text:
transparency in inventory modeling warrants a re-evaluation of the
   treatment of coproduction in analyses with attributional and
   consequential perspectives. In the present article, the main types of
   coproductions situations and of coproduction models are reviewed, along
   with key desirable characteristics of attributional and consequential
   studies. A concordance analysis leads to clear recommendations, which
   call for important refinements to current guidelines for both LCA/IO
   practitioners and database developers. We notably challenge the simple
   association between, on the one hand, attributional LCA and partition
   allocation, and on the one hand, consequential LCA and substitution
   modeling.}},
Publisher = {{WILEY}},
Address = {{111 RIVER ST, HOBOKEN 07030-5774, NJ USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Majeau-Bettez, G (Corresponding Author), Polytech Montreal, CIRAIG, Montreal, PQ, Canada.
   Majeau-Bettez, Guillaume; Dandres, Thomas; Samson, Rejean, Polytech Montreal, CIRAIG, Montreal, PQ, Canada.
   Majeau-Bettez, Guillaume; Wood, Richard; Stromman, Anders Hammer, Norwegian Univ Sci \& Technol NTNU, Ind Ecol Programme, Trondheim, Norway.
   Pauliuk, Stefan, Univ Freiburg, Freiburg, Germany.
   Hertwich, Edgar, Yale Univ, New Haven, CT USA.}},
DOI = {{10.1111/jiec.12604}},
ISSN = {{1088-1980}},
EISSN = {{1530-9290}},
Keywords = {{coproduction; by-product; joint production; system expansion;
   substitution; multifunctionality}},
Keywords-Plus = {{MULTI-FUNCTIONALITY; LCA; SYSTEM; MODEL; FRAMEWORK; METHODOLOGY;
   SCENARIOS; PRODUCTS; BALANCE; POLICY}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{guillaume.majeau-bettez@polymtl.ca}},
ResearcherID-Numbers = {{Hertwich, Edgar/Y-9537-2019
   Wood, Richard/E-4111-2015
   Stromman, Anders/AAE-9611-2019
   }},
ORCID-Numbers = {{Hertwich, Edgar/0000-0002-4934-3421
   Wood, Richard/0000-0002-7906-3324
   Majeau-Bettez, Guillaume/0000-0002-0151-2468
   Dandres, Thomas/0000-0002-6999-8468}},
Cited-References = {{Almon C., 2000, ECON SYST RES, V12, P27, DOI {[}DOI 10.1080/095353100111263, 10.1080/095353100111263].
   Ardente F, 2012, J IND ECOL, V16, P387, DOI 10.1111/j.1530-9290.2011.00434.x.
   AZAPAGIC A, 1995, COMPUT CHEM ENG, V19, pS229, DOI 10.1016/0098-1354(95)00077-F.
   AZAPAGIC A, 1999, J CLEAN PROD, V7, P101, DOI DOI 10.1016/S0959-6526(98)00046-8.
   Azapagic A., 1998, INT J LCA, V3, P305, DOI DOI 10.1007/BF02979340.
   Beltran AM, 2016, INT J LIFE CYCLE ASS, V21, P252, DOI 10.1007/s11367-015-0994-4.
   Bidard C, 1998, REV POLITICAL EC, V10, P427.
   Bohlin L., 2006, ECON SYST RES, V18, P205.
   Borjeson L, 2006, FUTURES, V38, P723, DOI 10.1016/j.futures.2005.12.002.
   Brander M., 2011, Greenhouse Gas Measurement and Management, V1, P161, DOI 10.1080/20430779.2011.637670.
   Chertow MR, 2007, J IND ECOL, V11, P11, DOI 10.1162/jiec.2007.1110.
   Cherubini F, 2011, RESOUR CONSERV RECY, V55, P1070, DOI 10.1016/j.resconrec.2011.06.001.
   Commission European, 2008, METH WORK PAP.
   Commission European, 2010, INT REF LIF CYCL DAT.
   Dandres T, 2012, RENEW SUST ENERG REV, V16, P1180, DOI 10.1016/j.rser.2011.11.003.
   Dandres T, 2011, RENEW SUST ENERG REV, V15, P3121, DOI 10.1016/j.rser.2011.04.004.
   Duchin F, 2011, ECON SYST RES, V23, P281, DOI 10.1080/09535314.2011.571238.
   Duchin F, 2009, ECO-EFFIC IND SCI, V23, P23.
   Earles JM, 2011, INT J LIFE CYCLE ASS, V16, P445, DOI 10.1007/s11367-011-0275-9.
   Ehrenfeld J, 2004, J CLEAN PROD, V12, P825, DOI 10.1016/j.jclepro.2004.02.003.
   Ekvall T, 2005, J CLEAN PROD, V13, P1225, DOI 10.1016/j.jclepro.2005.05.010.
   Ekvall T, 2004, INT J LIFE CYCLE ASS, V9, P161, DOI 10.1007/BF02994190.
   Ekvall T, 2006, INT J LIFE CYCLE ASS, V11, P344, DOI 10.1065/lca2005.05.208.
   Finnveden G, 2009, J ENVIRON MANAGE, V91, P1, DOI 10.1016/j.jenvman.2009.06.018.
   Fischer-Kowalski M, 1999, J IND ECOL, V2, P107, DOI DOI 10.1162/JIEC.1998.2.4.107.
   Frischknecht R, 2010, INT J LIFE CYCLE ASS, V15, P806, DOI 10.1007/s11367-010-0200-7.
   Gibon T, 2015, ENVIRON SCI TECHNOL, V49, P11218, DOI 10.1021/acs.est.5b01558.
   GIGANTES T, 1970, CONTRIBUTIONS INPUT.
   Gloria TP, 2006, INT J LIFE CYCLE ASS, V11, P26, DOI 10.1065/lca2006.01.229.
   Guinee J.B., 2002, HDB LIFE CYCLE ASSES, V7.
   Guinee J. B., 2010, ENVIRON SCI TECHNOL, V27, P1016.
   Guinee JB, 2004, INT J LIFE CYCLE ASS, V9, P23, DOI 10.1007/BF02978533.
   GUINEE JB, 2001, LIFE CYCLE ASSESSM 3.
   Heijungs R., 2002, COMPUTATIONAL STRUCT.
   Heijungs R., 1997, THESIS.
   HEIJUNGS R, 1998, INT J LCA, V3, P321, DOI DOI 10.1007/BF02979343.
   Heijungs R, 2007, WASTE MANAGE, V27, P997, DOI 10.1016/j.wasman.2007.02.013.
   Heijungs R, 2014, INT J LIFE CYCLE ASS, V19, P473, DOI 10.1007/s11367-013-0662-5.
   Hertwich E, 2014, J IND ECOL, V18, P464, DOI 10.1111/jiec.12150.
   HERTWICH EG, 2000, J IND ECOL, V4, P13, DOI DOI 10.1162/108819800569267.
   Hospido A, 2010, INT J LIFE CYCLE ASS, V15, P44, DOI 10.1007/s11367-009-0130-4.
   International Organization for Standardization, 1998, ISO14041.
   International Organization for Standardization, 2006, 14044 ISO.
   JANSEN PK, 1990, INT ECON REV, V31, P213, DOI 10.2307/2526639.
   Jung J, 2014, INT J LIFE CYCLE ASS, V19, P661, DOI 10.1007/s11367-013-0655-4.
   Koesler S., 2015, SUBSTITUTION ELASTIC.
   Konijn P. J. A., 1995, ECON SYST RES, V7, P31, DOI DOI 10.1080/09535319500000009.
   Konijn P. J. A., 1994, THESIS.
   Lan J, 2012, J IND ECOL, V16, P623, DOI 10.1111/j.1530-9290.2012.00518.x.
   LEONTIEF W, 1970, REV ECON STAT, V52, P262, DOI 10.2307/1926294.
   Leontief WW, 1936, REV ECON STATISTICS, V18, P105, DOI 10.2307/1927837.
   Lesage P, 2007, INT J LIFE CYCLE ASS, V12, P497, DOI 10.1065/lca2006.10.279.2.
   Levine S. H., 2009, HDB INPUT OUTPUT EC.
   Londero E., 1999, ECON SYST RES, V11, P195, DOI {[}DOI 10.1080/09535319900000014, 10.1080/09535319900000014].
   Majeau-Bettez G, 2016, ECON SYST RES, V28, P333, DOI 10.1080/09535314.2016.1166098.
   Majeau-Bettez G, 2016, ECOL ECON, V126, P188, DOI 10.1016/j.ecolecon.2016.02.017.
   Majeau-Bettez G, 2016, J IND ECOL, V20, P67, DOI 10.1111/jiec.12273.
   Majeau-Bettez G, 2014, J IND ECOL, V18, P747, DOI 10.1111/jiec.12142.
   Majeau-Bettez G, 2011, ENVIRON SCI TECHNOL, V45, P10170, DOI 10.1021/es201308x.
   Marvuglia A, 2013, RENEW SUST ENERG REV, V25, P768, DOI 10.1016/j.rser.2013.04.031.
   Miller R. E., 1984, INPUT OUTPUT ANAL FD.
   Nakamura Shinichiro, 2002, Journal of Industrial Ecology, V6, P39, DOI 10.1162/108819802320971632.
   Oosterhaven J, 1996, SOUTH ECON J, V62, P750, DOI 10.2307/1060892.
   Panzar J. C., 1981, AM ECON REV, V71, P2007.
   Pauliuk S., 2016, TAKING STOCK IND ECO, P21, DOI DOI 10.1007/978-3-319-20571-7\_2.
   Pauliuk S, 2015, J IND ECOL, V19, P728, DOI 10.1111/jiec.12306.
   Pelletier N, 2015, INT J LIFE CYCLE ASS, V20, P74, DOI 10.1007/s11367-014-0812-4.
   Rueda-Cantuche JM, 2009, ECON SYST RES, V21, P363, DOI 10.1080/09535310903208344.
   Sanden BA, 2007, J CLEAN PROD, V15, P1469, DOI 10.1016/j.jclepro.2006.03.005.
   Schrijvers DL, 2016, INT J LIFE CYCLE ASS, V21, P976, DOI 10.1007/s11367-016-1063-3.
   Smith NJ, 2011, ECON SYST RES, V23, P49, DOI 10.1080/09535314.2010.534977.
   Spielmann M, 2005, INT J LIFE CYCLE ASS, V10, P325, DOI 10.1065/lca2004.10.188.
   Steubing B, 2016, INT J LIFE CYCLE ASS, V21, P1269, DOI 10.1007/s11367-016-1109-6.
   Suh S, 2004, ENVIRON SCI TECHNOL, V38, P657, DOI 10.1021/es0263745.
   Suh S, 2002, INT J LIFE CYCLE ASS, V7, P134, DOI 10.1007/BF02994047.
   Suh S, 2014, INT J LIFE CYCLE ASS, V19, P1179, DOI 10.1007/s11367-014-0739-9.
