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- 2021-2025
- 13. Climate action
- 3. Good health
- University of Melbourne
Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Dix, Martin; Bi, Daohua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia M.; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CSIRO-ARCCSS.ACCESS-CM2.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:University of Melbourne Authors: SARAH MCCOLL-GAUSDEN (3871372);FROST summary outputs of the area burnt by short interval fires, high intensity fires, and overall area burnt of alpine ash stands in Victoria under various climate scenarios.
University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:University of Melbourne Authors: Alexei Trundle (2876768); Jessie Briggs (6610712); Michele Acuto (5271224);An editable communications and analysis tool for presenting the relevance of all UN SDGs and their subsidiary targets and indicators to a particular locality or municipality. Default selections are derived from UN-Habitat classifications of urban relevance across all 17 Goals, with shaded segments representative of relevant targets, and the stars within each segment denoting the indicators aligned with each target (current as of mid-2020). This communications tool has potential relevance and adaptability to other sectors beyond local government that apply the SDGs.
University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26188/16599293&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:RMIT University Higgs, Carl; Liu, Shiqin; Boeing, Geoff; Arundel, Jonathan; Lowe, Melanie; Adlakha, Deepti; Cerin, Ester; Hinckson, Erica; Sallis, James F.; Salvo, Deborah; Moudon, Anne Vernez; Giles-Corti, Billie;Output data prepared for analysis of 25 diverse global cities by the Global Healthy and Sustainable City-Indicator Collaboration study, published in The Lancet Global Health Series on urban design, transport, and health. 2022. https://www.thelancet.com/series/urban-design-2022 Boeing, G. et al. (2022) ‘Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities’, The Lancet Global Health, 10(6), pp. e907–e918. Available at: https://doi.org/10.1016/S2214-109X(22)00072-9. Data outputs were generated through use of the global-indicators software tool, designed for this study and available from: https://github.com/global-healthy-liveable-cities/global-indicators Further detail on the methods used is provided in the following publication: Liu, S., Higgs, C., Arundel, J., Boeing, G., Cerdera, N., Moctezuma, D., Cerin, E., Adlakha, D., Lowe, M. and Giles-Corti, B. (2021), A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data. Geogr Anal. https://doi.org/10.1111/gean.12290 The study made use of OpenStreetMap, Global Human Settlements and custom data, and is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
https://dx.doi.org/1... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 AustraliaPublisher:Mendeley Authors: Behzad Rismanchi; Lu Aye; Sheikh Khaleduzzaman Shah;handle: 11343/280154
This dataset includes data from the validation of double U-tube borehole and seasonal solar thermal energy storage system TRNSYS models. The simulated transient temperatures at various points of the systems were compared with the measured ones. To quantify the agreement between each simulated and measured temperature of interest, mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (CC) were applied.
Mendeley Data arrow_drop_down DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)The University of Melbourne: Digital RepositoryDataset . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Mendeley Data arrow_drop_down DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)The University of Melbourne: Digital RepositoryDataset . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/vy7mxkkgx5&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Dix, Martin; Bi, Daohua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia M.; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022 AustraliaPublisher:MDPI AG Authors: Mahima Kalla; Micheal Jerowsky; Benjamin Howes; Ann Borda;handle: 11343/324347
A key determinant and outcome of successful environmental education is ‘pro-environmental behavior’, i.e., behavior that involves conscious action to mitigate adverse environmental impacts at personal or community level, e.g., reducing resource consumption and waste generation, avoiding toxic substances, and organizing community awareness initiatives. However, some theorists have sought to move away from rationalist models of behavioral modification, towards holistic pedagogical initiatives that seek to develop action competence. In light of the global push towards achievement of the Sustainable Development Goals (SDGs), emerging evidence suggests that education initiatives should foster action competence so students may be equipped to contribute to sustainable development as part of their education. The UNESCO Education for Sustainable Development (ESD) Roadmap 2030 has also identified key priority areas to strengthen ESD in formal curricula. This article reports two informal environmental education initiatives for promoting action competence and pro-environmental behaviors in school-aged children. The authors recommend that formal education settings (e.g., schools) should incorporate self-directed, free-choice project-based learning to augment environmental education programs and promote students’ action competence for contribution to attainment of SDGs. To this end, we propose a Free-Choice Project-based Learning for Action Competence in Sustainable Development (ACiSD) Curriculum, comprising six implementation dimensions, namely: (1) project duration and teaming arrangements, (2) topic selection, (3) student support, (4) teacher support, (5) learning environments, and (6) digital access and equity. For each implementation dimension, we recommend action steps to help educators implement this curriculum in their own educational settings, with the aid of an illustrative worked example.
