text:
89, P1916, DOI 10.1126/science.289.5486.1916.
   Tian LD, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006249.
   Urrutia R, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011021.
   Wang B, 1999, B AM METEOROL SOC, V80, P629, DOI 10.1175/1520-0477(1999)080<0629:COSASM>2.0.CO;2.
   Wang B, 2009, J CLIMATE, V22, P3303, DOI 10.1175/2008JCLI2675.1.
   Wang NL, 2003, CHINESE SCI BULL, V48, P2134, DOI 10.1360/02wd0539.
   WEBSTER PJ, 1992, Q J ROY METEOR SOC, V118, P877, DOI 10.1256/smsqj.50704.
   Wu ZW, 2012, J CLIMATE, V25, P2481, DOI 10.1175/JCLI-D-11-00135.1.
   Wushiki H., 1993, P JAP CHIN INT S STU.
   Xavier PK, 2007, Q J ROY METEOR SOC, V133, P749, DOI 10.1002/qj.45.
   Yang MX, 2000, CLIMATIC CHANGE, V47, P401, DOI 10.1023/A:1005696702385.
   Yao TD, 2013, REV GEOPHYS, V51, DOI 10.1002/rog.20023.
   Yao TD, 2012, NAT CLIM CHANGE, V2, P663, DOI {[}10.1038/NCLIMATE1580, 10.1038/nclimate1580].
   Yao TD, 1996, J GEOPHYS RES-ATMOS, V101, P29531, DOI 10.1029/96JD02683.
   Zhao Y, 2014, J CLIMATE, V27, P4721, DOI 10.1175/JCLI-D-13-00456.1.}},
Number-of-Cited-References = {{46}},
Times-Cited = {{1}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{22}},
Journal-ISO = {{J. Geophys. Res.-Atmos.}},
Doc-Delivery-Number = {{GH0DC}},
Unique-ID = {{ISI:000433071200002}},
DA = {{2020-08-06}},
}

@article{ ISI:000433071200009,
Author = {Wang, Jiabao and Kim, Hye-Mi and Chang, Edmund K. M. and Son, Seok-Woo},
Title = {{Modulation of the MJO and North Pacific Storm Track Relationship by the
   QBO}},
Journal = {{JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}},
Year = {{2018}},
Volume = {{123}},
Number = {{8}},
Pages = {{3976-3992}},
Month = {{APR 27}},
Abstract = {{This study demonstrates a possible impact of the quasi-biennial
   oscillation (QBO) on the Madden-Julian Oscillation (MJO)-related North
   Pacific storm track (NPST) change during October-March for the period of
   1979-2016. The NPST shows significant intraseasonal changes in response
   to the MJO. In general, when the MJO convection is located over the
   Indian Ocean (western to central Pacific), the NPST tends to shift
   poleward (southward). This MJO-related NPST change has larger amplitude
   during the easterly phase of the QBO (EQBO) than during its westerly
   phase (WQBO). The spatial distribution of this NPST change also exhibits
   significant differences between the two QBO phases with a zonally
   elongated pattern during EQBO winters but separated into two centers
   during WQBO winters. Diagnoses of the dynamical processes associated
   with the NPST change indicate the dominant roles of the baroclinic
   energy conversion and downstream energy propagation. The analysis of
   intraseasonal flow change indicates a larger amplitude of the
   MJO-related baroclinicity over the North Pacific. This is likely due to
   a stronger MJO and associated Rossby wave source in EQBO winters, which
   may give rise to the enhanced amplitude of the NPST change. On the other
   hand, different spatial distribution of the NPST change is likely a
   result of a direct impact of the QBO on the NPST. These results suggest
   that the QBO impact needs to be considered for better reproduction of
   the MJO-NPST teleconnection in general circulation models, which may
   also benefit subseasonal prediction of extratropical storm activities.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Kim, HM (Corresponding Author), SUNY Stony Brook, Sch Marine \& Atmospher Sci, Stony Brook, NY 11794 USA.
   Wang, Jiabao; Kim, Hye-Mi; Chang, Edmund K. M., SUNY Stony Brook, Sch Marine \& Atmospher Sci, Stony Brook, NY 11794 USA.
   Son, Seok-Woo, Seoul Natl Univ, Sch Earth \& Environm Sci, Seoul, South Korea.}},
DOI = {{10.1029/2017JD027977}},
ISSN = {{2169-897X}},
EISSN = {{2169-8996}},
Keywords = {{MJO; North Pacific storm track; QBO}},
Keywords-Plus = {{MADDEN-JULIAN OSCILLATION; QUASI-BIENNIAL OSCILLATION; EXTRATROPICAL
   CYCLONE ACTIVITY; TELECONNECTION PATTERN; GEOPOTENTIAL HEIGHT;
   HEMISPHERE WINTER; BAROCLINIC WAVES; LIFE-CYCLES; MEAN-FLOW; TEMPERATURE}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{hyemi.kim@stonybrook.edu}},
ResearcherID-Numbers = {{Son, Seok-Woo/A-8797-2013
   }},
ORCID-Numbers = {{Chang, Edmund/0000-0002-8036-2372
   Kim, Hye-Mi/0000-0003-4713-4658}},
Funding-Acknowledgement = {{NOAA grantNational Oceanic Atmospheric Admin (NOAA) - USA
   {[}NA16OAR4310070]; NSF grantNational Science Foundation (NSF)
   {[}AGS-1652289]; KMA Research and Development Program {[}KMI2018-03110];
   Basic Science Research Program through the National Research Foundation
   - Ministry of Science and ICT of Korea {[}2017R1E1A1A01074889]}},
Funding-Text = {{Constructive and valuable comments by Editor Chidong Zhang and three
   anonymous reviewers are greatly appreciated. The sources of data used in
   this study are as follows: https://www.esrl.noaa.gov/psd/mjo/mjoindex/
   for the OMI, http://www.bom.gov.au/climate/mjo for the RMM index,
   http://apps.ecmwf.int/datasets/data/interim\_full\_daily for ERA-Interim
   reanalysis data,
   https://www.esrl.noaa.gov/psd/data/gridded/tables/daily.html for OLR
   daily data, and
   https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html for
   Extended Reconstructed Sea Surface Temperature version 4. This research
   has been conducted as part of the NOAA MAPP S2S Prediction Task Force
   and supported by NOAA grant NA16OAR4310070 and NSF grant AGS-1652289.
   This study was also supported by the KMA Research and Development
   Program under grant KMI2018-03110 (Kim) and the Basic Science Research
   Program through the National Research Foundation funded by the Ministry
   of Science and ICT of Korea (2017R1E1A1A01074889; Son).}},
Cited-References = {{Adames AF, 2014, J ATMOS SCI, V71, P2007, DOI 10.1175/JAS-D-13-0254.1.
   Alaka GJ, 2012, J CLIMATE, V25, P3219, DOI 10.1175/JCLI-D-11-00232.1.
   Athanasiadis PJ, 2010, J ATMOS SCI, V67, P1361, DOI 10.1175/2009JAS3270.1.
   Baldwin MP, 2001, REV GEOPHYS, V39, P179, DOI 10.1029/1999RG000073.
   Bao M, 2014, GEOPHYS RES LETT, V41, P1322, DOI 10.1002/2013GL057683.
   BLACKMON ML, 1976, J ATMOS SCI, V33, P1607, DOI 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2.
   Blade I, 1995, J ATMOS SCI, V52, P4448, DOI 10.1175/1520-0469(1995)052<4448:TLANER>2.0.CO;2.
   Brayshaw DJ, 2011, J ATMOS SCI, V68, P1784, DOI 10.1175/2011JAS3674.1.
   Cai M, 2007, TELLUS A, V59, P127, DOI 10.1111/j.1600-0870.2006.00213.x.
   Cassou C, 2008, NATURE, V455, P523, DOI 10.1038/nature07286.
   Chang EKM, 2016, CLIM DYNAM, V47, P1435, DOI 10.1007/s00382-015-2911-8.
   Chang EKM, 2016, GEOPHYS RES LETT, V43, P2200, DOI 10.1002/2016GL068172.
   Chang EKM, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD018578.
   Chang EKM, 2002, J CLIMATE, V15, P2163, DOI 10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.
   CHANG EKM, 1994, J ATMOS SCI, V51, P3823, DOI 10.1175/1520-0469(1994)051<3823:OEFAGV>2.0.CO;2.
   Colle BA, 2008, B AM METEOROL SOC, V89, P829, DOI 10.1175/2007BAMS2401.1.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Deng Y, 2011, J CLIMATE, V24, P1122, DOI 10.1175/2010JCLI3676.1.
   DOLE RM, 1990, MON WEATHER REV, V118, P824, DOI 10.1175/1520-0493(1990)118<0824:LCOPAP>2.0.CO;2.
   DUCHON CE, 1979, J APPL METEOROL, V18, P1016, DOI 10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
   EADY ET, 1949, TELLUS, V1, P33.
   Feldstein SB, 2002, Q J ROY METEOR SOC, V128, P775, DOI 10.1256/0035900021643683.
   Garfinkel CI, 2011, J ATMOS SCI, V68, P2026, DOI 10.1175/2011JAS3702.1.
   Guo YJ, 2017, J CLIMATE, V30, P4799, DOI {[}10.1175/jcli-d-16-0513.1, 10.1175/JCLI-D-16-0513.1].
   Harnik N, 2004, J ATMOS SCI, V61, P23, DOI 10.1175/1520-0469(2004)061<0023:TEOVIJ>2.0.CO;2.
   Henderson SA, 2017, J CLIMATE, V30, P4567, DOI 10.1175/JCLI-D-16-0789.1.
   Henderson SA, 2016, J CLIMATE, V29, P4597, DOI 10.1175/JCLI-D-15-0502.1.
   Hendon HH, 2007, J CLIMATE, V20, P531, DOI 10.1175/JCLI4003.1.
   HOREL JD, 1981, MON WEATHER REV, V109, P813, DOI 10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.
   HOSKINS BJ, 1983, J ATMOS SCI, V40, P1595, DOI 10.1175/1520-0469(1983)040<1595:TSPAMF>2.0.CO;2.
   HOSKINS BJ, 1981, J ATMOS SCI, V38, P1179, DOI 10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.
   Hsu HH, 1996, J CLIMATE, V9, P2386, DOI 10.1175/1520-0442(1996)009<2386:GVOTIO>2.0.CO;2.
   Huang BY, 2015, J CLIMATE, V28, P911, DOI 10.1175/JCLI-D-14-00006.1.
   Kiladis GN, 2014, MON WEATHER REV, V142, P1697, DOI 10.1175/MWR-D-13-00301.1.
   Kunkel KE, 2012, J HYDROMETEOROL, V13, P1131, DOI 10.1175/JHM-D-11-0108.1.
   Lau W. K. M., 2011, INTRASEASONAL VARIAB, P613.
   Lee SS, 2012, CLIM DYNAM, V39, P313, DOI 10.1007/s00382-011-1188-9.