   Suh S, 2010, J IND ECOL, V14, P335, DOI 10.1111/j.1530-9290.2010.00235.x.
   ten Raa T., 2013, J EC STRUCTURES, V2, P1, DOI DOI 10.1186/2193-2409-2-5>.
   TENRAA T, 1984, REV ECON STAT, V66, P88, DOI 10.2307/1924699.
   TILLMAN AM, 2000, ENVIRON IMPACT ASSES, V20, P113.
   Udo de Haes H.A.U., 2004, J IND ECOL, V8, P19, DOI DOI 10.1162/1088198042442351.
   UNEP, 2011, TECHNICAL REPORT.
   United Nations, 2009, SYST NAT ACC 2008.
   United Nations, 1999, STUD METH HDB NAT AC, V74.
   Vadenbo C., 2016, J IND ECOL, P1, DOI DOI 10.1111/JIEC.12519.
   VIET VQ, 1994, REG SCI URBAN ECON, V24, P27, DOI 10.1016/0166-0462(94)90018-3.
   Wardenaar T, 2012, INT J LIFE CYCLE ASS, V17, P1059, DOI 10.1007/s11367-012-0431-x.
   Weidema B., 2000, J IND ECOL, V4, P11, DOI DOI 10.1162/108819800300106366.
   Weidema B. P., 2009, 037075 IT NAT AG NEW.
   Weidema B. P., 2013, ECOINVENT REPORT, V1.
   Weidema B. P., 2003, MARKET INFORM LIFE C.
   Weidema B.P., 1999, INT J LIFE CYCLE ASS, V4, P48, DOI DOI 10.1007/BF02979395.
   Weidema B, 2014, J IND ECOL, V18, P324, DOI 10.1111/jiec.12139.
   Weidema BP, 2010, J IND ECOL, V14, P192, DOI 10.1111/j.1530-9290.2010.00236.x.
   Weisz H, 2006, ECOL ECON, V57, P534, DOI 10.1016/j.ecolecon.2005.05.011.
   Wernet G, 2016, INT J LIFE CYCLE ASS, V21, P1218, DOI 10.1007/s11367-016-1087-8.
   Zamagni A, 2012, INT J LIFE CYCLE ASS, V17, P904, DOI 10.1007/s11367-012-0423-x.
   Zamagni A, 2012, J IND ECOL, V16, pS39, DOI 10.1111/j.1530-9290.2012.00467.x.}},
Number-of-Cited-References = {{98}},
Times-Cited = {{16}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{9}},
Journal-ISO = {{J. Ind. Ecol.}},
Doc-Delivery-Number = {{GQ3OI}},
Unique-ID = {{ISI:000441574100003}},
OA = {{Green Accepted}},
DA = {{2020-08-06}},
}

@article{ ISI:000434702100005,
Author = {Bett, Philip E. and Scaife, Adam A. and Li, Chaofan and Hewitt, Chris
   and Golding, Nicola and Zhang, Peiqun and Dunstone, Nick and Smith, Doug
   M. and Thornton, Hazel E. and Lu, Riyu and Ren, Hong-Li},
Title = {{Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall}},
Journal = {{ADVANCES IN ATMOSPHERIC SCIENCES}},
Year = {{2018}},
Volume = {{35}},
Number = {{8, SI}},
Pages = {{918-926}},
Month = {{AUG}},
Abstract = {{The Yangtze River has been subject to heavy flooding throughout history,
   and in recent times severe floods such as those in 1998 have resulted in
   heavy loss of life and livelihoods. Dams along the river help to manage
   flood waters, and are important sources of electricity for the region.
   Being able to forecast high-impact events at long lead times therefore
   has enormous potential benefit. Recent improvements in seasonal
   forecasting mean that dynamical climate models can start to be used
   directly for operational services. The teleconnection from El Nio to
   Yangtze River basin rainfall meant that the strong El Nio in winter
   2015/16 provided a valuable opportunity to test the application of a
   dynamical forecast system. This paper therefore presents a case study of
   a real-time seasonal forecast for the Yangtze River basin, building on
   previous work demonstrating the retrospective skill of such a forecast.
   A simple forecasting methodology is presented, in which the forecast
   probabilities are derived from the historical relationship between
   hindcast and observations. Its performance for 2016 is discussed. The
   heavy rainfall in the May-June-July period was correctly forecast well
   in advance. August saw anomalously low rainfall, and the forecasts for
   the June-July-August period correctly showed closer to average levels.
   The forecasts contributed to the confidence of decision-makers across
   the Yangtze River basin. Trials of climate services such as this help to
   promote appropriate use of seasonal forecasts, and highlight areas for
   future improvements.}},
Publisher = {{SCIENCE PRESS}},
Address = {{16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Bett, PE (Corresponding Author), Met Off, Hadley Ctr, FitzRoy Rd, Exeter EX1 3PB, Devon, England.
   Bett, Philip E.; Scaife, Adam A.; Hewitt, Chris; Golding, Nicola; Dunstone, Nick; Smith, Doug M.; Thornton, Hazel E., Met Off, Hadley Ctr, FitzRoy Rd, Exeter EX1 3PB, Devon, England.
   Scaife, Adam A., Univ Exeter, Coll Engn Math \& Phys Sci, Exeter EX4 4QF, Devon, England.
   Li, Chaofan, Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100029, Peoples R China.
   Zhang, Peiqun; Ren, Hong-Li, China Meteorol Adm, Natl Climate Ctr, Lab Climate Studies, Beijing 100081, Peoples R China.
   Lu, Riyu, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing 100029, Peoples R China.}},
DOI = {{10.1007/s00376-018-7210-y}},
ISSN = {{0256-1530}},
EISSN = {{1861-9533}},
Keywords = {{seasonal forecasting; flood forecasting; Yangtze basin rainfall; ENSO;
   hydroelectricity}},
Keywords-Plus = {{GLOBAL COUPLED MODEL; CHINA; PREDICTION; PRECIPITATION; CLIMATE;
   MONSOON; ENERGY; PREDICTABILITY; CONFIGURATION; JULES}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{philip.bett@metoffice.gov.uk}},
ResearcherID-Numbers = {{Li, Chaofan/D-2735-2017
   Bett, Philip E/I-2682-2012
   Ren, Hong-Li/X-9632-2018
   Zhang, Peiqun/AAQ-5385-2020}},
ORCID-Numbers = {{Li, Chaofan/0000-0001-5157-5427
   Bett, Philip E/0000-0002-4508-7192
   Ren, Hong-Li/0000-0001-7194-0567
   }},
Funding-Acknowledgement = {{UK-China Research \& Innovation Partnership Fund through the Met Office
   Climate Science for Service Partnership China, Newton Fund; National
   Natural Science Foundation of ChinaNational Natural Science Foundation
   of China {[}41320104007]; Project for Development of Key Techniques in
   Meteorological Operation Forecasting {[}YBGJXM201705]}},
Funding-Text = {{This work and its contributors (PB, AS, ND, DS, CH, NG) were supported
   by the UK-China Research \& Innovation Partnership Fund through the Met
   Office Climate Science for Service Partnership China as part of the
   Newton Fund. CL and RL were supported by the National Natural Science
   Foundation of China (Grant No. 41320104007). HR was supported by the
   Project for Development of Key Techniques in Meteorological Operation
   Forecasting (Grant No. YBGJXM201705). The trial forecast service was
   first suggested by AS in 2015. The GPCP precipitation data were provided
   by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, via their website at
   http://www.esrl.noaa.gov/psd/. The Yangtze River basin shapefile used in
   the maps was obtained from
   http://worldmap.harvard.edu/data/geonode:ch\_wtrshed\_30mar11 and is
   based on the watersheds shown in the China Environmental Atlas (2000),
   (c) Chinese Academy of Sciences, Environmental Data Center.}},
Cited-References = {{Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
   Adler RF, 2016, NEW VERSION 2 3 GLOB.
   Best MJ, 2011, GEOSCI MODEL DEV, V4, P677, DOI 10.5194/gmd-4-677-2011.
   Bett PE, 2017, J APPL METEOROL CLIM, V56, P3099, DOI 10.1175/JAMC-D-17-0070.1.
   Clark RT, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa57ab.
   Eade R, 2014, GEOPHYS RES LETT, V41, P5620, DOI 10.1002/2014GL061146.
   Golding Nicola, 2017, Climate Services, V5, P39, DOI 10.1016/j.cliser.2017.03.004.
   Hardiman SC, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aaa172.
   He JH, 2016, J METEOROL RES-PRC, V30, P135, DOI 10.1007/s13351-016-5222-z.
   Hunke EC, 2010, CICE LOS ALAMOS SEA.
   Jiao MeiYan, 2013, Bulletin - World Meteorological Organization, V62, P49.
   Ke ZJ, 2011, ATMOS OCEAN SCI LETT, V4, P270, DOI 10.1080/16742834.2011.11446941.
   Kwon HH, 2009, HYDROLOG SCI J, V54, P582, DOI 10.1623/hysj.54.3.582.
   Li CF, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/9/094002.
   Li F, 2015, ADV ATMOS SCI, V32, P497, DOI 10.1007/s00376-014-4073-8.
   Lu B, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa739a.
   MacLachlan C, 2015, Q J ROY METEOR SOC, V141, P1072, DOI 10.1002/qj.2396.
   Madec G., 2008, NEMO OCEAN ENGINE NO.
   Megann A, 2014, GEOSCI MODEL DEV, V7, P1069, DOI 10.5194/gmd-7-1069-2014.
   Palin EJ, 2016, J APPL METEOROL CLIM, V55, P325, DOI 10.1175/JAMC-D-15-0102.1.
   Peng ZL, 2014, J HYDROL, V519, P792, DOI 10.1016/j.jhydrol.2014.08.012.
   Plate EJ, 2002, J HYDROL, V267, P2, DOI 10.1016/S0022-1694(02)00135-X.
   Podlaha A., 2016, JULY 2016 GLOBAL CAT.
   Rae JGL, 2015, GEOSCI MODEL DEV, V8, P2221, DOI 10.5194/gmd-8-2221-2015.
   Scaife AA, 2014, GEOPHYS RES LETT, V41, P2514, DOI 10.1002/2014GL059637.
   Stuecker MF, 2015, J CLIMATE, V28, P1093, DOI 10.1175/JCLI-D-14-00225.1.
   Svensson C, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/6/064006.
   Tung YL, 2013, J GEOPHYS RES-ATMOS, V118, P5147, DOI 10.1002/jgrd.50367.
   Walters D, 2017, GEOSCI MODEL DEV, V10, P1487, DOI 10.5194/gmd-10-1487-2017.
   Wang B, 2013, P NATL ACAD SCI USA, V110, P2718, DOI 10.1073/pnas.1214626110.
   Wilks D.S., 2011, INT GEOPHYS, V100, P215, DOI DOI 10.1016/B978-0-12-385022-5.00007-5.
