text:
, INT J LIFE CYCLE ASS, V18, P127, DOI 10.1007/s11367-012-0442-7.
   Ernst and Young, 2006, GLOB EX.
   ETI, 2015, LIV WAG GLOB SUPPL C.
   European Communities, 2008, SYST NAT ACC.
   FAOSTAT, 2015, FOOD BALANCE SHEETS, P1990.
   Glickman Lawrence, 1999, LIVING WAGE AM WORKE.
   Hosseinijou SA, 2014, INT J LIFE CYCLE ASS, V19, P620, DOI 10.1007/s11367-013-0658-1.
   ILO, 2011, WORK COND LAWS DAT I.
   Jorgensen A, 2009, INT J LIFE CYCLE ASS, V14, P204, DOI 10.1007/s11367-009-0073-9.
   Kalecki M, 1960, COLLECTED WORKS M KA, P3.
   Kondo Y, 2004, INT J LIFE CYCLE ASS, V9, P236, DOI 10.1007/BF02978599.
   Labour Behind the Label, 2015, LIV WAG.
   Lee S., 2007, WORKING TIME WORLD T.
   Li M, 2008, IND RELAT J, V39, P296, DOI 10.1111/j.1468-2338.2008.00489.x.
   Mair S., 2016, BETTER RATHER MORE E.
   Mair S, 2017, HDB SUSTAINABILITY S.
   Mair S, 2016, J CLEAN PROD, V132, P57, DOI 10.1016/j.jclepro.2015.08.082.
   McMullen A., 2014, TAILORED WAGES.
   Merk J, 2009, GLOBALIZATION WORKP, P429.
   Miller D, 2009, GLOB SOC POLICY, V9, P99, DOI 10.1177/1468018108100400.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   Neugebauer S, 2014, SUSTAINABILITY-BASEL, V6, P4839, DOI 10.3390/su6084839.
   New Look, 2011, NEW LOOK SUBM 2011 U.
   ONeill J., 2001, 66 GOLDM SACHS.
   Pickles J, 2012, EC SOCIAL UPGRADING.
   Pollin R, 2004, CAMBRIDGE J ECON, V28, P153, DOI 10.1093/cje/28.2.153.
   PWC, 2014, SOC SEC SYST GLOB.
   Rivoli P., 2006, TRAVELS T SHIRT GLOB.
   Roos S, 2016, J CLEAN PROD, V133, P691, DOI 10.1016/j.jclepro.2016.05.146.
   Scherman M, 2015, SUSTAINABILITY IS NO.
   Simas MS, 2014, SUSTAINABILITY-BASEL, V6, P7514, DOI 10.3390/su6117514.
   Tokatli N, 2011, URBAN STUD, V48, P1201, DOI 10.1177/0042098010370629.
   Traverso M, 2018, INT J LIFE CYCLE ASS, V23, P597, DOI 10.1007/s11367-016-1168-8.
   Umair S, 2015, RESOUR CONSERV RECY, V95, P46, DOI 10.1016/j.resconrec.2014.11.008.
   UNEP, 2009, GUID SOC LIF CYCL AS.
   Vaughan-Whitehead Daniel, 2010, FAIR WAGES STRENGTHE.
   Wang SW, 2016, INT J LIFE CYCLE ASS, V21, P1514, DOI 10.1007/s11367-016-1114-9.
   WHO, 2003, WHO TECHN REP SER.
   World Food Programme, 2015, WHAT IS HUNG.
   {[}WRC Workers Rights Consortium], 2005, GLOB WORKPL 219, P219.
   Xu Z, 2015, REV RADICAL POL ECON, V47, P446, DOI 10.1177/0486613414542780.
   Zamani B, 2016, INT J LIFE CYCLE ASS, DOI 10.1007/s11367-016-1113-x.}},
Number-of-Cited-References = {{60}},
Times-Cited = {{1}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{8}},
Journal-ISO = {{Int. J. Life Cycle Assess.}},
Doc-Delivery-Number = {{GQ6CM}},
Unique-ID = {{ISI:000441789600012}},
OA = {{Other Gold, Green Accepted, Green Published}},
DA = {{2020-08-06}},
}

@article{ ISI:000442067100068,
Author = {Lampiris, Georgios and Karelakis, Christos and Loizou, Efstratios},
Title = {{Evaluation of the impacts of CAP policy measures on a local economy: The
   case of a Greek region}},
Journal = {{LAND USE POLICY}},
Year = {{2018}},
Volume = {{77}},
Pages = {{745-751}},
Month = {{SEP}},
Abstract = {{The amendments of the 2014-2020 Common Agricultural Policy (CAP) have
   brought about significant changes to the European farming systems. These
   changes have an additional impact on the Greek economy and agriculture,
   within a period of ongoing recession. The present study attempts to
   estimate economy wide potential impacts that the implementation of the
   reformed CAP may induce on a regional economy, employing Input-Output
   analysis. An endeavor is made to analyze and evaluate the impact of
   Pillar II measures on output, income and employment levels of the Greek
   region of Central Macedonia for the period of 2014-2020. A regional
   Input-Output model was constructed employing the GRIT along with the FLQ
   formula for the regionalization procedure. The results indicate that the
   implementation of Pillar II measures has a weak positive impact on the
   regional economy, both on its output and income levels.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Karelakis, C (Corresponding Author), Democritus Univ Thrace, Sch Agr \& Forestry Sci, Dept Agr Dev, Lab Agr Policy \& Cooperat, Xanthi, Greece.
   Lampiris, Georgios; Karelakis, Christos, Democritus Univ Thrace, Sch Agr \& Forestry Sci, Dept Agr Dev, Lab Agr Policy \& Cooperat, Xanthi, Greece.
   Loizou, Efstratios, IUNG, Dept Bioecon \& Syst Anal, Pulawy, Poland.
   Loizou, Efstratios, Technol Educ Inst TEI Western Macedonia, Dept Agr Technol, Div Agr Econ, Kila, Kila Kozanis, Greece.}},
DOI = {{10.1016/j.landusepol.2018.06.019}},
ISSN = {{0264-8377}},
EISSN = {{1873-5754}},
Keywords = {{CAP; Rural development; Impacts; I-O model; Regional economy}},
Keywords-Plus = {{INPUT-OUTPUT TABLES; RURAL-DEVELOPMENT; LOCATION QUOTIENTS; MARINE
   SECTOR; FLQ FORMULA; MULTIPLIERS; URBAN; COEFFICIENTS; SCOTLAND; AREAS}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Environmental Studies}},
Author-Email = {{lampiris@sch.gr
   chkarel@agro.duth.gr
   lstratos@agro.auth.gr}},
ResearcherID-Numbers = {{Karelakis, Christos/AAF-2873-2020
   Loizou, Efstratios/S-4313-2019}},
ORCID-Numbers = {{Karelakis, Christos/0000-0002-4426-7044
   Loizou, Efstratios/0000-0002-9779-0226}},
Cited-References = {{Balamou E., 2009, 113 EAAE SEM ROL KNO.
   Ballas D, 2006, J RURAL STUD, V22, P367, DOI 10.1016/j.jrurstud.2006.01.002.
   Banouei A., 2016, 24 IIOA C SEOUL KOR.
   Bednarikova Z, 2015, PRAGUE ECON PAP, V24, P416, DOI 10.18267/j.pep.545.
   Bergmann H., 2008, 48 ANN C GEWISOLA.
   Bess R., 2011, 50 SO REG SCI ASS C.
   Bonfiglio A., 2005, QUADERNO DIRICERCA, V226.
   Bonfiglio A, 2008, ECON SYST RES, V20, P243, DOI 10.1080/09535310802344315.
   Boster R. S., 1972, ANN REGIONAL SCI, V6, P35.
   Bournaris T, 2016, ENVIRON IMPACT ASSES, V57, P159, DOI 10.1016/j.eiar.2015.12.004.
   Bournaris T, 2014, LAND USE POLICY, V38, P1, DOI 10.1016/j.landusepol.2013.10.008.
   Bradley J, 2006, REG STUD, V40, P189, DOI 10.1080/00343400600600512.
   Ciobanu C, 2004, REG STUD, V38, P603, DOI 10.1080/003434042000240914.
   CZAMANSKI S, 1974, STUDY CLUSTERING IND.
   De Propris L, 2005, REG STUD, V39, P197, DOI 10.1080/003434005200059983.
   European Commission, 2013, OV CAP REF 2014 2020.
   Feser E, 2009, REG STUD, V43, P89, DOI 10.1080/00343400801968429.
   Flegg A., 2014, EC WORKING PAPER SER, P1406.
   Flegg AT, 2013, REG STUD, V47, P703, DOI 10.1080/00343404.2011.592138.
   Flegg AT, 1997, REG STUD, V31, P795, DOI 10.1080/713693401.
   Flegg AT, 2000, REG STUD, V34, P563, DOI 10.1080/00343400050085675.
   Giannakis E., 2014, Agricultural Economics Review, V15, P48.
   Giannakis E., 2011, Agricultural Economics Review, V12, P81.
   Gigante R., 2014, ASSESSMENT CAP REFOR, DOI {[}10.1007/978-3-319-06635-620.2014, DOI 10.1007/978-3-319-06635-620.2014].
   GILCHRIST DA, 1994, REG SCI URBAN ECON, V24, P115, DOI 10.1016/0166-0462(94)90022-1.
   Harris RID, 1998, REG STUD, V32, P851, DOI 10.1080/00343409850118004.
   Hellenic Statistical Service (HELSTAT), 2017, NAT ACC ANN NAT ACC.
   Hill B, 2008, EUROCHOICES, V7, P28, DOI 10.1111/j.1746-692X.2008.00082.x.
   JENSEN RC, 1979, REGIONAL EC PLANNING.
   Jiang X., 2009, ENV PLAN, V42, P2508.
   JOHNS PM, 1987, J AGR ECON, V38, P243.
   Johnson TG, 2010, EUROCHOICES, V9, P30, DOI 10.1111/j.1746-692X.2010.00152.x.
   Kahouli S., 2017, CAN OFFSHORE WIND EN, DOI {[}10.1007/s10666-017-9580-4, DOI 10.1007/S10666-017-9580-4].
   Keating M, 2006, REG STUD, V40, P397, DOI 10.1080/00343400600632820.
   Kowalewksi J, 2015, REG STUD, V49, P240, DOI 10.1080/00343404.2013.766318.
   Kulisic B, 2007, ENERG POLICY, V35, P6036, DOI 10.1016/j.enpol.2007.08.025.
   Lamonica GR, 2018, PAP REG SCI, V97, P1169, DOI 10.1111/pirs.12297.
