FUME projection data

# FUME data Population data and migration flows from FUME projections ## Update 1.0.0 Major corrections of the projections ## Update 0.3.0 - Corrected error in No Migration scenario data - Added IGC (Intensifying Global Competition) scenario - Updated scenario names ## Update 0.2.0 Updated input rat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Bucaro, Orlando Olaya, Kluge, Lucas, KC, Samir, Schewe, Jacob
Format: Dataset
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Bucaro, Orlando Olaya
Kluge, Lucas
KC, Samir
Schewe, Jacob
description # FUME data Population data and migration flows from FUME projections ## Update 1.0.0 Major corrections of the projections ## Update 0.3.0 - Corrected error in No Migration scenario data - Added IGC (Intensifying Global Competition) scenario - Updated scenario names ## Update 0.2.0 Updated input rates ## Introduction International projection model with dimensions Age, Sex, Education and Country of Birth. Projected from 2015 to 2050, four different scenarios; Benchmark, Ukraine War, Recovery in Europe and Rise of the East. Additional scenario with no migration also included. Benchmark scenario: Identical to SSP2 from Koch & Leimbach (2022), including COVID shock but not Ukraine war. Ukraine War (previously called Short-War) scenario: Same as the benchmark scenario but we use the latest IMF GDP estimates till the year 2027 which include the Ukraine war. Afterwards, a linear transition over 1 years back to SSP2 growth rates is implemented. Exception: Ukraine is estimated to have a 35% decrease in GDP in 2022 and the IMF does not provide estimates for the following year. Using a linear transition to SSP2 growth rates till 2028 would reduce Ukraines GDP very drastically. Instead we assume that in 2023 Ukraine has another 17.5% GDP decrease and in 2024 the growth is zero. Afterwards (2025 and ongoing), we estimate Ukraine to again follow the SSP2 growth rates. Recovery in Europe (Scenario B): The same as the Ukraine War scenario till 2027 but afterwards European countries will transition towards the SSP with highest growth rates and developing countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. Rise of the East (Scenario C): The same as the Ukraine War scenario till 2027 but afterwards developing countries will transition towards the SSP with highest growth rates and European countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. IGC (Intensifying Global Competition): The same as the Ukraine War scenario till 2027 but afterwards every country with a projected GDPc less than 15000$ in 2040 will linearly transition to 15000$ in 2040 and afterwards keep on growing with SSP2 growth rates. All countries with a GDPc higher than 15000$ will behave the same as in the short-war scenario. This “catch-up” behaviour of poorer countries is inspired by the “CAP” and “FAIR” GDP scenarios in Bodirsky et al., 2022. No Migration: Same as Benchmark scena
doi_str_mv 10.5281/zenodo.7033061
format Dataset
fullrecord <record><control><sourceid>datacite_PQ8</sourceid><recordid>TN_cdi_datacite_primary_10_5281_zenodo_7033061</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_5281_zenodo_7033061</sourcerecordid><originalsourceid>FETCH-LOGICAL-d791-c2cc44ded16cdcd171169d94a772b423cde8c909dde31f9aaa415e952873b6653</originalsourceid><addsrcrecordid>eNotzj0PgjAUheEuDkbdjDN_AOxtS0tHQ_xKNC44N_XekmCUEmTRX69Gp7O952FsATzLRQHLV2gjxcxwKbmGMZtvzsd10vXxGnBoYpuQH_yUjWp_e4TZfyes2qyrcpceTtt9uTqkZCykKBCVokCgkZDAAGhLVnljxEUJiRQKtNwSBQm19d4ryIP9MIy8aJ3LCct-2e8nNkNwXd_cff90wN1X635a99fKNztqOBM</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>dataset</recordtype></control><display><type>dataset</type><title>FUME projection data</title><source>DataCite</source><creator>Bucaro, Orlando Olaya ; Kluge, Lucas ; KC, Samir ; Schewe, Jacob</creator><creatorcontrib>Bucaro, Orlando Olaya ; Kluge, Lucas ; KC, Samir ; Schewe, Jacob</creatorcontrib><description># FUME data Population data and migration flows from FUME projections ## Update 1.0.0 Major corrections of the projections ## Update 0.3.0 - Corrected error in No Migration scenario data - Added IGC (Intensifying Global Competition) scenario - Updated scenario names ## Update 0.2.0 Updated input rates ## Introduction International projection model with dimensions Age, Sex, Education and Country of Birth. Projected from 2015 to 2050, four different scenarios; Benchmark, Ukraine War, Recovery in Europe and Rise of the East. Additional scenario with no migration also included. Benchmark scenario: Identical to SSP2 from Koch &amp; Leimbach (2022), including