   Lee YY, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016417.
   Li Y, 2011, THESIS.
   Lin H, 2009, J CLIMATE, V22, P364, DOI 10.1175/2008JCLI2515.1.
   Linkin ME, 2008, J CLIMATE, V21, P1979, DOI 10.1175/2007JCLI2048.1.
   MADDEN RA, 1971, J ATMOS SCI, V28, P702, DOI 10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.
   MADDEN RA, 1972, J ATMOS SCI, V29, P1109, DOI 10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.
   MADDEN RA, 1994, MON WEATHER REV, V122, P814, DOI 10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.
   Marshall AG, 2017, CLIM DYNAM, V49, P1365, DOI 10.1007/s00382-016-3392-0.
   Moon JY, 2011, CLIM DYNAM, V37, P1133, DOI 10.1007/s00382-010-0902-3.
   Mori M, 2008, J METEOROL SOC JPN, V86, P213, DOI 10.2151/jmsj.86.213.
   Mundhenk BD, 2016, J CLIMATE, V29, P4885, DOI 10.1175/JCLI-D-15-0655.1.
   Nakazawa T, 2000, ADV SPACE RES, V25, P953, DOI 10.1016/S0273-1177(99)00928-X.
   Nishimoto E, 2017, J ATMOS SCI, V74, P1105, DOI 10.1175/JAS-D-16-0205.1.
   ORLANSKI I, 1993, MON WEATHER REV, V121, P2929, DOI 10.1175/1520-0493(1993)121<2929:ACODBD>2.0.CO;2.
   Pohl B, 2007, J CLIMATE, V20, P2659, DOI 10.1175/JCLI4230.1.
   Salathe EP, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026882.
   SARDESHMUKH PD, 1988, J ATMOS SCI, V45, P1228, DOI 10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.
   Seo J, 2013, GEOPHYS RES LETT, V40, P5949, DOI 10.1002/2013GL058266.
   Seo KH, 2017, J ATMOS SCI, V74, P1767, DOI 10.1175/JAS-D-16-0343.1.
   SIMMONS AJ, 1978, J ATMOS SCI, V35, P414, DOI 10.1175/1520-0469(1978)035<0414:TLCOSN>2.0.CO;2.
   SIMMONS AJ, 1983, J ATMOS SCI, V40, P1363, DOI 10.1175/1520-0469(1983)040<1363:BWPAIA>2.0.CO;2.
   Son SW, 2017, J CLIMATE, V30, P1909, DOI {[}10.1175/JCLI-D-16-0620.1, 10.1175/jcli-d-16-0620.1].
   Son SW, 2009, J ATMOS SCI, V66, P393, DOI 10.1175/2008JAS2742.1.
   Stan C, 2017, REV GEOPHYS, V55, P902, DOI 10.1002/2016RG000538.
   Takahashi C, 2014, J GEOPHYS RES-ATMOS, V119, P10663, DOI 10.1002/2014JD021973.
   Taylor KE, 2001, J GEOPHYS RES-ATMOS, V106, P7183, DOI 10.1029/2000JD900719.
   WALLACE JM, 1981, MON WEATHER REV, V109, P784, DOI 10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
   WALLACE JM, 1988, J ATMOS SCI, V45, P439, DOI 10.1175/1520-0469(1988)045<0439:RBCTAT>2.0.CO;2.
   Wang JB, 2018, GEOPHYS RES LETT, V45, P2786, DOI 10.1002/2017GL076929.
   Wang JB, 2016, CLIM DYNAM, V46, P3965, DOI 10.1007/s00382-015-2815-7.
   Wheeler MC, 2004, MON WEATHER REV, V132, P1917, DOI 10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.
   Woollings T, 2012, NAT GEOSCI, V5, P313, DOI {[}10.1038/NGEO1438, 10.1038/ngeo1438].
   Yadav P, 2017, MON WEATHER REV, V145, P1577, DOI 10.1175/MWR-D-16-0352.1.
   Yoo C, 2016, GEOPHYS RES LETT, V43, P1392, DOI 10.1002/2016GL067762.
   Zhang CD, 2005, REV GEOPHYS, V43, DOI 10.1029/2004RG000158.
   Zhang CD, 2018, J GEOPHYS RES-ATMOS, V123, P2957, DOI 10.1002/2017JD028171.
   Zhang CD, 2013, GEOPHYS RES LETT, V40, P1223, DOI 10.1002/grl.50244.}},
Number-of-Cited-References = {{74}},
Times-Cited = {{15}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{8}},
Journal-ISO = {{J. Geophys. Res.-Atmos.}},
Doc-Delivery-Number = {{GH0DC}},
Unique-ID = {{ISI:000433071200009}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000430795000046,
Author = {Ma, Tianjiao and Chen, Wen and Nath, Debashis and Graf, Hans-F. and
   Wang, Lin and Huangfu, Jingliang},
Title = {{East Asian Winter Monsoon Impacts the ENSO-related Teleconnections and
   North American Seasonal Air Temperature Prediction}},
Journal = {{SCIENTIFIC REPORTS}},
Year = {{2018}},
Volume = {{8}},
Month = {{APR 25}},
Abstract = {{El Nino-Southern Oscillation (ENSO) is a key feature for seasonal
   weather and climate prediction in the extra-tropics since related sea
   surface temperature anomalies induce precipitation anomalies that
   generate poleward propagating Rossby waves and teleconnections. The East
   Asian winter monsoon (EAWM) is driven by processes originating over the
   Asian continent and, to a lesser degree, by ENSO-related tropical
   convection. EAWM also strongly affects convection and precipitation
   patterns over the western tropical Pacific by cold air outbreaks
   reaching equatorial latitudes. Hence, one can expect a modulating effect
   of EAWM on the generation of Rossby wave trains related to ENSO. By
   increasing the convective heating over the western Pacific, strong EAWM
   strengthens the Pacific Walker circulation, and weakens (strengthens)
   the El Nino (La Nina) related effects on the extra-tropics via a
   modulation of the Pacific North America teleconnection pattern. Our
   results indicate that, for seasonal prediction over North America, along
   with ENSO the variability of EAWM should also be taken into account. The
   climate anomalies over the North America for the same phase of ENSO are
   significantly different for strong and weak EAWM.}},
Publisher = {{NATURE PUBLISHING GROUP}},
Address = {{MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Chen, W (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100190, Peoples R China.
   Chen, W (Corresponding Author), Univ Chinese Acad Sci, Sch Earth Sci, Beijing 100049, Peoples R China.
   Ma, Tianjiao; Chen, Wen; Nath, Debashis; Graf, Hans-F.; Wang, Lin; Huangfu, Jingliang, Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100190, Peoples R China.
   Ma, Tianjiao; Chen, Wen, Univ Chinese Acad Sci, Sch Earth Sci, Beijing 100049, Peoples R China.}},
DOI = {{10.1038/s41598-018-24552-3}},
Article-Number = {{6547}},
ISSN = {{2045-2322}},
Keywords-Plus = {{SEA-SURFACE TEMPERATURE; SUMMER MONSOON; EL-NINO; PRECIPITATION;
   VARIABILITY; MODULATION; CLIMATE}},
Research-Areas = {{Science \& Technology - Other Topics}},
Web-of-Science-Categories  = {{Multidisciplinary Sciences}},
Author-Email = {{cw@post.iap.ac.cn}},
ResearcherID-Numbers = {{Chen, Wen/G-6058-2011
   Wang, Lin/G-5245-2010
   Ma, Tianjiao/I-7370-2018}},
ORCID-Numbers = {{Chen, Wen/0000-0001-9327-9079
   Wang, Lin/0000-0002-3557-1853
   }},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}41721004]; Chinese Academy of Sciences Key
   Research Program of Frontier Sciences {[}QYZDY-SSW-DQC024]; ``The Belt
   and Road Initiatives{''} Program on International Cooperation: Climate
   Change Research and Observation Project {[}134111KYSB20160010]}},
Funding-Text = {{This work is supported jointly by the National Natural Science
   Foundation of China (Grant No. 41721004), the Chinese Academy of
   Sciences Key Research Program of Frontier Sciences (QYZDY-SSW-DQC024)
   and ``The Belt and Road Initiatives{''} Program on International
   Cooperation: Climate Change Research and Observation Project
   (134111KYSB20160010). We are also thankful to the agencies which provide
   us the datasets for our analysis. The NCEP Reanalysis Derived data,
   Precipitation reconstruction dataset (PREC), OLR data, 20th Century
   Reanalysis V2c data, and NOAA\_ ERSST\_V4 data are provided by the
   NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at
   http://www.esrl.noaa.gov/psd/. The CRU data are provided by the Centre
   for Environmental Data Analysis from their web site
   http://catalogue.ceda.ac.uk. ERA-20C dataset is obtained from the web
   site of ECMWF at https://www.ecmwf.int.Hans-F. Graf is a Visiting Fellow
   of the Chinese Academy of Sciences.}},
Cited-References = {{Abdillah MR, 2017, J CLIMATE, V30, P2989, DOI {[}10.1175/jcli-d-16-0152.1, 10.1175/JCLI-D-16-0152.1].
   Boschat G, 2013, GEOPHYS RES LETT, V40, P1605, DOI 10.1002/grl.50229.
   Chang C. P., 2011, GLOBAL MONSOON SYSTE, P85.
   CHANG CP, 1980, MON WEATHER REV, V108, P298, DOI 10.1175/1520-0493(1980)108<0298:NCSANE>2.0.CO;2.
   Chen MY, 2002, J HYDROMETEOROL, V3, P249, DOI 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.
   Chen SF, 2013, GEOPHYS RES LETT, V40, P6384, DOI 10.1002/2013GL058628.
   Chen W, 2000, ADV ATMOS SCI, V17, P48.
   Chen W, 2013, J CLIMATE, V26, P622, DOI 10.1175/JCLI-D-12-00021.1.
   Chen Z, 2014, J CLIMATE, V27, P835, DOI 10.1175/JCLI-D-13-00314.1.
   Compo GP, 2011, Q J ROY METEOR SOC, V137, P1, DOI 10.1002/qj.776.
   Compo GP, 1999, Q J ROY METEOR SOC, V125, P29, DOI 10.1256/smsqj.55302.
   Efron B., 1993, INTRO BOOTSTRAP.
   Eroglu D, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms12929.
   Feng J, 2017, CLIM DYNAM, V48, P2135, DOI 10.1007/s00382-016-3196-2.
   Feng J, 2014, ADV ATMOS SCI, V31, P344, DOI 10.1007/s00376-013-3118-8.
   Feng JA, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014761.
   Gershunov A, 1998, B AM METEOROL SOC, V79, P2715, DOI 10.1175/1520-0477(1998)079<2715:IMOET>2.0.CO;2.
   Harris I. C., 1901, CRU TS4 00 CLIMATIC.