The University of Me... arrow_drop_down The University of Melbourne: Digital RepositoryArticle . 2022License: CC BYFull-Text: http://hdl.handle.net/11343/324347Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su142316315&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!
more_vert The University of Me... arrow_drop_down The University of Melbourne: Digital RepositoryArticle . 2022License: CC BYFull-Text: http://hdl.handle.net/11343/324347Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su142316315&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV Authors: James Helal; André Stephan; Robert H. Crawford;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enbuild.2023.113462&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enbuild.2023.113462&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Copernicus GmbH Funded by:EC | GENIE, EC | RESCUE, EC | ESM2025EC| GENIE ,EC| RESCUE ,EC| ESM2025Matthew J. Gidden; Thomas Gasser; Giacomo Grassi; Nicklas Forsell; Iris Janssens; William F. Lamb; Jan Minx; Zebedee Nicholls; Jan Steinhauser; Keywan Riahi;Global mitigation pathways play a critical role in informing climate policies and targets that are in line with international climate goals. However, it is not possible to directly compare modelled results with national inventories used to assess progress under the UNFCCC due to differences in how land-based fluxes are accounted for.National inventories consider carbon flux on managed land using an area-based approach with managed land-areas determined by nations. Emissions scenarios consider a different managed land area and are calibrated against data from detailed global carbon cycle models that account for natural (indirect) and anthropogenic (direct) fluxes separately by design. To disentangle the direct and indirect components of land-based carbon fluxes, we use a reduced complexity climate model with explicit treatment of the land-use sector, OSCAR, one of the models used by the Global Carbon Project. We find the discrepancy between model and NGHGI-based accounting methods globally to be 4.4 ± 1.0 Gt CO2 yr-1 averaged over the 2000-2020 time period, which is in line with existing estimates. We then apply OSCAR to the set of pathways assessed by the IPCC to quantify how this gap evolves over time and estimate how key mitigation benchmarks change.Across both 1.5°C and 2°C scenarios, LULUCF emissions pathways aligned with NGHGI accounting practices show a strong increase in the total land sink until around mid-century. However, the ‘NGHGI alignment gap’  decreases over this period, converging in the 2050-2060s for 1.5°C scenarios and 2070s-2080s for 2°C scenarios. The convergence is primarily a result of the simulated stabilization and then decrease of the CO2-fertilization effect as well as background climate warming reducing the overall effectiveness of the land sink, which in turn reduces the indirect removals considered by NGHGIs. These dynamics lead to land-based emissions reversing their downward trend in most NGHGI-aligned scenarios by mid-century, and result in the LULUCF sector becoming a net-source of emissions by 2100 in about 25% of both 1.5°C and 2°C scenarios.Assessing emission pathways using LULUCF definitions from national inventory accounting results in downward revisions to emissions benchmarks derived from scenarios. NGHGI-aligned pathways result in earlier net-zero CO2 emissions by around 2-5 years for both 1.5°C and 2°C scenarios, and 2030 emission reductions relative to 2020 are enhanced by about 5 percentage points for both pathway categories. When incorporating the additional land removals considered by NGHGIs, the assessed cumulative net CO2 emissions to global net-zero CO2 also decreases systematically by 15-18% for both 1.5°C and 2°C scenarios.We find that increasing removals from direct fluxes in 1.5C scenarios overtake estimated removals using NGHGI conventions in the near term. However, by midcentury, the strengthening of direct removals is balanced by weakening of indirect removals, meaning that, on average, carbon removal on land accounted for using NGHGI conventions in 1.5C scenarios results in about half of the LULUCF removals in current policy scenarios. We discuss the implications of our results for future Global Stocktakes and market mechanisms under the Paris Agreement.