COVID shock but not Ukraine war. Ukraine War (previously called Short-War) scenario: Same as the benchmark scenario but we use the latest IMF GDP estimates till the year 2027 which include the Ukraine war. Afterwards, a linear transition over 1 years back to SSP2 growth rates is implemented. Exception: Ukraine is estimated to have a 35% decrease in GDP in 2022 and the IMF does not provide estimates for the following year. Using a linear transition to SSP2 growth rates till 2028 would reduce Ukraines GDP very drastically. Instead we assume that in 2023 Ukraine has another 17.5% GDP decrease and in 2024 the growth is zero. Afterwards (2025 and ongoing), we estimate Ukraine to again follow the SSP2 growth rates. Recovery in Europe (Scenario B): The same as the Ukraine War scenario till 2027 but afterwards European countries will transition towards the SSP with highest growth rates and developing countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. Rise of the East (Scenario C): The same as the Ukraine War scenario till 2027 but afterwards developing countries will transition towards the SSP with highest growth rates and European countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. IGC (Intensifying Global Competition): The same as the Ukraine War scenario till 2027 but afterwards every country with a projected GDPc less than 15000$ in 2040 will linearly transition to 15000$ in 2040 and afterwards keep on growing with SSP2 growth rates. All countries with a GDPc higher than 15000$ will behave the same as in the short-war scenario. This “catch-up” behaviour of poorer countries is inspired by the “CAP” and “FAIR” GDP scenarios in Bodirsky et al., 2022. No Migration: Same as Benchmark scenario but with no international migration. ## Variables ### Population data period: Start year of projection step dest: Country of residency / Migration destination country CoB: Country of Birth area: ISO3 numeric country code of destination age: Age, five year groups, 0 - 100+ edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper Secondary, e6 = Post Secondary) sex: Sex, two categories pop: Population ### Migration rate data specific variable names POB: Place Of Birth (Country of Birth) Orig: Country of origin Dest: Country of destination flow: Migration rate Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+)) age: Age groups (1 (0-24), 2 (25-64), 3 (65+)) flowM: Male specific migration rate flowF: Female specific migration rate ## Countries Countries currently included in the model are in total 171 (given in ISO3 country codes): ``` "AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU" "SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA" "BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE" "CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA" "ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY" "HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ" "KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR" "MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS" "NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI" "PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM" "SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN" "TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF" "ZMB" "ZWE" ```</description><identifier>DOI: 10.5281/zenodo.7033061</identifier><language>eng</language><publisher>Zenodo</publisher><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-9847-1374</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.5281/zenodo.7033061$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Bucaro, Orlando Olaya</creatorcontrib><creatorcontrib>Kluge, Lucas</creatorcontrib><creatorcontrib>KC, Samir</creatorcontrib><creatorcontrib>Schewe, Jacob</creatorcontrib><title>FUME projection data</title><description># FUME data Population data and migration flows from FUME projections ## Update 1.0.0 Major corrections of the projections ## Update 0.3.0 - Corrected error in No Migration scenario data - Added IGC (Intensifying Global Competition) scenario - Updated scenario names ## Update 0.2.0 Updated input rates ## Introduction International projection model with dimensions Age, Sex, Education and Country of Birth. Projected from 2015 to 2050, four different scenarios; Benchmark, Ukraine War, Recovery in Europe and Rise of the East. Additional scenario with no migration also included. Benchmark scenario: Identical to SSP2 from Koch &amp; Leimbach (2022), including COVID shock but not Ukraine war. Ukraine War (previously called Short-War) scenario: Same as the benchmark scenario but we use the