   Hoerling MP, 1997, J CLIMATE, V10, P1769, DOI 10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2.
   HOREL JD, 1981, MON WEATHER REV, V109, P813, DOI 10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.
   Huang BY, 2015, J CLIMATE, V28, P911, DOI 10.1175/JCLI-D-14-00006.1.
   Huang P, 2015, NAT GEOSCI, V8, P922, DOI {[}10.1038/ngeo2571, 10.1038/NGEO2571].
   Jia XJ, 2016, INT J CLIMATOL, V36, P3384, DOI 10.1002/joc.4562.
   Jia XJ, 2015, J GEOPHYS RES-ATMOS, V120, P430, DOI 10.1002/2014JD022324.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   KRISHNAMURTI TN, 1971, J ATMOS SCI, V28, P1342, DOI 10.1175/1520-0469(1971)028<1342:TEWCDT>2.0.CO;2.
   Kumar A, 1998, J CLIMATE, V11, P3295, DOI 10.1175/1520-0442(1998)011<3295:ACOPNA>2.0.CO;2.
   Larkin NK, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022860.
   Lee HT, 2014, CDRPATBD0526.
   Li YQ, 2010, J CLIMATE, V23, P4255, DOI 10.1175/2010JCLI3375.1.
   Liu W, 2015, J CLIMATE, V28, P931, DOI 10.1175/JCLI-D-14-00007.1.
   Lu QM, 2010, J GEOPHYS RES-SPACE, V115, DOI 10.1029/2009JA014580.
   Poli P, 2016, J CLIMATE, V29, P4083, DOI 10.1175/JCLI-D-15-0556.1.
   RASMUSSON EM, 1982, MON WEATHER REV, V110, P354, DOI 10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   ROPELEWSKI CF, 1987, MON WEATHER REV, V115, P1606, DOI 10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.
   Shoji T, 2014, J CLIMATE, V27, P9337, DOI 10.1175/JCLI-D-14-00307.1.
   SIMMONS AJ, 1983, J ATMOS SCI, V40, P1363, DOI 10.1175/1520-0469(1983)040<1363:BWPAIA>2.0.CO;2.
   Trenberth KE, 2000, J CLIMATE, V13, P4358, DOI 10.1175/1520-0442(2000)013<4358:TSORSL>2.0.CO;2.
   Vimont DJ, 2003, J CLIMATE, V16, P2653, DOI 10.1175/1520-0442(2003)016<2653:TSFMIT>2.0.CO;2.
   Wang B, 2000, J CLIMATE, V13, P1517, DOI 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
   Wang B, 2006, ASIAN MONSOON, P89.
   Wang SY, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL050909.
   Webster PJ, 1998, J GEOPHYS RES-OCEANS, V103, P14451, DOI 10.1029/97JC02719.
   Weng HY, 2009, CLIM DYNAM, V32, P663, DOI 10.1007/s00382-008-0394-6.
   Wu RG, 2014, J GEOPHYS RES-ATMOS, V119, P5046, DOI 10.1002/2013JD021095.
   Zhang RH, 1996, J METEOROL SOC JPN, V74, P49, DOI 10.2151/jmsj1965.74.1\_49.
   Zhou W, 2007, INT J CLIMATOL, V27, P157, DOI 10.1002/joc.1380.}},
Number-of-Cited-References = {{48}},
Times-Cited = {{15}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{18}},
Journal-ISO = {{Sci Rep}},
Doc-Delivery-Number = {{GD8WN}},
Unique-ID = {{ISI:000430795000046}},
OA = {{DOAJ Gold, Green Published}},
DA = {{2020-08-06}},
}

@article{ ISI:000435743400044,
Author = {Ogawa, Fumiaki and Keenlyside, Noel and Gao, Yongqi and Koenigk, Torben
   and Yang, Shuting and Suo, Lingling and Wang, Tao and Gastineau,
   Guillaume and Nakamura, Tetsu and Cheung, Ho Nam and Omrani, Nour-Eddine
   and Ukita, Jinro and Semenov, Vladimir},
Title = {{Evaluating Impacts of Recent Arctic Sea Ice Loss on the Northern
   Hemisphere Winter Climate Change}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{7}},
Pages = {{3255-3263}},
Month = {{APR 16}},
Abstract = {{Wide disagreement among individual modeling studies has contributed to a
   debate on the role of recent sea ice loss in the Arctic amplification of
   global warming and the Siberian wintertime cooling trend. We perform
   coordinated experiments with six atmospheric general circulation models
   forced by the observed and climatological daily sea ice concentration
   and sea surface temperature. The results indicate that the impact of the
   recent sea ice decline is rather limited to the high-latitude lower
   troposphere in winter, and the sea ice changes do not significantly lead
   to colder winters over Siberia. The observed wintertime Siberian
   temperature and corresponding circulation trends are reproduced in a
   small number of ensemble members but not by the multimodel ensemble
   mean, suggesting that atmospheric internal dynamics could have played a
   major role in the observed trends.
   Plain Language Summary Understanding the mechanism governing the ongoing
   global warming is a major challenge facing our society and its
   sustainable growth. Together with the CO2-forced warming, the concurrent
   polar sea ice loss might also have contributed to the observed Arctic
   warming amplification and also to the cooling trends over Eurasia
   through a dynamical teleconnection. However, previous individual
   modeling studies suggest widely different findings on the role of sea
   ice loss in Northern Hemisphere climate change. To help resolve this
   controversy, we used satellite-derived sea ice and sea-surface
   temperature to run coordinated hindcast experiments with five different
   atmospheric general circulation models. The multimodel ensemble-mean
   results presented in the paper reduce biases of each model and eliminate
   atmospheric internal unforced variability, and thus provide the best
   estimate to date of the signal of the polar sea ice loss. The results
   suggest that the impact of sea ice seems critical for the Arctic surface
   temperature changes, but the temperature trends elsewhere seem rather
   due to either sea-surface temperature changes or atmospheric internal
   variability. They give clear guidance on how to provide society with
   more accurate climate change attributions. Our work is of interest to
   stakeholders of countries in the Northern Hemisphere middle and high
   latitudes.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Ogawa, F (Corresponding Author), Univ Bergen, Geophys Inst, Bergen, Norway.
   Ogawa, F (Corresponding Author), Bjerknes Ctr Climate Res, Bergen, Norway.
   Ogawa, Fumiaki; Keenlyside, Noel; Cheung, Ho Nam; Omrani, Nour-Eddine, Univ Bergen, Geophys Inst, Bergen, Norway.
   Ogawa, Fumiaki; Keenlyside, Noel; Gao, Yongqi; Suo, Lingling; Cheung, Ho Nam; Omrani, Nour-Eddine, Bjerknes Ctr Climate Res, Bergen, Norway.
   Keenlyside, Noel; Gao, Yongqi; Suo, Lingling, Nansen Environm \& Remote Sensing Ctr, Bergen, Norway.
   Koenigk, Torben, Swedish Meteorol \& Hydrol Inst, Norrkoping, Sweden.
   Yang, Shuting, Danish Meteorol Inst, Copenhagen, Denmark.
   Wang, Tao, Chinese Acad Sci, Inst Atmospher Phys, Nansen Zhu Int Res Ctr, Beijing, Peoples R China.
   Gastineau, Guillaume, Sorbonne Univ, UPMC, CNRS, IRD, Paris, France.
   Nakamura, Tetsu, Hokkaido Univ, Fac Earth Environm Sci, Sapporo, Hokkaido, Japan.
   Ukita, Jinro, Niigata Univ, Inst Sci \& Technol, Niigata, Japan.
   Semenov, Vladimir, Russian Acad Sci, AM Obukhov Inst Atmospher Phys, Moscow, Russia.
   Semenov, Vladimir, Russian Acad Sci, Inst Geog, Moscow, Russia.}},
DOI = {{10.1002/2017GL076502}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords-Plus = {{CIRCULATION RESPONSE; SURFACE TEMPERATURE; MIDLATITUDE WEATHER; EURASIAN
   CLIMATE; COLD WINTERS; AMPLIFICATION; SYSTEM; MODEL; ASIA}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{fumiaki.ogawa@gfi.uib.no}},
ResearcherID-Numbers = {{Keenlyside, Noel S/E-8303-2013
   gao, yongqi/N-9347-2014
   Wang, Tao/AAI-9782-2020
   Semenov, Vladimir/U-9488-2019
   Nakamura, Tetsu/M-7914-2015}},
ORCID-Numbers = {{Keenlyside, Noel S/0000-0002-8708-6868
   Wang, Tao/0000-0002-6170-340X
   Ogawa, Fumiaki/0000-0003-4079-9518
   gao, yongqi/0000-0003-3474-3526
   Nakamura, Tetsu/0000-0002-2056-7392}},
Funding-Acknowledgement = {{NordForsk GREENICE project {[}61841]; National Natural Science
   Foundation of ChinaNational Natural Science Foundation of China
   {[}41661144005]; ERC STERCP {[}648982]; UNITETT Sigma AS {[}NN9390K,
   NS9064K]; TGCC {[}2017-017403]; ERC Ice2Ice {[}610055]}},
Funding-Text = {{This study is supported by the NordForsk GREENICE project (Project
   61841). The simulation data used in this study are freely available (see
   Ogawa et al., 2018). We thank Bjerknes Climate Data Centre and Norstore
   research data archive for publishing the data. IAP4 experiments are
   supported by the National Natural Science Foundation of China (grant
   41661144005). N. K. was also supported by the ERC STERCP (grant 648982).
   Computing resources were provided by UNITETT Sigma AS (NN9390K, NS9064K)
   and TGCC (allocation 2017-017403 made by GENCI). T. N. and J. U. thank
   ArCS project. N. K., Y. G., T. K., T. W., and J. U. also benefited from
   JPI-Climate-Belmont Forum project 407, InterDec. S. Y. also received
   support from the ERC Ice2Ice (grant 610055). The authors are grateful to
   John Fyfe and an anonymous reviewer for their insightful comments and
   suggestions.}},
Cited-References = {{Balsamo G, 2009, J HYDROMETEOROL, V10, P623, DOI 10.1175/2008JHM1068.1.
   Cheung HHN, 2018, ADV ATMOS SCI, V35, P38, DOI 10.1007/s00376-017-7156-5.
   Cohen J, 2014, NAT GEOSCI, V7, P627, DOI {[}10.1038/ngeo2234, 10.1038/NGEO2234].
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Deser C, 2015, J CLIMATE, V28, P2168, DOI 10.1175/JCLI-D-14-00325.1.
   Ding QH, 2014, NATURE, V509, P209, DOI 10.1038/nature13260.
   Dong X, 2012, ATMOS OCEAN SCI LETT, V5, P435, DOI 10.1080/16742834.2012.11447031.
   Gao YQ, 2015, ADV ATMOS SCI, V32, P92, DOI 10.1007/s00376-014-0009-6.
   Graversen RG, 2008, NATURE, V451, P53, DOI 10.1038/nature06502.