IIASA DARE arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 26 citations 26 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 New Zealand, AustraliaPublisher:MDPI AG Vasiliki Summerson; Claudia Gonzalez Viejo; Alexis Pang; Damir D. Torrico; Sigfredo Fuentes;Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = −0.93), decanoic acid, ethyl ester (r = −0.94), and octanoic acid, 3-methylbutyl ester (r = −0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.
Lincoln University (... arrow_drop_down Lincoln University (New Zealand): Lincoln U Research ArchiveArticle . 2021License: CC BYFull-Text: https://doi.org/10.3390/molecules26165108Data sources: Bielefeld Academic Search Engine (BASE)The University of Melbourne: Digital RepositoryArticle . 2021License: CC BYFull-Text: http://hdl.handle.net/11343/287783Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/molecules26165108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 19 citations 19 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Lincoln University (... arrow_drop_down Lincoln University (New Zealand): Lincoln U Research ArchiveArticle . 2021License: CC BYFull-Text: https://doi.org/10.3390/molecules26165108Data sources: Bielefeld Academic Search Engine (BASE)The University of Melbourne: Digital RepositoryArticle . 2021License: CC BYFull-Text: http://hdl.handle.net/11343/287783Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Dix, Martin; Bi, Daohua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia M.; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CSIRO-ARCCSS.ACCESS-CM2.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6cmcsiacchi&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:University of Melbourne Authors: SARAH MCCOLL-GAUSDEN (3871372);FROST summary outputs of the area burnt by short interval fires, high intensity fires, and overall area burnt of alpine ash stands in Victoria under various climate scenarios.
University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26188/16458132.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26188/16458132.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:University of Melbourne Authors: Alexei Trundle (2876768); Jessie Briggs (6610712); Michele Acuto (5271224);An editable communications and analysis tool for presenting the relevance of all UN SDGs and their subsidiary targets and indicators to a particular locality or municipality. Default selections are derived from UN-Habitat classifications of urban relevance across all 17 Goals, with shaded segments representative of relevant targets, and the stars within each segment denoting the indicators aligned with each target (current as of mid-2020). This communications tool has potential relevance and adaptability to other sectors beyond local government that apply the SDGs.
University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26188/16599293&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Melbou... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26188/16599293&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:RMIT University Higgs, Carl; Liu, Shiqin; Boeing, Geoff; Arundel, Jonathan; Lowe, Melanie; Adlakha, Deepti; Cerin, Ester; Hinckson, Erica; Sallis, James F.; Salvo, Deborah; Moudon, Anne Vernez; Giles-Corti, Billie;Output data prepared for analysis of 25 diverse global cities by the Global Healthy and Sustainable City-Indicator Collaboration study, published in The Lancet Global Health Series on urban design, transport, and health. 2022. https://www.thelancet.com/series/urban-design-2022 Boeing, G. et al. (2022) ‘Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities’, The Lancet Global Health, 10(6), pp. e907–e918. Available at: https://doi.org/10.1016/S2214-109X(22)00072-9. Data outputs were generated through use of the global-indicators software tool, designed for this study and available from: https://github.com/global-healthy-liveable-cities/global-indicators Further detail on the methods used is provided in the following publication: Liu, S., Higgs, C., Arundel, J., Boeing, G., Cerdera, N., Moctezuma, D., Cerin, E., Adlakha, D., Lowe, M. and Giles-Corti, B. (2021), A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data. Geogr Anal. https://doi.org/10.1111/gean.12290 The study made use of OpenStreetMap, Global Human Settlements and custom data, and is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
https://dx.doi.org/1... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.25439/rmt.15072009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.25439/rmt.15072009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 AustraliaPublisher:Mendeley Authors: Behzad Rismanchi; Lu Aye; Sheikh Khaleduzzaman Shah;handle: 11343/280154
This dataset includes data from the validation of double U-tube borehole and seasonal solar thermal energy storage system TRNSYS models. The simulated transient temperatures at various points of the systems were compared with the measured ones. To quantify the agreement between each simulated and measured temperature of interest, mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (CC) were applied.
