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  • 1
    Publication Date: 2023-08-26
    Keywords: D11810#1; D177; Density, wet bulk; DEPTH, sediment/rock; Discovery (1962); Multi-Sensor Core Logger; PC; Piston corer; Susceptibility; Velocity, compressional wave
    Type: Dataset
    Format: text/tab-separated-values, 1926 data points
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  • 2
    Publication Date: 2023-08-26
    Keywords: D11957#5; D187; Density, wet bulk; DEPTH, sediment/rock; Discovery (1962); Multi-Sensor Core Logger; PC; Piston corer; Susceptibility; Velocity, compressional wave
    Type: Dataset
    Format: text/tab-separated-values, 925 data points
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  • 3
    Publication Date: 2023-08-26
    Keywords: D11958#6; D187; Density, wet bulk; DEPTH, sediment/rock; Discovery (1962); Multi-Sensor Core Logger; PC; Piston corer; Susceptibility; Velocity, compressional wave
    Type: Dataset
    Format: text/tab-separated-values, 2512 data points
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  • 4
    Publication Date: 2023-08-26
    Keywords: D12616; D205; Density, wet bulk; DEPTH, sediment/rock; Discovery (1962); Multi-Sensor Core Logger; PC; Piston corer; Susceptibility; Velocity, compressional wave
    Type: Dataset
    Format: text/tab-separated-values, 1087 data points
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  • 5
    Publication Date: 2023-08-26
    Keywords: D11810#1; D177; Description; Discovery (1962); File format; File size; ORDINAL NUMBER; PC; Piston corer; Principal investigator; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 6
    Publication Date: 2023-08-26
    Keywords: D11957#5; D187; Description; Discovery (1962); File format; File size; ORDINAL NUMBER; PC; Piston corer; Principal investigator; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 7
    Publication Date: 2023-08-26
    Keywords: D12616; D205; Description; Discovery (1962); File format; File size; ORDINAL NUMBER; PC; Piston corer; Principal investigator; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 8
    Publication Date: 2023-08-26
    Keywords: D11958#6; D187; Description; Discovery (1962); File format; File size; ORDINAL NUMBER; PC; Piston corer; Principal investigator; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 9
    Publication Date: 2015-02-01
    Description: The potential greenhouse gas benefits of displacing fossil energy with biofuels are driving policy development in the absence of complete information. The potential carbon neutrality of forest biomass is a source of considerable scientific debate because of the complexity of dynamic forest ecosystems, varied feedstock types, and multiple energy production pathways. The lack of scientific consensus leaves decision makers struggling with contradicting technical advice. Analyzing previously published studies, our goal was to identify and prioritize those attributes of bioenergy greenhouse gas (GHG) emissions analysis that are most influential on length of carbon payback period. We investigated outcomes of 59 previously published forest biomass greenhouse gas emissions research studies published between 1991 and 2014. We identified attributes for each study and classified study cases by attributes. Using Classification and Regression Tree analysis, we identified those attributes that are strong predictors of carbon payback period (e.g. the time required by the forest to recover through sequestration the carbon dioxide from biomass combusted for energy). The inclusion of wildfire dynamics proved to be the most influential in determining carbon payback period length compared to other factors such as feedstock type, baseline choice, and the incorporation of leakage calculations. Additionally we demonstrate that evaluation criteria consistency is required to facilitate equitable comparison between projects. For carbon payback period calculations to provide operational insights to decision makers, future research should focus on creating common accounting principles for the most influential factors including temporal scale, natural disturbances, system boundaries, GHG emission metrics, and baselines. This article is protected by copyright. All rights reserved.
    Print ISSN: 1757-1693
    Electronic ISSN: 1757-1707
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Wiley
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  • 10
    Publication Date: 2012-04-15
    Description: In the current debate over the CO 2 emissions implications of switching from fossil fuel energy sources to include a substantial amount of woody biomass energy, many scientists and policy makers hold the view that emissions from the two sources should not be equated. Their rationale is that the combustion or decay of woody biomass is simply part of the global cycle of biogenic carbon and does not increase the amount of carbon in circulation. This view is frequently presented as justification to implement policies that encourage the substitution of fossil fuel energy sources with biomass. We present the opinion that this is an inappropriate conceptual basis to assess the atmospheric greenhouse gas (GHG) accounting of woody biomass energy generation. While there are many other environmental, social, and economic reasons to move to woody biomass energy, we argue that the inferred benefits of biogenic emissions over fossil fuel emissions should be reconsidered.
    Print ISSN: 1757-1693
    Electronic ISSN: 1757-1707
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Wiley
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