text:
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Number-of-Cited-References = {{45}},
Times-Cited = {{0}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{9}},
Journal-ISO = {{Int. J. Climatol.}},
Doc-Delivery-Number = {{GS5FV}},
Unique-ID = {{ISI:000443683600011}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000441789600001,
Author = {Levy, Jonathan I. and Woo, May K. and Tebbens, Radboud Duintjer and
   Nishioka, Yurika},
Title = {{Emission payback periods for increased residential insulation using
   marginal electricity modeling: a life cycle approach}},
Journal = {{INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT}},
Year = {{2018}},
Volume = {{23}},
Number = {{9}},
Pages = {{1723-1734}},
Month = {{SEP}},
Abstract = {{Increases in residential insulation can reduce energy consumption and
   corresponding life cycle emissions, but with increased manufacturing and
   transportation of insulation and the associated impacts. In this study,
   we conducted life cycle analyses of residential insulation and estimated
   payback periods for carbon dioxide (CO2), nitrogen oxides (NOx), and
   sulfur dioxide (SO2) emissions, using modeling techniques that account
   for regional variability in climate, fuel utilization, and marginal
   power plant emissions.
   We simulated the increased production of insulation and energy savings
   if all single-family homes in the USA increased insulation levels to the
   2012 International Energy Conservation Code, using an energy simulation
   model (EnergyPlus) applied to a representative set of home templates. We
   estimated hourly marginal changes in electricity production and
   emissions using the Avoided Emissions and Generation Tool (AVERT), and
   we estimated emissions related to direct residential combustion. We
   determined changes in upstream emissions for both insulation and energy
   using openLCA and ecoinvent. Payback periods were estimated by pollutant
   and region. In sensitivity analyses, we considered the importance of
   marginal versus average power plant emissions, transportation emissions,
   emission factors for fiberglass insulation, and sensitivity of emission
   factors to the magnitude of electricity reduction.
   Combining the life cycle emissions associated with both increased
   insulation manufacturing and decreased energy consumption, the payback
   period for increased residential insulation is 1.9 years for CO2
   (regional range 1.4-2.9), 2.5 years for NOx (regional range 1.8-3.9),
   and 2.7 years for SO2 (regional range 1.9-4.8). For insulation,
   transportation emissions are limited in comparison with manufacturing
   emissions. Emission benefits displayed strong regional patterns
   consistent with relative demands for heating versus cooling and the
   dominant fuels used. Payback periods were generally longer using average
   instead of marginal emissions and were insensitive to the magnitude of
   electricity savings, which reflects the structure of the intermediate
   complexity electricity dispatch model.
   The life cycle benefits of increased residential insulation greatly
   exceed the adverse impacts related to increased production across all
   regions, given insulation lifetimes of multiple decades. The strong
   regionality in benefits and the influence of a marginal modeling
   approach reinforce the importance of site-specific attributes and
   time-dynamic modeling within LCA.}},
Publisher = {{SPRINGER HEIDELBERG}},
Address = {{TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Levy, JI (Corresponding Author), Boston Univ, Sch Publ Hlth, 715 Albany St T4W, Boston, MA 02118 USA.
   Levy, Jonathan I.; Woo, May K., Boston Univ, Sch Publ Hlth, 715 Albany St T4W, Boston, MA 02118 USA.
   Tebbens, Radboud Duintjer; Nishioka, Yurika, 102 Sylvester Ave, Winchester, MA 01890 USA.}},
DOI = {{10.1007/s11367-017-1412-x}},
ISSN = {{0948-3349}},
EISSN = {{1614-7502}},
Keywords = {{Carbon dioxide; Electricity; Energy efficiency; Insulation; Marginal;
   Nitrogen oxides; Residential; Sulfur dioxide}},
Keywords-Plus = {{HEALTH IMPACT ASSESSMENT; INPUT-OUTPUT-ANALYSIS; RISK-BASED APPROACH}},
Research-Areas = {{Engineering; Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Engineering, Environmental; Environmental Sciences}},
Author-Email = {{jonlevy@bu.edu}},
ResearcherID-Numbers = {{Levy, Jonathan/B-4542-2011
   }},
ORCID-Numbers = {{Levy, Jonathan/0000-0002-1116-4006
   Woo, May/0000-0003-1283-2202}},
Funding-Acknowledgement = {{North American Insulation Manufacturers Association (NAIMA)}},
Funding-Text = {{This research was supported by the North American Insulation
   Manufacturers Association (NAIMA). NAIMA suggested the research topic
   and was provided the opportunity to give comments on the manuscript, but
   the authors had full editorial control of the content and the findings
   should not be attributed to NAIMA or its member companies. The authors
   thank Yann Tambouret for his contribution to the energy simulation
   modeling.}},
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Number-of-Cited-References = {{26}},
Times-Cited = {{0}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{16}},
Journal-ISO = {{Int. J. Life Cycle Assess.}},
Doc-Delivery-Number = {{GQ6CM}},
Unique-ID = {{ISI:000441789600001}},
DA = {{2020-08-06}},
}

@article{ ISI:000438003200002,
Author = {Wang, Zhaohua and Li, Yiming and Cai, Hailin and Wang, Bo},
Title = {{Comparative analysis of regional carbon emissions accounting methods in
   China: Production-based versus consumption-based principles}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{194}},
Pages = {{12-22}},
Month = {{SEP 1}},
Abstract = {{The analysis of the characteristics of carbon flows between regions is
   critical for China to deploy effective regional mitigation strategies.