Mendeley Data arrow_drop_down DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)The University of Melbourne: Digital RepositoryDataset . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Mendeley Data arrow_drop_down DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)The University of Melbourne: Digital RepositoryDataset . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/vy7mxkkgx5&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Dix, Martin; Bi, Daohua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia M.; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spcsiaccs245&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spcsiaccs245&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022 AustraliaPublisher:MDPI AG Authors: Mahima Kalla; Micheal Jerowsky; Benjamin Howes; Ann Borda;handle: 11343/324347
A key determinant and outcome of successful environmental education is ‘pro-environmental behavior’, i.e., behavior that involves conscious action to mitigate adverse environmental impacts at personal or community level, e.g., reducing resource consumption and waste generation, avoiding toxic substances, and organizing community awareness initiatives. However, some theorists have sought to move away from rationalist models of behavioral modification, towards holistic pedagogical initiatives that seek to develop action competence. In light of the global push towards achievement of the Sustainable Development Goals (SDGs), emerging evidence suggests that education initiatives should foster action competence so students may be equipped to contribute to sustainable development as part of their education. The UNESCO Education for Sustainable Development (ESD) Roadmap 2030 has also identified key priority areas to strengthen ESD in formal curricula. This article reports two informal environmental education initiatives for promoting action competence and pro-environmental behaviors in school-aged children. The authors recommend that formal education settings (e.g., schools) should incorporate self-directed, free-choice project-based learning to augment environmental education programs and promote students’ action competence for contribution to attainment of SDGs. To this end, we propose a Free-Choice Project-based Learning for Action Competence in Sustainable Development (ACiSD) Curriculum, comprising six implementation dimensions, namely: (1) project duration and teaming arrangements, (2) topic selection, (3) student support, (4) teacher support, (5) learning environments, and (6) digital access and equity. For each implementation dimension, we recommend action steps to help educators implement this curriculum in their own educational settings, with the aid of an illustrative worked example.
The University of Me... arrow_drop_down The University of Melbourne: Digital RepositoryArticle . 2022License: CC BYFull-Text: http://hdl.handle.net/11343/324347Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su142316315&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!