   He SP, 2013, J CLIMATE, V26, P3377, DOI 10.1175/JCLI-D-12-00355.1.
   Honda M, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL037079.
   Hourdin F, 2013, CLIM DYNAM, V40, P2193, DOI 10.1007/s00382-012-1343-y.
   Hurrell JW, 2008, J CLIMATE, V21, P5145, DOI 10.1175/2008JCLI2292.1.
   Kim BM, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5646.
   Kug JS, 2015, NAT GEOSCI, V8, P759, DOI {[}10.1038/ngeo2517, 10.1038/NGEO2517].
   Lang A, 2017, GEOPHYS RES LETT, V44, P409, DOI 10.1002/2016GL071274.
   Lindsay R, 2014, J CLIMATE, V27, P2588, DOI 10.1175/JCLI-D-13-00014.1.
   Liu JP, 2012, P NATL ACAD SCI USA, V109, P4074, DOI 10.1073/pnas.1114910109.
   Marsh DR, 2013, J CLIMATE, V26, P7372, DOI 10.1175/JCLI-D-12-00558.1.
   McCusker KE, 2017, GEOPHYS RES LETT, V44, P7955, DOI 10.1002/2017GL074327.
   McCusker KE, 2016, NAT GEOSCI, V9, P838, DOI {[}10.1038/ngeo2820, 10.1038/NGEO2820].
   Mori M, 2014, NAT GEOSCI, V7, P869, DOI 10.1038/NGEO2277.
   Nakamura T, 2015, J GEOPHYS RES-ATMOS, V120, P3209, DOI 10.1002/2014JD022848.
   Neale RB, 2013, J CLIMATE, V26, P5150, DOI 10.1175/JCLI-D-12-00236.1.
   Ogawa F., 2018, OUTPUTS GREENICE COO, DOI {[}10.11582/2018.00007, DOI 10.11582/2018.00007].
   Ohfuchi W., 2004, J EARTH SIMUL, V1, P8, DOI DOI 10.1029/2004GL019895.
   Overland JE, 2016, NAT CLIM CHANGE, V6, P992, DOI 10.1038/NCLIMATE3121.
   Peings Y, 2014, ENVIRON RES LETT, V9, DOI 10.1088/1748-9326/9/3/034018.
   Perlwitz J, 2015, J CLIMATE, V28, P2154, DOI 10.1175/JCLI-D-14-00095.1.
   Reynolds RW, 2007, J CLIMATE, V20, P5473, DOI 10.1175/2007JCLI1824.1.
   Sato K, 2014, ENVIRON RES LETT, V9, DOI 10.1088/1748-9326/9/8/084009.
   Screen JA, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051598.
   Screen JA, 2017, J CLIMATE, V30, P3945, DOI 10.1175/JCLI-D-16-0197.1.
   Screen JA, 2013, J CLIMATE, V26, P1230, DOI 10.1175/JCLI-D-12-00063.1.
   Semenov VA, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/5/054020.
   Serreze MC, 2011, GLOBAL PLANET CHANGE, V77, P85, DOI 10.1016/j.gloplacha.2011.03.004.
   Sorokina SA, 2016, J CLIMATE, V29, P495, DOI 10.1175/JCLI-D-15-0046.1.
   Sun LT, 2015, J CLIMATE, V28, P7824, DOI 10.1175/JCLI-D-15-0169.1.
   Takaya K, 2001, J ATMOS SCI, V58, P608, DOI 10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.
   Wang SYS, 2017, GEOPHYS RES LETT, V44, P3806, DOI 10.1002/2017GL073012.}},
Number-of-Cited-References = {{40}},
Times-Cited = {{41}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{12}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GJ9RZ}},
Unique-ID = {{ISI:000435743400044}},
OA = {{Other Gold, Green Published}},
ESI-Highly-Cited-Paper = {{Y}},
ESI-Hot-Paper = {{N}},
DA = {{2020-08-06}},
}

@article{ ISI:000429385700007,
Author = {Zhang, Wei and Wang, Feng and Hubacek, Klaus and Liu, Yu and Wang,
   Jinnan and Feng, Kuishuang and Jiang, Ling and Jiang, Hongqiang and
   Zhang, Bing and Bi, Jun},
Title = {{Unequal Exchange of Air Pollution and Economic Benefits Embodied in
   China's Exports}},
Journal = {{ENVIRONMENTAL SCIENCE \& TECHNOLOGY}},
Year = {{2018}},
Volume = {{52}},
Number = {{7}},
Pages = {{3888-3898}},
Month = {{APR 3}},
Abstract = {{As the world's factory, China has enjoyed huge economic benefits from
   international export but also suffered severe environmental
   consequences. Most studies investigating unequal environmental exchange
   associated with trade took China as a homogeneous entity ignoring
   considerable inequality and outsourcing of pollution within China. This
   paper traces the regional mismatch of export-induced economic benefits
   and environmental costs along national supply chains by using the latest
   multiregional input-output model and emission inventory for 2012. The
   results indicate that approximately 56\% of the national GDP induced by
   exports has been received by developed coastal regions, while about 72\%
   of air pollution embodied in national exports, measured as aggregated
   atmospheric pollutant equivalents (APE), has been mainly incurred by
   less developed central and western regions. For each yuan of
   export-induced GDP, developed regions only incurred 0.4-0.6 g APE
   emissions, whereas less developed regions from western or central China
   had to suffer 4-8 times the amount of emissions. This is due to poorer
   regions providing lower value added and higher emission-intensive inputs
   and having lower environmental standards and less efficient
   technologies. Our results may pave a way to mitigate the unequal
   relationship between developed and less developed regions from the
   perspective of environment-economy nexus.}},
Publisher = {{AMER CHEMICAL SOC}},
Address = {{1155 16TH ST, NW, WASHINGTON, DC 20036 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Bi, J (Corresponding Author), Nanjing Univ, Sch Environm, State Key Lab Pollut Control \& Resource Reuse, Nanjing 210023, Jiangsu, Peoples R China.
   Wang, JN (Corresponding Author), Chinese Acad Environm Planning, State Environm Protect Key Lab Environm Planning, Beijing 100012, Peoples R China.
   Zhang, Wei; Wang, Feng; Zhang, Bing; Bi, Jun, Nanjing Univ, Sch Environm, State Key Lab Pollut Control \& Resource Reuse, Nanjing 210023, Jiangsu, Peoples R China.
   Zhang, Wei; Wang, Jinnan; Jiang, Hongqiang, Chinese Acad Environm Planning, State Environm Protect Key Lab Environm Planning, Beijing 100012, Peoples R China.
   Hubacek, Klaus; Feng, Kuishuang, Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.
   Hubacek, Klaus, Masaryk Univ, Dept Environm Studies, Brno, Czech Republic.
   Liu, Yu, Chinese Acad Sci, Inst Sci \& Dev, Beijing 100190, Peoples R China.
   Liu, Yu, Univ Chinese Acad Sci, Sch Publ Policy \& Management, Beijing 100049, Peoples R China.
   Jiang, Ling, Cent Univ Finance \& Econ, Sch Govt, Beijing 100081, Peoples R China.}},
DOI = {{10.1021/acs.est.7b05651}},
ISSN = {{0013-936X}},
EISSN = {{1520-5851}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; INTERNATIONAL-TRADE; CO2 EMISSIONS; HEALTH
   IMPACTS; INTERPROVINCIAL TRADE; CONSUMPTION; POLICY; FLOWS; INVENTORY;
   TRANSPORT}},
Research-Areas = {{Engineering; Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Engineering, Environmental; Environmental Sciences}},
Author-Email = {{wangjn1962@126.com
   jbi@nju.edu.cn}},
ResearcherID-Numbers = {{Feng, Kuishuang/Y-2541-2019
   }},
ORCID-Numbers = {{Wang, Feng/0000-0003-1774-7701}},
Funding-Acknowledgement = {{National Science Foundation of ChinaNational Natural Science Foundation
   of China {[}71603097, 71433007, 71628301, 71473242, 71373294]; Peking
   University-Lincoln Institute Centre for Urban Development and Land
   Policy; Outstanding PhD Candidate Program of Nanjing University
   {[}201601B009, 201601B011]; Clean Development Mechanism Project of
   China's National Development and Reform Commission {[}2013056]}},
Funding-Text = {{This work was supported by the National Science Foundation of China
   (Grants 71603097, 71433007, 71628301, 71473242 and 71373294). W.Z. is
   supported by a scholarship from the Peking University-Lincoln Institute
   Centre for Urban Development and Land Policy. W.Z. and F.W. are
   supported by the Outstanding PhD Candidate Program of Nanjing University
   (201601B009, 201601B011). J.B. is supported by Clean Development
   Mechanism Project of China's National Development and Reform Commission
   (grant 2013056).}},
Cited-References = {{Alex H., IPHONE IS MADE COMP.
   Chen YY, 2013, P NATL ACAD SCI USA, V110, P12936, DOI 10.1073/pnas.1300018110.
   Davis SJ, 2010, P NATL ACAD SCI USA, V107, P5687, DOI 10.1073/pnas.0906974107.
   El-Said M., 2001, ECON SYST RES, V13, P47, DOI DOI 10.1080/09535310120026247.
   Feng KS, 2014, ENVIRON SCI TECHNOL, V48, P7704, DOI 10.1021/es500502q.
   Feng KS, 2013, P NATL ACAD SCI USA, V110, P11654, DOI 10.1073/pnas.1219918110.
   Feng KS, 2012, APPL GEOGR, V32, P691, DOI 10.1016/j.apgeog.2011.08.004.
   Fritz MMC, 2018, ENVIRON SCI POLICY, V81, P63, DOI 10.1016/j.envsci.2017.12.011.
   Guo JE, 2012, ENERG POLICY, V42, P486, DOI 10.1016/j.enpol.2011.12.015.
   Jiang XJ, 2015, ENVIRON SCI TECHNOL, V49, P4381, DOI 10.1021/es506121s.
   Kagawa S, 2015, GLOBAL ENVIRON CHANG, V35, P486, DOI 10.1016/j.gloenvcha.2015.04.003.
   Liang S, 2017, J CLEAN PROD, V167, P133, DOI 10.1016/j.jclepro.2017.08.139.
   Liang S, 2014, ENVIRON SCI TECHNOL, V48, P2807, DOI 10.1021/es500310t.
   Lin JT, 2016, NAT GEOSCI, V9, P790, DOI {[}10.1038/ngeo2798, 10.1038/NGEO2798].
   Lin JT, 2014, P NATL ACAD SCI USA, V111, P1736, DOI 10.1073/pnas.1312860111.
   Liu QL, 2017, J CLEAN PROD, V161, P735, DOI 10.1016/j.jclepro.2017.05.003.
   Liu QL, 2015, ECOL ECON, V113, P39, DOI 10.1016/j.ecolecon.2015.02.026.
   Liu X., 2016, DEMYSTIFYING GLOBAL.