   Williams KD, 2015, GEOSCI MODEL DEV, V8, P1509, DOI 10.5194/gmd-8-1509-2015.
   WMO, 2017, 1189 WMO.
   Wu ZW, 2016, CLIM DYNAM, V46, P3067, DOI 10.1007/s00382-015-2753-4.
   Xie SP, 2016, ADV ATMOS SCI, V33, P411, DOI 10.1007/s00376-015-5192-6.
   Xing W, 2016, J CLIMATE, V29, P1783, DOI 10.1175/JCLI-D-15-0016.1.
   Ye Q., 2005, Mitigation and Adaptation Strategies for Global Change, V10, P159, DOI 10.1007/s11027-005-7838-7.
   Ying L, 2012, METEOROL ATMOS PHYS, V117, P121, DOI 10.1007/s00703-012-0201-0.
   Yu FL, 2009, GEOMORPHOLOGY, V113, P210, DOI 10.1016/j.geomorph.2009.03.008.
   Yuan Y, 2017, J METEOROL RES-PRC, V31, P261, DOI 10.1007/s13351-017-6192-5.
   Zhai PM, 2016, J METEOROL RES-PRC, V30, P283, DOI 10.1007/s13351-016-6101-3.
   Zhang WJ, 2016, GEOPHYS RES LETT, V43, P11375, DOI 10.1002/2016GL071190.
   Zhang WJ, 2016, J CLIMATE, V29, P4347, DOI 10.1175/JCLI-D-15-0104.1.
   Zhu CW, 2008, ADV ATMOS SCI, V25, P867, DOI 10.1007/s00376-008-0867-x.
   Zong YQ, 2000, NAT HAZARDS, V22, P165, DOI 10.1023/A:1008119805106.}},
Number-of-Cited-References = {{45}},
Times-Cited = {{12}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{25}},
Journal-ISO = {{Adv. Atmos. Sci.}},
Doc-Delivery-Number = {{GI7SI}},
Unique-ID = {{ISI:000434702100005}},
DA = {{2020-08-06}},
}

@article{ ISI:000434702100008,
Author = {Li, Chaofan and Lu, Riyu and Bett, Philip E. and Scaife, Adam A. and
   Martin, Nicola},
Title = {{Skillful Seasonal Forecasts of Summer Surface Air Temperature in Western
   China by Global Seasonal Forecast System Version 5}},
Journal = {{ADVANCES IN ATMOSPHERIC SCIENCES}},
Year = {{2018}},
Volume = {{35}},
Number = {{8, SI}},
Pages = {{955-964}},
Month = {{AUG}},
Abstract = {{Variations of surface air temperature (SAT) are key in affecting the
   hydrological cycle, ecosystems and agriculture in western China in
   summer. This study assesses the seasonal forecast skill and reliability
   of SAT in western China, using the GloSea5 operational forecast system
   from the UK Met Office. Useful predictions are demonstrated, with
   considerable skill over most regions of western China. The temporal
   correlation coefficients of SAT between model predictions and
   observations are larger than 0.6, in both northwestern China and the
   Tibetan Plateau. There are two important sources of skill for these
   predictions in western China: interannual variation of SST in the
   western Pacific and the SST trend in the tropical Pacific. The tropical
   SST change in the recent two decades, with a warming in the western
   Pacific and cooling in the eastern Pacific, which is reproduced well by
   the forecast system, provides a large contribution to the skill of SAT
   in northwestern China. Additionally, the interannual variation of SST in
   the western Pacific gives rise to the reliable prediction of SAT around
   the Tibetan Plateau. It modulates convection around the Maritime
   Continent and further modulates the variation of SAT on the Tibetan
   Plateau via the surrounding circulation. This process is evident
   irrespective of detrending both in observations and the model
   predictions, and acts as a source of skill in predictions for the
   Tibetan Plateau. The predictability and reliability demonstrated in this
   study is potentially useful for climate services providing early warning
   of extreme climate events and could imply useful economic benefits.}},
Publisher = {{SCIENCE PRESS}},
Address = {{16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Li, CF (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100029, Peoples R China.
   Li, Chaofan, Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100029, Peoples R China.
   Lu, Riyu, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modelling Atmospher Sci \& Geo, Beijing 100029, Peoples R China.
   Lu, Riyu, Univ Chinese Acad Sci, Beijing 100029, Peoples R China.
   Bett, Philip E.; Scaife, Adam A.; Martin, Nicola, Met Off, Hadley Ctr, FitzRoy Rd, Exeter EX1 3PB, Devon, England.
   Scaife, Adam A., Univ Exeter, Coll Engn Math \& Phys Sci, Exeter EX4 4QF, Devon, England.}},
DOI = {{10.1007/s00376-018-7291-7}},
ISSN = {{0256-1530}},
EISSN = {{1861-9533}},
Keywords = {{seasonal forecast; western China; surface air temperature;
   predictability; warming trend}},
Keywords-Plus = {{NCEP-NCAR REANALYSIS; CIRCUMGLOBAL TELECONNECTION; COUPLED MODEL;
   CLIMATE; PREDICTABILITY; CONFIGURATION; RAINFALL; PACIFIC; ENERGY; OCEAN}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{lichaofan@mail.iap.ac.cn}},
ResearcherID-Numbers = {{Bett, Philip E/I-2682-2012
   Li, Chaofan/D-2735-2017}},
ORCID-Numbers = {{Bett, Philip E/0000-0002-4508-7192
   Li, Chaofan/0000-0001-5157-5427}},
Funding-Acknowledgement = {{National Key R\&D Program of China {[}2016YFA0600603]; National Natural
   Science Foundation of ChinaNational Natural Science Foundation of China
   {[}U1502233, 41320104007, 41775083]; UK-China Research \& Innovation
   Partnership Fund through the Met Office Climate Science for Service
   Partnership (CSSP) China, Newton Fund}},
Funding-Text = {{This work was supported by the National Key R\&D Program of China (Grant
   No. 2016YFA0600603) and the National Natural Science Foundation of China
   (Grant Nos. U1502233, 41320104007 and 41775083). This work and its
   contributors were also supported by the UK-China Research \& Innovation
   Partnership Fund through the Met Office Climate Science for Service
   Partnership (CSSP) China as part of the Newton Fund.}},
Cited-References = {{Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
   Best MJ, 2011, GEOSCI MODEL DEV, V4, P677, DOI 10.5194/gmd-4-677-2011.
   Bett PE, 2018, ADV ATMOS SCI, V35, P918, DOI 10.1007/s00376-018-7210-y.
   Bett PE, 2017, J APPL METEOROL CLIM, V56, P3099, DOI 10.1175/JAMC-D-17-0070.1.
   Camp J, 2015, Q J ROY METEOR SOC, V141, P2206, DOI 10.1002/qj.2516.
   Chen JM, 2009, ADV ATMOS SCI, V26, P803, DOI 10.1007/s00376-009-9018-2.
   Clark RT, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa57ab.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Ding H, 2013, J CLIMATE, V26, P7650, DOI 10.1175/JCLI-D-12-00626.1.
   Ding QH, 2005, J CLIMATE, V18, P3483, DOI 10.1175/JCLI3473.1.
   Eade R, 2014, GEOPHYS RES LETT, V41, P5620, DOI 10.1002/2014GL061146.
   Gamo M, 1996, BOUND-LAY METEOROL, V79, P265, DOI 10.1007/BF00119441.
   GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904.
   Hong XW, 2016, J CLIMATE, V29, P3753, DOI 10.1175/JCLI-D-15-0541.1.
   Huang BY, 2015, J CLIMATE, V28, P911, DOI 10.1175/JCLI-D-14-00006.1.
   Huang G, 2011, ADV ATMOS SCI, V28, P257, DOI 10.1007/s00376-010-9225-x.
   Inoue T, 2004, J METEOROL SOC JPN, V82, P951, DOI 10.2151/jmsj.2004.951.
   Kosaka Y, 2016, NAT GEOSCI, V9, P669, DOI {[}10.1038/ngeo2770, 10.1038/NGEO2770].
   Kosaka Y, 2012, J CLIMATE, V25, P7574, DOI 10.1175/JCLI-D-12-00009.1.
   Kumar A, 2013, J CLIMATE, V26, P5674, DOI 10.1175/JCLI-D-12-00731.1.
   Kumar A, 2009, MON WEATHER REV, V137, P2622, DOI 10.1175/2009MWR2814.1.
   Li CF, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa71bd.
   Li CF, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/9/094002.
   Li CF, 2012, CLIM DYNAM, V39, P329, DOI 10.1007/s00382-011-1274-z.
   Lin ZD, 2017, CLIM DYNAM, V48, P3855, DOI 10.1007/s00382-016-3306-1.
   Lin ZD, 2016, ATMOS SCI LETT, V17, P258, DOI 10.1002/asl.652.
   Lu B, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa739a.
   Ma F, 2015, J GEOPHYS RES-ATMOS, V120, P8264, DOI 10.1002/2015JD023185.
   MacLachlan C, 2015, Q J ROY METEOR SOC, V141, P1072, DOI 10.1002/qj.2396.
   Matsuno T., 1966, J METEOR SOC JAPAN, V44, P25, DOI {[}DOI 10.2151/JMSJ1965.44.1\_25, 10.2151/jmsj1965.44.1\_25].
   Megann A, 2014, GEOSCI MODEL DEV, V7, P1069, DOI 10.5194/gmd-7-1069-2014.
   Monerie PA, 2018, CLIM DYNAM, V51, P473, DOI 10.1007/s00382-017-3935-z.
   Palin EJ, 2016, J APPL METEOROL CLIM, V55, P325, DOI 10.1175/JAMC-D-15-0102.1.
   {[}钱永甫 Qian Yongfu], 2004, {[}高原气象, Plateau Meteorology], V23, P417.
   Qin DH, 2006, J CLIMATE, V19, P1820, DOI 10.1175/JCLI3694.1.
   Rae JGL, 2015, GEOSCI MODEL DEV, V8, P2221, DOI 10.5194/gmd-8-2221-2015.
   Scaife AA, 2014, GEOPHYS RES LETT, V41, P2514, DOI 10.1002/2014GL059637.
   Scaife AA, 2017, Q J ROY METEOR SOC, V143, P1, DOI 10.1002/qj.2910.
   Smith DM, 2016, NAT CLIM CHANGE, V6, P936, DOI {[}10.1038/NCLIMATE3058, 10.1038/nclimate3058].
   Svensson C, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/6/064006.
   Wang B, 2009, CLIM DYNAM, V33, P93, DOI 10.1007/s00382-008-0460-0.
   Wang B, 2008, ACTA METEOROL SIN, V22, P383.
   Wei K, 2009, ATMOS OCEAN SCI LETT, V2, P153, DOI 10.1080/16742834.2009.11446795.
   Williams KD, 2015, GEOSCI MODEL DEV, V8, P1509, DOI 10.5194/gmd-8-1509-2015.