more_vert The University of Me... arrow_drop_down The University of Melbourne: Digital RepositoryArticle . 2022License: CC BYFull-Text: http://hdl.handle.net/11343/324347Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su142316315&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV Authors: James Helal; André Stephan; Robert H. Crawford;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enbuild.2023.113462&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Copernicus GmbH Funded by:EC | GENIE, EC | RESCUE, EC | ESM2025EC| GENIE ,EC| RESCUE ,EC| ESM2025Matthew J. Gidden; Thomas Gasser; Giacomo Grassi; Nicklas Forsell; Iris Janssens; William F. Lamb; Jan Minx; Zebedee Nicholls; Jan Steinhauser; Keywan Riahi;Global mitigation pathways play a critical role in informing climate policies and targets that are in line with international climate goals. However, it is not possible to directly compare modelled results with national inventories used to assess progress under the UNFCCC due to differences in how land-based fluxes are accounted for.National inventories consider carbon flux on managed land using an area-based approach with managed land-areas determined by nations. Emissions scenarios consider a different managed land area and are calibrated against data from detailed global carbon cycle models that account for natural (indirect) and anthropogenic (direct) fluxes separately by design. To disentangle the direct and indirect components of land-based carbon fluxes, we use a reduced complexity climate model with explicit treatment of the land-use sector, OSCAR, one of the models used by the Global Carbon Project. We find the discrepancy between model and NGHGI-based accounting methods globally to be 4.4 ± 1.0 Gt CO2 yr-1 averaged over the 2000-2020 time period, which is in line with existing estimates. We then apply OSCAR to the set of pathways assessed by the IPCC to quantify how this gap evolves over time and estimate how key mitigation benchmarks change.Across both 1.5°C and 2°C scenarios, LULUCF emissions pathways aligned with NGHGI accounting practices show a strong increase in the total land sink until around mid-century. However, the ‘NGHGI alignment gap’  decreases over this period, converging in the 2050-2060s for 1.5°C scenarios and 2070s-2080s for 2°C scenarios. The convergence is primarily a result of the simulated stabilization and then decrease of the CO2-fertilization effect as well as background climate warming reducing the overall effectiveness of the land sink, which in turn reduces the indirect removals considered by NGHGIs. These dynamics lead to land-based emissions reversing their downward trend in most NGHGI-aligned scenarios by mid-century, and result in the LULUCF sector becoming a net-source of emissions by 2100 in about 25% of both 1.5°C and 2°C scenarios.Assessing emission pathways using LULUCF definitions from national inventory accounting results in downward revisions to emissions benchmarks derived from scenarios. NGHGI-aligned pathways result in earlier net-zero CO2 emissions by around 2-5 years for both 1.5°C and 2°C scenarios, and 2030 emission reductions relative to 2020 are enhanced by about 5 percentage points for both pathway categories. When incorporating the additional land removals considered by NGHGIs, the assessed cumulative net CO2 emissions to global net-zero CO2 also decreases systematically by 15-18% for both 1.5°C and 2°C scenarios.We find that increasing removals from direct fluxes in 1.5C scenarios overtake estimated removals using NGHGI conventions in the near term. However, by midcentury, the strengthening of direct removals is balanced by weakening of indirect removals, meaning that, on average, carbon removal on land accounted for using NGHGI conventions in 1.5C scenarios results in about half of the LULUCF removals in current policy scenarios. We discuss the implications of our results for future Global Stocktakes and market mechanisms under the Paris Agreement.
IIASA DARE arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/egusphere-egu24-218&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 26 citations 26 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert IIASA DARE arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/egusphere-egu24-218&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 New Zealand, AustraliaPublisher:MDPI AG Vasiliki Summerson; Claudia Gonzalez Viejo; Alexis Pang; Damir D. Torrico; Sigfredo Fuentes;Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = −0.93), decanoic acid, ethyl ester (r = −0.94), and octanoic acid, 3-methylbutyl ester (r = −0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.
Lincoln University (... arrow_drop_down Lincoln University (New Zealand): Lincoln U Research ArchiveArticle . 2021License: CC BYFull-Text: https://doi.org/10.3390/molecules26165108Data sources: Bielefeld Academic Search Engine (BASE)The University of Melbourne: Digital RepositoryArticle . 2021License: CC BYFull-Text: http://hdl.handle.net/11343/287783Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/molecules26165108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 19 citations 19 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Lincoln University (... arrow_drop_down Lincoln University (New Zealand): Lincoln U Research ArchiveArticle . 2021License: CC BYFull-Text: https://doi.org/10.3390/molecules26165108Data sources: Bielefeld Academic Search Engine (BASE)The University of Melbourne: Digital RepositoryArticle . 2021License: CC BYFull-Text: http://hdl.handle.net/11343/287783Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/molecules26165108&type=result"></script>'); --> </script>
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