   Lopez LA, 2014, ENVIRON SCI TECHNOL, V48, P36, DOI 10.1021/es403708m.
   Meng J, 2016, P ROY SOC A-MATH PHY, V472, DOI 10.1098/rspa.2016.0380.
   Mi ZF, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01820-w.
   Ministry of Environmental Protection of China (MEP), 2013, ANN STAT REP ENV CHI.
   Minx JC, 2011, ENVIRON SCI TECHNOL, V45, P9144, DOI 10.1021/es201497m.
   Nagashima F, 2017, ECON SYST RES, V29, P105, DOI 10.1080/09535314.2016.1266992.
   National Bureau of Statistics, 2016, CHIN REG INP OUTP TA.
   National Bureau of Statistics (NBS), 2016, CHIN REG INP OUTP TA.
   National Bureau of Statistics of China (NBS), 2016, CHIN STAT YB 2015.
   OGULEDO VI, 1994, APPL ECON, V26, P107, DOI 10.1080/00036849400000066.
   Peters GP, 2008, ENVIRON SCI TECHNOL, V42, P1401, DOI 10.1021/es072023k.
   Peters GP, 2011, P NATL ACAD SCI USA, V108, P8903, DOI 10.1073/pnas.1006388108.
   Prell C., 2015, ENV SOCIOL, V1, P256, DOI DOI 10.1080/23251042.2015.1114208.
   Prell C., 2015, J IND ECOL, V20, P537, DOI DOI 10.1111/jiec.12377.
   Prell C, 2016, SOC SCI J, V53, P111, DOI 10.1016/j.soscij.2015.08.003.
   Prell C, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0144453.
   Prell C, 2014, SOC FORCES, V93, P405, DOI 10.1093/sf/sou048.
   Takahashi K, 2014, ATMOS ENVIRON, V97, P406, DOI 10.1016/j.atmosenv.2014.04.028.
   Tang X, 2016, J CLEAN PROD, V134, P310, DOI 10.1016/j.jclepro.2015.08.122.
   Wang F, 2017, J CLEAN PROD, V140, P1236, DOI 10.1016/j.jclepro.2016.10.036.
   Wiedmann T, 2011, ECOL ECON, V70, P1937, DOI 10.1016/j.ecolecon.2011.06.014.
   World Trade Organization, 2017, WORLD TRAD STAT REV.
   Yang J., 1998, REFORMING DESIGN POL.
   Yu C., 2017, CHINA ECON REV, V47, P263.
   Yu Y, 2014, ECOL INDIC, V47, P156, DOI 10.1016/j.ecolind.2014.01.044.
   Zhang Q, 2017, NATURE, V543, P705, DOI 10.1038/nature21712.
   Zhang YX, 2014, RENEW SUST ENERG REV, V40, P60, DOI 10.1016/j.rser.2014.07.178.
   Zhang ZK, 2014, ENERG ECON, V46, P161, DOI 10.1016/j.eneco.2014.09.011.
   Zhao HY, 2015, ATMOS CHEM PHYS, V15, P5443, DOI 10.5194/acp-15-5443-2015.
   Zhao HY, 2017, ATMOS CHEM PHYS, V17, P10367, DOI 10.5194/acp-17-10367-2017.
   Zhao HY, 2016, APPL ENERG, V184, P926, DOI 10.1016/j.apenergy.2016.04.038.
   Zhao Y, 2011, ATMOS CHEM PHYS, V11, P2295, DOI 10.5194/acp-11-2295-2011.}},
Number-of-Cited-References = {{50}},
Times-Cited = {{35}},
Usage-Count-Last-180-days = {{11}},
Usage-Count-Since-2013 = {{97}},
Journal-ISO = {{Environ. Sci. Technol.}},
Doc-Delivery-Number = {{GB9HU}},
Unique-ID = {{ISI:000429385700007}},
DA = {{2020-08-06}},
}

@article{ ISI:000429385700040,
Author = {Plank, Barbara and Eisenmenger, Nina and Schaffartzik, Anke and
   Wiedenhofer, Dominik},
Title = {{International Trade Drives Global Resource Use: A Structural
   Decomposition Analysis of Raw Material Consumption from 1990-2010}},
Journal = {{ENVIRONMENTAL SCIENCE \& TECHNOLOGY}},
Year = {{2018}},
Volume = {{52}},
Number = {{7}},
Pages = {{4190-4198}},
Month = {{APR 3}},
Abstract = {{Globalization led to an immense increase of international trade and the
   emergence of complex global value chains. At the same time, global
   resource use and pressures on the environment are increasing steadily.
   With these two processes in parallel, the question arises whether trade
   contributes positively to resource efficiency, or to the contrary is
   further driving resource use? In this article, the socioeconomic driving
   forces of increasing global raw material consumption (RMC) are
   investigated to assess the role of changing trade relations, extended
   supply chains and increasing consumption. We apply a structural
   decomposition analysis of changes in RMC from 1990 to 2010, utilizing
   the Eora multi-regional input-output (MRIO) model. We find that changes
   in international trade patterns significantly contributed to an increase
   of global RMC. Wealthy developed countries play a major role in driving
   global RMC growth through changes in their trade structures, as they
   shifted production processes increasingly to less material-efficient
   input suppliers. Even the dramatic increase in material consumption in
   the emerging economies has not diminished the role of industrialized
   countries as drivers of global RMC growth.}},
Publisher = {{AMER CHEMICAL SOC}},
Address = {{1155 16TH ST, NW, WASHINGTON, DC 20036 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Plank, B (Corresponding Author), Inst Social Ecol, Schottenfeldgasse 29, A-1070 Vienna, Austria.
   Plank, Barbara; Eisenmenger, Nina; Schaffartzik, Anke; Wiedenhofer, Dominik, Inst Social Ecol, Schottenfeldgasse 29, A-1070 Vienna, Austria.}},
DOI = {{10.1021/acs.est.7b06133}},
ISSN = {{0013-936X}},
EISSN = {{1520-5851}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; MATERIAL FLOW; ECONOMIC-GROWTH; SOCIOECONOMIC
   METABOLISM; MATERIAL EQUIVALENTS; CARBON FOOTPRINT; SUSTAINABILITY;
   20TH-CENTURY; INDICATORS; NATIONS}},
Research-Areas = {{Engineering; Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Engineering, Environmental; Environmental Sciences}},
Author-Email = {{barbara.plank@boku.ac.at}},
ResearcherID-Numbers = {{Eisenmenger, Nina/AAB-7805-2020
   Wiedenhofer, Dominik/AAA-5678-2020
   }},
ORCID-Numbers = {{Eisenmenger, Nina/0000-0002-8833-3883
   Wiedenhofer, Dominik/0000-0001-7418-3477
   Plank, Barbara/0000-0002-0306-3715}},
Funding-Acknowledgement = {{Austrian Federal Ministry of Agriculture, Forestry, Environment and
   Water Management {[}BMLFUW-UW.1.4.18/0076-I/2/2016]}},
Funding-Text = {{The research presented in this article was supported by the Austrian
   Federal Ministry of Agriculture, Forestry, Environment and Water
   Management, grant number BMLFUW-UW.1.4.18/0076-I/2/2016. We would like
   to thank our colleague Maria Niedertscheider for her technical support.}},
Cited-References = {{{[}Anonymous], 2016, WORLD EC OUTLOOK DAT.
   Arto I, 2014, ENVIRON SCI TECHNOL, V48, P5388, DOI 10.1021/es5005347.
   Baldwin Richard, 2016, GREAT CONVERGENCE IN.
   Bruckner M, 2012, GLOBAL ENVIRON CHANG, V22, P568, DOI 10.1016/j.gloenvcha.2012.03.011.
   Daniels PL, 2011, ECON SYST RES, V23, P353, DOI 10.1080/09535314.2011.633500.
   Dietzenbacher E., 2000, ECON SYST RES, V12, P497, DOI DOI 10.1080/09535310020003793.
   Dietzenbacher E., 1998, ECON SYST RES, V10, P307, DOI DOI 10.1080/09535319800000023.
   Eisenmenger N, 2016, ECOL ECON, V128, P177, DOI 10.1016/j.ecolecon.2016.03.010.
   Elbadawi IA, 2012, WORLD DEV, V40, P681, DOI 10.1016/j.worlddev.2011.09.012.
   Erb KH, 2009, ECOL ECON, V69, P328, DOI 10.1016/j.ecolecon.2009.06.025.
   {*}EUR, 2001, EC WID MAT FLOW ACC.
   Fischer-Kowalski M, 1998, INT SOC SCI J, V50, P573, DOI 10.1111/1468-2451.00169.
   Fischer-Kowalski M, 2011, J IND ECOL, V15, P855, DOI 10.1111/j.1530-9290.2011.00366.x.
   Fischer-Kowalski M, 2011, REPORT WORKING GROUP.
   Giljum S, 2015, J IND ECOL, V19, P792, DOI 10.1111/jiec.12214.
   Hashimoto S, 2008, J IND ECOL, V12, P657, DOI 10.1111/j.1530-9290.2008.00072.x.
   Hertwich EG, 2009, ENVIRON SCI TECHNOL, V43, P6414, DOI 10.1021/es803496a.
   Hoekstra AY, 2007, INTEGRATED ASSESSMENT OF WATER RESOURCES AND GLOBAL CHANGE, P35, DOI 10.1007/s11269-006-9039-x.
   Hoekstra R, 2002, ENVIRON RESOUR ECON, V23, P357, DOI 10.1023/A:1021234216845.
   Hoekstra R, 2016, ECON SYST RES, V28, P151, DOI 10.1080/09535314.2016.1166099.
   Hornborg A, 2016, J POLIT ECOL, V23, P328.
   Inomata S, 2014, ECON SYST RES, V26, P239, DOI 10.1080/09535314.2014.940856.
   Kanemoto K, 2014, GLOBAL ENVIRON CHANG, V24, P52, DOI 10.1016/j.gloenvcha.2013.09.008.
   Krausmann F, 2017, P NATL ACAD SCI USA, V114, P1880, DOI 10.1073/pnas.1613773114.
   Krausmann F, 2013, P NATL ACAD SCI USA, V110, P10324, DOI 10.1073/pnas.1211349110.
   Krausmann F, 2009, ECOL ECON, V68, P2696, DOI 10.1016/j.ecolecon.2009.05.007.
   Lan J, 2016, APPL ENERG, V163, P436, DOI 10.1016/j.apenergy.2015.10.178.
   Lenzen M, 2012, NATURE, V486, P109, DOI 10.1038/nature11145.
   Lenzen M, 2013, ECON SYST RES, V25, P20, DOI 10.1080/09535314.2013.769938.
   Lenzen M, 2012, ENVIRON SCI TECHNOL, V46, P8374, DOI 10.1021/es300171x.
   Liu JG, 2013, ECOL SOC, V18, DOI 10.5751/ES-05873-180226.