   Wu GX, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2002GL016209.
   Wu RG, 2005, J CLIMATE, V18, P3048, DOI 10.1175/JCLI3465.1.
   Yan L, 2010, ADV ATMOS SCI, V27, P921, DOI 10.1007/s00376-009-9062-y.
   Zhou LT, 2010, INT J CLIMATOL, V30, P549, DOI 10.1002/joc.1923.
   {[}周连童 Zhou Liantong], 2003, {[}气候与环境研究, Climatic and environmental research], V8, P274.}},
Number-of-Cited-References = {{49}},
Times-Cited = {{4}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{17}},
Journal-ISO = {{Adv. Atmos. Sci.}},
Doc-Delivery-Number = {{GI7SI}},
Unique-ID = {{ISI:000434702100008}},
DA = {{2020-08-06}},
}

@article{ ISI:000434702100014,
Author = {Lu, Bo and Ren, Hong-Li and Eade, Rosie and Andrews, Martin},
Title = {{Indian Ocean SST modes and Their Impacts as Simulated in BCC\_CSM1.1(m)
   and HadGEM3}},
Journal = {{ADVANCES IN ATMOSPHERIC SCIENCES}},
Year = {{2018}},
Volume = {{35}},
Number = {{8, SI}},
Pages = {{1035-1048}},
Month = {{AUG}},
Abstract = {{The sea surface temperature anomalies (SSTAs) in the tropical Indian
   Ocean (TIO) show two dominant modes at interannual time scales, referred
   to as the Indian Ocean basin mode (IOBM) and dipole mode (IOD). Recent
   studies have shown that the IOBM and IOD not only affect the local
   climate, but also induce remarkable influences in East Asia via
   teleconnections. In this study, we assess simulations of the IOBM and
   IOD, as well as their teleconnections, using the operational seasonal
   prediction models from the Met Office (HadGEM3) and Beijing Climate
   Center {[}BCC CSM1.1(m)]. It is demonstrated that the spatial patterns
   and seasonal cycles are generally reproduced by the control simulations
   of BCC CSM1.1(m) and HadGEM3, although spectra biases exist. The
   relationship between the TIO SSTA and El Nio is successfully simulated
   by both models, including the persistent IOBM warming following El Nio
   and the IOD-El Nio interactions. BCC CSM1.1(m) and HadGEM3 are capable
   of simulating the observed local impact of the IOBM, such as the
   strengthening of the South Asian high. The influences of the IOBM on
   Yangtze River rainfall are also captured well by both models, although
   this teleconnection is slightly weaker in BCC CSM1.1(m) due to the
   underestimation of the northwestern Pacific subtropical high. The local
   effect of the IOD on East African rainfall is reproduced by both models.
   However, the remote control of the IOD on rainfall over southwestern
   China is not clear in either model. It is shown that the realistic
   simulations of TIO SST modes and their teleconnections give rise to the
   source of skillful seasonal predictions over China.}},
Publisher = {{SCIENCE PRESS}},
Address = {{16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Ren, HL (Corresponding Author), China Meteorol Adm, Natl Climate Ctr, Lab Climate Studies, Beijing 100081, Peoples R China.
   Ren, HL (Corresponding Author), Nanjing Univ, Inst Climate \& Global Change Res, Sch Atmospher Sci, CMA NJU Joint Lab Climate Predict Studies, Nanjing 210023, Jiangsu, Peoples R China.
   Ren, HL (Corresponding Author), China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan 430074, Hubei, Peoples R China.
   Lu, Bo; Ren, Hong-Li, China Meteorol Adm, Natl Climate Ctr, Lab Climate Studies, Beijing 100081, Peoples R China.
   Lu, Bo; Ren, Hong-Li, Nanjing Univ, Inst Climate \& Global Change Res, Sch Atmospher Sci, CMA NJU Joint Lab Climate Predict Studies, Nanjing 210023, Jiangsu, Peoples R China.
   Lu, Bo, Xin Jiang Climate Ctr, Urumqi 830002, Peoples R China.
   Ren, Hong-Li, China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan 430074, Hubei, Peoples R China.
   Eade, Rosie; Andrews, Martin, Met Off Hadley Ctr, Exeter EX1 3PB, Devon, England.}},
DOI = {{10.1007/s00376-018-7279-3}},
ISSN = {{0256-1530}},
EISSN = {{1861-9533}},
Keywords = {{Indian Ocean SST; teleconnection; simulation; seasonal prediction}},
Keywords-Plus = {{SEA-SURFACE TEMPERATURE; EAST-AFRICAN RAINFALL; ASIAN SUMMER MONSOON;
   EL-NINO; WESTERN PACIFIC; DIPOLE MODE; CLIMATE VARIABILITY; ATMOSPHERIC
   BRIDGE; TROPICAL CLIMATE; ENSO}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{renhl@cma.gov.cn}},
ResearcherID-Numbers = {{Ren, Hong-Li/X-9632-2018
   }},
ORCID-Numbers = {{Ren, Hong-Li/0000-0001-7194-0567
   Eade, Rosie/0000-0002-3566-4232}},
Funding-Acknowledgement = {{National Key Research and Development Program of China
   {[}2016YFA0602104]; China Meteorological Special Program
   {[}GYHY201506013]; National Science FoundationNational Science
   Foundation (NSF) {[}41605116]; UK-China Research \& Innovation
   Partnership Fund through the Met Office Climate Science for Service
   Partnership (CSSP) China, Newton Fund}},
Funding-Text = {{This work is jointly supported by the National Key Research and
   Development Program of China (Grant No. 2016YFA0602104), the China
   Meteorological Special Program (Grant No. GYHY201506013), and the
   National Science Foundation (Grant No. 41605116). This work and its
   contributors (Bo LU, Hong-Li REN, Rosie EADE, and Martin ANDREWS) were
   supported by the UK-China Research \& Innovation Partnership Fund
   through the Met Office Climate Science for Service Partnership (CSSP)
   China as part of the Newton Fund.}},
Cited-References = {{Alexander MA, 2002, J CLIMATE, V15, P2205, DOI 10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
   ALLAN R, 2001, CLIVAR EXCHANGES, V6, P18.
   Annamalai H, 2005, J CLIMATE, V18, P302, DOI 10.1175/JCLI-3268.1.
   Ashok K, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017926.
   Baquero-Bernal A, 2002, J CLIMATE, V15, P1358, DOI 10.1175/1520-0442(2002)015<1358:ODVOSS>2.0.CO;2.
   Behera SK, 1999, GEOPHYS RES LETT, V26, P3001, DOI 10.1029/1999GL010434.
   Birkett C, 1999, GEOPHYS RES LETT, V26, P1031, DOI 10.1029/1999GL900165.
   Black E, 2003, MON WEATHER REV, V131, P74, DOI 10.1175/1520-0493(2003)131<0074:AOSOTR>2.0.CO;2.
   Chan SC, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034204.
   Chiang JCH, 2002, J CLIMATE, V15, P2616, DOI 10.1175/1520-0442(2002)015<2616:TTTVCB>2.0.CO;2.
   Dommenget D, 2002, J CLIMATE, V15, P216, DOI 10.1175/1520-0442(2002)015<0216:ACNOTI>2.0.CO;2.
   Dommenget D, 2011, CLIM DYNAM, V36, P2129, DOI 10.1007/s00382-010-0787-1.
   Dommenget D, 2009, J CLIMATE, V22, P4930, DOI 10.1175/2009JCLI2846.1.
   Du Y, 2013, J CLIMATE, V26, P7240, DOI 10.1175/JCLI-D-12-00678.1.
   Du Y, 2011, J CLIMATE, V24, P315, DOI 10.1175/2010JCLI3890.1.
   Du Y, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL033631.
   Du Y, 2009, J CLIMATE, V22, P2023, DOI 10.1175/2008JCLI2590.1.
   GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904.
   Harris I, 2014, INT J CLIMATOL, V34, P623, DOI 10.1002/joc.3711.
   Huang G, 2011, ADV ATMOS SCI, V28, P257, DOI 10.1007/s00376-010-9225-x.
   HUANG RH, 1992, J METEOROL SOC JPN, V70, P243, DOI 10.2151/jmsj1965.70.1B\_243.
   Johnson SJ, 2017, CLIM DYNAM, V48, P1447, DOI 10.1007/s00382-016-3151-2.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Klein SA, 1999, J CLIMATE, V12, P917, DOI 10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.
   Kug JS, 2006, J CLIMATE, V19, P1784, DOI 10.1175/JCLI3660.1.
   Latif M, 1999, J CLIMATE, V12, P3497, DOI 10.1175/1520-0442(1999)012<3497:TROIOS>2.0.CO;2.
   Li CF, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/9/094002.
   Li G, 2015, J CLIMATE, V28, P3058, DOI 10.1175/JCLI-D-14-00740.1.
   Li T, 2003, J ATMOS SCI, V60, P2119, DOI 10.1175/1520-0469(2003)060<2119:ATFTIO>2.0.CO;2.
   Liu L, 2011, J CLIMATE, V24, P4941, DOI 10.1175/2011JCLI4041.1.
   Liu ZY, 2007, REV GEOPHYS, V45, DOI 10.1029/2005RG000172.
   Lu B, 2018, CLIM DYNAM, V51, P89, DOI 10.1007/s00382-017-3908-2.
   Lu B, 2016, DYNAM ATMOS OCEANS, V76, P127, DOI 10.1016/j.dynatmoce.2016.10.005.
   Lu B, 2016, J GEOPHYS RES-ATMOS, V121, P2719, DOI 10.1002/2015JD024393.
   MacLachlan C, 2015, Q J ROY METEOR SOC, V141, P1072, DOI 10.1002/qj.2396.
   Qiu Y, 2014, SCI REP-UK, V4, DOI 10.1038/srep04943.
   Rayner NA, 2006, J CLIMATE, V19, P446, DOI 10.1175/JCLI3637.1.
   Ren HL, 2017, J METEOROL RES-PRC, V31, P204, DOI 10.1007/s13351-017-6097-3.
   Saji NH, 1999, NATURE, V401, P360, DOI 10.1038/43855.
   Saji NH, 2003, CLIM RES, V25, P151, DOI 10.3354/cr025151.
   Schott FA, 2009, REV GEOPHYS, V47, DOI 10.1029/2007RG000245.
   Stevenson S, 2010, J CLIMATE, V23, P5540, DOI 10.1175/2010JCLI3609.1.
   Ummenhofer CC, 2009, J CLIMATE, V22, P993, DOI 10.1175/2008JCLI2493.1.
   Wallace JM, 1998, J GEOPHYS RES-OCEANS, V103, P14241, DOI 10.1029/97JC02905.
   Webster PJ, 1999, NATURE, V401, P356, DOI 10.1038/43848.
   Wittenberg AT, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL038710.
