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121 results for “Scenario Modelling”
Model to: Participatory modelling of scenarios to restore nitrogen cycles in a nutrient-saturated area
<p>Based on this model we wrote the manuscript: "Participatory modelling of scenarios to restore nitrogen cycles in a nutrient-saturated area"</p><p><strong>Abstract</strong></p><p>This paper aims to find socially acceptable solutions of circularity as measure to reduce nitrogen (N) losses and prevent environmental damage by combining participatory modelling and scenario Substance Flow Analyses (SFA). A local perspective was taken on the agro-food-waste system in the animal production-dominated German district Cleves. Three scenarios were programmed as Monte Carlo simulation of SFA with stakeholder input regarding crop allocation, livestock composition, livestock reduction, and manure allocation following the elimination of feed imports. One scenario with the unaltered stakeholder input (PS), one with altered crop allocation to satisfy the demand for feed (CBS), and one with the livestock numbers changed to match the locally available feed (LBS). In the reference year (2020) agricultural losses amounted to 0.07 t N year-1 ha -1 agricultural land and 0.12 t N in feed was imported year-1 ha -1 agricultural land. In the PS feed import elimination led to deficits in feed availability. The LBS showed the biggest reduction of agricultural N losses and improved N use efficiency (+6%), however agricultural losses were still high (0.05 t N year-1 ha -1 agricultural land). The results show a limited effect of feed import elimination on N losses if no further measures are taken, such as increased sufficiency of consumers, e.g. less animal-based products. Further, the study shows, that it is important to improve stakeholders´ knowledge about approaches to circular agro-food-waste systems. The discrepancy between stakeholder visions and N circularity provide policy makers with the recommendation to improve stakeholders´ visions of a circular agro-food-waste system.</p><p> </p>
Freshwater thickness, pycnocline depth, and depth averaged velocities from 17 model scenarios in Admiralty Bay, Antarctica.
<p>The dataset contains model results from the Admiralty Bay hydrodynamic model calculated using the Delft3D Flow. It contains freshwater thickness (FWT), pycnocline depth, and depth-averaged velocities mean values from period 1.1.2022-28.01.2022, from 17 model scenarios.</p><p>Scenarios details:</p><p>1–14 scenarios with increasing glacial influx (m^3/s per ~1 km of ice/water boundary) spread homogenously across glacial fronts with 0 m/s initial velocity: 1 - 0; 2 - 0.15 3 - 0.3; 4 - 0.6; 5 - 0.9; 6 -1.7; 7 - 3.0; 8 - 4.5; 9 - 6.0 (also described as test run H0); 10 - 8.0; 11 -11.0; 12 - 14.0; 13 - 28.0 ; 14 - 60.0;</p><p>15 - H2 test run with glacial water discharged from all glaciers, homogenously through the entirety of glacial front, with an initial velocity of 2 m/s</p><p>16 - S0 test run with glacial water discharged from all glaciers subglacially, with zero initial velocity</p><p>17 - S2 test run with glacial water discharged from all glaciers subglacially, with an initial velocity of 2 m/s</p>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
AWESOME CGE scenarios and modelling outputs
<p>The primary goal of this analysis was to study the impacts of climate change on food security given the potential supply of alternative water sources: desalination and reused (treated) water within the WEFE nexus in the Mediterranean Sea Basin. Accordingly, the first step of the analysis is to explicitly introduce desalination and treated water into the global CGE model and database. Using the standard GTAP model (Corong, Hertel, McDougall, Tsigas, & van der Mensbrugghe, 2017) and GTAP10A dataset (Aguiar, Chepeliev, Corong, McDougall, & & van der Mensbrugghe, 2019), the water sector was divided into three main categories using the SplitCom application (Horridge, 2008) and including (a) natural water that refers to distribution of water, (b) desalinated water that refers mostly to seawater desalination and (c) treated water that refers primarily to wastewater and brackish water treatment. The resulting modelling framework is used as the basis of the second step, that assesses the externalities associated with the different water sources incorporated in the model.</p> <p>To introduce alternative water sources in the CGE, several assumptions were made. First, desalinated water is produced from the abundant resource – seawater and therefore the scarcity value that is attributed to depletable natural resources is not applicable. Accordingly, desalinated water production can be treated as a sector disaggregated from the existing water sector in the GTAP database. For example, the economic activities of the desalination sector were reported as part of the water sector in input-output tables (United Nations, 2008).</p>
Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis
<p><strong>Dataset Name:</strong><br><em>Literature Data, Archetype Parameter Sheets, and Schedules for the publication, named Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis</em>.</p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources, archetypal data, and schedules for Nigerian residential dwelling typologies.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> Nigeria<em>_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> Nigeria<em>_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> Nigeria<em>_Schedules.xlsx:</em> Excel sheet containing the operation schedules compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format.</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Nigerian residential buildings. The literature sources included in the Nigeria_LiteratureSources.xlsx and Nigeria_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Chibuikem Chrysogonus Nwagwu, Sahin Akin, and Edgar G. Hertwich. 2024. “Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis” https://doi.org/10.5281/zenodo.10995123</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) as well as full material and energy use result sheets can be provided on request. If you have any questions or comments about the dataset, please contact <strong>chibuikem.nwagwu@sintef.no, the corresponding author.</strong></p>
Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario
<p>The database is built over three main sources</p> <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> <p>Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset.</p> <p>Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset.</p> <p>The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above. </p> <p>Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.</p> <p> </p>
REMix model input data for the THG95/GHG95 scenario analysed within the MuSeKo project
<ul> <li>This file contains data used in the REMix energy system model in a scenario assessment for the years 2020, 2030, 2040, and 2050</li> <li>The dataset comprises techno-economic data, energy demand data, renewable energy potentials, fuel as well as emission prices, and energy infrastructure capacities</li> <li>This data is considered in the THG95/GHG95 (Treibhausgas / green house gas) scenario developed within the project MuSeKo</li> <li>This scenario comprises Germany, its neighbours as well as Italy, Norway and Sweden</li> <li>Further descriptions and data is available in the project report of MuSeKo (in German), which can be downloaded from <a href="https://elib.dlr.de/135971/">https://elib.dlr.de/135971/</a></li> </ul> <p>Version 2 provides a correction of biogas potentials in Germany</p>
Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality
<p>Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.</p>
Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.
Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
CDR deployment in Europe, NEGEM-scenario results from Pan-European TIMES-VTT energy system modelling as reported in Markkanen et al. (2024)
<p>This dataset includes cumulative and yearly carbon dioxide removal (CDR) deployment in NEGEM-scenarios for Europe.</p> <p>The results originate from Pan-European TIMES-VTT energy system model and are published in Markkanen et al. (2024), manuscript submitted to Environmental Research Letters, Focus issue on Carbon Dioxide Removals on 31/05/2024. </p> <p>Regional coverage: EU-31. Temporal coverage: until 2060. </p> <p>Cumulative values are reported for the period 2025-2050. Yearly values are reported for 2010, 2020, 2030, 2040, 2050 and 2060.</p> <p>Negative emission technologies and practises (NETPs) included: bioenergy with carbon capture and storage (BECCS), biochar, direct air carbon capture and storage (DACCS), enhanced weathering (EW), forestry (A/R; afforestation and reforestation) and soil carbon sequestration (SCS). Additionally, sum of total CDR is reported, which is the sum of NETPs. For the yearly data, absolute CO2 emissions and net CO2 emissions are reported. </p> <p>Data covers six (6) NEGEM-scenarios, TEC, ENV and SEC, and their limited variants, which exclude the use of EW and SCS. Storylines and main assumptions for NEGEM-scenarios are reported in NEGEM Deliverable 8.2 Quantifying the NEGEM pathways and impact assessments with global TIMES-VTT and PET-VTT IAMs by <a href="https://www.negemproject.eu/wp-content/uploads/2023/11/NEGEM_D8.2_NEGEM-scenarios.pdf" target="_blank" rel="noopener">Lehtilä et al. (2023).</a></p>