   Different accounting principles exert a significant influence on China's
   regional carbon emissions, especially the fairness of carbon emission
   reduction targets, and the responsibility for reducing emissions. An
   input-output model was established, based on the input-output data of
   2007 and 2012, to evaluate the characteristics of regional carbon flow
   and the change in carbon emission from different industries, and to
   analyse the changes in carbon emissions under production based, and
   consumption-based, principles in 30 provinces. The results show that the
   carbon emissions in each region and sector are different: from the
   eastern region to the western region they show a downward trend, and the
   embodied carbon flow accounts for a large proportion of the total.
   Secondary industries are a major contributor to carbon emissions and
   there is a signifiant influence exerted on the results of carbon
   emission accounting in various provinces of China under different
   accounting principles. Therefore, to meet national emission reduction
   targets, the provinces need to pay considerable attention to the choice
   of accounting principles and achieve the principle of fairness and
   impartiality. It will provide a reference for the future division of
   responsibility for carbon emissions in provinces of China. (C) 2018
   Elsevier Ltd. All rights reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Wang, B (Corresponding Author), Beijing Inst Technol, Sch Management \& Econ, Beijing 100081, Peoples R China.
   Wang, Zhaohua; Li, Yiming; Cai, Hailin; Wang, Bo, Beijing Inst Technol, Sch Management \& Econ, Beijing 100081, Peoples R China.
   Wang, Zhaohua; Li, Yiming; Cai, Hailin; Wang, Bo, Beijing Inst Technol, Ctr Energy \& Environm Policy Res, Beijing 100081, Peoples R China.
   Wang, Zhaohua, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China.
   Wang, Zhaohua, Beijing Key Lab Energy Econ \& Environm Management, Beijing 100081, Peoples R China.
   Wang, Zhaohua, Sustainable Dev Res Inst Econ \& Soc Beijing, Beijing 100081, Peoples R China.}},
DOI = {{10.1016/j.jclepro.2018.05.018}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{CO2 emissions embodied; Input-output analysis; Accounting principle}},
Keywords-Plus = {{CO2 EMISSIONS; ENERGY-REQUIREMENTS; GHG EMISSIONS; TIANJIN; FOOTPRINT;
   SHANGHAI; IMPACTS; POLICY; MODEL}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{51022080@qq.com}},
Funding-Acknowledgement = {{National Science Fund for Distinguished Young ScholarsNational Natural
   Science Foundation of ChinaNational Science Fund for Distinguished Young
   Scholars {[}71625003]; Yangtze River Distinguished Professor of MOE;
   National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}91746208, 71573016, 71403021, 71521002,
   71774014]; Humanities and Social science Fund of Ministry of Education
   of ChinaMinistry of Education, China {[}17YJC630145]; China Postdoctoral
   Science FoundationChina Postdoctoral Science Foundation {[}2017M620648];
   National Key Research and Development Program of China
   {[}2016YFA0602504]}},
Funding-Text = {{This study is supported by the National Science Fund for Distinguished
   Young Scholars (Reference No. 71625003), Yangtze River Distinguished
   Professor of MOE, National Key Research and Development Program of China
   (Reference No. 2016YFA0602504), National Natural Science Foundation of
   China (Reference No. 91746208, 71573016, 71403021, 71521002, 71774014),
   Humanities and Social science Fund of Ministry of Education of China
   (Reference No. 17YJC630145), China Postdoctoral Science Foundation
   (Reference No. 2017M620648).}},
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Number-of-Cited-References = {{39}},
Times-Cited = {{8}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{76}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GM3KJ}},
Unique-ID = {{ISI:000438003200002}},
DA = {{2020-08-06}},
}

@article{ ISI:000438003200011,
Author = {Chen, Shuzhen and Wu, Desheng},
Title = {{A revealed damage cost method to evaluate environmental performance of
   production: Evaluating treatment efficiency of emissions and scaling
   treatment cost bounds}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{194}},
Pages = {{101-111}},
Month = {{SEP 1}},
Abstract = {{Environmental performance indices are in great demand for environmental
   policy support. This paper addresses the particular problem of
   evaluating environmental performance of industries and identifying the
   worst performed industries that should be strictly regulated.