   Wu TW, 2010, CLIM DYNAM, V34, P123, DOI 10.1007/s00382-008-0487-2.
   Xie SP, 2016, ADV ATMOS SCI, V33, P411, DOI 10.1007/s00376-015-5192-6.
   Xie SP, 2009, J CLIMATE, V22, P730, DOI 10.1175/2008JCLI2544.1.
   Yamagata T, 2003, B AM METEOROL SOC, V84, P1418, DOI 10.1175/BAMS-84-10-1418.
   Yang JL, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028571.
   {[}杨建玲 YANG Jianling], 2008, {[}海洋学报, Acta Oceanologica Sinica], V30, P12.
   Yang Y, 2015, J CLIMATE, V28, P8021, DOI 10.1175/JCLI-D-15-0078.1.
   Yu JY, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014098.
   Yuan Y, 2008, INT J CLIMATOL, V28, P1849, DOI 10.1002/joc.1678.
   Zhang Q, 2002, J METEOROL SOC JPN, V80, P733, DOI 10.2151/jmsj.80.733.
   Zhang Q Y, 1998, ACTA METEOROLOGICA S, V56, P199, DOI DOI 10.11676/QXXB1998.019.
   {[}赵俊虎 Zhao Junhu], 2016, {[}大气科学, Chinese Journal of Atmospheric Sciences], V40, P1182.}},
Number-of-Cited-References = {{58}},
Times-Cited = {{5}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{10}},
Journal-ISO = {{Adv. Atmos. Sci.}},
Doc-Delivery-Number = {{GI7SI}},
Unique-ID = {{ISI:000434702100014}},
DA = {{2020-08-06}},
}

@article{ ISI:000439440200005,
Author = {Leung, Marco Y. T. and Zhou, Wen},
Title = {{Circumglobal teleconnection and eddy control of variation in summer
   precipitation over Northwest China}},
Journal = {{CLIMATE DYNAMICS}},
Year = {{2018}},
Volume = {{51}},
Number = {{4}},
Pages = {{1351-1362}},
Month = {{AUG}},
Abstract = {{In this study, authors investigated both year-to-year variation and
   long-term changes in precipitation in Northwest China. Based on
   statistical analysis, anomalous regional rising motion and convergence
   of moisture flux are identified as the causes of year-to-year variation
   of precipitation in Northwest China. Aside from the year-to-year
   variation, long-term changes in these regional circulations also led to
   a significant enhancement of precipitation in Northwest China after the
   mid-1980 s. In addition, the changes in these regional circulations are
   controlled by anomalous eddies in the upper troposphere. The anomalous
   regional rising motion and the convergence of moisture flux are a result
   of the anomalous divergence of wind in the upper troposphere and
   southeasterly wind in the mid to lower troposphere. Both are induced by
   a negative center of eddy height in the upper troposphere to the west of
   Northwest China. It is also noted that the year-to-year variation of the
   negative center is concurrent with the circumglobal teleconnection. On
   the other hand, the long-term change in the negative center is
   concurrent with the weakening of midlatitude eddies in the mid-1980 s.}},
Publisher = {{SPRINGER}},
Address = {{233 SPRING ST, NEW YORK, NY 10013 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhou, W (Corresponding Author), City Univ Hong Kong, Guy Carpenter Asia Pacific Climate Impact Ctr, Sch Energy \& Environm, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China.
   Leung, Marco Y. T.; Zhou, Wen, City Univ Hong Kong, Guy Carpenter Asia Pacific Climate Impact Ctr, Sch Energy \& Environm, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China.}},
DOI = {{10.1007/s00382-017-3958-5}},
ISSN = {{0930-7575}},
EISSN = {{1432-0894}},
Keywords = {{Circumglobal teleconnection; Climate shift; Moisture flux; Midlatitude
   eddies}},
Keywords-Plus = {{WATER-VAPOR TRANSPORT; INTERDECADAL VARIABILITY; PATTERNS; REGIONS}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{wenzhou@cityu.edu.hk}},
ResearcherID-Numbers = {{Zhou, Wen/C-3750-2012}},
ORCID-Numbers = {{Leung, Yu-Ting/0000-0002-8314-627X
   Zhou, Wen/0000-0002-3297-4841}},
Funding-Acknowledgement = {{Research Grants Council of the Hong Kong Special Administrative Region,
   ChinaHong Kong Research Grants Council {[}CityU 11305715, 11335316]}},
Funding-Text = {{This work is supported by Research Grants Council of the Hong Kong
   Special Administrative Region, China (CityU 11305715 and 11335316).}},
Cited-References = {{Barnes S. L., 1964, J APPL METEOROL, V3, P396, DOI {[}DOI 10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 10.1175/1520-0450(1964)003,0396:ATFMDI.2.0.CO;2].
   Chen GS, 2012, J CLIMATE, V25, P7834, DOI 10.1175/JCLI-D-11-00684.1.
   Ding QH, 2005, J CLIMATE, V18, P3483, DOI 10.1175/JCLI3473.1.
   Ding QH, 2011, J CLIMATE, V24, P1878, DOI 10.1175/2011JCLI3621.1.
   Ding YH, 2009, INT J CLIMATOL, V29, P1926, DOI 10.1002/joc.1759.
   DUCHON CE, 1979, J APPL METEOROL, V18, P1016, DOI 10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
   Gong DY, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014523.
   Huang W, 2015, CHINA J CLIM, V28, P3579, DOI {[}10.1175/JCLI-D-14-00395.1, DOI 10.1175/JCLI-D-14-00395.1].
   Kobayashi S, 2015, J METEOROL SOC JPN, V93, P5, DOI 10.2151/jmsj.2015-001.
   Li RCY, 2015, CLIM DYNAM, V45, P1043, DOI 10.1007/s00382-014-2347-6.
   Li XZ, 2015, J CLIMATE, V28, P7025, DOI 10.1175/JCLI-D-14-00403.1.
   Li XZ, 2016, CLIM DYNAM, V46, P147, DOI 10.1007/s00382-015-2575-4.
   Li XZ, 2014, J CLIMATE, V27, P8778, DOI 10.1175/JCLI-D-14-00049.1.
   Peixoto J.P., 1992, PHYS CLIMATE.
   Wang H, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052371.
   Wang L, 2015, CLIM DYNAM, V45, P2527, DOI 10.1007/s00382-015-2490-8.
   Wei W, 2015, J CLIMATE, V28, P2623, DOI 10.1175/JCLI-D-14-00454.1.
   Wei W, 2014, CLIM DYNAM, V43, P1257, DOI 10.1007/s00382-013-1938-y.
   Wu B, 2016, J CLIMATE, V29, P3253, DOI 10.1175/JCLI-D-15-0105.1.
   Xu ZQ, 2015, J CLIMATE, V28, P4086, DOI 10.1175/JCLI-D-14-00464.1.
   Yatagai A, 1998, J METEOROL SOC JPN, V76, P799, DOI 10.2151/jmsj1965.76.5\_799.
   Yatagai A, 2003, HYDROL PROCESS, V17, P2871, DOI 10.1002/hyp.1439.
   YATAGAI A, 1995, J METEOROL SOC JPN, V73, P909, DOI 10.2151/jmsj1965.73.5\_909.
   Yuan F, 2012, ADV ATMOS SCI, V29, P769, DOI 10.1007/s00376-012-2018-7.
   Zhai PM, 2005, J CLIMATE, V18, P1096, DOI 10.1175/JCLI-3318.1.
   Zhou LT, 2010, INT J CLIMATOL, V30, P549, DOI 10.1002/joc.1923.
   Zhu YL, 2011, CLIM DYNAM, V36, P1463, DOI 10.1007/s00382-010-0852-9.}},
Number-of-Cited-References = {{27}},
Times-Cited = {{3}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{8}},
Journal-ISO = {{Clim. Dyn.}},
Doc-Delivery-Number = {{GN8RY}},
Unique-ID = {{ISI:000439440200005}},
DA = {{2020-08-06}},
}

@article{ ISI:000439440200014,
Author = {Gong, Zhiqiang and Feng, Guolin and Dogar, Muhammad Mubashar and Huang,
   Gang},
Title = {{The possible physical mechanism for the EAP-SR co-action}},
Journal = {{CLIMATE DYNAMICS}},
Year = {{2018}},
Volume = {{51}},
Number = {{4}},
Pages = {{1499-1516}},
Month = {{AUG}},
Abstract = {{The anomalous characteristics of summer precipitation and atmospheric
   circulation in the East Asia-West Pacific Region (EA-WP) associated with
   the co-action of East Asia/Pacific teleconnection-Silk Road
   teleconnection (EAP-SR) are investigated in this study. The compositions
   of EAP-SR phase anomalies can be expressed as pattern I (+ +), pattern
   II (+ -), pattern III (- -), and pattern IV (- +) using EAP and SR
   indices. It is found that the spatial distribution of summer
   precipitation anomalies in EA-WP corresponding to pattern I (III) shows
   a tripole structure in the meridional direction and a zonal dipole
   structure in the subtropical region, while pattern II (IV) presents a
   tripole pattern in meridional direction with compressed and continuous
   anomalies in the zonal direction over the subtropical region. The
   similar meridional and zonal structures are also found in the
   geopotential height anomalies at 500-hPa, as well as wind anomalies and
   moisture convergence at 850-hPa. Finally, a schematic mechanism for the
   EAP-SR co-action upon the summer precipitation in EA-WP is built: (1)
   Pattern I (III) exhibits that the negative (positive) sea surface
   temperature (SST) anomalies over tropical East Pacific may cause the
   enhanced (weakened) convective activity dominating the West Pacific,
   trigger the positive (negative) EAP teleconnection and produce more
   (less) precipitation. Besides, the negative (positive) SST anomalies
   over the Indonesia Maritime Continent (IMC) may further weaken
   (strengthen) anomalous downward (upward) motion over the South China Sea
   (SCS), cause negative (positive) geopotential height anomalies at the
   middle troposphere and surrounding regions through the function of the
   tropical Hadley circulation. Then the negative (positive) geopotential
   height anomalies could motivate the positive (negative) EAP
   teleconnection through the northward propagation of wave-activity
   perturbation. Meanwhile, a positive (negative) geopotential height
   anomalous pattern over Eastern Europe motivates a Rossby wave train
   propagation from Western Europe to west-central Asia. This circumstance
   can cause suppressed (enhanced) convection and less (more) precipitation
   over northwestern India and Pakistan, which could strengthen the
   negative (positive) geopotential height and positive (negative)
   vorticity anomalies over central East Asia, resulting in a negative
   (positive) SR teleconnection along the Asian jet stream. A positive
   (negative) lobe over the Korean Peninsula and Japan corresponding to SR
   overlaps with a positive (negative) lobe of EAP, which strengthens the
   anomalous phase contrast on both sides of 120A degrees E. Accordingly,
   summer precipitation anomalies in EA-WP exhibit the meridional tripole
   pattern and the zonal dipole pattern. (2) Pattern II (IV) indicates that
   the normal SST anomalies over the tropical East Pacific cause the weak
   tele-impact on the tropical West Pacific, while the positive (negative)
   SST anomalies over the IMC will lead to a negative (positive) lobe of
   EAP over the subtropical region. This circumstance can weaken the
   positive (negative) lobe of SR over subtropical region, causing
   compressed and continuous negative (positive) anomalies of 500-hPa
   geopotential height and positive (negative) surface precipitation
   anomalies from central East China to Japan.}},
Publisher = {{SPRINGER}},
Address = {{233 SPRING ST, NEW YORK, NY 10013 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Huang, G (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China.