   Lehtonen O, 2014, REG STUD, V48, P382, DOI 10.1080/00343404.2012.657619.
   Loizou E, 2014, LAND USE POLICY, V38, P41, DOI 10.1016/j.landusepol.2013.10.017.
   Loizou S., 2000, INT ADV EC RES, V6, P373, DOI DOI 10.1007/BF02294958.
   Los B, 2004, PAP REG SCI, V83, P669, DOI 10.1007/s10110-004-0221-3.
   Malagon-Zaldua E, 2018, AGRICULTURE-BASEL, V8, DOI 10.3390/agriculture8010010.
   Manos B, 2013, INT J ENVIRON SUSTAI, V12, P103, DOI 10.1504/IJESD.2013.052938.
   Mattas K., 2005, 89 EAAE SEM 3 5 FEBR.
   Mattas K., 2010, P OECD WORKSH, P251.
   Mattas K., 2006, RURAL BALKANS EU INT, P5.
   Mattas K, 2017, EUROCHOICES, V16, P22, DOI 10.1111/1746-692X.12170.
   Mattas K, 2009, SPRINGER SER OPTIM A, V25, P273, DOI 10.1007/978-0-387-75181-8\_13.
   Midmore P, 2010, EUROCHOICES, V9, P24, DOI 10.1111/j.1746-692X.2010.00155.x.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   Morrissey K, 2014, OCEAN COAST MANAGE, V91, P58, DOI 10.1016/j.ocecoaman.2014.02.006.
   Morrissey K, 2013, MAR POLICY, V37, P230, DOI 10.1016/j.marpol.2012.05.004.
   Moulogianni C, 2015, J ENVIRON PROT ECOL, V16, P1447.
   Porter M.E., 1990, COMPETITIVE ADVANTAG.
   PORTER M. E., 1998, HARVARD BUSINESS SCH.
   Pouliakas K., 2007, FP6SSP2005006469 TER.
   Psaltopoulos D, 2004, EUR REV AGRIC ECON, V31, P149, DOI 10.1093/erae/31.2.149.
   PSALTOPOULOS D, 1993, J RURAL STUD, V9, P351, DOI 10.1016/0743-0167(93)90047-N.
   Psaltopoulos D., 2005, MODELLING EFFECTS TR, P14.
   Psaltopoulos D, 2006, J AGR ECON, V57, P441, DOI 10.1111/j.1477-9552.2006.00059.x.
   Psaltopoulos D, 2011, J POLICY MODEL, V33, P53, DOI 10.1016/j.jpolmod.2010.09.005.
   RICHARDSON HW, 1972, INPUT OUTPUT REGIONA.
   Roberts D, 1998, EUR REV AGRIC ECON, V25, P506, DOI 10.1093/erae/25.4.506.
   ROUND JI, 1978, J REGIONAL SCI, V18, P179, DOI 10.1111/j.1467-9787.1978.tb00540.x.
   Sanchez-Moral S, 2009, REG STUD, V43, P949, DOI 10.1080/00343400801932326.
   Shucksmith M., 2005, CAP REGIONS TERRITOR.
   Sila U., 2005, JB OSTERREICHISCHEN, V13, P121.
   Smaniotto Silveira T., 2015, RIV EC AGRARIA, V1, P33, DOI DOI 10.13128/REA-16976.
   Thomson KJ, 2005, FOREST POLICY ECON, V7, P515, DOI 10.1016/j.forpol.2003.07.005.
   Tohmo T, 2004, REG STUD, V38, P43, DOI 10.1080/00343400310001632262.
   van Kammen J, 2006, B WORLD HEALTH ORGAN, V84, P608, DOI 10.2471/BLT.05.028308.}},
Number-of-Cited-References = {{71}},
Times-Cited = {{3}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{8}},
Journal-ISO = {{Land Use Pol.}},
Doc-Delivery-Number = {{GQ9DW}},
Unique-ID = {{ISI:000442067100068}},
DA = {{2020-08-06}},
}

@article{ ISI:000440402800002,
Author = {Gerlitz, Lars and Steirou, Eva and Schneider, Christoph and Moron,
   Vincent and Vorogushyn, Sergiy and Merz, Bruno},
Title = {{Variability of the Cold Season Climate in Central Asia. Part I: Weather
   Types and Their Tropical and Extratropical Drivers}},
Journal = {{JOURNAL OF CLIMATE}},
Year = {{2018}},
Volume = {{31}},
Number = {{18}},
Pages = {{7185-7207}},
Month = {{SEP}},
Abstract = {{To understand the atmospheric mechanisms resulting in a pronounced cold
   season climate variability in central Asia, an objective weather-type
   classification is conducted, utilizing a k-means-based clustering
   approach applied to 500-hPa geopotential height (GPH) fields. Eight
   weather types (WT) are identified and analyzed with regard to
   characteristic pressure patterns and moisture fluxes over Eurasia and
   specific near-surface climate conditions over central Asia. To identify
   remote drivers of the central Asian climate, WT frequencies are analyzed
   for their relationships with tropical and extratropical teleconnection
   modes. The results indicate an influence of Northern Hemispheric
   planetary wave tracks on westerly moisture fluxes with positive
   anomalies of precipitation associated with the formation of a Rossby
   trough over central Asia. Particularly the propagation of the east
   Atlantic-western Russia and the Scandinavian patterns is shown to
   modulate regional climate conditions. Variations of ENSO are shown to
   affect the frequency of particular WTs because of the formation of an
   anticyclonic anomaly over the Indian Ocean and an increase of tropical
   fluxes of moisture and heat into central Asia during El Nino events.
   Further a WT internal influence of ENSO is distinctly defined, with
   enhanced moisture supply during the ENSO warm phase. The analysis of
   climatic trends shows that 50\% of observed temperature changes can be
   assigned to variations of the WT composition, indicating that most
   likely changing regional circulation characteristics account for the
   enhanced warming rates in central Asia. Trends of precipitation sums are
   likewise shown to be associated with changing WT frequencies, although
   the WT-precipitation relationships include large uncertainties.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Gerlitz, L (Corresponding Author), GFZ German Res Ctr Geosci, Dept Hydrol, Potsdam, Germany.
   Gerlitz, Lars; Steirou, Eva; Vorogushyn, Sergiy; Merz, Bruno, GFZ German Res Ctr Geosci, Dept Hydrol, Potsdam, Germany.
   Schneider, Christoph, Humboldt Univ, Inst Geog, Berlin, Germany.
   Moron, Vincent, Aix Marseille Univ, Coll France, CEREGE, INRA,CNRS,IRD, Aix En Provence, France.
   Moron, Vincent, Columbia Univ, Int Res Inst Climate \& Soc, Palisades, NY USA.}},
DOI = {{10.1175/JCLI-D-17-0715.1}},
ISSN = {{0894-8755}},
EISSN = {{1520-0442}},
Keywords = {{El Nino; Planetary waves; Climate variability; Interannual variability;
   Regional effects}},
Keywords-Plus = {{CENTRAL-SOUTHWEST ASIA; NORTH-ATLANTIC OSCILLATION; EURASIAN SNOW COVER;
   TIBETAN PLATEAU; INTERANNUAL VARIABILITY; ATMOSPHERIC CIRCULATION;
   SOUTHERN-OSCILLATION; WINTER CLIMATE; UNITED-STATES; ERA-INTERIM}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{lars.gerlitz@gfz-potsdam.de}},
ResearcherID-Numbers = {{Schneider, Christoph/V-2150-2017
   }},
ORCID-Numbers = {{Schneider, Christoph/0000-0002-9914-3217}},
Funding-Acknowledgement = {{German Federal Foreign Office as part of the German Water Initiative for
   Central Asia {[}AA7090002]}},
Funding-Text = {{This work was undertaken within the frame of the CAWa project
   (www.cawa-project.net) funded by the German Federal Foreign Office as
   part of the German Water Initiative for Central Asia (Grant AA7090002).}},
Cited-References = {{Allen RJ, 2011, J CLIMATE, V24, P6528, DOI 10.1175/2011JCLI4157.1.
   Apel H, 2018, HYDROL EARTH SYST SC, V22, P2225, DOI 10.5194/hess-22-2225-2018.
   Balsamo G, 2015, HYDROL EARTH SYST SC, V19, P389, DOI 10.5194/hess-19-389-2015.
   Bao XH, 2013, J CLIMATE, V26, P206, DOI 10.1175/JCLI-D-12-00056.1.
   Barlow M, 2002, J CLIMATE, V15, P697, DOI 10.1175/1520-0442(2002)015\<0697:DICASA\>2.0.CO;2.
   Barlow M, 2016, J CLIMATE, V29, P8547, DOI 10.1175/JCLI-D-13-00692.1.
   Barlow M, 2015, B AM METEOROL SOC, V96, pS71, DOI 10.1175/BAMS-D-15-00127.1.
   Barlow MA, 2008, J HYDROMETEOROL, V9, P1334, DOI 10.1175/2008JHM976.1.
   Barnett TP, 2005, NATURE, V438, P303, DOI 10.1038/nature04141.
   Bastos A, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms10315.
   Bhandari G., 2014, J I SCI TECHNOL, V19, P106, DOI {[}10. 3126/jist. v19i1. 13835, DOI 10.3126/JIST.V19I1.13835].
   Bohner J, 2006, BOREAS, V35, P279, DOI 10.1080/03009480500456073.
   Boer GJ, 2008, CLIM DYNAM, V31, P987, DOI 10.1007/s00382-008-0379-5.
   Bothe O, 2012, THEOR APPL CLIMATOL, V108, P293, DOI 10.1007/s00704-011-0538-1.
   Brands S, 2012, J CLIMATE, V25, P4023, DOI 10.1175/JCLI-D-12-00083.1.
   Bueh C, 2007, Q J ROY METEOR SOC, V133, P2117, DOI 10.1002/qj.173.
   Cassou C, 2004, J CLIMATE, V17, P1055, DOI 10.1175/1520-0442(2004)017<1055:NAWCRS>2.0.CO;2.
   Cohen J, 1999, GEOPHYS RES LETT, V26, P345, DOI 10.1029/1998GL900321.
   Cohen J, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL049626.
   Cohen JL, 2012, ENVIRON RES LETT, V7, DOI 10.1088/1748-9326/7/1/014007.
   Coleman JSM, 2007, J CLIMATE, V20, P3485, DOI 10.1175/JCLI4201.1.
   Copsey D, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027175.
   Czaja A, 2002, J CLIMATE, V15, P606, DOI 10.1175/1520-0442(2002)015<0606:OIOASA>2.0.CO;2.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Diday E., 1980, COMMUNICATION CYBERN, V10, P47.