   Lutter S, 2016, ECOL ECON, V127, P1, DOI 10.1016/j.ecolecon.2016.03.012.
   Meng J, 2016, P ROY SOC A-MATH PHY, V472, DOI 10.1098/rspa.2016.0380.
   Meyfroidt P, 2013, CURR OPIN ENV SUST, V5, P438, DOI 10.1016/j.cosust.2013.04.003.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   Moran D, 2017, NAT ECOL EVOL, V1, DOI {[}10.1038/s41559-016-0023, 10.1038/41559-016-0023].
   Munoz P, 2008, ECOL ECON, V65, P136, DOI 10.1016/j.ecolecon.2007.06.010.
   Muradian R, 2001, ECOL ECON, V36, P281, DOI 10.1016/S0921-8009(00)00229-9.
   Neumann K, 2010, AGR SYST, V103, P316, DOI 10.1016/j.agsy.2010.02.004.
   OECD, 2013, INT EC BEN GLOB VAL.
   OECD, 2015, MAT RES PROD ENV.
   Pauliuk S, 2015, ECOL ECON, V119, P83, DOI 10.1016/j.ecolecon.2015.08.012.
   PERSKY J, 1990, J ECON PERSPECT, V4, P187, DOI 10.1257/jep.4.2.187.
   Peters GP, 2011, ECON SYST RES, V23, P131, DOI 10.1080/09535314.2011.563234.
   Peters GP, 2011, P NATL ACAD SCI USA, V108, P8903, DOI 10.1073/pnas.1006388108.
   Pifiero P., 2015, CHIN EN STAT YB 2012, V119, P217.
   Pothen F, 2017, ECOL ECON, V141, P154, DOI 10.1016/j.ecolecon.2017.05.032.
   Schaffartzik A, 2015, SUSTAINABILITY-BASEL, V7, P5345, DOI 10.3390/su7055345.
   Schaffartzik A, 2014, GLOBAL ENVIRON CHANG, V26, P87, DOI 10.1016/j.gloenvcha.2014.03.013.
   Schaffartzik A, 2014, J IND ECOL, V18, P102, DOI 10.1111/jiec.12055.
   Schandl H, 2018, J IND ECOL, V22, P827, DOI 10.1111/jiec.12626.
   Schandl H, 2010, GLOBAL ENVIRON CHANG, V20, P636, DOI 10.1016/j.gloenvcha.2010.06.003.
   Seto KC, 2012, P NATL ACAD SCI USA, V109, P7687, DOI 10.1073/pnas.1117622109.
   Steen-Olsen K, 2016, ECON SYST RES, V28, P78, DOI 10.1080/09535314.2016.1141751.
   Steinberger JK, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070385.
   Su B, 2012, ENERG ECON, V34, P177, DOI 10.1016/j.eneco.2011.10.009.
   Su B, 2010, ENERG ECON, V32, P166, DOI 10.1016/j.eneco.2009.07.010.
   Teixido-Figueras J, 2016, ECOL INDIC, V62, P163, DOI 10.1016/j.ecolind.2015.11.041.
   Tukker A, 2016, GLOBAL ENVIRON CHANG, V40, P171, DOI 10.1016/j.gloenvcha.2016.07.002.
   Tukker A, 2013, ECON SYST RES, V25, P50, DOI 10.1080/09535314.2012.761952.
   UNEP, 2016, GLOB MAT FLOWS RES P.
   Wallerstein I, 2011, MOD WORLD SYST, V1, P1.
   Wang HM, 2014, ENVIRON SCI TECHNOL, V48, P5025, DOI 10.1021/es405812w.
   Weber CL, 2009, ENERG POLICY, V37, P1561, DOI 10.1016/j.enpol.2008.12.027.
   Weinzettel J, 2011, J IND ECOL, V15, P893, DOI 10.1111/j.1530-9290.2011.00378.x.
   Weisz H, 2015, P NATL ACAD SCI USA, V112, P6260, DOI 10.1073/pnas.1506532112.
   Wenzlik M, 2015, J IND ECOL, V19, P814, DOI 10.1111/jiec.12341.
   Wiebe KS, 2012, J IND ECOL, V16, P636, DOI 10.1111/j.1530-9290.2012.00504.x.
   Wiedmann T, 2011, ECOL ECON, V70, P1937, DOI 10.1016/j.ecolecon.2011.06.014.
   Wiedmann T, 2010, ECON SYST RES, V22, P19, DOI 10.1080/09535311003612591.
   Wiedmann TO, 2015, P NATL ACAD SCI USA, V112, P6271, DOI 10.1073/pnas.1220362110.
   Wilting HC, 2017, ENVIRON SCI TECHNOL, V51, P3298, DOI 10.1021/acs.est.6b05296.
   Wood R, 2015, SUSTAINABILITY-BASEL, V7, P138, DOI 10.3390/su7010138.
   Wood R, 2009, J IND ECOL, V13, P847, DOI 10.1111/j.1530-9290.2009.00177.x.
   World Economic Forum, 2016, GLOB ALL TRAD FAC.
   Yang ZF, 2012, ENVIRON SCI TECHNOL, V46, P1796, DOI 10.1021/es203657t.
   Zhang C, 2017, ECOL ECON, V140, P265, DOI 10.1016/j.ecolecon.2017.04.021.}},
Number-of-Cited-References = {{77}},
Times-Cited = {{22}},
Usage-Count-Last-180-days = {{6}},
Usage-Count-Since-2013 = {{46}},
Journal-ISO = {{Environ. Sci. Technol.}},
Doc-Delivery-Number = {{GB9HU}},
Unique-ID = {{ISI:000429385700040}},
DA = {{2020-08-06}},
}

@article{ ISI:000428974500031,
Author = {Shao, Ling and Li, Yuan and Feng, Kuishuang and Meng, Jing and Shan,
   Yuli and Guan, Dabo},
Title = {{Carbon emission imbalances and the structural paths of Chinese regions}},
Journal = {{APPLIED ENERGY}},
Year = {{2018}},
Volume = {{215}},
Pages = {{396-404}},
Month = {{APR 1}},
Abstract = {{As the Chinese regions become more and more connected to each other and
   foreign countries, this study aims to address carbon imbalance and
   outsourcing issues in China. Using a production-based carbon emission
   inventory and a China-global multi-regional input-output model, this
   study estimates the consumption-based carbon emissions in 30 Chinese
   regions in 2007 and 2010. Our results reveal that the carbon imbalances
   of most Chinese provinces and cities have decreased between 2007 and
   2010, but disparities in the regional per capita carbon footprint have
   widened. Our Structural Path Analysis (SPA) results shows that most
   Chinese regions have higher ratios of both imported (to
   consumption-based) and exported (to production-based) carbon emissions
   in further upstream supplier tiers than that of direct imports and
   exports in the first tier, thus it's vital to trace emissions in the
   upstream supply chain to understand emission outsourcing. Our result
   from four case study provinces suggests that Beijing should import more
   electricity products from nearby Hebei and Shandong rather than Inner
   Mongolia to lower its consumption-based carbon emissions given the
   smaller emission coefficients of their electricity production.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Shao, L (Corresponding Author), China Univ Geosci, Sch Humanities \& Econ Management, Beijing 100083, Peoples R China.
   Li, Y (Corresponding Author), Jinan Univ, Inst Resource Environm \& Sustainable Dev, Guangzhou 510632, Guangdong, Peoples R China.
   Shao, Ling, China Univ Geosci, Sch Humanities \& Econ Management, Beijing 100083, Peoples R China.
   Shao, Ling, Minist Land \& Resource, Key Lab Carrying Capac Assessment Resource \& Envi, Beijing 100083, Peoples R China.
   Shao, Ling; Li, Yuan; Shan, Yuli; Guan, Dabo, Univ East Anglia, Sch Int Dev, Tyndall Ctr Climate Change Res, Norwich NR4 7TJ, Norfolk, England.
   Li, Yuan, Jinan Univ, Inst Resource Environm \& Sustainable Dev, Guangzhou 510632, Guangdong, Peoples R China.
   Feng, Kuishuang, Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.
   Meng, Jing, Univ Cambridge, Dept Polit \& Int Studies, Cambridge CB3 9D7, England.}},
DOI = {{10.1016/j.apenergy.2018.01.090}},
ISSN = {{0306-2619}},
EISSN = {{1872-9118}},
Keywords = {{Production-based carbon emissions; Consumption-based carbon emissions;
   Multi-regional input-output analysis; Carbon emission imbalance;
   Structural path analysis; Carbon emission pathways}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; CO2 EMISSIONS; CONSUMPTION; TRADE}},
Research-Areas = {{Energy \& Fuels; Engineering}},
Web-of-Science-Categories  = {{Energy \& Fuels; Engineering, Chemical}},
Author-Email = {{lingshao@pku.edu.cn
   y.li4@uea.ac.uk}},
ResearcherID-Numbers = {{Guan, Dabo/Y-2406-2019
   shao, ling/A-9483-2011
   Feng, Kuishuang/Y-2541-2019
   Meng, Jing/J-9740-2013
   Shan, Yuli/N-7747-2015}},
ORCID-Numbers = {{Guan, Dabo/0000-0003-3773-3403
   Meng, Jing/0000-0001-8708-0485
   Shan, Yuli/0000-0002-5215-8657}},
Funding-Acknowledgement = {{Natural Science Foundation of ChinaNational Natural Science Foundation
   of China {[}71503236, 41629501, 71628301]; National Key R\&D Program of
   China {[}2016YFA0602604]; Social Science Foundation of Beijing
   {[}16LJC013]; UK Economic and Social Research CouncilEconomic \& Social
   Research Council (ESRC) {[}ES/L016028/1]; UK Natural Environment
   Research CouncilNERC Natural Environment Research Council
   {[}NE/N00714X/1]; British Academy Grant {[}AF150310]}},
Funding-Text = {{This study was supported by the Natural Science Foundation of China
   (71503236, 41629501), the National Key R\&D Program of China
   (2016YFA0602604), Social Science Foundation of Beijing (16LJC013), the
   UK Economic and Social Research Council (ES/L016028/1), the UK Natural
   Environment Research Council (NE/N00714X/1), and British Academy Grant
   (AF150310). Kuishuang Feng acknowledges the support of the Natural
   Science Foundation of China (71628301).}},
Cited-References = {{Acquaye AA, 2011, ENVIRON SCI TECHNOL, V45, P2471, DOI 10.1021/es103410q.
   Brizga J, 2017, APPL ENERG, V189, P780, DOI 10.1016/j.apenergy.2016.01.102.
   Chen JD, 2016, APPL ENERG, V184, P1154, DOI 10.1016/j.apenergy.2016.03.032.
   Chen SQ, 2016, ENVIRON SCI TECHNOL, V50, P4731, DOI 10.1021/acs.est.5b06299.
   Chen ZM, 2011, ENERG POLICY, V39, P2899, DOI 10.1016/j.enpol.2011.02.068.