   Specifically, an input-output analysis method is developed to
   disentangle the environmental pressures of production processes;
   treatment costs are introduced to reveal the damage costs of relevant
   pressures which is integrated in the DEA based index to restrict the
   weight assignment. The proposed method is advantageous in data
   requirements as well as definition of process boundaries and can
   alleviate the underestimation of damages from predominant pressures. The
   results of evaluation provide a more reliable reference to industrial
   regulation. (C) 2018 Published by Elsevier Ltd.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Chen, SZ (Corresponding Author), Univ Chinese Acad Sci, Sch Management, Beijing 100869, Peoples R China.
   Chen, Shuzhen, Univ Chinese Acad Sci, Sch Management, Beijing 100869, Peoples R China.
   Wu, Desheng, Univ Chinese Acad Sci, Econ \& Management Sch, Beijing 100869, Peoples R China.
   Wu, Desheng, Stockholm Univ, Stockholm Business Sch, Stockholm, Sweden.}},
DOI = {{10.1016/j.jclepro.2018.04.220}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Data envelopment analysis; Environmental performance; Green production;
   Input-output modelling; Treatment cost}},
Keywords-Plus = {{DATA ENVELOPMENT ANALYSIS; LIFE-CYCLE ASSESSMENT; ECO-EFFICIENCY;
   INDUSTRY; IMPACTS; BENCHMARKING; INDICATORS; SYSTEMS; MODELS; INDEX}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{szchen@ucas.ac.cn
   dash@risklab.ca}},
ORCID-Numbers = {{, shuzhen/0000-0001-8265-4486}},
Funding-Acknowledgement = {{Ministry of Science and Technology of ChinaMinistry of Science and
   Technology, China {[}2016YFC0503606]; National Natural Science
   Foundation of China (NSFC)National Natural Science Foundation of China
   {[}71471055, 91546102]; Key Research Program of Frontier Science, CAS
   {[}QYZDB-SSW-SYS021]; CAS Strategic Research and Decision Support System
   Development {[}GHJ-ZLZX-2017-36]; China Postdoctoral Science
   FoundationChina Postdoctoral Science Foundation {[}2017M620883];
   Marianne and Marcus Wallenberg Foundation {[}MMW 2015.0007]}},
Funding-Text = {{This work is supported by the Ministry of Science and Technology of
   China under Grant 2016YFC0503606, by National Natural Science Foundation
   of China (NSFC) under Grant No. 71471055 and 91546102, by Key Research
   Program of Frontier Science, CAS under Grant No. QYZDB-SSW-SYS021, by a
   CAS Strategic Research and Decision Support System Development grants
   under Grant GHJ-ZLZX-2017-36, by the China Postdoctoral Science
   Foundation funded project under Grant 2017M620883 and by the Marianne
   and Marcus Wallenberg Foundation under Grant \# MMW 2015.0007.}},
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Number-of-Cited-References = {{27}},
Times-Cited = {{1}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{19}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GM3KJ}},
Unique-ID = {{ISI:000438003200011}},
DA = {{2020-08-06}},
}

@article{ ISI:000438003200023,
Author = {Wiebe, Kirsten S.},
Title = {{Identifying emission hotspots for low carbon technology transfers}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{194}},
Pages = {{243-252}},
Month = {{SEP 1}},
Abstract = {{Data on consumption-based CO2 emissions has become increasingly
   available over the past years. These data raise the awareness of the
   link between final goods and the environmental pollution caused by
   upstream production processes. Consumers of final products learn where
   in the world CO2 was emitted along the upstream production chain. For
   producers of final products these data provide benchmarks for total CO2
   emitted in upstream production processes. These are used together with
   an extended version of the inverse important coefficient methodology to
   identify `emission hotspots'. `Emission hotspots' are defined as
   countries/industries where a bulk of the upstream emissions occur and
   where a change in technology brings about the largest decrease in
   upstream emissions. This knowledge provides a basis for well-targeted
   technology transfers to clean up the upstream production chain, thus
   reducing the emission footprint of final goods production. The highest
   impact overall in a significant number global value chains analyzed here
   would be replacing upstream use of coal electricity by low carbon
   electricity. These results support the call of the `Powering Past Coal
   Alliance' at the COP23 of ending the use of coal power sooner rather
   than later. (C) 2018 Elsevier Ltd. All rights reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Wiebe, KS (Corresponding Author), Norwegian Univ Sci \& Technol, Dept Energy \& Proc Engn, Ind Ecol Programme, Trondheim, Norway.