   Dimri AP, 2013, GLOBAL PLANET CHANGE, V106, P39, DOI 10.1016/j.gloplacha.2013.03.002.
   Duethmann D, 2015, WATER RESOUR RES, V51, P4727, DOI 10.1002/2014WR016716.
   El Kenawy AM, 2014, FRONT ENV SCI-SWITZ, V2, DOI 10.3389/fenvs.2014.00037.
   Fahu Chen, 2009, Frontiers of Earth Science in China, V3, P42, DOI 10.1007/s11707-009-0013-9.
   Garcia-Serrano J., 2014, NAT GEOSCI, V7, pE2, DOI DOI 10.1038/NGEO2164.
   Gerlitz L, 2015, EARTH SYST DYNAM, V6, P61, DOI 10.5194/esd-6-61-2015.
   Gerlitz L, 2016, HYDROL EARTH SYST SC, V20, P4605, DOI 10.5194/hess-20-4605-2016.
   Giorgi F, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL025734.
   Golian S, 2015, THEOR APPL CLIMATOL, V119, P679, DOI 10.1007/s00704-014-1139-6.
   Handorf D, 2015, GEOPHYS RES LETT, V42, P2367, DOI 10.1002/2015GL063203.
   Hennig C, 2007, COMPUT STAT DATA AN, V52, P258, DOI 10.1016/j.csda.2006.11.025.
   Hoell A, 2014, CLIM DYNAM, V42, P3289, DOI 10.1007/s00382-013-1799-4.
   Iglesias I, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0086439.
   IPCC Climate Change, 2013, PHYS SCI BASIS, DOI {[}10.1017/CB09781107415324, DOI 10.1017/CB09781107415324, 10.1017/CBO9781107415324].
   Krichak SO, 2005, INT J CLIMATOL, V25, P183, DOI 10.1002/joc.1124.
   Lee CC, 2017, INT J CLIMATOL, V37, P3714, DOI 10.1002/joc.4950.
   Liu JP, 2012, P NATL ACAD SCI USA, V109, P4074, DOI 10.1073/pnas.1114910109.
   Liu WB, 2016, CLIM DYNAM, V46, P3481, DOI 10.1007/s00382-015-2782-z.
   Mariotti A, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030078.
   Marshall AG, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011737.
   Maussion F, 2014, J CLIMATE, V27, P1910, DOI 10.1175/JCLI-D-13-00282.1.
   MICHELANGELI PA, 1995, J ATMOS SCI, V52, P1237, DOI 10.1175/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2.
   Murawski A, 2018, J GEOPHYS RES-ATMOS, V123, P1562, DOI 10.1002/2017JD026654.
   Palazzi E, 2013, J GEOPHYS RES-ATMOS, V118, P85, DOI 10.1029/2012JD018697.
   Philipp A, 2010, PHYS CHEM EARTH, V35, P360, DOI 10.1016/j.pce.2009.12.010.
   Roghani R, 2016, THEOR APPL CLIMATOL, V124, P411, DOI 10.1007/s00704-015-1423-0.
   Roller CD, 2016, J CLIMATE, V29, P2963, DOI 10.1175/JCLI-D-15-0274.1.
   Schar C, 2004, J HYDROMETEOROL, V5, P959, DOI 10.1175/1525-7541(2004)005<0959:SRFUPF>2.0.CO;2.
   Schiemann R, 2008, INT J CLIMATOL, V28, P295, DOI 10.1002/joc.1532.
   Schiemann R, 2009, J CLIMATE, V22, P2940, DOI 10.1175/2008JCLI2625.1.
   Shepherd TG, 2014, NAT GEOSCI, V7, P703, DOI 10.1038/NGEO2253.
   Sheridan SC, 2003, INT J CLIMATOL, V23, P27, DOI 10.1002/joc.863.
   Shirvani A, 2016, INT J CLIMATOL, V36, P1887, DOI 10.1002/joc.4467.
   Smith TM, 2003, J CLIMATE, V16, P1495, DOI 10.1175/1520-0442-16.10.1495.
   Sohn BJ, 2013, CLIM DYNAM, V40, P1721, DOI 10.1007/s00382-012-1484-z.
   Sutton RT, 2000, ATMOS SCI LETT, V1, P89, DOI 10.1006/asle.2000.0018.
   Syed FS, 2006, THEOR APPL CLIMATOL, V86, P147, DOI 10.1007/s00704-005-0217-1.
   Syed FS, 2010, INT J CLIMATOL, V30, P220, DOI 10.1002/joc.1887.
   Terray P, 2005, J CLIMATE, V18, P1351, DOI 10.1175/JCLI3338.1.
   Trigo RM, 2010, AGR FOREST METEOROL, V150, P1245, DOI 10.1016/j.agrformet.2010.05.006.
   Wang AH, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016553.
   Wulf H, 2010, GEOMORPHOLOGY, V118, P13, DOI 10.1016/j.geomorph.2009.12.003.
   Yadav RK, 2010, J CLIMATE, V23, P1979, DOI 10.1175/2009JCLI3202.1.
   Yin ZY, 2014, J CLIMATE, V27, P7830, DOI 10.1175/JCLI-D-14-00052.1.}},
Number-of-Cited-References = {{69}},
Times-Cited = {{7}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{20}},
Journal-ISO = {{J. Clim.}},
Doc-Delivery-Number = {{GO9CX}},
Unique-ID = {{ISI:000440402800002}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000438003200045,
Author = {Duan, Yuwan and Jiang, Xuemei},
Title = {{Visualizing the change of embodied CO2 emissions along global production
   chains}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{194}},
Pages = {{499-514}},
Month = {{SEP 1}},
Abstract = {{Network analysis has become one of the most widely used tools for
   visualizing the major inter-country/inter-industry CO2 emission flows
   along the global production chains. Based on the World Input-Output
   Database (WIOD), we estimate worldwide CO2 emission flows across
   economies from 1995 to 2011, and visualize the major flows and their
   temporal changes through network analysis. More specifically, we adapt
   the recognition of crucial nodes in network, and present four types of
   networks: networks based on absolute size and relative size of CO2
   emission flows for four individual years (1995, 2000, 2007, and 2011)
   and networks based on absolute change and relative change of CO2
   emission flows for three sub periods (1995-2000, 2000-2007, and
   2007-2011). The results highlight the different roles economies play in
   worldwide CO2 trade networks and their temporary changes over time.
   While ``large{''} economies such as the U.S. and China were often
   recognized as the major importers of worldwide CO2 emissions, the very
   rapid growth rates of a series of ``medium{''} and ``small{''}
   economies, such as Turkey, Mexico, and Cyprus, in CO2 imports throughout
   the entire period of 1995-2011 are not negligible. The global warming
   mitigation requires joint inputs from ``large{''} economies as well as
   ``medium{''} and ``small{''} economies. (C) 2018 Elsevier Ltd. All
   rights reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Jiang, XM (Corresponding Author), Capital Univ Econ \& Business, Sch Econ, 121 Zhang Jia Lu Kou, Beijing 100070, Peoples R China.
   Duan, Yuwan, Cent Univ Finance \& Econ, Sch Int Trade \& Econ, Beijing, Peoples R China.
   Jiang, Xuemei, Capital Univ Econ \& Business, Sch Econ, 121 Zhang Jia Lu Kou, Beijing 100070, Peoples R China.}},
DOI = {{10.1016/j.jclepro.2018.05.133}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Embodied CO2 emissions flows; Global production chain; Network analysis}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; INTERNATIONAL OIL TRADE; ENVIRONMENTAL IMPACTS;
   NETWORK PERSPECTIVE; POLLUTION TERMS; SOCIAL NETWORK; CONSUMPTION;
   CHINA; SECTORS; GROWTH}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{jiangxuem@gmail.com}},
ResearcherID-Numbers = {{Duan, Yuwan/AAP-3781-2020
   }},
ORCID-Numbers = {{?, ??/0000-0002-5639-0453}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71473246]; Natural Science Foundation of
   BeijingBeijing Natural Science Foundation {[}9172006]}},
Funding-Text = {{This work was supported by the National Natural Science Foundation of
   China {[}grant numbers 71473246] and Natural Science Foundation of
   Beijing {[}grant number: 9172006]. The authors thank the Editor and two
   anonymous referees for their constructive suggestions. Yuwan Duan thanks
   Xiaohai He for his excellent research assistance.}},
Cited-References = {{AHMAD N, 2003, 15 OECD SCI.
   Amador J, 2017, WORLD ECON, V40, P1291, DOI 10.1111/twec.12469.
   Antras P., 2017, DP12063 CEPR.
   Chen B, 2018, APPL ENERG, V210, P98, DOI 10.1016/j.apenergy.2017.10.113.
   Chen ZM, 2011, ENERG POLICY, V39, P2899, DOI 10.1016/j.enpol.2011.02.068.
   Cingolani I., 2018, INT EC, V153, P11.
   de Andrade RL, 2018, PHYSICA A, V491, P249, DOI 10.1016/j.physa.2017.08.126.
   Du RJ, 2017, APPL ENERG, V196, P142, DOI 10.1016/j.apenergy.2016.12.042.
   Duan YW, 2017, ECOL ECON, V132, P31, DOI 10.1016/j.ecolecon.2016.10.001.
   Fan Y, 2014, ECON MODEL, V39, P71, DOI 10.1016/j.econmod.2014.02.027.
   Feng KS, 2013, P NATL ACAD SCI USA, V110, P11654, DOI 10.1073/pnas.1219918110.
   Fernandez-Amador O, 2016, ECOL ECON, V125, P14, DOI 10.1016/j.ecolecon.2016.01.005.
   Genty A., 2012, TECHNICAL REPORT.
   Grether JM, 2013, J DEV ECON, V100, P19, DOI 10.1016/j.jdeveco.2012.06.007.
   Hanaka T, 2017, ENERG ECON, V68, P44, DOI 10.1016/j.eneco.2017.09.012.
   Hubacek K, 2014, J IND ECOL, V18, P7, DOI 10.1111/jiec.12082.
   Jiang XM, 2017, ENERG POLICY, V109, P734, DOI 10.1016/j.enpol.2017.07.058.
   Jiang XM, 2016, APPL ENERG, V184, P1132, DOI 10.1016/j.apenergy.2016.06.142.
   Jiang XM, 2015, J SYST SCI COMPLEX, V28, P907, DOI 10.1007/s11424-015-2152-2.
   Kagawa S, 2015, GLOBAL ENVIRON CHANG, V35, P486, DOI 10.1016/j.gloenvcha.2015.04.003.
