps/data/co2-cars-emission-20">Source: https://www.eea.europa.eu/en</a></p> <p>https://www.eea.europa.eu/data-and-maps/data/co2-cars-emission-20</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Data from: Built structures influence patterns of energy demand and CO2 emissions across countries
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SPARCS_WP3_Espoo_City_CO2 emissions in Espoo, Finland
<p>CO2 emissions in Espoo, Finland, divided by sector. Provided by the Helsinki Region Environmental Authority. 2000-2023</p>
Dataset CO2 Emission per GDP Forecast 2020-2100
<p>The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100.</p> <p>The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase.</p> <p>The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase.</p> <p>CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year.</p> <p>The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.</p>
Energy-related CO2 Emission Accounts and Datasets for 40 Emerging Economies in 2010 - 2019
<p>The Carbon Emission Accounts and Datasets for emerging economies (CEADs, <a href="https://ceads.net">https://ceads.net</a>) aims to provide transparent, verifiable, open-access data on the CO<sub>2</sub> emissions of 40 emerging economies (accounting for 17.5% of the emissions and 12.9% of GDP of the world) for the period 2010-2019. Given the variety of statistical capacity among these countries, we standardized sectors using relevant data on energy and economic statistics and compiled emissions inventories at the regional level using sub-national statistics. For energy-related data, we included emissions from burning biomass, given its significance as an emissions source in these economies. The emissions dataset covers 47 economic sectors and 8 major energy categories in the 40 emerging economies, and in 28 we provide a subnational inventory. The emissions inventories are compiled for 47 economic sectors and 8 major categories.</p>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Data set for fossil Co2 emission in Nigeria
<p>Here is a dataset that captures fossil Co2 emission in Nigeria. </p>
Data from: Hydroxymethylbutenyl diphosphate accumulation reveals MEP pathway regulation for high CO2-induced suppression of isoprene emission
<p>Isoprene is emitted by some plants and is the most abundant biogenic hydrocarbon entering the atmosphere. Multiple studies have elucidated protective roles of isoprene against several environmental stresses, including high temperature, excessive ozone, and herbivory attack. However, isoprene emission adversely affects atmospheric chemistry by contributing to ozone production and aerosol formation. Thus, understanding the regulation of isoprene emission in response to varying environmental conditions, for example elevated CO<sub>2</sub>, is critical to comprehend how plants will respond to climate change. Isoprene emission decreases with increasing CO<sub>2</sub> concentration; however, the underlying mechanism of this response is currently unknown. We demonstrated that high-CO<sub>2</sub>-mediated suppression of isoprene emission is independent of photosynthesis and light intensity, but it is reduced with increasing temperature. Furthermore, we measured methylerythritol 4-phosphate pathway metabolites in poplar leaves harvested at ambient and high CO<sub>2</sub> to identify why isoprene emission is reduced under high CO<sub>2</sub>. We found that hydroxymethylbutenyl diphosphate (HMBDP) was increased and dimethylallyl diphosphate (DMADP) decreased at high CO<sub>2</sub>. This implies that high CO<sub>2</sub> impeded the conversion of HMBDP to DMADP, possibly through the inhibition of HMBDP reductase activity, resulting in reduced isoprene emission. We further demonstrated that although this phenomenon appears similar to ABA-dependent stomatal regulation, it is unrelated as abscisic acid treatment did not alter the effect of elevated CO<sub>2</sub> on the suppression of isoprene emission. Thus, this study provides a comprehensive understanding of the regulation of the MEP pathway and isoprene emission in the face of increasing CO<sub>2</sub>.</p>
Ocean alkalinity destruction by anthropogenic seafloor disturbances generates a hidden CO2 emission
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Divergent terrestrial responses of soil N2O emissions to different levels of elevated CO2 and temperature
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OpenNeuro
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