latest IMF GDP estimates till the year 2027 which include the Ukraine war. Afterwards, a linear transition over 1 years back to SSP2 growth rates is implemented. Exception: Ukraine is estimated to have a 35% decrease in GDP in 2022 and the IMF does not provide estimates for the following year. Using a linear transition to SSP2 growth rates till 2028 would reduce Ukraines GDP very drastically. Instead we assume that in 2023 Ukraine has another 17.5% GDP decrease and in 2024 the growth is zero. Afterwards (2025 and ongoing), we estimate Ukraine to again follow the SSP2 growth rates. Recovery in Europe (Scenario B): The same as the Ukraine War scenario till 2027 but afterwards European countries will transition towards the SSP with highest growth rates and developing countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. Rise of the East (Scenario C): The same as the Ukraine War scenario till 2027 but afterwards developing countries will transition towards the SSP with highest growth rates and European countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. IGC (Intensifying Global Competition): The same as the Ukraine War scenario till 2027 but afterwards every country with a projected GDPc less than 15000$ in 2040 will linearly transition to 15000$ in 2040 and afterwards keep on growing with SSP2 growth rates. All countries with a GDPc higher than 15000$ will behave the same as in the short-war scenario. This “catch-up” behaviour of poorer countries is inspired by the “CAP” and “FAIR” GDP scenarios in Bodirsky et al., 2022. No Migration: Same as Benchmark scenario but with no international migration. ## Variables ### Population data period: Start year of projection step dest: Country of residency / Migration destination country CoB: Country of Birth area: ISO3 numeric country code of destination age: Age, five year groups, 0 - 100+ edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper Secondary, e6 = Post Secondary) sex: Sex, two categories pop: Population ### Migration rate data specific variable names POB: Place Of Birth (Country of Birth) Orig: Country of origin Dest: Country of destination flow: Migration rate Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+)) age: Age groups (1 (0-24), 2 (25-64), 3 (65+)) flowM: Male specific migration rate flowF: Female specific migration rate ## Countries Countries currently included in the model are in total 171 (given in ISO3 country codes): ``` "AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU" "SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA" "BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE" "CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA" "ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY" "HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ" "KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR" "MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS" "NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI" "PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM" "SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN" "TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF" "ZMB" "ZWE" ```</description><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2023</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNotzj0PgjAUheEuDkbdjDN_AOxtS0tHQ_xKNC44N_XekmCUEmTRX69Gp7O952FsATzLRQHLV2gjxcxwKbmGMZtvzsd10vXxGnBoYpuQH_yUjWp_e4TZfyes2qyrcpceTtt9uTqkZCykKBCVokCgkZDAAGhLVnljxEUJiRQKtNwSBQm19d4ryIP9MIy8aJ3LCct-2e8nNkNwXd_cff90wN1X635a99fKNztqOBM</recordid><startdate>20230620</startdate><enddate>20230620</enddate><creator>Bucaro, Orlando Olaya</creator><creator>Kluge, Lucas</creator><creator>KC, Samir</creator><creator>Schewe, Jacob</creator><general>Zenodo</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0000-0002-9847-1374</orcidid></search><sort><creationdate>20230620</creationdate><title>FUME projection data</title><author>Bucaro, Orlando Olaya ; Kluge, Lucas ; KC, Samir ; Schewe, Jacob</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d791-c2cc44ded16cdcd171169d94a772b423cde8c909dde31f9aaa415e952873b6653</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Bucaro, Orlando Olaya</creatorcontrib><creatorcontrib>Kluge, Lucas</creatorcontrib><creatorcontrib>KC, Samir</creatorcontrib><creatorcontrib>Schewe, Jacob</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bucaro, Orlando Olaya</au><au>Kluge, Lucas</au><au>KC, Samir</au><au>Schewe, Jacob</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>FUME projection data</title><date>2023-06-20</date><risdate>2023</risdate><abstract># FUME data Population data and