   CSY, 2008, CHIN STAT YB.
   Davis SJ, 2010, P NATL ACAD SCI USA, V107, P5687, DOI 10.1073/pnas.0906974107.
   Department of Climate Change National Development and Reform Commission of China 2015, 2015, ENH ACT CLIM CHANG C.
   Doyle A, 2015, WORLD SEALS LANDMARK.
   Duan CC, 2018, APPL ENERGY IN PRESS.
   Feng KS, 2014, ECOL INDIC, V47, P26, DOI 10.1016/j.ecolind.2014.04.045.
   Feng KS, 2013, P NATL ACAD SCI USA, V110, P11654, DOI 10.1073/pnas.1219918110.
   Guan D, 2008, GLOBAL ENVIRON CHANG, V18, P626, DOI 10.1016/j.gloenvcha.2008.08.001.
   Ji P., 2016, PEOPLES DAILY.
   Lenzen M., 2004, ECON SYST RES, V16, P391, DOI DOI 10.1080/0953531042000304272.
   Leontief W, 1986, INPUT OUTPUT EC.
   Leontief WW, 1936, REV ECON STATISTICS, V18, P105, DOI 10.2307/1927837.
   Liu HG, 2015, ENERG ECON, V50, P294, DOI 10.1016/j.eneco.2015.06.006.
   Liu W., 2012, THEORIES PRACTICE CO.
   Liu Y, 2017, J CLEAN PROD, V163, pS410, DOI 10.1016/j.jclepro.2016.02.085.
   Liu Z, 2016, NAT CLIM CHANGE, V6, P201, DOI {[}10.1038/NCLIMATE2800, 10.1038/nclimate2800].
   Liu Z, 2015, NATURE, V524, P335, DOI 10.1038/nature14677.
   Mi ZF, 2016, APPL ENERG, V184, P1073, DOI 10.1016/j.apenergy.2016.06.094.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   National Development and Reform Commission People's Republic of China, 2015, EN RED EV RES EACH P.
   Peter G. P, 2006, ECON SYST RES, V18, P155, DOI DOI 10.1080/09535310600653008.
   Peters GP, 2008, ECOL ECON, V65, P13, DOI 10.1016/j.ecolecon.2007.10.014.
   Project GTA, 2015, GLOB TRAD AN PROJ.
   Project GTA, 2012, GLOB TRAD AN PROJ.
   Shan YL, 2018, SCI DATA, V5, DOI 10.1038/sdata.2017.201.
   Shao L, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/11/114028.
   Skelton A, 2013, GLOBAL ENVIRON CHANG, V23, P1795, DOI 10.1016/j.gloenvcha.2013.07.024.
   Skelton A, 2011, ENVIRON SCI TECHNOL, V45, P10516, DOI 10.1021/es202313e.
   Su B, 2014, APPL ENERG, V114, P377, DOI 10.1016/j.apenergy.2013.09.036.
   Su B, 2011, ECOL ECON, V71, P42, DOI 10.1016/j.ecolecon.2011.08.024.
   The Chinese Government, 2016, KEY TARG CHIN 13 5 Y.
   The State Council of People's Republic of China, 2016, WORK PLAN CONTR GREE.
   The State Council of People's Republic of China, 2011, WORK PLAN CONTR GREE.
   The State Council `s second national economic census leading group office, 2010, CHIN EC CENS YB 2008.
   Tian X, 2014, APPL ENERG, V123, P19, DOI 10.1016/j.apenergy.2014.02.016.
   UNSD, 2015, NAT ACC MAIN AGGR DA.
   Wiebe KS, 2012, J IND ECOL, V16, P636, DOI 10.1111/j.1530-9290.2012.00504.x.
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003.
   Wood R, 2009, ENERG ECON, V31, P335, DOI 10.1016/j.eneco.2008.11.003.
   Ye B, 2017, APPL ENERG, V198, P223, DOI 10.1016/j.apenergy.2017.04.063.
   Zhang B, 2016, APPL ENERG, V184, P1093, DOI 10.1016/j.apenergy.2015.09.076.
   Zhang B, 2016, APPL ENERG, V184, P1184, DOI 10.1016/j.apenergy.2016.04.088.}},
Number-of-Cited-References = {{47}},
Times-Cited = {{31}},
Usage-Count-Last-180-days = {{13}},
Usage-Count-Since-2013 = {{89}},
Journal-ISO = {{Appl. Energy}},
Doc-Delivery-Number = {{GB3RG}},
Unique-ID = {{ISI:000428974500031}},
OA = {{Green Accepted}},
DA = {{2020-08-06}},
}

@article{ ISI:000428974500063,
Author = {Gao, Cuixia and Su, Bin and Sun, Mei and Zhang, Xiaoling and Zhang,
   Zhonghua},
Title = {{Interprovincial transfer of embodied primary energy in China: A complex
   network approach}},
Journal = {{APPLIED ENERGY}},
Year = {{2018}},
Volume = {{215}},
Pages = {{792-807}},
Month = {{APR 1}},
Abstract = {{The energy supply demand security and climate change has continued to be
   problematic, making it significant and necessary to investigate embodied
   energy flow, particularly in a large and fast-growing developing country
   like China. One of the effective approaches is the energy/emissions
   embodied in bilateral trade (EEBT) aiming to locate the destination of
   energy bi-directionally to evaluate how energy flow between producer and
   consumer sectors. However, in addition to the flow of energy and
   resources, the topological structure and impact of underlying components
   from a system science perspective are equally important for
   policy-making. This study therefore constructs an energy embodied in
   trade network (EETN) model to track multi-layer primary energy flow by
   integrating the EEBT approach and complex network analysis. The embodied
   coal, oil, natural gas, and non-fossil fuels associated with China's 30
   provinces/municipalities are quantified at the provincial level. By the
   joint analysis of the network-oriented metrics, the EETN model elicits
   the possibility of understanding the heterogeneity distribution of
   different types of energy flow and the potential impact of
   province-specific policy interventions. We explain how resource
   endowment, economic growth, income inequality, cross-provincial
   industrial transfer, and infrastructures affect China's provincial
   energy embodiments as well as the clustering features. Other findings
   and policy recommendations are also presented.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Sun, M (Corresponding Author), Jiangsu Univ, Inst Appl Syst Anal, Zhenjiang, Peoples R China.
   Su, B (Corresponding Author), Natl Univ Singapore, Energy Studies Inst, Singapore, Singapore.
   Gao, Cuixia; Sun, Mei, Jiangsu Univ, Inst Appl Syst Anal, Zhenjiang, Peoples R China.
   Gao, Cuixia; Su, Bin, Natl Univ Singapore, Energy Studies Inst, Singapore, Singapore.
   Zhang, Xiaoling, City Univ Hong Kong, Dept Publ Policy, Hong Kong, Hong Kong, Peoples R China.
   Zhang, Xiaoling, City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China.
   Zhang, Zhonghua, Tsinghua Univ, Sch Environm, Beijing, Peoples R China.}},
DOI = {{10.1016/j.apenergy.2018.02.075}},
ISSN = {{0306-2619}},
EISSN = {{1872-9118}},
Keywords = {{Primary energy; EEBT; Complex network theory; Communities; China}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; CO2 EMISSIONS; INTERNATIONAL-TRADE; WATER NEXUS;
   CONSUMPTION; CARBON; IMPACTS; FLOWS; PART}},
Research-Areas = {{Energy \& Fuels; Engineering}},
Web-of-Science-Categories  = {{Energy \& Fuels; Engineering, Chemical}},
Author-Email = {{subin@nus.edu.sg
   sunm@ujs.edu.cn}},
ResearcherID-Numbers = {{SU, BIN/B-7990-2011
   Zhang, Xiaoling/AAT-4748-2020
   Zhang, Xiaoling/AAT-4795-2020
   Gao, Cuixia/S-8133-2017}},
ORCID-Numbers = {{SU, BIN/0000-0003-4273-7054
   Gao, Cuixia/0000-0002-4345-2934}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71473108]; China Scholarship CouncilChina
   Scholarship Council {[}201608320273]; Energy Studies Institute (ESI) of
   the National University of Singapore (NUS)}},
Funding-Text = {{This work was supported by the National Natural Science Foundation of
   China (Grant No. 71473108). Ms. Cuixia Gao also expresses her profound
   gratitude to the China Scholarship Council (No. 201608320273) and the
   Energy Studies Institute (ESI) of the National University of Singapore
   (NUS) for their continued support.}},
Cited-References = {{An Q, 2015, ECOL MODEL, V318, P26, DOI 10.1016/j.ecolmodel.2015.01.020.
   Anandarajah G, 2014, APPL ENERG, V131, P79, DOI 10.1016/j.apenergy.2014.06.026.
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008.
   Chen B, 2018, APPL ENERG, V210, P98, DOI 10.1016/j.apenergy.2017.10.113.
   Chen WM, 2017, SCI TOTAL ENVIRON, V584, P990, DOI 10.1016/j.scitotenv.2017.01.152.
   China National Petroleum Corporation, OV W E GAS PIP PROJ.
   Cortes-Borda D, 2015, ENERGY, V82, P578, DOI 10.1016/j.energy.2015.01.067.
   Duan CC, 2017, APPL ENERG, V194, P725, DOI 10.1016/j.apenergy.2016.05.139.
   Gao CX, 2015, APPL ENERG, V156, P542, DOI 10.1016/j.apenergy.2015.07.054.
   Gao CX, 2014, ENERGY, V77, P890, DOI 10.1016/j.energy.2014.09.075.
   Gasim AA, 2015, ENERG POLICY, V86, P186, DOI 10.1016/j.enpol.2015.06.043.
   Hall CAS, 2014, ENERG POLICY, V64, P141, DOI 10.1016/j.enpol.2013.05.049.
   Hong JK, 2016, RENEW SUST ENERG REV, V53, P1303, DOI 10.1016/j.rser.2015.09.068.
   International Energy Agency, 2016, KEY CO2 EM TRENDS.
   International Energy Agency, 2016, KEY WORLD EN TRENDS.
   International Energy Agency (IEA), 2016, NAT GAS INF DAT DOC.
   Jiang XM, 2016, APPL ENERG, V184, P1132, DOI 10.1016/j.apenergy.2016.06.142.
   Kaika D, 2013, ENERG POLICY, V62, P1392, DOI 10.1016/j.enpol.2013.07.131.
   Kanemoto K, 2012, ENVIRON SCI TECHNOL, V46, P172, DOI 10.1021/es202239t.
   Lin W, 2016, HDB BRIEF ENERGY DAT.
   Liu YS, 2015, APPL ENERG, V155, P904, DOI 10.1016/j.apenergy.2015.06.051.
   Liu Z, 2012, ENERG POLICY, V49, P751, DOI 10.1016/j.enpol.2012.07.016.
   Machado G, 2001, ECOL ECON, V39, P409, DOI 10.1016/S0921-8009(01)00230-0.