   Kanemoto K, 2014, GLOBAL ENVIRON CHANG, V24, P52, DOI 10.1016/j.gloenvcha.2013.09.008.
   Kitamura T, 2017, APPL ENERG, V208, P361, DOI 10.1016/j.apenergy.2017.10.028.
   Li ZL, 2017, J CLEAN PROD, V162, P61, DOI 10.1016/j.jclepro.2017.05.200.
   Liang S, 2016, PHYSICA A, V452, P311, DOI 10.1016/j.physa.2016.01.090.
   Liu Z, 2013, NATURE, V500, P143, DOI 10.1038/500143a.
   Los B, 2015, J REGIONAL SCI, V55, P66, DOI 10.1111/jors.12121.
   Moran DD, 2013, ECOL ECON, V89, P177, DOI 10.1016/j.ecolecon.2013.02.013.
   Mozner ZV, 2013, J CLEAN PROD, V42, P83, DOI 10.1016/j.jclepro.2012.10.014.
   Peng SJ, 2016, ECOL ECON, V128, P147, DOI 10.1016/j.ecolecon.2016.04.020.
   Peters GP, 2011, P NATL ACAD SCI USA, V108, P8903, DOI 10.1073/pnas.1006388108.
   Prell C, 2016, SOC SCI J, V53, P111, DOI 10.1016/j.soscij.2015.08.003.
   Su B, 2013, ECOL ECON, V88, P119, DOI 10.1016/j.ecolecon.2013.01.017.
   Su B, 2010, ECOL ECON, V70, P10, DOI 10.1016/j.ecolecon.2010.08.016.
   Sun QR, 2017, PHYSICA A, V482, P345, DOI 10.1016/j.physa.2017.04.047.
   Timmer MP, 2015, REV INT ECON, V23, P575, DOI 10.1111/roie.12178.
   Timmer MP, 2014, J ECON PERSPECT, V28, P99, DOI 10.1257/jep.28.2.99.
   Timmer MP, 2013, ECON POLICY, V28, P613, DOI 10.1111/1468-0327.12018.
   Tsekeris T, 2017, PHYSICA A, V488, P187, DOI 10.1016/j.physa.2017.06.019.
   Tukker A, 2016, GLOBAL ENVIRON CHANG, V40, P171, DOI 10.1016/j.gloenvcha.2016.07.002.
   Tukker A, 2013, ECON SYST RES, V25, P1, DOI 10.1080/09535314.2012.761179.
   Wakeel M, 2017, J CLEAN PROD, V142, P3322, DOI 10.1016/j.jclepro.2016.10.138.
   Wang SG, 2016, ENRGY PROCED, V104, P580, DOI 10.1016/j.egypro.2016.12.098.
   Wang Z., 2009, 200910C USITC EC.
   Wang Z, 2017, APPL ENERG, V206, P1544, DOI 10.1016/j.apenergy.2017.09.108.
   Wang Z, 2017, ECOL INDIC, V80, P114, DOI 10.1016/j.ecolind.2017.05.013.
   Wiebe K.S., 2016, OECD SCI TECHNOLOGY, DOI {[}10.1787/5jlrcm216xkl-en, DOI 10.1787/5JLRCM216XKL-EN].
   Wiebe KS, 2012, ECON SYST RES, V24, P113, DOI 10.1080/09535314.2011.643293.
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003.
   Xiao H, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0169549.
   Xu Y, 2014, ECOL ECON, V101, P10, DOI 10.1016/j.ecolecon.2014.02.015.
   Yang SY, 2016, APPL ENERG, V184, P882, DOI 10.1016/j.apenergy.2016.04.087.
   Zhang XH, 2017, PHYSICA A, V467, P380, DOI 10.1016/j.physa.2016.10.024.
   Zhang Y, 2018, J CLEAN PROD, V170, P34, DOI 10.1016/j.jclepro.2017.08.244.
   Zhong WQ, 2017, ENERG POLICY, V100, P365, DOI 10.1016/j.enpol.2016.07.025.}},
Number-of-Cited-References = {{54}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{3}},
Usage-Count-Since-2013 = {{52}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GM3KJ}},
Unique-ID = {{ISI:000438003200045}},
DA = {{2020-08-06}},
}

@article{ ISI:000437079300034,
Author = {Tao, Feng and Xu, Zhou and Duncan, Adrew A. and Xia, Xiaohua and Wu,
   Xiaofang and Li, Jinyi},
Title = {{Driving forces of energy embodied in China-EU manufacturing trade from
   1995 to 2011}},
Journal = {{RESOURCES CONSERVATION AND RECYCLING}},
Year = {{2018}},
Volume = {{136}},
Pages = {{324-334}},
Month = {{SEP}},
Abstract = {{In this study, an empirically validated Environmental Input-Output Life
   Cycle Assessment (EIO-LCA) model was applied to calculate and decompose
   the amount of energy embodied in the manufacturing trade between China
   and the European Union (EU) in 1995-2011. The main findings are as
   follows: China's entry to the WTO (World Trade Organization) in 2001 has
   spurred the growth of energy embodied in exports from China to the EU,
   while environmental policies issued by Chinese government since 2006
   have pulled it down. The export sectoral structure change and energy
   consumption intensity decrease are two key drivers of China's embodied
   energy export changes. China's embodied energy exports are mainly
   contributed by coal and other fossil fuels. Germany is the largest
   importer of China's embodied energy among the EU member states. This
   study can provide data support and reference basis for international
   trade and climate negotiations, and help Chinese government to improve
   its policies on industrial structure, primary energy structure and
   export state structure.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Xia, XH (Corresponding Author), Renmin Univ China, Sch Econ, Beijing 100872, Peoples R China.
   Tao, Feng; Xu, Zhou, Jinan Univ, Inst Ind Econ, Guangzhou 510632, Guangdong, Peoples R China.
   Duncan, Adrew A., St Norbert Coll, Div Social Sci, De Pere, WI 54115 USA.
   Xia, Xiaohua, Renmin Univ China, Sch Econ, Beijing 100872, Peoples R China.
   Xia, Xiaohua, Renmin Univ China, Inst Chinas Econ Reform \& Dev, Beijing 100872, Peoples R China.
   Wu, Xiaofang, Zhongnan Univ Econ \& Law, Econ Sch, Wuhan 430073, Hubei, Peoples R China.
   Li, Jinyi, Jinan Univ, Sch Management, Guangzhou 510632, Guangdong, Peoples R China.}},
DOI = {{10.1016/j.resconrec.2018.04.021}},
ISSN = {{0921-3449}},
EISSN = {{1879-0658}},
Keywords = {{Embodied energy; China-EU trade; EIO-LCA model; Driving forces}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; INTERNATIONAL-TRADE; US TRADE; EMISSIONS; CARBON}},
Research-Areas = {{Engineering; Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Engineering, Environmental; Environmental Sciences}},
Author-Email = {{xia.email@ruc.edu.cn}},
ResearcherID-Numbers = {{Wu, Xiaofang/O-7253-2014}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71333007, 71673277, 71673114]; Construction Funds
   of High Level Universities in Guangdong Province of China}},
Funding-Text = {{This work is supported by the National Natural Science Foundation of
   China (Grant Nos. 71333007, 71673277 and 71673114), and the Construction
   Funds of High Level Universities in Guangdong Province of China for
   ``applied economics and industrial transformation{''}.}},
Cited-References = {{Bin Chen, 2016, Energy, Ecology and Environment, V1, P1, DOI 10.1007/s40974-016-0017-8.
   Bordigoni M, 2012, ENERG POLICY, V43, P335, DOI 10.1016/j.enpol.2012.01.011.
   Chen B, 2018, APPL ENERG, V210, P98, DOI 10.1016/j.apenergy.2017.10.113.
   Chen B, 2017, J CLEAN PROD, V141, P1259, DOI 10.1016/j.jclepro.2016.09.200.
   Cortes-Borda D, 2015, ENERGY, V82, P578, DOI 10.1016/j.energy.2015.01.067.
   Cui LB, 2015, ENERGY, V82, P457, DOI 10.1016/j.energy.2015.01.056.
   Dente SMR, 2018, RESOUR CONSERV RECY, V133, P395, DOI 10.1016/j.resconrec.2017.12.011.
   Dietzenbacher E, 2013, ECON SYST RES, V25, P71, DOI 10.1080/09535314.2012.761180.
   Du HB, 2011, ENERG POLICY, V39, P5980, DOI 10.1016/j.enpol.2011.06.060.
   Gasim AA, 2015, ENERG POLICY, V86, P186, DOI 10.1016/j.enpol.2015.06.043.
   Genty A., 2012, FINAL DATABASE ENV S.
   Hendrickson C, 1998, ENVIRON SCI TECHNOL, V32, p184A, DOI 10.1021/es983471i.
   HENDRICKSON CT, 2006, ENV LIFE CYCLE ASSES.
   Hu Y, 2015, ENERGIES, V8, P9392, DOI 10.3390/en8099392.
   Ji SY, 2016, APPL ENERG, V180, P416, DOI 10.1016/j.apenergy.2016.07.114.
   Jiang XM, 2017, RESOUR CONSERV RECY, V120, P209, DOI 10.1016/j.resconrec.2016.12.013.
   Li H, 2007, ECOL ECON, V62, P136, DOI 10.1016/j.ecolecon.2006.06.007.
   Liu HT, 2010, ENERG POLICY, V38, P3957, DOI 10.1016/j.enpol.2010.03.019.
   Machado G, 2001, ECOL ECON, V39, P409, DOI 10.1016/S0921-8009(01)00230-0.
   Shao L, 2013, ECOL MODEL, V252, P63, DOI 10.1016/j.ecolmodel.2012.09.004.
   Su B, 2013, ENERG POLICY, V56, P83, DOI 10.1016/j.enpol.2013.01.041.
   Tang X, 2018, RESOUR CONSERV RECY, V129, P307, DOI 10.1016/j.resconrec.2016.07.018.
   Tang X, 2016, J CLEAN PROD, V134, P310, DOI 10.1016/j.jclepro.2015.08.122.
   Tang X, 2013, ENERG POLICY, V57, P418, DOI 10.1016/j.enpol.2013.02.009.
   Wiedmann T, 2009, ECOL ECON, V68, P1975, DOI 10.1016/j.ecolecon.2008.06.023.
   WYCKOFF AW, 1994, ENERG POLICY, V22, P187, DOI 10.1016/0301-4215(94)90158-9.
   Xia XH, 2014, ENERG POLICY, V66, P292, DOI 10.1016/j.enpol.2013.11.013.
   Xia XH, 2012, ENERG POLICY, V45, P449, DOI 10.1016/j.enpol.2012.02.055.