   Wiebe, Kirsten S., Norwegian Univ Sci \& Technol, Dept Energy \& Proc Engn, Ind Ecol Programme, Trondheim, Norway.}},
DOI = {{10.1016/j.jclepro.2018.05.003}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Consumption-based emissions; Embodied emissions; Low-carbon
   technologies; Technology transfer; Technology diffusion; Multi-regional
   input-output analysis}},
Keywords-Plus = {{SUPPLY CHAIN MANAGEMENT; LIFE-CYCLE ASSESSMENT; INPUT-OUTPUT DATABASE;
   AUTOMOBILE-INDUSTRY; ENVIRONMENTAL-IMPACT; GLOBAL TRADE; SUSTAINABILITY;
   FOOTPRINT; CLIMATE; SECTOR}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{kirsten.s.wiebe@ntnu.no}},
ResearcherID-Numbers = {{Wiebe, Kirsten Svenja/I-4498-2019}},
ORCID-Numbers = {{Wiebe, Kirsten Svenja/0000-0003-3105-4194}},
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Number-of-Cited-References = {{61}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{29}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GM3KJ}},
Unique-ID = {{ISI:000438003200023}},
DA = {{2020-08-06}},
}

@article{ ISI:000439445100005,
Author = {Gong, Hainan and Wang, Lin and Chen, Wen and Wu, Renguang and Huang,
   Gang and Nath, Debashis},
Title = {{Diversity of the Pacific-Japan Pattern among CMIP5 Models: Role of SST
   Anomalies and Atmospheric Mean Flow}},
Journal = {{JOURNAL OF CLIMATE}},
Year = {{2018}},
Volume = {{31}},
Number = {{17}},
Pages = {{6857-6877}},
Month = {{SEP}},
Abstract = {{This study investigates the reproducibility of the spatial structure and
   amplitude of the observed Pacific-Japan (PJ) pattern in the phase 5 of
   the Coupled Model Intercomparison Project (CMIP5) models. In particular,
   the role of sea surface temperature anomalies (SSTAs) and atmospheric
   mean flow in the diverse reproducibility of the PJ pattern among models
   is investigated. Based on the pattern correlation between simulated and
   observed PJ patterns, models are categorized into high and low
   correlation groups, referred to as HCG and LCG, respectively. The
   observed cold SSTAs in the western North Pacific (WNP) and equatorial
   central Pacific, organized convection and precipitation anomalies, and
   Rossby wave response are reproduced well in HCG models, whereas these
   features are not present in LCG models. The summer SSTAs are closely
   tied to the preceding El Nino-Southern Oscillation and its temporal
   evolution in the tropical Indo-Pacific Ocean in both observations and
   models, but the SSTAs in the Indian Ocean are weak in both HCG and LCG,
   implying a weak Indian Ocean capacitor effect. As a result, the
   reproducibility of the amplitude of the WNP center of the PJ pattern is
   mainly modulated by the SSTAs and local air-sea feedback over the WNP in
   the models. On the other hand, a model with stronger climatological
   southerly along the coast of East Asia tends to produce more realistic
   amplitude of the midlatitude center of the PJ pattern with clearer
   poleward wave-activity fluxes due to more efficient local barotropic
   energy conversion from the mean flow.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Wang, L (Corresponding Author), Chinese Acad Sci, Joint Ctr Global Change Studies, Ctr Monsoon Syst Res, Beijing, Peoples R China.
   Wang, Lin, Chinese Acad Sci, Joint Ctr Global Change Studies, Ctr Monsoon Syst Res, Beijing, Peoples R China.