migration flows from FUME projections ## Update 1.0.0 Major corrections of the projections ## Update 0.3.0 - Corrected error in No Migration scenario data - Added IGC (Intensifying Global Competition) scenario - Updated scenario names ## Update 0.2.0 Updated input rates ## Introduction International projection model with dimensions Age, Sex, Education and Country of Birth. Projected from 2015 to 2050, four different scenarios; Benchmark, Ukraine War, Recovery in Europe and Rise of the East. Additional scenario with no migration also included. Benchmark scenario: Identical to SSP2 from Koch &amp; Leimbach (2022), including COVID shock but not Ukraine war. Ukraine War (previously called Short-War) scenario: Same as the benchmark scenario but we use the latest IMF GDP estimates till the year 2027 which include the Ukraine war. Afterwards, a linear transition over 1 years back to SSP2 growth rates is implemented. Exception: Ukraine is estimated to have a 35% decrease in GDP in 2022 and the IMF does not provide estimates for the following year. Using a linear transition to SSP2 growth rates till 2028 would reduce Ukraines GDP very drastically. Instead we assume that in 2023 Ukraine has another 17.5% GDP decrease and in 2024 the growth is zero. Afterwards (2025 and ongoing), we estimate Ukraine to again follow the SSP2 growth rates. Recovery in Europe (Scenario B): The same as the Ukraine War scenario till 2027 but afterwards European countries will transition towards the SSP with highest growth rates and developing countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. Rise of the East (Scenario C): The same as the Ukraine War scenario till 2027 but afterwards developing countries will transition towards the SSP with highest growth rates and European countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2. IGC (Intensifying Global Competition): The same as the Ukraine War scenario till 2027 but afterwards every country with a projected GDPc less than 15000$ in 2040 will linearly transition to 15000$ in 2040 and afterwards keep on growing with SSP2 growth rates. All countries with a GDPc higher than 15000$ will behave the same as in the short-war scenario. This “catch-up” behaviour of poorer countries is inspired by the “CAP” and “FAIR” GDP scenarios in Bodirsky et al., 2022. No Migration: Same as Benchmark scenario but with no international migration. ## Variables ### Population data period: Start year of projection step dest: Country of residency / Migration destination country CoB: Country of Birth area: ISO3 numeric country code of destination age: Age, five year groups, 0 - 100+ edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper Secondary, e6 = Post Secondary) sex: Sex, two categories pop: Population ### Migration rate data specific variable names POB: Place Of Birth (Country of Birth) Orig: Country of origin Dest: Country of destination flow: Migration rate Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+)) age: Age groups (1 (0-24), 2 (25-64), 3 (65+)) flowM: Male specific migration rate flowF: Female specific migration rate ## Countries Countries currently included in the model are in total 171 (given in ISO3 country codes): ``` "AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU" "SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA" "BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE" "CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA" "ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY" "HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ" "KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR" "MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS" "NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI" "PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM" "SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN" "TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF" "ZMB" "ZWE" ```</abstract><pub>Zenodo</pub><doi>10.5281/zenodo.7033061</doi><orcidid>https://orcid.org/0000-0002-9847-1374</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.5281/zenodo.7033061
ispartof
issn
language eng
recordid cdi_datacite_primary_10_5281_zenodo_7033061
source DataCite
title FUME projection data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T09%3A14%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-datacite_PQ8&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.au=Bucaro,%20Orlando%20Olaya&rft.date=2023-06-20&rft_id=info:doi/10.5281/zenodo.7033061&rft_dat=%3Cdatacite_PQ8%3E10_5281_zenodo_7033061%3C/datacite_PQ8%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true