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios
<p>This data were presented in the research paper “How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios”, currently accepted in the journal Global Change Biology Bioenergy (https://onlinelibrary.wiley.com/journal/17571707).<br>The study delivers a model-based evaluation of how much energy, in the form of biomethane and bioethanol, can be produced by giant reed and Miscanthus across Italy in 2000, 2055 and 2085. Marginal lands were defined as low profitable non-irrigated lands, without mechanization and/or nature conservation limitations. Our findings offer an estimation of achievable energy yields and related stability under current/future climate, identifying critical spots and opportunities at province and regional level across Italy.<br>This work was conducted by the Council for Agricultural Research and Economics and supported by the Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) under i) the AGROENER project (D.D. n. 26329, April 1, 2016, http://agroener.crea.gov.it/) and ii) the AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018, https://www.progettoagridigit.it/il-progetto).</p> <p><br>The database used was split in two main datasets, one for the national case study and one for the provincial case study (Bologna province).<br>The national dataset consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_National.shp; 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across Italy (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_National.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. USDA soil texture classification: 1= Loamy, 2=Sandy−loam, 3=Silty−loam, 4= Clay−loam, 5= Sandy−clay−loam, 6=Silty−clay−loam, 7=Loamy−sand, 8=Sandy−clay, 9=Silty−clay, 10=Silty, 11=Clay, 12=Heavy−clay, 13=Sandy.<br>b. soil organic carbon (SOC) classification: SOC≤1.5%=low, 1.5%<SOC≤3%,=medium, otherwise=high;<br>c. maximum soil depth (depth) classification: depth≤50 cm=shallow, otherwise=deep;<br>d. absolute values of aboveground biomass (AGB, Mg ha-1) and energy yields (Giga J ha-1) obtainable from bioethanol (ETA) and biomethane (MET) energy carriers simulated for giant reed (GR) and Miscanthus (MI) in the current scenario;<br>e. minimum (Mn) and maximum (Mx) AGB percentage (%) variations (compared to the baseline) estimated in 2055 (55) and 2085 (85) for RCP 4.5 (4.5) and RCP 8.5 (8.5) scenarios;<br>f. potentially assignable marginal lands to Miscanthus (2) and giant reed (1) crop species in Italy based on attainable energy yields under current (C_Base) and future (2085) time slices, considering the more pessimistic (C_8.5_85_MIN) and optimistic (C_4.5_85_MAX) AGB projection for both crops.</p> <p><br>The provincial dataset (case study in the Bologna province) consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_Provincial.shp, 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across the Bologna province (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_Provincial.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. absolute values of simulated energy (EN, Giga J ha-1) from bioethanol (ETA) and biomethane (MET) for giant reed (GR) and Miscanthus (MI) in 1995,<br>b. energy percentage variations (compared to the baseline) estimated in 2085 for more optimistic (EN_Mx, i.e., RCP 4.5_max) and pessimistic (EN_Mn, i.e., RCP 8.5_min) projections for giant reed (GR) and Miscanthus (MI) and<br>c. coefficients of variations (CV, %) computed for the whole 30-year period centred on 1995 (B) and 2085 for RCP 4.5_max (CV_Mx) and RCP 8.5_min (CV_Mn) for giant reed (GR) and Miscanthus (MI) in the Bologna province.</p>
Figure 7 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 7. Map of potential invasion range of S. woodiana in Europe under the RCP 8.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 6 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 6. Map of potential invasion range of S. woodiana in Europe under the RCP 4.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 4 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 4. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 8.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 5 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 5. Map of potential invasion range of S. woodiana in Europe under the recent climate conditions: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 3 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 3. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 4.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
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