   Xia XH, 2011, ENERG POLICY, V39, P3520, DOI 10.1016/j.enpol.2011.03.051.
   Yang RR, 2014, ENERG POLICY, V72, P110, DOI 10.1016/j.enpol.2014.04.024.
   Zhang B, 2015, ECOL INFORM, V26, P101, DOI 10.1016/j.ecoinf.2014.01.009.
   Zhang B, 2011, ENERGIES, V4, P1897, DOI 10.3390/en4111897.
   Zhao YH, 2016, J CLEAN PROD, V131, P678, DOI 10.1016/j.jclepro.2016.04.114.}},
Number-of-Cited-References = {{33}},
Times-Cited = {{8}},
Usage-Count-Last-180-days = {{6}},
Usage-Count-Since-2013 = {{33}},
Journal-ISO = {{Resour. Conserv. Recycl.}},
Doc-Delivery-Number = {{GL3XW}},
Unique-ID = {{ISI:000437079300034}},
DA = {{2020-08-06}},
}

@article{ ISI:000443071400069,
Author = {Chakraborty, Abhijit and Kichikawa, Yuichi and Iino, Takashi and
   Iyetomi, Hiroshi and Inoue, Hiroyasu and Fujiwara, Yoshi and Aoyama,
   Hideaki},
Title = {{Hierarchical communities in the walnut structure of the Japanese
   production network}},
Journal = {{PLOS ONE}},
Year = {{2018}},
Volume = {{13}},
Number = {{8}},
Month = {{AUG 29}},
Abstract = {{This paper studies the structure of the Japanese production network,
   which includes one million firms and five million supplier-customer
   links. This study finds that this network forms a tightly-knit structure
   with a core giant strongly connected component (GSCC) surrounded by IN
   and OUT components constituting two half-shells of the GSCC, which we
   call awalnut structure because of its shape. The hierarchical structure
   of the communities is studied by the Infomap method, and most of the
   irreducible communities are found to be at the second level. The
   composition of some of the major communities, including overexpressions
   regarding their industrial or regional nature, and the connections that
   exist between the communities are studied in detail. The findings
   obtained here cause us to question the validity and accuracy of using
   the conventional input-output analysis, which is expected to be useful
   when firms in the same sectors are highly connected to each other.}},
Publisher = {{PUBLIC LIBRARY SCIENCE}},
Address = {{1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Chakraborty, A (Corresponding Author), Univ Hyogo, Grad Sch Simulat Studies, Kobe, Hyogo, Japan.
   Chakraborty, Abhijit; Inoue, Hiroyasu; Fujiwara, Yoshi, Univ Hyogo, Grad Sch Simulat Studies, Kobe, Hyogo, Japan.
   Kichikawa, Yuichi; Iino, Takashi; Iyetomi, Hiroshi, Niigata Univ, Fac Sci, Niigata, Japan.
   Aoyama, Hideaki, Kyoto Univ, Grad Sch Sci, Kyoto, Japan.}},
DOI = {{10.1371/journal.pone.0202739}},
Article-Number = {{e0202739}},
ISSN = {{1932-6203}},
Keywords-Plus = {{SEGREGATION}},
Research-Areas = {{Science \& Technology - Other Topics}},
Web-of-Science-Categories  = {{Multidisciplinary Sciences}},
Author-Email = {{abhiphyiitg@gmail.com}},
ORCID-Numbers = {{Chakraborty, Abhijit/0000-0001-5866-1985}},
Funding-Acknowledgement = {{Project ``Large-scale Simulation and Analysis of Economic Network for
   Macro Prudential Policy{''} undertaken at Research Institute of Economy,
   Trade and Industry (RIETI); MEXTMinistry of Education, Culture, Sports,
   Science and Technology, Japan (MEXT); JSPSMinistry of Education,
   Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for
   the Promotion of Science {[}25400393, 17H02041]; Kyoto University
   Supporting Program for Interaction-based Initiative Team Studies:
   SPIRITS, as part of the Program for Promoting the Enhancement of
   Research Universities, MEXT, JAPAN}},
Funding-Text = {{This study was supported in part by the Project ``Large-scale Simulation
   and Analysis of Economic Network for Macro Prudential Policy{''}
   undertaken at Research Institute of Economy, Trade and Industry (RIETI),
   MEXT as Exploratory Challenges on Post-K computer (Studies of
   Multi-level Spatiotemporal Simulation of Socioeconomic Phenomena),
   Grant-in-Aid for Scientific Research (KAKENHI) by JSPS Grant Numbers
   25400393, 17H02041 and the Kyoto University Supporting Program for
   Interaction-based Initiative Team Studies: SPIRITS, as part of the
   Program for Promoting the Enhancement of Research Universities, MEXT,
   JAPAN.}},
Cited-References = {{Abergel F., 2013, ECONOPHYSICS AGENT B.
   Aoyama H, 2017, MACRO ECONOPHYSICS N.
   Arata Y, 2017, INT WORKSH COMPL NET, P1100.
   Battiston S, 2012, SCI REP-UK, V2, DOI 10.1038/srep00541.
   Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9.
   Caiani A, 2016, EC HETEROGENEOUS INT.
   Chakraborty A, 2017, ARXIV170600203.
   Chen JC, 2006, BIOINFORMATICS, V22, P2283, DOI 10.1093/bioinformatics/btl370.
   Clauset A, 2009, SIAM REV, V51, P661, DOI 10.1137/070710111.
   Currarini S, 2009, ECONOMETRICA, V77, P1003, DOI 10.3982/ECTA7528.
   DRAGHICI S, 2003, DATA ANAL TOOLS DNA.
   Echenique F, 2007, Q J ECON, V122, P441, DOI 10.1162/qjec.122.2.441.
   Esquivel AV, 2011, PHYS REV X, V1, DOI 10.1103/PhysRevX.1.021025.
   Fortunato S, 2007, P NATL ACAD SCI USA, V104, P36, DOI 10.1073/pnas.0605965104.
   Fortunato S, 2010, PHYS REP, V486, P75, DOI 10.1016/j.physrep.2009.11.002.
   FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102.
   Fujiwara Y, 2010, EUR PHYS J B, V77, P565, DOI 10.1140/epjb/e2010-00275-2.
   Fujiwara Y, 2012, PROG THEOR PHYS SUPP, P158.
   Iino T, 2015, ADV JPN BUS ECON, V4, P39, DOI 10.1007/978-4-431-55390-8\_3.
   Kawamoto T, 2015, PHYS REV E, V91, DOI 10.1103/PhysRevE.91.012809.
   Krichene H, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0186467.
   Krishene H, 2018, APPL EXPONENTIAL RAN.
   Lancichinetti A, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.056117.
   Leontief WW, 1936, REV ECON STATISTICS, V18, P105, DOI 10.2307/1927837.
   Leskovec J, 2009, INTERNET MATH, V6, P29, DOI 10.1080/15427951.2009.10129177.
   Miller Jr. R.G., 1981, SIMULTANEOUS STATIST, P37.
   Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133.
   Onnela JP, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.056110.
   Rosvall M, 2008, P NATL ACAD SCI USA, V105, P1118, DOI 10.1073/pnas.0706851105.
   Rosvall M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018209.
   Tesfatsion L., 2006, HDB COMPUTATIONAL EC, V2.
   Tumminello M, 2011, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2011/01/P01019.}},
Number-of-Cited-References = {{32}},
Times-Cited = {{2}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{2}},
Journal-ISO = {{PLoS One}},
Doc-Delivery-Number = {{GR9JD}},
Unique-ID = {{ISI:000443071400069}},
OA = {{DOAJ Gold, Green Published}},
DA = {{2020-08-06}},
}

@article{ ISI:000445612500067,
Author = {Xue, Jiaqing and Sun, Cheng and Li, Jianping and Mao, Jiangyu},
Title = {{South Atlantic Forced Multidecadal Teleconnection to the Midlatitude
   South Indian Ocean}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{16}},
Pages = {{8480-8489}},
Month = {{AUG 28}},
Abstract = {{Sea surface temperature (SST) in the midlatitude south Indian Ocean
   (MSIO) exhibits prominent multidecadal fluctuations that have profound
   climate impacts for regions around the Indian Ocean. Observational
   analysis suggests that these multidecadal fluctuations can be explained
   by remote forcing from South Atlantic multidecadal variability. A suite
   of Atlantic Pacemaker experiments performs well in reproducing the
   observed MSIO SST multidecadal variation and its association with the
   South Atlantic. This transbasin covariability can be described by an
   atmospheric bridge mechanism, in which South Atlantic multidecadal
   variability-related SST warming weakens regional meridional circulation
   over the South Atlantic, suppressing convection over tropical Africa.
   The reduced diabatic heating then drives a cold atmospheric Kelvin wave
   that penetrates into the tropical Indian Ocean, leading to an anomalous
   cyclonic circulation there that weakens the westerlies over the MSIO and
   subsequently triggers the warming of MSIO. This initial warming is
   further amplified by local SST-water vapor positive feedback over the
   MSIO.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Li, JP (Corresponding Author), Qingdao Natl Lab Marine Sci \& Technol, Lab Reg Oceanog \& Numer Modeling, Qingdao, Peoples R China.
   Li, JP (Corresponding Author), Beijing Normal Univ, Coll Global Change \& Earth Syst Sci, Beijing, Peoples R China.
   Xue, Jiaqing; Mao, Jiangyu, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing, Peoples R China.
   Xue, Jiaqing; Li, Jianping, Qingdao Natl Lab Marine Sci \& Technol, Lab Reg Oceanog \& Numer Modeling, Qingdao, Peoples R China.
   Xue, Jiaqing, Univ Chinese Acad Sci, Coll Earth Sci, Beijing, Peoples R China.