   Gong, Zhiqiang; Feng, Guolin, Natl Climate Res Ctr CMA, Lab Climate Studies, Beijing 100081, Peoples R China.
   Dogar, Muhammad Mubashar, King Abdullah Univ Sci \& Technol, Earth Sci \& Engn, Thuwal, Saudi Arabia.
   Dogar, Muhammad Mubashar, Minist Climate Change, Global Change Impact Studies Ctr, Islamabad, Pakistan.
   Huang, Gang, Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China.}},
DOI = {{10.1007/s00382-017-3967-4}},
ISSN = {{0930-7575}},
EISSN = {{1432-0894}},
Keywords = {{East Asia/Pacific teleconnection; Silk Road teleconnection; Co-action;
   Precipitation}},
Keywords-Plus = {{WESTERN NORTH PACIFIC; ASIAN SUMMER MONSOON; INTERANNUAL VARIABILITY;
   SEASONAL PREDICTABILITY; EAST-ASIA; TELECONNECTION; PRECIPITATION;
   CLIMATE; DYNAMICS; CHINA}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{gzq0929@126.com}},
ResearcherID-Numbers = {{Huang, Gang/E-9539-2016
   Dogar, Muhammad/AAK-3964-2020
   huang, gang/D-6427-2012}},
ORCID-Numbers = {{Huang, Gang/0000-0003-2518-8145
   huang, gang/0000-0002-8692-7856}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}41575082, 41530531, 41475064]; Special Scientific
   Research Project for Public Interest {[}GYHY201306021]}},
Funding-Text = {{The authors wish to thank anonymous reviewers' meaningful comments that
   led to a much-improved manuscript. Thanks also are extended to Dr. Pete
   Saunders for his great help in improving the writing of this manuscript.
   This work is supported by the National Natural Science Foundation of
   China (Grant Nos. 41575082, 41530531, and 41475064), the Special
   Scientific Research Project for Public Interest (Grant No.
   GYHY201306021).}},
Cited-References = {{Chen GS, 2013, J ATMOS SCI, V70, P2875, DOI 10.1175/JAS-D-12-0326.1.
   CORNEJOGARRIDO AG, 1977, J ATMOS SCI, V34, P1155, DOI 10.1175/1520-0469(1977)034<1155:OTHBOT>2.0.CO;2.
   Ding QH, 2005, J CLIMATE, V18, P3483, DOI 10.1175/JCLI3473.1.
   Ding QH, 2011, J CLIMATE, V24, P1878, DOI 10.1175/2011JCLI3621.1.
   Ding YH, 2005, METEOROL ATMOS PHYS, V89, P117, DOI 10.1007/s00703-005-0125-z.
   Ding YH, 2007, J METEOROL SOC JPN, V85B, P21, DOI 10.2151/jmsj.85B.21.
   Dogar MM, 2017, J EARTH SYST SCI, V126, DOI 10.1007/s12040-017-0804-4.
   Enomoto T, 2003, Q J ROY METEOR SOC, V129, P157, DOI 10.1256/qj.01.211.
   {[}封国林 Feng Guolin], 2013, {[}应用气象学报, Journal of Applied Meteorolgical Science], V24, P656.
   Gong ZQ, 2015, CHINESE PHYS B, V24, DOI 10.1088/1674-1056/24/4/049204.
   Gong ZQ, 2016, WEATHER FORECAST, V31, P1381, DOI 10.1175/WAF-D-16-0007.1.
   Gong ZQ, 2015, SCI CHINA EARTH SCI, V58, P404, DOI 10.1007/s11430-014-4967-4.
   Gong ZQ, 2014, THEOR APPL CLIMATOL, V117, P195, DOI 10.1007/s00704-013-0997-7.
   HOSKINS BJ, 1981, J ATMOS SCI, V38, P1179, DOI 10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.
   Hsu HH, 2007, J CLIMATE, V20, P4443, DOI 10.1175/JCLI4246.1.
   Huang G, 2004, ADV ATMOS SCI, V21, P41, DOI 10.1007/BF02915679.
   HUANG JP, 1993, Q J ROY METEOR SOC, V119, P547.
   Huang R., 2012, CHINESE SCI BULL, V58, P1339.
   Huang R.H., 1998, CHIN J ATMOS SCI, V22, P469.
   Huang R H, 1987, P INT C GEN CIRC E A, P40.
   {[}黄荣辉 HUANG RongHui], 2006, {[}大气科学, Chinese Journal of Atmospheric Sciences], V30, P545.
   Huang RH, 2007, ADV ATMOS SCI, V24, P993, DOI 10.1007/s00376-007-0993-x.
   Huang RH, 2012, ADV ATMOS SCI, V29, P910, DOI 10.1007/s00376-012-2015-x.
   Ishii M, 2005, INT J CLIMATOL, V25, P865, DOI 10.1002/joc.1169.
   Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI {[}10.1175/BAMS-83-11-1631(2002)083<1631:NAR>2.3.CO;2, 10.1175/Bams-83-11-1631].
   Kosaka Y, 2006, Q J ROY METEOR SOC, V132, P2009, DOI 10.1256/qj.05.204.
   Kosaka Y, 2013, P NATL ACAD SCI USA, V110, P7574, DOI 10.1073/pnas.1215582110.
   Kosaka Y, 2012, J CLIMATE, V25, P7574, DOI 10.1175/JCLI-D-12-00009.1.
   Kosaka Y, 2009, J METEOROL SOC JPN, V87, P561, DOI 10.2151/jmsj.87.561.
   Lau WKM, 2012, J HYDROMETEOROL, V13, P392, DOI 10.1175/JHM-D-11-016.1.
   Li HM, 2010, CLIM DYNAM, V34, P501, DOI 10.1007/s00382-008-0482-7.
   Li XZ, 2012, J METEOROL SOC JPN, V90, P587, DOI 10.2151/jmsj.2012-501.
   Lu RY, 2006, J METEOROL SOC JPN, V84, P447, DOI 10.2151/jmsj.84.447.
   Lu RY, 2009, J CLIMATE, V22, P2058, DOI 10.1175/2008JCLI2444.1.
   Nitta T, 1996, J METEOROL SOC JPN, V74, P425, DOI 10.2151/jmsj1965.74.4\_425.
   NITTA T, 1987, J METEOROL SOC JPN, V65, P373, DOI 10.2151/jmsj1965.65.3\_373.
   Qiao SB, 2015, ATMOS SCI LETT, V16, P479, DOI 10.1002/asl.585.
   Sato N, 2006, J CLIMATE, V19, P1531, DOI 10.1175/JCLI3697.1.
   Stuecker MF, 2015, J CLIMATE, V28, P1093, DOI 10.1175/JCLI-D-14-00225.1.
   Takaya K, 2001, J ATMOS SCI, V58, P608, DOI 10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.
   Wang B, 1999, B AM METEOROL SOC, V80, P629, DOI 10.1175/1520-0477(1999)080<0629:COSASM>2.0.CO;2.
   Wang B, 2001, J CLIMATE, V14, P4073, DOI 10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2.
   Wang B, 2013, P NATL ACAD SCI USA, V110, P2718, DOI 10.1073/pnas.1214626110.
   Wang B, 2009, J CLIMATE, V22, P3864, DOI 10.1175/2009JCLI2850.1.
   WEBSTER PJ, 1992, Q J ROY METEOR SOC, V118, P877, DOI 10.1256/smsqj.50704.
   Xu ZQ, 2015, J CLIMATE, V28, P4086, DOI 10.1175/JCLI-D-14-00464.1.
   Yang FL, 2004, INT J CLIMATOL, V24, P1625, DOI 10.1002/joc.1094.
   Zhou TJ, 2009, J CLIMATE, V22, P1159, DOI 10.1175/2008JCLI2245.1.}},
Number-of-Cited-References = {{48}},
Times-Cited = {{5}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{18}},
Journal-ISO = {{Clim. Dyn.}},
Doc-Delivery-Number = {{GN8RY}},
Unique-ID = {{ISI:000439440200014}},
DA = {{2020-08-06}},
}

@article{ ISI:000445713500049,
Author = {Yu, Shiwei and Zheng, Shuhong and Li, Xia},
Title = {{The achievement of the carbon emissions peak in China: The role of
   energy consumption structure optimization}},
Journal = {{ENERGY ECONOMICS}},
Year = {{2018}},
Volume = {{74}},
Pages = {{693-707}},
Month = {{AUG}},
Abstract = {{Optimizing the coal-dominated structure of energy consumption would
   effectively contribute toward ensuring that China's carbon emissions
   peak by 2030. To investigate whether this goal can be achieved through
   such optimization, the present study proposed a new economic-carbon
   emission-costs (ECC) multi-objective optimization model. Our findings
   show that the carbon emission peak will be achieved between 2025 and
   2028, most likely in 2028, with 10.12 billion tonnes CO2. The peak of
   coal consumption was achieved in 2013 whereas the oil consumption cannot
   be peaked before 2035 in the GDP preference. In the process, China's GDP
   will be able to maintain an average annual growth rate of 5.9\%-63\%
   between 2017 and 2030, while the carbon emission average annual growth
   will be 0.5\%-1.1\%. The carbon intensity in 2030 can drop to 71.9\%
   based on the 2005 level, which would exceed the committed reduction goal
   of carbon intensity equal to 60-65\%. Furthermore, we find that the
   volume and time of the CO2 emission peak are not sensitive to renewable
   electricity price, but are sensitive to the assumptions both the minimum
   average annual growth rates of GDP and the average annual reduction rate
   of energy intensity. (C) 2018 Elsevier B.V. All rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Yu, SW (Corresponding Author), China Univ Geosci, Sch Econ \& Management, Wuhan 430074, Hubei, Peoples R China.
   Yu, Shiwei, China Univ Geosci, Sch Econ \& Management, Wuhan 430074, Hubei, Peoples R China.
