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4 results for “input-output analysis”
Can green hydrogen drive economic transformation in Saudi Arabia? - An input-output analysis of different Power-to-X configurations. Supplementary Data
<p>Supplementary material for peer review</p> <ul> <li>Modelling Data (input & results)</li> <li>Literature Review</li> </ul>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture
<p>The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.</p>
Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) Database
<p>A commonly used method to examine the relationship between global water consumption and production is input--output analysis. However, between approximately 70% and 90% of freshwater consumption occurs in agricultural primary production, which is often represented by only a small percentage of the total number of sectors in input-output databases. In addition, the assessment of the impact of water consumption is usually carried out at the national level.</p> <p><br> Therefore, the primary objective of the Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) approach was to improve assessments of water use and its impacts in input-output analysis.</p> <p><br> To achieve this objective, a global spatial model of agricultural primary production <em>MapSPAM</em> (IFPRI, 2019) was integrated into the existing input-output database <em>GLORIA</em> (Lenzen et al., 2017, 2021) via prorating. The resulting IO-GHAAPP approach includes (1) a disaggregated input-output database and novel environmental extensions for freshwater consumption and scarcity. The IO-GHAAPP database consists of 150 categories and 164 regions, resulting in a total of 24,600 region-category combinations. Forty-two of the categories are dedicated to agricultural primary production (28%). In comparison, the source input--output data consist of 120 categories and 164 regions, resulting in a total of 19,680 region-category combinations, of which 14 are dedicated to agricultural primary production (12%).</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Bunsen, Jonas, Vlad Coroamă, and Matthias Finkbeiner. 2023. ‘Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) Database’. <em>Sustainability</em> 15 (2). <a href="https://doi.org/10.3390/su15129351">https://doi.org/10.3390/su15129351</a>.</p> <p> </p> <p><strong>References:</strong></p> <ul> <li>IFPRI. 2019. ‘Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0’. Harvard Dataverse. <a href="https://doi.org/10.7910/DVN/PRFF8V">https://doi.org/10.7910/DVN/PRFF8V</a>.</li> <li>Lenzen, Manfred, Arne Geschke, Muhammad Daaniyall Abd Rahman, Yanyan Xiao, Jacob Fry, Rachel Reyes, Erik Dietzenbacher, et al. 2017. ‘The Global MRIO Lab - Charting the World Economy’. <em>Economic Systems Research</em> 29 (2): 158–86. <a href="https://doi.org/10.1080/09535314.2017.1301887">https://doi.org/10.1080/09535314.2017.1301887</a>.</li> <li>Lenzen, Manfred, Arne Geschke, James West, Jacob Fry, Arunima Malik, Stefan Giljum, Llorenç Milà i Canals, et al. 2021. ‘Implementing the Material Footprint to Measure Progress towards Sustainable Development Goals 8 and 12’. <em>Nature Sustainability</em>, December. <a href="https://doi.org/10.1038/s41893-021-00811-6">https://doi.org/10.1038/s41893-021-00811-6</a>.</li> </ul>
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