   Sun, Cheng; Li, Jianping, Beijing Normal Univ, Coll Global Change \& Earth Syst Sci, Beijing, Peoples R China.}},
DOI = {{10.1029/2018GL078990}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords = {{South Atlantic multidecadal variability; the south Indian Ocean;
   teleconnection; atmospheric bridge; local air-sea feedback}},
Keywords-Plus = {{MERIDIONAL OVERTURNING CIRCULATION; SEA-SURFACE TEMPERATURE; ASIAN
   SUMMER MONSOON; CLIMATE VARIABILITY; SST VARIABILITY; ATMOSPHERIC
   CIRCULATION; PACIFIC OSCILLATION; DECADAL MODULATIONS; REANALYSIS
   PROJECT; AUSTRAL SUMMER}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{ljp@bnu.edu.cn}},
ResearcherID-Numbers = {{Mao, Jiangyu/AAJ-5420-2020
   Sun, Cheng/O-7918-2015
   Li, Jianping/I-2661-2012}},
ORCID-Numbers = {{Sun, Cheng/0000-0003-0474-7593
   Li, Jianping/0000-0003-0625-1575}},
Funding-Acknowledgement = {{National Key R\&D Program of China {[}2016YFA0601801]; National Natural
   Science Foundation of China (NSFC) ProjectNational Natural Science
   Foundation of China {[}41790474]; SOA International Cooperation Program
   on Global Change and Air-Sea Interactions {[}GASI-IPOVAI-03]; State Key
   Laboratory of Tropical Oceanography, South China Sea Institute of
   Oceanology, Chinese Academy of Sciences {[}LTO1801]}},
Funding-Text = {{We appreciate the anonymous reviewers for their helpful comments. This
   work was jointly sponsored by the National Key R\&D Program of China
   (2016YFA0601801), National Natural Science Foundation of China (NSFC)
   Project (41790474) and SOA International Cooperation Program on Global
   Change and Air-Sea Interactions (GASI-IPOVAI-03). C. Sun is supported by
   the State Key Laboratory of Tropical Oceanography, South China Sea
   Institute of Oceanology, Chinese Academy of Sciences (Project No.
   LTO1801). We thank Dr. Fred Kucharski at ICTP for providing the ICTPAGCM
   simulation data. The NCEP/NCAR and 20CRv2 reanalyses were provided by
   NOAA and are available at https://www.esrl.noaa.gov/psd/data/gridded/.
   Moreover, the HadISST data set was obtained from
   https://www.metoffice.gov.uk/hadobs/.}},
Cited-References = {{ALLAN RJ, 1995, J CLIMATE, V8, P1853, DOI 10.1175/1520-0442(1995)008<1853:MVITCS>2.0.CO;2.
   Biastoch A, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms10082.
   Cheung KKW, 2015, INT J CLIMATOL, V35, P1519, DOI 10.1002/joc.4073.
   Cole JE, 2000, SCIENCE, V287, P617, DOI 10.1126/science.287.5453.617.
   Compo GP, 2011, Q J ROY METEOR SOC, V137, P1, DOI 10.1002/qj.776.
   Dong L, 2016, SCI REP-UK, V6, DOI 10.1038/srep21251.
   Fauchereau N, 2003, INT J CLIMATOL, V23, P663, DOI 10.1002/joc.905.
   Feudale L, 2013, CLIM DYNAM, V41, P243, DOI 10.1007/s00382-013-1827-4.
   Han WQ, 2014, B AM METEOROL SOC, V95, P1679, DOI 10.1175/BAMS-D-13-00028.1.
   Hermes JC, 2005, J CLIMATE, V18, P2864, DOI 10.1175/JCLI3422.1.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Krishnamurthy L, 2016, CLIM DYNAM, V46, P57, DOI 10.1007/s00382-015-2568-3.
   Kucharski F, 2009, Q J ROY METEOR SOC, V135, P569, DOI 10.1002/qj.406.
   Kucharski F, 2016, CLIM DYNAM, V46, P2337, DOI 10.1007/s00382-015-2705-z.
   Kucharski F, 2016, ATMOSPHERE-BASEL, V7, DOI 10.3390/atmos7020029.
   Le Bars D, 2016, GEOPHYS RES LETT, V43, P2102, DOI 10.1002/2016GL068177.
   Lee SK, 2015, NAT GEOSCI, V8, P445, DOI 10.1038/NGEO2438.
   Li JP, 2013, GEOPHYS RES LETT, V40, P5497, DOI 10.1002/2013GL057877.
   Lopez H, 2016, GEOPHYS RES LETT, V43, P8250, DOI 10.1002/2016GL069067.
   Lopez H, 2016, J CLIMATE, V29, P1831, DOI 10.1175/JCLI-D-15-0491.1.
   Marshall J, 2012, NAT GEOSCI, V5, P171, DOI {[}10.1038/ngeo1391, 10.1038/NGEO1391].
   Morioka Y, 2017, J GEOPHYS RES-OCEANS, V122, P5611, DOI 10.1002/2017JC012706.
   Morioka Y, 2015, J CLIMATE, V28, P8695, DOI 10.1175/JCLI-D-15-0201.1.
   Morioka Y, 2014, J CLIMATE, V27, P4486, DOI 10.1175/JCLI-D-13-00506.1.
   Morioka Y, 2012, J CLIMATE, V25, P4029, DOI 10.1175/JCLI-D-11-00396.1.
   Nnamchi HC, 2016, J CLIMATE, V29, P7295, DOI 10.1175/JCLI-D-15-0894.1.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   Reason CJC, 1998, INT J CLIMATOL, V18, P391, DOI 10.1002/(SICI)1097-0088(19980330)18:4<391::AID-JOC253>3.0.CO;2-C.
   Reason CJC, 2000, TELLUS A, V52, P203, DOI 10.1034/j.1600-0870.2000.00978.x.
   Sun C, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15998.
   Sun C, 2017, CLIM DYNAM, V48, P3903, DOI 10.1007/s00382-016-3309-y.
   Sutton RT, 2007, J CLIMATE, V20, P891, DOI 10.1175/JCLI4038.1.
   Tozuka T, 2007, J CLIMATE, V20, P2881, DOI 10.1175/JCLI4168.1.
   Trenary LL, 2013, J GEOPHYS RES-OCEANS, V118, P381, DOI 10.1029/2012JC008317.
   Trenberth KE, 2013, EARTHS FUTURE, V1, P19, DOI 10.1002/2013EF000165.
   Venegas SA, 1998, J GEOPHYS RES-OCEANS, V103, P24723, DOI 10.1029/98JC02443.
   Wainer I, 2002, J CLIMATE, V15, P1408, DOI 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2.
   Wang F, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL042750.
   Xie SP, 2009, J CLIMATE, V22, P730, DOI 10.1175/2008JCLI2544.1.
   Xue F, 2001, ADV ATMOS SCI, V18, P567.
   Xue JQ, 2018, CLIM DYNAM, V50, P2687, DOI 10.1007/s00382-017-3764-0.
   Zhang HY, 2017, CLIM DYNAM, V48, P1125, DOI 10.1007/s00382-016-3131-6.}},
Number-of-Cited-References = {{42}},
Times-Cited = {{1}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{5}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GU8SO}},
Unique-ID = {{ISI:000445612500067}},
DA = {{2020-08-06}},
}

@article{ ISI:000445331900020,
Author = {Liu, Jianyu and Zhang, Yongqiang and Yang, Yuting and Gu, Xihui and
   Xiao, Mingzhong},
Title = {{Investigating Relationships Between Australian Flooding and Large-Scale
   Climate Indices and Possible Mechanism}},
Journal = {{JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}},
Year = {{2018}},
Volume = {{123}},
Number = {{16}},
Pages = {{8708-8723}},
Month = {{AUG 27}},
Abstract = {{Flooding is one of the most severe natural disasters in Australia. While
   flood spatial and temporal variability in the Australian continent has
   been well documented in the literature, the relationship between
   seasonal flood magnitude and frequency (FMF) and climatic driving forces
   influence, and the possible physical mechanism behind the relationship
   remain unclear. This study attempts to quantify relationships between
   four climatic indices (Nino-Southern Oscillation {[}ENSO], Indian Ocean
   Dipole {[}IOD], Interdecadal Pacific Oscillation {[}IPO], and Southern
   Annular Mode {[}SAM]) and seasonal FMF across 413 unregulated Australian
   catchments during 1975-2010. Results indicate that the climate indices
   have noticeable impacts on flood variability across Australia.
   Specifically, ENSO plays a dominant role in influencing summer and
   spring flood variability. However, IOD and IPO tend to have larger
   effects on autumn and winter floods, respectively. Relationships between
   the climate indices and FMF can be well interpreted by atmospheric
   circulation related to the dominant climate index. Our results provide
   relevant information for flood risk management and have implications for
   mitigating flood hazards.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhang, YQ (Corresponding Author), CSIRO Land \& Water, Canberra, ACT, Australia.
   Yang, YT (Corresponding Author), Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci \& Engn, Beijing, Peoples R China.
   Liu, Jianyu, China Univ Geosci, Sch Earth Sci, Lab Crit Zone Evolut, Wuhan, Hubei, Peoples R China.
   Liu, Jianyu; Zhang, Yongqiang, CSIRO Land \& Water, Canberra, ACT, Australia.
   Yang, Yuting, Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci \& Engn, Beijing, Peoples R China.
   Gu, Xihui, China Univ Geosci, Sch Environm Studies, Wuhan, Hubei, Peoples R China.
   Xiao, Mingzhong, Hohai Univ, Coll Hydrol \& Water Resources, Nanjing, Jiangsu, Peoples R China.}},
DOI = {{10.1029/2017JD028197}},
ISSN = {{2169-897X}},
EISSN = {{2169-8996}},
Keywords-Plus = {{NINO-SOUTHERN-OSCILLATION; INDIAN-OCEAN DIPOLE; RIVER-BASIN;
   NON-STATIONARITY; ANNULAR MODE; MULTIDECADAL VARIABILITY; ENSO
   TELECONNECTION; PACIFIC VARIABILITY; FREQUENCY-ANALYSIS; RESERVOIR
   INDEXES}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{yongqiang.zhang@csiro.au
   yyt.1986617@gmail.com}},
ResearcherID-Numbers = {{YONGQIANG, ZHANG/C-5708-2008
   Yang, Yuting/C-8527-2014
   Xiao, Mingzhong/C-4694-2014
   yang, yuting/Y-7885-2018}},
ORCID-Numbers = {{YONGQIANG, ZHANG/0000-0002-3562-2323
   Yang, Yuting/0000-0002-4573-1929
   Xiao, Mingzhong/0000-0002-9462-2577
   yang, yuting/0000-0002-4573-1929}},
Funding-Acknowledgement = {{Strategic Priority Research Program Grant of the Chinese Academy of
   Sciences {[}XDA19070402]; National Key Research and Development Program
   of China {[}2018YFA0605603]; Fundamental Research Funds for the Central
   Universities, China University of Geosciences (Wuhan)Fundamental
   Research Funds for the Central Universities {[}CUG180614]; International
   Program for Ph.D. Candidates, Sun Yat-Sen University}},
Funding-Text = {{This work is financially supported by the Strategic Priority Research
   Program Grant of the Chinese Academy of Sciences (Grant No.
   XDA19070402); the National Key Research and Development Program of China
   (Grant No. 2018YFA0605603); the Fundamental Research Funds for the
   Central Universities, China University of Geosciences (Wuhan; Grant No.