   Miro L, 2015, APPL ENERG, V137, P793, DOI 10.1016/j.apenergy.2014.06.062.
   Mousavi B, 2017, APPL ENERG, V206, P804, DOI 10.1016/j.apenergy.2017.08.199.
   National Energy Administration of China, 2016, 13 5 YEAR PLAN EN 20.
   NBS, 2014, CHIN REG INP OUTP TA.
   Peters GP, 2008, ECOL ECON, V65, P13, DOI 10.1016/j.ecolecon.2007.10.014.
   Rocco MV, 2016, J CLEAN PROD, V139, P1449, DOI 10.1016/j.jclepro.2016.09.026.
   Sato M, 2017, ENERG POLICY, V105, P41, DOI 10.1016/j.enpol.2017.02.024.
   Sato M, 2014, J ECON SURV, V28, P831, DOI 10.1111/joes.12027.
   Shi JL, 2017, ENERGY, V140, P395, DOI 10.1016/j.energy.2017.08.124.
   Sinden GE, 2011, CLIM POLICY, V11, P1226, DOI 10.1080/14693062.2011.602549.
   Su B, 2017, ENERG POLICY, V105, P484, DOI 10.1016/j.enpol.2017.03.027.
   Su B, 2014, APPL ENERG, V114, P377, DOI 10.1016/j.apenergy.2013.09.036.
   Su B, 2010, ECOL ECON, V70, P10, DOI 10.1016/j.ecolecon.2010.08.016.
   Vora N, 2017, ACS SUSTAIN CHEM ENG, V5, P2119, DOI 10.1021/acssuschemeng.6b02122.
   White DJ, 2018, APPL ENERG, V210, P550, DOI 10.1016/j.apenergy.2017.05.159.
   Wiebe K, 2016, OECD SCI TECHNOLOGY, V2016/05.
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003.
   Wiedmann T, 2009, ECOL ECON, V68, P1975, DOI 10.1016/j.ecolecon.2008.06.023.
   Wu R, 2016, J CLEAN PROD, V112, P4151, DOI 10.1016/j.jclepro.2015.07.017.
   Wu XD, 2016, APPL ENERG, V184, P936, DOI 10.1016/j.apenergy.2016.03.027.
   Yang J, 2013, RENEW SUST ENERG REV, V27, P559, DOI 10.1016/j.rser.2013.07.024.
   Zhang B, 2016, APPL ENERG, V184, P1093, DOI 10.1016/j.apenergy.2015.09.076.
   Zhang B, 2015, ECOL MODEL, V318, P138, DOI 10.1016/j.ecolmodel.2014.10.007.
   Zhang HR, 2018, J CLEAN PROD, V170, P96, DOI 10.1016/j.jclepro.2017.09.139.
   Zhang XC, 2016, BUILD ENVIRON, V104, P188, DOI 10.1016/j.buildenv.2016.05.018.
   Zhang ZH, 2017, RENEW SUST ENERG REV, V68, P492, DOI 10.1016/j.rser.2016.10.009.}},
Number-of-Cited-References = {{49}},
Times-Cited = {{39}},
Usage-Count-Last-180-days = {{14}},
Usage-Count-Since-2013 = {{114}},
Journal-ISO = {{Appl. Energy}},
Doc-Delivery-Number = {{GB3RG}},
Unique-ID = {{ISI:000428974500063}},
DA = {{2020-08-06}},
}

@article{ ISI:000425202700015,
Author = {Bae, Jinwon and Dall'erba, Sandy},
Title = {{Crop Production, Export of Virtual Water and Water-saving Strategies in
   Arizona}},
Journal = {{ECOLOGICAL ECONOMICS}},
Year = {{2018}},
Volume = {{146}},
Pages = {{148-156}},
Month = {{APR}},
Abstract = {{Growing world population and the uncertain hazards that accompany
   climate change put an increasing pressure on the management and
   sustainability of scarce environmental resources, notably water. In
   spite of its water scarcity, the state of Arizona permits as much as
   73\% of its water to be consumed by a single sector, crop production.
   Since 79\% of such crop production is not consumed in Arizona, it
   corresponds to exporting up to 67\% of the water available in the state
   to the rest of the country and abroad. It has certain and glooming
   consequences on the availability of water for a state expected to see
   its population grow and its climate get drier. Based on input-output
   techniques, we simulate three scenarios aiming at saving 19\% of the
   water available, a figure set by the first of them based on improving
   the efficiency of the current irrigation system. The same savings could
   also be reached by a twenty-seven-fold increase in the price of water or
   a 19.5\% reduction in crop exports. Estimates indicate that the least
   costly solution is a more efficient irrigation system while export
   reduction is the second-best choice.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Dall'erba, S (Corresponding Author), Univ Illinois, Dept Agr \& Consumer Econ, Urbana, IL 61801 USA.
   Dall'erba, S (Corresponding Author), Univ Illinois, Reg Econ Applicat Lab, Urbana, IL 61801 USA.
   Bae, Jinwon, Univ Arizona, Sch Geog \& Dev, Tucson, AZ 85721 USA.
   Dall'erba, Sandy, Univ Illinois, Dept Agr \& Consumer Econ, Urbana, IL 61801 USA.
   Dall'erba, Sandy, Univ Illinois, Reg Econ Applicat Lab, Urbana, IL 61801 USA.}},
DOI = {{10.1016/j.ecolecon.2017.10.018}},
ISSN = {{0921-8009}},
EISSN = {{1873-6106}},
Keywords = {{input-output analysis; Virtual water flow; Agricultural water saving}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; CLIMATE-CHANGE; UNITED-STATES; RIVER-BASIN;
   TRADE; CHINA; RESOURCES; FOOTPRINT; AGRICULTURE; CALIFORNIA}},
Research-Areas = {{Environmental Sciences \& Ecology; Business \& Economics}},
Web-of-Science-Categories  = {{Ecology; Economics; Environmental Sciences; Environmental Studies}},
Author-Email = {{dallerba@illinois.edu}},
ResearcherID-Numbers = {{BAE, JINWON/AAM-6695-2020
   Dall'erba, Sandy/AAR-3267-2020}},
Funding-Acknowledgement = {{USDA NIFA Hatch {[}1006816]; Renewable Energy Network, Water,
   Environmental and Energy Solutions Initiatives}},
Funding-Text = {{Financial support from USDA NIFA Hatch (grant 1006816) and from the
   Renewable Energy Network, Water, Environmental and Energy Solutions
   Initiatives is gratefully acknowledged.}},
Cited-References = {{Alcon F, 2011, TECHNOL FORECAST SOC, V78, P991, DOI 10.1016/j.techfore.2011.02.001.
   Allan J. A., 1993, PRIORITIES WATER RES, V13, P26.
   ALLAN JA, 1994, WATER IN THE ARAB WORLD, P65.
   Allan JA, 1998, GROUND WATER, V36, P545, DOI 10.1111/j.1745-6584.1998.tb02825.x.
   CAP, 2009, 2009 2010 WAT RAT SC.
   Cazcarro I, 2013, ENVIRON SCI TECHNOL, V47, P12275, DOI 10.1021/es4019964.
   Christensen NS, 2004, CLIMATIC CHANGE, V62, P337, DOI 10.1023/B:CLIM.0000013684.13621.1f.
   Dang Q, 2015, WATER RESOUR RES, V51, P973, DOI 10.1002/2014WR015919.
   Dasberg S, 1999, DRIP IRRIGATION.
   Deng XP, 2006, AGR WATER MANAGE, V80, P23, DOI 10.1016/j.agwat.2005.07.021.
   Dietzenbacher E, 2007, REG STUD, V41, P185, DOI 10.1080/00343400600929077.
   Dominguez F, 2010, CLIMATIC CHANGE, V99, P499, DOI 10.1007/s10584-009-9672-5.
   Dunning H.C., 1977, J CONT L, V4.
   Finster R.D., 1971, JAWRA J AM WATER RES, V7, P17.
   FISHELSON G, 1994, STUD ENVIRON SCI, V58, P427.
   Garin G., 2013, US REPORT PREPARED N.
   GRIMM NB, 1992, GLOBAL CLIMATE CHANGE AND FRESHWATER ECOSYSTEMS, P211.
   Guan D, 2007, ECOL ECON, V61, P159, DOI 10.1016/j.ecolecon.2006.02.022.
   Han MY, 2014, FRONT EARTH SCI-PRC, V8, P414, DOI 10.1007/s11707-014-0430-2.
   Heckscher EF, 1991, HECKSCHER OHLIN TRAD.
   Hoekstra AY, 2007, INTEGRATED ASSESSMENT OF WATER RESOURCES AND GLOBAL CHANGE, P35, DOI 10.1007/s11269-006-9039-x.
   Hoekstra A. Y., 2002, VIRTUAL WATER TRADE.
   Howitt R., 2014, EC ANAL 2014 DROUGHT.
   Hu S., 2001, AGR RES ARID AREAS, V20, P66.
   IMPLAN, 2010, IMPLAN SYST DAT SOFT.
   Lenzen M, 2009, WATER RESOUR RES, V45, DOI 10.1029/2008WR007649.
   Leontief WW, 1936, REV ECON STATISTICS, V18, P105, DOI 10.2307/1927837.
   Marston L, 2015, P NATL ACAD SCI USA, V112, P8561, DOI 10.1073/pnas.1500457112.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   MOORE MR, 1994, AM J AGR ECON, V76, P859, DOI 10.2307/1243747.
   Mubako S, 2013, ECOL ECON, V93, P230, DOI 10.1016/j.ecolecon.2013.06.005.
   Murphree J., 2014, VOICE 143 BILLION FO.
   NWIS, 2010, WAT US DAT AR.
   Phare M.-A. S, 2009, DENYING SOURCE CRISI.
   Ribot JC, 2005, CLIMATE VARIABILITY.
   Scheierling S.M., 2006, WATER RESOUR RES, V42.
   Schoengold K, 2006, WATER RESOUR RES, V42, DOI 10.1029/2005WR004096.
   Statistics O.o.E.a.P, 2012, AR STAT COUNT POP PR, P1.
   Swain DL, 2014, B AM METEOROL SOC, V95, pS3.
   USDA, 2012, 2012 CENS PUBL RANK.
   USDA, 2008, EST QUANT WAT APPL P.
   USGS, 2010, EST US WAT US COUNTR.
   USGS, 2016, USGS WAT SCI SCH IRR.
   USGS, 2000, USGS WAT SCI SCH IRR.
   Velazquez E, 2006, ECOL ECON, V56, P226, DOI 10.1016/j.ecolecon.2004.09.026.
   Vorosmarty CJ, 2000, SCIENCE, V289, P284, DOI 10.1126/science.289.5477.284.
   Wang HR, 2009, WATER INT, V34, P451, DOI 10.1080/02508060903370077.
   Wang ZY, 2013, J CLEAN PROD, V42, P172, DOI

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