   China Univ Geosci, Ctr Resources \& Environm Econ Res, Wuhan 430074, Hubei, Peoples R China.}},
DOI = {{10.1016/j.eneco.2018.07.017}},
ISSN = {{0140-9883}},
EISSN = {{1873-6181}},
Keywords = {{Carbon emission peak; Energy consumption structure; Multi-objective
   optimization}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; CO2 EMISSIONS; MULTIOBJECTIVE OPTIMIZATION;
   DECOMPOSITION ANALYSIS; TRADE-OFFS; REDUCTIONS; INVESTMENT; INTENSITY;
   ALGORITHM; ECONOMY}},
Research-Areas = {{Business \& Economics}},
Web-of-Science-Categories  = {{Economics}},
Author-Email = {{ysw81993@sina.com}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71573236]}},
Funding-Text = {{Financial supports from the National Natural Science Foundation of China
   (Grant No. 71573236) are gratefully acknowledged.}},
Cited-References = {{Ang BW, 2005, ENERG POLICY, V33, P867, DOI 10.1016/j.enpol.2003.10.010.
   Ang BW, 2003, ENERG POLICY, V31, P1561, DOI 10.1016/S0301-4215(02)00206-9.
   {[}Anonymous], 2017, CHINADAILY.
   {[}Anonymous], 2016, CHINADAILY.
   BP, 2016, BP STAT REV WORLD EN, V2016.
   BP (British Petroleum), 2017, BP STAT REV WORLD EN, V2017.
   CEC (China Electricity Council), 2016, EL IND STAT EXPR BEI.
   Chai QM, 2014, ADV CLIM CHANG RES, V5, P169, DOI 10.1016/j.accre.2015.06.001.
   Cheng Q, 2015, STUDY OPTIMIZATION C.
   CNREC, 2015, CHIN REN EN IND DEV, P5.
   CSC (China's State Council), 2017, 13 5 YEAR COMPR WORK.
   CSC (China's State Council), 2016, 13 5 YEAR WORK PROGR.
   CSC (China's State Council), 2016, 13 5 YEAR EN DEV PLA.
   de Carvalho AL, 2016, RENEW SUST ENERG REV, V54, P1429, DOI 10.1016/j.rser.2015.10.064.
   Deb K, 2002, IEEE T EVOLUT COMPUT, V6, P182, DOI 10.1109/4235.996017.
   Deb K., 2001, MULTIOBJECTIVE OPTIM.
   den Elzen M, 2016, ENERG POLICY, V89, P224, DOI 10.1016/j.enpol.2015.11.030.
   ERI, 2015, CHIN 2050 HIGH REN E.
   Fu F, 2014, ENERG CONVERS MANAGE, V85, P573, DOI 10.1016/j.enconman.2014.05.046.
   Green F, 2015, CHINAS NEW NORMAL BE.
   Green F, 2017, CLIM POLICY, V17, P423, DOI 10.1080/14693062.2016.1156515.
   Guan DB, 2014, NAT CLIM CHANGE, V4, P1017, DOI 10.1038/NCLIMATE2388.
   Hao Y, 2015, ENVIRON DEV ECON, V20, P723, DOI 10.1017/S1355770X15000017.
   He JK, 2014, ADV CLIM CHANG RES, V5, P155, DOI 10.1016/j.accre.2015.04.002.
   He JK, 2014, CHIN J POPUL RESOUR, V12, P189, DOI 10.1080/10042857.2014.932266.
   Huskova I, 2016, GLOBAL ENVIRON CHANG, V41, P216, DOI 10.1016/j.gloenvcha.2016.10.007.
   Jugen L, 2006, WATER POWER, V1, P3.
   Korsbakken JI, 2016, NAT CLIM CHANGE, V6, P687, DOI {[}10.1038/NCLIMATE2963, 10.1038/nclimate2963].
   Li XD, 2003, LECT NOTES COMPUT SC, V2723, P37.
   Liu Z, 2015, NATURE, V524, P335, DOI 10.1038/nature14677.
   Liu Z, 2015, NATURE, V522, P279, DOI 10.1038/522279a.
   Ma G, 2017, IET RENEW POWER GEN, V11, P194, DOI 10.1049/iet-rpg.2016.0646.
   Martinez-Gomez J, 2017, J ENVIRON MANAGE, V203, P962, DOI 10.1016/j.jenvman.2017.07.001.
   Mi ZF, 2017, J CLEAN PROD, V142, P2227, DOI 10.1016/j.jclepro.2016.11.055.
   MLR (Ministry of Land and Resources of China), 2016, CHIN MIN RES 2016.
   NDRC, 2016, 13 5 YEAR PLAN REN E.
   NDRC (National Development and Reform Commission), 2016, NOT ADJ BENCHM PRIC.
   NDRC (National Development and Reform Commission), 2014, CHIN NAT PLAN COP CL.
   NDRC (National Development and Reform Commission), 2015, NOT RED PRIC NONR NA.
   NDRC (National Development and Reform Commission), 2016, REG PIP TRANSP PRIC.
   NEA (Nation Energy Administration), 2016, CHIN 13 5 YEAR PLAN.
   Ni JR, 2001, J ENVIRON MANAGE, V61, P37, DOI 10.1006/jema.2000.0398.
   Niu SW, 2016, RENEW SUST ENERG REV, V58, P782, DOI 10.1016/j.rser.2015.12.274.
   Parreiras RO, 2009, NONLINEAR ANAL-THEOR, V71, pE191, DOI 10.1016/j.na.2008.10.060.
   Qi TY, 2014, ENERG POLICY, V68, P60, DOI 10.1016/j.enpol.2013.12.035.
   QianzhanNet, 2017, QIANZHANNET.
   Su B, 2015, APPL ENERG, V154, P13, DOI 10.1016/j.apenergy.2015.04.101.
   Tian H, 2014, ENERG CONVERS MANAGE, V81, P504, DOI 10.1016/j.enconman.2014.02.053.
   Tuladhar S.R, 2014, P INT C UT EXH GREEN, P1.
   UNFCCC, 2015, ENH ACT CLIM CHANG C.
   Wang QW, 2015, ENERG ECON, V51, P252, DOI 10.1016/j.eneco.2015.07.009.
   Wang YF, 2013, ENERG POLICY, V58, P312, DOI 10.1016/j.enpol.2013.03.022.
   Wang Z., 2017, CHINA RENEWABLE ENER.
   Xu SC, 2014, APPL ENERG, V127, P182, DOI 10.1016/j.apenergy.2014.03.093.
   Yu SW, 2018, ENERG ECON, V73, P91, DOI 10.1016/j.eneco.2018.05.012.
   Yu SW, 2017, EUR J OPER RES, V260, P335, DOI 10.1016/j.ejor.2016.12.023.
   Yuan JH, 2014, ENERG POLICY, V68, P508, DOI 10.1016/j.enpol.2014.01.019.
   Zhang Q, 2004, DECIS SUPPORT SYST, V38, P247, DOI 10.1016/s0167-9236(03)00094-0.
   Zhang XL, 2016, ENERG ECON, V54, P388, DOI 10.1016/j.eneco.2015.12.002.
   Zhang YJ, 2015, RENEW SUST ENERG REV, V41, P1255, DOI 10.1016/j.rser.2014.09.021.
   Zitzler E., 1999, THESIS.}},
Number-of-Cited-References = {{61}},
Times-Cited = {{18}},
Usage-Count-Last-180-days = {{6}},
Usage-Count-Since-2013 = {{34}},
Journal-ISO = {{Energy Econ.}},
Doc-Delivery-Number = {{GU9YP}},
Unique-ID = {{ISI:000445713500049}},
DA = {{2020-08-06}},
}

@article{ ISI:000439671200031,
Author = {Long, Yin and Yoshid, Yoshikuni and Zhang, Runsen and Sun, Lu and Dou,
   Yi},
Title = {{Policy implications from revealing consumption-based carbon footprint of
   major economic sectors in Japan}},
Journal = {{ENERGY POLICY}},
Year = {{2018}},
Volume = {{119}},
Pages = {{339-348}},
Month = {{AUG}},
Abstract = {{In Japan, the Great East Earthquake caused numerous casualties and the
   subsequent suspension of operations at the Fukushima Daiichi Nuclear
   Power Plant continues to have an adverse impact on the national energy
   supply chain. The structural changes in energy supply have served to
   reinforce the importance of national emissions accounting in promoting
   overall CO2 reduction policies. However, traditional approaches for
   evaluating industrial emissions have been criticized for their
   production-based perspective, which fails to consider emissions embodied
   in sectoral indirect consumption. The purpose of this study is therefore
   to understand the sectoral emission transfer. By applying economic
   input-output tables, detailed sectoral economic interactions can be
   usefully identified. It was found that the emissions embodied in final
   consumption generated largely by households. Besides, although emission
   intensity of manufacturing sector is extremely high in direct emission,
   agriculture sector is found to exceed other sectors in indirect emission
   intensities. From the consumption-side, these findings strongly suggest
   that a high priority be given to mitigation efforts aimed at reducing
   residential consumption, whist policy priorities are expected to be
   located on agriculture-related sectors from production-side. The study
   hence provides insights into how we can better allocate emissions
   responsibility and set reduction priorities among major economic
   sectors.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Long, Y (Corresponding Author), Univ Tokyo, Grad Sch Frontier Sci, Dept Environm Syst, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778563, Japan.
   Long, Yin; Yoshid, Yoshikuni; Sun, Lu, Univ Tokyo, Grad Sch Frontier Sci, Dept Environm Syst, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778563, Japan.
   Zhang, Runsen, Hiroshima Univ, Grad Sch Int Dev \& Cooperat, Hiroshima 7398529, Japan.
   Dou, Yi, Nagoya Univ, Grad Sch Environm Studies, Chikusa Ku, Furo Cho, Nagoya, Aichi 4648601, Japan.
   Sun, Lu; Dou, Yi, NIES, Ctr Social \& Environm Syst Res, 16-2 Onogawa, Tsukuba, Ibaraki 3058506, Japan.}},
DOI = {{10.1016/j.enpol.2018.04.052}},
ISSN = {{0301-4215}},
EISSN = {{1873-6777}},
Keywords = {{Consumption-based accounting; Input-output analysis; Japan; Life-cycle
   assessment; Household consumption}},
Keywords-Plus = {{INPUT-OUTPUT; CO2 EMISSIONS; INTERNATIONAL-TRADE; DRIVING FORCES; CHINA;
   ENERGY; DECOMPOSITION; CHAINS; URBAN; MODEL}},
Research-Areas = {{Business \& Economics; Energy \& Fuels; Environmental Sciences \&
   Ecology}},
Web-of-Science-Categories  = {{Economics; Energy \& Fuels; Environmental Sciences; Environmental
   Studies}},
Author-Email = {{long@globalenv.k.u-tokyo.ac.jp}},
ResearcherID-Numbers = {{Sun, Lu/L-2238-2019
   Zhang, Runsen/U-1815-2017
   }},
ORCID-Numbers = {{Sun, Lu/0000-0001-7356-7866
   Long, Yin/0000-0002-6761-9735}},
Funding-Acknowledgement = {{Center for Low Carbon Society Strategy (LCS), Japan Science and
   Technology Agency (JST)}},
Funding-Text = {{This work was supported by Center for Low Carbon Society Strategy (LCS),
   Japan Science and Technology Agency (JST).}},
Cited-References = {{Brizga J, 2017, APPL ENERG, V189, P780, DOI 10.1016/j.apenergy.2016.01.102.