   CUG180614); and the International Program for Ph.D. Candidates, Sun
   Yat-Sen University. We thank Nick Potter for his comments and
   suggestions on this manuscript. Carol Zhang proofread the manuscript. We
   thank two anonymous reviewers for their constructive comments for
   improving the quality of our work. The streamflow data used in this
   study are available in
   https://publications.csiro.au/rpr/pub?pid=csiro:EP113194 and
   http://www.bom.gov.au/water/hrs/index.shtml. NCEP/NCAR reanalysis data
   and climate indices data can be downloaded from Earth System Research
   Laboratory
   (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived
   .surface.html; http://www.esrl.noaa.gov/psd/data/climateindices/list/).}},
Cited-References = {{Archfield SA, 2016, GEOPHYS RES LETT, V43, P10232, DOI 10.1002/2016GL070590.
   Brodie IM, 2016, HYDROLOG SCI J, V61, P2213, DOI 10.1080/02626667.2015.1099790.
   Cai W, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039902.
   Cai WJ, 2013, J CLIMATE, V26, P2796, DOI 10.1175/JCLI-D-12-00458.1.
   Cai WJ, 2012, J CLIMATE, V25, P6318, DOI 10.1175/JCLI-D-11-00501.1.
   Cai WJ, 2010, J CLIMATE, V23, P4944, DOI 10.1175/2010JCLI3501.1.
   Callaghan J, 2014, AUST METEOROL OCEAN, V64, P183, DOI 10.22499/2.6403.002.
   Cook BI, 2016, J GEOPHYS RES-ATMOS, V121, P12820, DOI 10.1002/2016JD024892.
   Eltahir EAB, 1996, REV GEOPHYS, V34, P367, DOI 10.1029/96RG01927.
   FitzGerald G, 2010, EMERG MED AUSTRALAS, V22, P180, DOI 10.1111/j.1742-6723.2010.01284.x.
   Franks SW, 2002, WATER RESOUR RES, V38, DOI 10.1029/2001WR000232.
   Gu XH, 2017, GLOBAL PLANET CHANGE, V149, P1, DOI 10.1016/j.gloplacha.2016.12.016.
   Gu X, 2016, GLOBAL PLANET CHANGE, V142, P1, DOI 10.1016/j.gloplacha.2016.04.004.
   Hendon HH, 2007, J CLIMATE, V20, P2452, DOI 10.1175/JCLI4134.1.
   Henley BJ, 2015, CLIM DYNAM, V45, P3077, DOI 10.1007/s00382-015-2525-1.
   Hirschboeck K. K., 1987, BINGHAMTON S GEOMORP, V18.
   Huang SZ, 2016, INT J CLIMATOL, V36, P280, DOI 10.1002/joc.4344.
   Ishak E., 2010, WORLD ENV WAT RES C, V2010, P115, DOI {[}10.1061/41114(371)14, DOI 10.1061/41114(371)14].
   Ishak EH, 2013, J HYDROL, V494, P134, DOI 10.1016/j.jhydrol.2013.04.021.
   Johnson F, 2016, CLIMATIC CHANGE, V139, P21, DOI 10.1007/s10584-016-1689-y.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Kendall M.G., 1975, RANK CORRELATION MET.
   Kiem AS, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2002GL015992.
   King AD, 2013, GEOPHYS RES LETT, V40, P2271, DOI 10.1002/grl.50427.
   Lang M, 1999, J HYDROL, V225, P103, DOI 10.1016/S0022-1694(99)00167-5.
   LINDESAY JA, 1991, INT J CLIMATOL, V11, P609.
   Liu JY, 2017, J HYDROL, V555, P744, DOI 10.1016/j.jhydrol.2017.10.072.
   Liu JY, 2017, HYDROLOG SCI J, V62, P1809, DOI 10.1080/02626667.2017.1349909.
   Lopez J, 2013, HYDROL EARTH SYST SC, V17, P3189, DOI 10.5194/hess-17-3189-2013.
   Mallakpour I, 2016, ADV WATER RESOUR, V92, P159, DOI 10.1016/j.advwatres.2016.04.008.
   Mann HB, 1945, ECONOMETRICA, V13, P245, DOI 10.2307/1907187.
   Marshall GJ, 2003, J CLIMATE, V16, P4134, DOI 10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2.
   McCullagh P., 1989, GEN LINEAR MODELS, DOI {[}10. 1007/978-1-4899-3242-6, DOI 10.1007/978-1-4899-3242-6].
   Meredith EP, 2015, NAT GEOSCI, V8, P615, DOI {[}10.1038/ngeo2483, 10.1038/NGEO2483].
   Messner F., 2016, FLOOD RISK MANAGEMEN, P149.
   Min SK, 2013, J GEOPHYS RES-ATMOS, V118, P643, DOI 10.1002/jgrd.50164.
   Nicholls N, 1989, J CLIMATE, V2, P965, DOI 10.1175/1520-0442(1989)002<0965:SSTAAW>2.0.CO;2.
   Noji E., 1995, WILDERNESS MED MANAG, P644.
   Pepler A, 2014, J CLIMATE, V27, P3816, DOI 10.1175/JCLI-D-13-00554.1.
   Power S, 1999, CLIM DYNAM, V15, P319, DOI 10.1007/s003820050284.
   Power S, 1998, AUST METEOROL MAG, V47, P85.
   Pui A, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009420.
   Pui A, 2012, MON WEATHER REV, V140, P1665, DOI 10.1175/MWR-D-11-00238.1.
   Rigby RA, 2005, J R STAT SOC C-APPL, V54, P507, DOI 10.1111/j.1467-9876.2005.00510.x.
   Risbey JS, 2009, MON WEATHER REV, V137, P3233, DOI 10.1175/2009MWR2861.1.
   Risko SL, 2014, J HYDROL, V519, P1130, DOI 10.1016/j.jhydrol.2014.08.043.
   Ropelewski C. F., 1987, MON WEA REV, V115, P985, DOI DOI 10.1175/1520-0493(1987)115<1606:GARSPP>2.0.00;2.
   Rustomji P, 2009, J HYDROL, V374, P196, DOI 10.1016/j.jhydrol.2009.06.017.
   Saji NH, 2003, CLIM RES, V25, P151, DOI 10.3354/cr025151.
   Singh J, 2016, GEOPHYS RES LETT, V43, P11269, DOI 10.1002/2016GL071238.
   Sivapalan M, 2005, WATER RESOUR RES, V41, DOI 10.1029/2004WR003439.
   Tarhule A, 2005, CLIMATIC CHANGE, V72, P355, DOI 10.1007/s10584-005-6792-4.
   Thompson DWJ, 2000, J CLIMATE, V13, P1000, DOI 10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.
   Ummenhofer CC, 2015, GEOPHYS RES LETT, V42, P9942, DOI 10.1002/2015GL065948.
   Verdon DC, 2004, WATER RESOUR RES, V40, DOI 10.1029/2004WR003234.
   Villarini G, 2016, ADV WATER RESOUR, V87, P80, DOI 10.1016/j.advwatres.2015.11.009.
   Villarini G, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR008395.
   Wang G, 2007, J CLIMATE, V20, P4211, DOI 10.1175/JCLI4228.1.
   Ward PJ, 2016, J HYDROL, V539, P358, DOI 10.1016/j.jhydrol.2016.05.045.
   Ward PJ, 2014, HYDROL EARTH SYST SC, V18, P47, DOI 10.5194/hess-18-47-2014.
   Ward PJ, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043215.
   Watterson IG, 2009, INT J CLIMATOL, V29, P809, DOI 10.1002/joc.1772.
   Westra S, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD005996.
   Wunsch C, 1999, B AM METEOROL SOC, V80, P245, DOI 10.1175/1520-0477(1999)080<0245:TIOSCR>2.0.CO;2.
   Xiao MZ, 2016, J HYDROL, V534, P397, DOI 10.1016/j.jhydrol.2016.01.012.
   Xiao MZ, 2015, INT J CLIMATOL, V35, P3556, DOI 10.1002/joc.4228.
   Yilmaz AG, 2017, INT J CLIMATOL, V37, P430, DOI 10.1002/joc.4716.
   Zhang Q, 2016, GLOBAL PLANET CHANGE, V139, P44, DOI 10.1016/j.gloplacha.2015.10.005.
   Zhang Q, 2016, J HYDROL, V533, P308, DOI 10.1016/j.jhydrol.2015.12.003.
   Zhang Q, 2015, J HYDROL, V527, P565, DOI 10.1016/j.jhydrol.2015.05.029.
   Zhang XS, 2016, HYDROL EARTH SYST SC, V20, P3947, DOI 10.5194/hess-20-3947-2016.
   Zhang Y, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0056732.
   Zhang YQ, 2009, WATER RESOUR RES, V45, DOI 10.1029/2008WR007504.
   Zhou YC, 2013, AGR FOREST METEOROL, V182, P332, DOI 10.1016/j.agrformet.2013.04.018.}},
Number-of-Cited-References = {{74}},
Times-Cited = {{6}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{25}},
Journal-ISO = {{J. Geophys. Res.-Atmos.}},
Doc-Delivery-Number = {{GU5NB}},
Unique-ID = {{ISI:000445331900020}},
DA = {{2020-08-06}},
}

@article{ ISI:000442739700001,
Author = {Vannitsem, Stephane and Ekelmans, Pierre},
Title = {{Causal dependences between the coupled ocean-atmosphere dynamics over
   the tropical Pacific, the North Pacific and the North Atlantic}},
Journal = {{EARTH SYSTEM DYNAMICS}},
Year = {{2018}},
Volume = {{9}},
Number = {{3}},
Pages = {{1063-1083}},
Month = {{AUG 27}},
Abstract = {{The causal dependences (in a dynamical sense) between the dynamics of
   three different coupled ocean-atmosphere basins, the North Atlantic, the
   North Pacific and the tropical Pacific region (Nino3.4), have been
   explored using data from three reanalysis datasets, namely ORA-20C,
   ORAS4 and ERA-20C. The approach is based on convergent cross mapping
   (CCM) developed by Sugihara et al. (2012) that allows for evaluating the
   dependences between variables beyond the classical teleconnection
   patterns based on correlations.
   The use of CCM on these data mostly reveals that (i) the tropical
   Pacific (Nino3.4 region) only influences the dynamics of the North
   Atlantic region through its annual climatological cycle; (ii) the
   atmosphere over the North Pacific is dynamically forcing the North
   Atlantic on a monthly basis; (iii) on longer timescales (interannual),
   the dynamics of the North Pacific and the North Atlantic are influencing
   each other through the ocean dynamics, suggesting a connection through
   the thermohaline circulation.
