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18 results for “Environmental footprints”
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
Environmental Footprints of Greater Melbourne
<p>Environmental footprints of Greater Melbourne, 2021</p><p>Primary data sources: EXIOBASE MR EE SUT/IOT; ABS Australian National Accounts; ABS State Accounts; ABS Household Expenditure Survey (HES)</p><p>For more information on approach and methodology, contact <a href="mailto:research@opencorridor.org">research@opencorridor.org</a></p>
Data for - EU's bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol
<p>To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU’s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU’s “advanced bioethanol” potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector’s energy consumption. EU’s advanced bioethanol has a land footprint of 0.28 m<sup>2</sup> MJ<sup>−1</sup> for wheat straw and 0.18 m<sup>2</sup> MJ<sup>−1</sup> for maize stover. The average water footprint of advanced bioethanol is 173 L MJ<sup>−1</sup> for wheat straw and 113 L MJ<sup>−1</sup> for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO<sub>2</sub>eq MJ<sup>−1</sup> for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU’s advanced bioethanol production potential is insufficient to achieve EU’s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.</p>
Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.
<p>Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.:</p> <p><br> - Trait-based distances calculated for each pair of taxa;</p> <p>- CWM, CWM(LN) values, and their difference (CWMDIFF)</p> <p>Refer to the published articles for further details.</p>
Fig. 9 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 9: Histogram of the Mantel test assessing the relationship between genetic and morphologic distance for Gobius niger. Sim: simulations; Frequency: frequency values of the correlation between the genetic and morphologic distances. The dot represents the original value of the correlation between the distance matrices.
Fig. 6 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 6: PCA of the morphological variables of Gobius niger (standard length, SL; body height, BH; head length, HL; snout length, SnL; eye diameter, ED; first dorsal fin, DF1; second dorsal fin, DF2; anal fin, AF; pectoral fin, PF; ventral fin, VF) with projection of phenotypic groups. PC1 vs. PC2 and PC2 vs. PC3. The percentage of variation explained by each PC axis is given within parentheses.
Fig. 3 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 3: Cluster analysis associated with the similarity profile test (SIMPROF), based on abundances of Gobius niger, reveals reciprocal relations among the 20 sampled stations in the Marchica Lagoon using the Bray–Curtis distance.
Fig. 2 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 2: Picture of Gobius niger from the Marchica Lagoon showing the main measurements taken: total length (TL), standard length (SL), head length (LT), snout length (SnL), body height (BH), and eye diameter (ED).
Fig. 7 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 7: Linear regression of the principal component score axis (PC1) from morphometric measurements on the log standard length of Gobius niger with projection of phenotypic groups.
Fig. 8 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 8: Haplotype network constructed from 16S rDNA sequences of Gobius niger. The size of a particular circle reflects the haplotype frequency. The numbers indicate the nodes.
Fig. 1 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 1: Map showing the geographical localization of the Marchica Lagoon and the sampling stations of Gobius niger.
Fig. 4 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 4: Two-dimensional redundancy analysis (RDA) ordination representing the spatial distribution of Gobius niger related to the predictor variables selected through the best linear models based on distance (DISTLM). SM: suspended matter.
Fig. 5 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 5: Spatial and temporal distribution of Gobius niger in the Marchica Lagoon.
The Legacy Environmental Footprints of Manufactured Capital
<p>To enhance the clarity and coherence of the statement, you may consider revising it as follows:</p> <p>By utilizing the data and Matlab scripts made available here, one can produce all the visual components featured in the article entitled "The Legacy Environmental Footprint of Manufactured Capital." This comprehensive work is the result of collaborative research among various distinguished scholars and prestigious institutions, namely:</p> <p>Ranran Wang<sup>1*</sup>, Edgar G. Hertwich<sup>2*</sup>, Tomer Fishman<sup>1</sup>, Sebastiaan Deetman<sup>1</sup>, Paul Behrens<sup>1</sup>, Wei-qiang Chen<sup>3</sup>, Arjan de Koning<sup>1</sup>, Ming Xu<sup>4</sup>, Kira Matus<sup>5</sup>, Hauke Ward<sup>1</sup>, Arnold Tukker<sup>1</sup>, Julie B. Zimmerman<sup>6</sup></p> <p><sup>1</sup>Institute of Environmental Sciences (CML), Leiden University; Leiden, The Netherlands.</p> <p><sup>2</sup>Department of Energy and Process Engineering, Norwegian University of Science and Technology; Trondheim, Norway.</p> <p><sup>3</sup>Institute of Urban Environment, Chinese Academy of Sciences; Xiamen, China.</p> <p><sup>4</sup>School of Environment, Tsinghua University; Beijing, China.</p> <p><sup>5</sup>Division of Public Policy, Hongkong University of Science and Technology; Hong Kong, China.</p> <p><sup>6</sup>School of the Environment, Yale University; New Haven, United States.</p> <p>Here is the Abstract of the article:</p> <p>The foundations of today's societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production-consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical materials extractions, greenhouse gas (GHG) emissions, and health impacts accrued in today's manufactured capital. We show that between 1995-2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed and less-developed economies while economic returns from global asset supply chains disproportionately flow to developed economies, resulting in a double burden for less-developed economies. Our results show ensuring best-practice in asset production while prioritizing wellbeing outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation.</p>
Datasets for "Levelling foods for priority micronutrient value can provide more meaningful environmental footprint comparisons"
<p>Supplementary Data Tables for the article "Levelling foods for priority micronutrient value can provide more meaningful environmental footprint comparison".</p>
Carbon Footprint Assessment for Robotic, Laparoscopic and Open Colorectal Operations to Enhance Environmental Sustainability
ClinicalTrials.gov study NCT06844604. IPD Sharing: NO. Countries: 1. Publications: 3.
Play Sustainaball: An environmental footprint for an MLB team season
Open the record for dataset details and reuse information.
Compendium of Environmental Sustainability Indicator Collections: 2006 National Footprint Accounts (NFA)
The 2006 National Footprint Accounts (NFA) portion of the Compendium of Environmental Sustainability Indicator Collections, version 1.1 is a data set that measures how much land and water area a human population requires to produce the resources it consumes and to absorb its wastes under prevailing technology and management. It includes Footprints for cropland, grazing land, carbon, nuclear, forest, built-up and fishing ground for 147 countries. It also identifies countries that are considered to have ecological deficits and reserves. These data are drawn from the National Footprint Accounts, 2006 Edition, produced by the Global Footprint Network and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.