   Caro D, 2014, ECOL INDIC, V36, P640, DOI 10.1016/j.ecolind.2013.09.021.
   Chen GQ, 2010, ENERG POLICY, V38, P6180, DOI 10.1016/j.enpol.2010.06.004.
   Druckman A, 2009, ECOL ECON, V68, P2066, DOI 10.1016/j.ecolecon.2009.01.013.
   Fan JL, 2016, APPL ENERG, V184, P790, DOI 10.1016/j.apenergy.2016.06.076.
   Fan Y, 2007, ENVIRON MODELL SOFTW, V22, P378, DOI 10.1016/j.envsoft.2005.12.007.
   Fang K, 2014, ECOL INDIC, V36, P508, DOI 10.1016/j.ecolind.2013.08.017.
   Hasegawa R., 2015, J EC STRUCT, V4.
   Jones C, 2014, ENVIRON SCI TECHNOL, V48, P895, DOI 10.1021/es4034364.
   Kerkhof AC, 2009, ECOL ECON, V68, P1160, DOI 10.1016/j.ecolecon.2008.08.004.
   LEONTIEF W, 1970, REV ECON STAT, V52, P262, DOI 10.2307/1926294.
   Li N, 2017, APPL ENERG, V185, P1840, DOI 10.1016/j.apenergy.2015.12.112.
   Lin BQ, 2010, ENERG POLICY, V38, P613, DOI 10.1016/j.enpol.2009.10.014.
   Liu HG, 2015, ENERG ECON, V50, P294, DOI 10.1016/j.eneco.2015.06.006.
   Liu HG, 2015, J CLEAN PROD, V103, P362, DOI 10.1016/j.jclepro.2014.09.077.
   Liu HT, 2010, ENERG POLICY, V38, P3957, DOI 10.1016/j.enpol.2010.03.019.
   Liu LC, 2011, J CLEAN PROD, V19, P1754, DOI 10.1016/j.jclepro.2011.06.011.
   Long Y, 2017, J CLEAN PROD, V167, P571, DOI 10.1016/j.jclepro.2017.08.159.
   Machado G, 2001, ECOL ECON, V39, P409, DOI 10.1016/S0921-8009(01)00230-0.
   MIC, 2015, 2011 INP OUTP TABL J.
   MOE, 2011, JAP NAT GREENH GAS E.
   Mongelli I, 2006, ENERG POLICY, V34, P88, DOI 10.1016/j.enpol.2004.06.004.
   Munksgaard J, 2001, ENERG POLICY, V29, P327, DOI 10.1016/S0301-4215(00)00120-8.
   Shan YL, 2016, APPL ENERG, V166, P245, DOI 10.1016/j.apenergy.2015.04.091.
   Steen-Olsen K, 2016, J IND ECOL, V20, P582, DOI 10.1111/jiec.12405.
   Su B, 2015, APPL ENERG, V154, P13, DOI 10.1016/j.apenergy.2015.04.101.
   TMG, 2016, 2011 INP OUTP TABL F.
   Tukker A, 2006, ANALYSIS, V22284, P1.
   Virtanen Y, 2011, J CLEAN PROD, V19, P1849, DOI 10.1016/j.jclepro.2011.07.001.
   Wei J, 2017, J CLEAN PROD, V163, P58, DOI 10.1016/j.jclepro.2016.05.086.
   Weinzettel J, 2014, ECOL ECON, V101, P115, DOI 10.1016/j.ecolecon.2014.02.020.
   Wiedmann TO, 2016, J IND ECOL, V20, P676, DOI 10.1111/jiec.12346.
   Xie SC, 2014, ENERG POLICY, V73, P401, DOI 10.1016/j.enpol.2014.05.035.
   Yu SW, 2012, APPL ENERG, V92, P552, DOI 10.1016/j.apenergy.2011.11.068.
   Zhang Y.-J., 2015, J CLEAN PROD.
   Zhang Y, 2015, ENERG POLICY, V86, P651, DOI 10.1016/j.enpol.2015.08.014.
   Zhu Q, 2012, ENERG POLICY, V48, P618, DOI 10.1016/j.enpol.2012.05.068.}},
Number-of-Cited-References = {{37}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{30}},
Journal-ISO = {{Energy Policy}},
Doc-Delivery-Number = {{GO0XY}},
Unique-ID = {{ISI:000439671200031}},
DA = {{2020-08-06}},
}

@article{ ISI:000441784000087,
Author = {Du, Qiang and Xu, Yadan and Wu, Min and Sun, Qiang and Bai, Libiao and
   Yu, Ming},
Title = {{A network analysis of indirect carbon emission flows among different
   industries in China}},
Journal = {{ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH}},
Year = {{2018}},
Volume = {{25}},
Number = {{24}},
Pages = {{24469-24487}},
Month = {{AUG}},
Abstract = {{Indirect carbon emissions account for a large ratio of the total carbon
   emissions in processes to make the final products, and this implies
   indirect carbon emission flow across industries. Understanding these
   flows is crucial for allocating a carbon allowance for each industry. By
   combining input-output analysis and complex network theory, this study
   establishes an indirect carbon emission flow network (ICEFN) for 41
   industries from 2005 to 2014 to investigate the interrelationships among
   different industries. The results show that the ICEFN was consistent
   with a small-world nature based on an analysis of the average path
   lengths and the clustering coefficients. Moreover, key industries in the
   ICEFN were identified using complex network theory on the basis of
   degree centrality and betweenness centrality. Furthermore, the 41
   industries of the ICEFN were divided into four industrial subgroups that
   are related closely to one another. Finally, possible policy
   implications were provided based on the knowledge of the structure of
   the ICEFN and its trend.}},
Publisher = {{SPRINGER HEIDELBERG}},
Address = {{TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Bai, LB (Corresponding Author), Changan Univ, Sch Econ \& Management, Xian 710064, Shaanxi, Peoples R China.
   Du, Qiang; Bai, Libiao, Changan Univ, Sch Econ \& Management, Xian 710064, Shaanxi, Peoples R China.
   Xu, Yadan; Wu, Min; Sun, Qiang; Yu, Ming, Changan Univ, Sch Civil Engn, Xian 710061, Shaanxi, Peoples R China.}},
DOI = {{10.1007/s11356-018-2533-x}},
ISSN = {{0944-1344}},
EISSN = {{1614-7499}},
Keywords = {{Complex network; Input-output analysis; Indirect carbon emission flows;
   Industry}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; ENERGY-WATER NEXUS; COMPLEX NETWORK; CO2
   EMISSIONS; DIOXIDE EMISSIONS; EMBODIED ENERGY; EXERGY FLOW; CONSUMPTION;
   SECTORS; PERSPECTIVE}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Environmental Sciences}},
Author-Email = {{q.du@chd.edu.cn
   yadan\_xu@chd.edu.cn
   min\_wu@chd.edu.cn
   sun.q@chd.edu.cn
   hanshannuanyang@chd.edu.cn
   ming\_yu@chd.edu.cn}},
ResearcherID-Numbers = {{bai, libiao/L-8511-2019
   }},
ORCID-Numbers = {{libiao, bai/0000-0003-4105-6476}},
Funding-Acknowledgement = {{National Social Science Foundation of China {[}16CJY028]; Humanity and
   Social Science Program Foundation of the Ministry of Education of China
   {[}15YJC790015]; Fundamental Research Funds for the Central
   UniversitiesFundamental Research Funds for the Central Universities
   {[}300102238620, 300102238303]}},
Funding-Text = {{The research work was supported by the National Social Science
   Foundation of China {[}Grant No. 16CJY028]. Humanity and Social Science
   Program Foundation of the Ministry of Education of China {[}Grant No.
   15YJC790015]. The Fundamental Research Funds for the Central
   Universities {[}Grant No. 300102238620, 300102238303].}},
Cited-References = {{An Q, 2015, ECOL MODEL, V318, P26, DOI 10.1016/j.ecolmodel.2015.01.020.
   Batallas DA, 2006, IEEE T ENG MANAGE, V53, P570, DOI 10.1109/TEM.2006.883706.
   Benevides LD, 2016, GENET MOL BIOL, V39, P665, DOI {[}10.1590/1678-4685-GMB-2015-0164, 10.1590/1678-4685-gmb-2015-0164].
   Brown MT, 1996, ECOL ECON, V19, P219, DOI 10.1016/S0921-8009(96)00046-8.
   Buchs M, 2013, ECOL ECON, V90, P114, DOI 10.1016/j.ecolecon.2013.03.007.
   Carvalho VM, 2014, J ECON PERSPECT, V28, P23, DOI 10.1257/jep.28.4.23.
   Chang N, 2015, J CLEAN PROD, V103, P40, DOI 10.1016/j.jclepro.2014.03.003.
   Chen SQ, 2016, APPL ENERG, V184, P905, DOI 10.1016/j.apenergy.2016.03.042.
   Chen SQ, 2015, APPL ENERG, V138, P99, DOI 10.1016/j.apenergy.2014.10.055.
   Chen WQ, 2016, ENVIRON SCI TECHNOL, V50, P3905, DOI 10.1021/acs.est.5b05095.
   Chen WD, 2017, RESOUR CONSERV RECY, V117, P264, DOI 10.1016/j.resconrec.2016.10.008.
   Dai J, 2012, RENEW SUST ENERG REV, V16, P4796, DOI 10.1016/j.rser.2012.04.027.
   Domenech T, 2011, PROCD SOC BEHV, V10, DOI 10.1016/j.sbspro.2011.01.011.
   Dong L, 2013, J CLEAN PROD, V59, P226, DOI 10.1016/j.jclepro.2013.06.048.
   Geng Y, 2013, ENERG POLICY, V60, P820, DOI 10.1016/j.enpol.2013.05.054.
   Gluckler J, 2016, SOC NETWORKS, V47, P156, DOI 10.1016/j.socnet.2016.07.002.
   Goes-Neto A, 2010, BIOSYSTEMS, V101, P59, DOI 10.1016/j.biosystems.2010.04.006.
   Han MY, 2013, ENERG BUILDINGS, V64, P62, DOI 10.1016/j.enbuild.2013.04.006.
   Han R, 2017, ENERG POLICY, V106, P298, DOI 10.1016/j.enpol.2017.03.071.
   Hao XQ, 2016, APPL ENERG, V162, P1515, DOI 10.1016/j.apenergy.2015.04.032.
   Hendrickson C, 1998, ENVIRON SCI TECHNOL, V32, p184A, DOI 10.1021/es983471i.
   Huang JN, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0109996.
   Li LJ, 2017, J CLEAN PROD, V141, P394, DOI 10.1016/j.jclepro.2016.09.134.

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