   These findings shed a new light on the coupling between these three
   different regions of the globe. In particular, they call for a deep
   reassessment of the way teleconnections are interpreted and for a more
   rigorous way to evaluate dynamical dependences between the different
   components of the climate system.}},
Publisher = {{COPERNICUS GESELLSCHAFT MBH}},
Address = {{BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Vannitsem, S (Corresponding Author), Royal Meteorol Inst Belgium, Ave Circulaire 3, B-1180 Brussels, Belgium.
   Vannitsem, Stephane, Royal Meteorol Inst Belgium, Ave Circulaire 3, B-1180 Brussels, Belgium.
   Ekelmans, Pierre, Max Planck Inst Brain Res, Theory Neural Dynam Grp, Max von Laue Str 4, D-60438 Frankfurt, Germany.}},
DOI = {{10.5194/esd-9-1063-2018}},
ISSN = {{2190-4979}},
EISSN = {{2190-4987}},
Keywords-Plus = {{GALACTIC COSMIC-RAYS; AIR-SEA INTERACTION; INTERANNUAL VARIATION; GLOBAL
   TEMPERATURE; GRANGER CAUSALITY; TIME-SERIES; OSCILLATION; CLIMATE;
   TELECONNECTIONS; VARIABILITY}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{stephane.vannitsem@meteo.be}},
ResearcherID-Numbers = {{Vannitsem, Stephane/R-7328-2019}},
ORCID-Numbers = {{Vannitsem, Stephane/0000-0002-1734-1042}},
Funding-Acknowledgement = {{EU project MEDSCOPE (ERA4CS); Mass2Ant of the Belgian Scientific Policy
   Office {[}BR/165/A2/Mass2Ant]}},
Funding-Text = {{This work is supported in part by the EU project MEDSCOPE (ERA4CS) and
   the project Mass2Ant of the Belgian Scientific Policy Office under
   contract BR/165/A2/Mass2Ant.}},
Cited-References = {{Alexander MA, 2002, J CLIMATE, V15, P2205, DOI 10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
   Balmaseda MA, 2013, Q J ROY METEOR SOC, V139, P1132, DOI 10.1002/qj.2063.
   BozorgMagham AE, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0131226.
   Bronnimann S, 2007, REV GEOPHYS, V45, DOI 10.1029/2006RG000199.
   Casdagli M., 1991, 199107029 SANT FEE I.
   de Boisseson E., 2016, ERA REPORT SERIES, V24.
   Deyle ER, 2016, P NATL ACAD SCI USA, V113, P13081, DOI 10.1073/pnas.1607747113.
   Drouard M, 2015, J CLIMATE, V28, P3957, DOI 10.1175/JCLI-D-14-00552.1.
   ELSNER JB, 1992, B AM METEOROL SOC, V73, P49, DOI 10.1175/1520-0477(1992)073<0049:NPCAN>2.0.CO;2.
   FRAEDRICH K, 1992, INT J CLIMATOL, V12, P25, DOI 10.1002/joc.3370120104.
   Goss M, 2017, J ATMOS SCI, V74, P487, DOI 10.1175/JAS-D-16-0067.1.
   Granger C. W. J., 2004, PRIZE LECT TIME SERI.
   GRANGER CWJ, 1969, ECONOMETRICA, V37, P424, DOI 10.2307/1912791.
   Johnson NC, 2016, CLIM DYNAM, V47, P3737, DOI 10.1007/s00382-016-3039-1.
   KANTZ H, 1995, CHAOS, V5, P143, DOI 10.1063/1.166096.
   Lau NC, 2016, B AM METEOROL SOC, V97, P981, DOI 10.1175/BAMS-D-15-00220.1.
   Liang XS, 2015, PHYS REV E, V92, DOI 10.1103/PhysRevE.92.022126.
   Liang XS, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.052150.
   Liang XS, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.244101.
   Lopez-Parages J, 2016, CLIM DYNAM, V47, P2071, DOI 10.1007/s00382-015-2951-0.
   Luo M, 2015, P NATL ACAD SCI USA, V112, pE4638, DOI 10.1073/pnas.1510571112.
   Mokhov II, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL045932.
   Monster D, 2017, FUTURE GENER COMP SY, V73, P52, DOI 10.1016/j.future.2016.12.009.
   Mosedale TJ, 2006, J CLIMATE, V19, P1182, DOI 10.1175/JCLI3653.1.
   Nicolis C, 1998, J ATMOS SCI, V55, P465, DOI 10.1175/1520-0469(1998)055<0465:AAARTS>2.0.CO;2.
   Parker T.S., 1989, PRACTICAL NUMERICAL.
   Philander S. G. H., 1990, EL NINO SO OSCILLATI.
   Poli P., 2015, ERA REPORT SERIES, V14.
   Runge J, 2012, PHYS REV E, V86, DOI 10.1103/PhysRevE.86.061121.
   SARDESHMUKH PD, 1988, J ATMOS SCI, V45, P1228, DOI 10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.
   SUGIHARA G, 1990, NATURE, V344, P734, DOI 10.1038/344734a0.
   Sugihara G, 2012, SCIENCE, V338, P496, DOI 10.1126/science.1227079.
   Tirabassi G, 2015, INT J CLIMATOL, V35, P3440, DOI 10.1002/joc.4218.
   Tsonis AA, 2015, P NATL ACAD SCI USA, V112, P3253, DOI 10.1073/pnas.1420291112.
   VANDENDOOL HM, 1994, TELLUS A, V46, P314, DOI 10.1034/j.1600-0870.1994.t01-2-00006.x.
   Vannitsem S., 2018, TIME SERIES USED MAN, DOI {[}10.5281/zenodo.1135134, DOI 10.5281/ZENODO.1135134].
   Vannitsem S, 2017, GEOPHYS RES LETT, V44, P2016, DOI 10.1002/2016GL072229.
   Vannitsem S, 2015, GEOPHYS RES LETT, V42, P8615, DOI 10.1002/2015GL065974.
   Vannitsem S, 2015, PHYSICA D, V309, P71, DOI 10.1016/j.physd.2015.07.006.
   Ye H, 2015, P NATL ACAD SCI USA, V112, pE4640, DOI 10.1073/pnas.1511080112.
   Yu B, 2016, J CLIMATE, V29, P1755, DOI 10.1175/JCLI-D-15-0583.1.}},
Number-of-Cited-References = {{41}},
Times-Cited = {{4}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{6}},
Journal-ISO = {{Earth Syst. Dynam.}},
Doc-Delivery-Number = {{GR6EY}},
Unique-ID = {{ISI:000442739700001}},
OA = {{DOAJ Gold, Green Published}},
DA = {{2020-08-06}},
}

@article{ ISI:000437997200045,
Author = {Tian, Yushen and Xiong, Siqin and Ma, Xiaoming and Ji, Junping},
Title = {{Structural path decomposition of carbon emission: A study of China's
   manufacturing industry}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{193}},
Pages = {{563-574}},
Month = {{AUG 20}},
Abstract = {{Manufacturing is the foundation of China's economy and accounts for a
   large proportion of China's CO2 emissions. In this study, the structural
   path decomposition (SPD) methodology, based on an environmental
   input-output model, was used to find critical supply chain paths that
   drive changes in CO2 life cycle in China's manufacturing industry from
   1992 to 2012. The changes in CO2 emissions were decomposed into three
   main factors: carbon emissions intensity, input-output structure, and
   final demand. In this study, the ``weighted average decompositions{''}
   method of structural decomposition analysis and the non-comparable
   input-output table were first applied to the SPD method to obtain
   accurate results. The results indicate that higher-order paths,
   especially the paths that started from the ``Smelting and Rolling of
   Metals{''} and the ``Production and Supply of Electricity and Steam{''}
   sectors, led to an increase in carbon emissions. This was mainly due to
   the extension of the industrial chain as well as policy priorities.
   Moreover, CO2 emissions from the ``Chemical Industry{''}, ``Manufacture
   of Nonmetallic Mineral Products{''}, and ``Smelting and Rolling of
   Metals{''} sectors were mainly induced by the increase of final export
   demands. Based on the results of this study, relevant policy changes
   have also been recommended. (C) 2018 Elsevier Ltd. All rights reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Ji, JP (Corresponding Author), Peking Univ, Shenzhen Grad Sch, Sch Environm \& Energy, Shenzhen 518055, Peoples R China.
   Tian, Yushen; Xiong, Siqin; Ma, Xiaoming; Ji, Junping, Peking Univ, Shenzhen Grad Sch, Sch Environm \& Energy, Shenzhen 518055, Peoples R China.
   Ji, Junping, Lawrence Berkeley Natl Lab, Energy Anal \& Environm Impacts Div, Berkeley, CA 94720 USA.}},
DOI = {{10.1016/j.jclepro.2018.05.047}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Climate change; CO2 emission; Environment input-output model; Structural
   path decomposition; China'S manufacturing industry}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; CO2 EMISSIONS; ENERGY EFFICIENCY; STEEL-INDUSTRY;
   SUPPLY CHAIN; CHEMICAL-INDUSTRY; DRIVING FACTORS; TRADE; CONSERVATION;
   REGRESSION}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{jackyji@pku.edu.cn}},
ResearcherID-Numbers = {{Ji, Junping/D-2094-2015
   }},
ORCID-Numbers = {{Ji, Junping/0000-0003-0305-4285
   Tian, Yushen/0000-0003-4218-3178}},
Cited-References = {{{[}Anonymous], 2017, 47542017 GBT.
   Chen GQ, 2010, ENERG POLICY, V38, P6180, DOI 10.1016/j.enpol.2010.06.004.
   DEFOURNY J, 1984, ECON J, V94, P111, DOI 10.2307/2232220.
   Deng GY, 2017, ENERGY, V137, P140, DOI 10.1016/j.energy.2017.07.064.
   Dietzenbacher E., 1998, ECON SYST RES, V10, P307, DOI DOI 10.1080/09535319800000023.
   Dong F, 2018, RESOUR CONSERV RECY, V129, P187, DOI 10.1016/j.resconrec.2017.10.035.
   Du Z., 2017, J CLEAN PROD, DOI {[}10.1016/jjclepro.2017.11.178, DOI 10.1016/JJCLEPRO.2017.11.178].
   Guan D, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036540.
   Gui SS, 2014, ENERGY, V74, P405, DOI 10.1016/j.energy.2014.06.116.
   Guo XY, 2017, ENERGY, V134, P392, DOI 10.1016/j.energy.201

path: /data/wos_io/savedrecs (3).bib

license_type: permissive

branch_name: refs/heads/master

extension: bib

filename: savedrecs (3).bib