   Chinese Acad Sci, Joint Ctr Global Change Studies, State Key Lab Numer Modeling Atmospher Sci \& Geop, Inst Atmospher Phys, Beijing, Peoples R China.}},
DOI = {{10.1175/JCLI-D-17-0541.1}},
ISSN = {{0894-8755}},
EISSN = {{1520-0442}},
Keywords = {{Atmosphere-ocean interaction; Teleconnections; Monsoons; ENSO; Climate
   models}},
Keywords-Plus = {{WESTERN NORTH PACIFIC; ASIAN SUMMER MONSOON; CLIMATE FORECAST SYSTEM;
   INTERANNUAL VARIABILITY; EL-NINO; TELECONNECTION PATTERN; INDIAN-OCEAN;
   EAST-ASIA; INTERDECADAL VARIATIONS; COUPLED MODELS}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{wanglin@mail.iap.ac.cn}},
ResearcherID-Numbers = {{Wang, Lin/G-5245-2010
   Gong, Hainan/R-2934-2019
   huang, gang/D-6427-2012
   Chen, Wen/G-6058-2011}},
ORCID-Numbers = {{Wang, Lin/0000-0002-3557-1853
   Gong, Hainan/0000-0002-6501-162X
   huang, gang/0000-0002-8692-7856
   Chen, Wen/0000-0001-9327-9079}},
Funding-Acknowledgement = {{National Key R\&D Program of China {[}2016YFA0600600]; National Natural
   Science Foundation of ChinaNational Natural Science Foundation of China
   {[}41661144016, 41605060]; Fundamental Research Funds for the Central
   UniversitiesFundamental Research Funds for the Central Universities}},
Funding-Text = {{We are very grateful to the three anonymous reviewers and the editor
   Hisashi Nakamura for their insightful comments that helped to improve
   the manuscript. We acknowledge the climatemodeling groups (listed in
   section 2) for producing and making available their model output. For
   CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis
   and Intercomparison provided coordinating support and led development of
   software infrastructure in partnership with the Global Organization for
   Earth System Science Portals. This work was supported by the National
   Key R\&D Program of China (2016YFA0600600), the National Natural Science
   Foundation of China (41661144016, and 41605060), and the Fundamental
   Research Funds for the Central Universities.}},
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Number-of-Cited-References = {{80}},
Times-Cited = {{10}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{18}},
Journal-ISO = {{J. Clim.}},
Doc-Delivery-Number = {{GN8TF}},
Unique-ID = {{ISI:000439445100005}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000439919300006,
Author = {Chen, Jiepeng and Wang, Xin and Zhou, Wen and Wang, Chunzai and Xie,
   Qiang and Li, Gang and Chen, Sheng},
Title = {{Unusual Rainfall in Southern China in Decaying August during Extreme El
   Nino 2015/16: Role of the Western Indian Ocean and North Tropical
   Atlantic SST}},
Journal = {{JOURNAL OF CLIMATE}},
Year = {{2018}},
Volume = {{31}},
Number = {{17}},
Pages = {{7019-7034}},
Month = {{SEP}},
Abstract = {{Previous research has suggested that the anomalous western North Pacific
   anticyclone (WNPAC) can generally persist from an El Nino mature winter
   to the subsequent summer, influencing southern China precipitation
   significantly, where southern China includes the Yangtze River valley
   and South China. Since the late 1970s, three extreme El Nino events have
   been recorded: 1982/83, 1997/98, and 2015/16. There was a sharp contrast
   in the change in southern China rainfall and corresponding atmospheric
   circulations in the decaying August between the 2015/16 extreme El Nino
   event and the earlier two extreme El Nino events. Enhanced rainfall in
   the middle and upper reaches of the Yangtze River and suppressed
   rainfall over South China resulted from basinwide warming in the
   tropical Indian Ocean induced by the extreme El Nino in August 1983 and
   1998, which was consistent with previous studies. However, an anomalous
   western North Pacific cyclone emerged in August 2016 and then caused
   positive rainfall anomalies over South China and negative rainfall
   anomalies from the Yangtze River to the middle and lower reaches of the
   Yellow River. Without considering the effect of the long-term global
   warming trend, in August 2016 the negative SST anomalies over the
   western Indian Ocean and cooling in the north tropical Atlantic
   contributed to the anomalous western North Pacific cyclone and a
   rainfall anomaly pattern with opposite anomalies in South China and the
   Yangtze River region. Numerical experiments with the CAM5 model are
   conducted to confirm that cooler SST in the western Indian Ocean
   contributed more than cooler SST in the north tropical Atlantic to the
   anomalous western North Pacific cyclone and anomalous South China
   rainfall.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Wang, X (Corresponding Author), Chinese Acad Sci, State Key Lab Trop Oceanog, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China.
   Wang, X (Corresponding Author), Qingdao Natl Lab Marine Sci \& Technol, Lab Reg Oceanog \& Numer Modeling, Qingdao, Peoples R China.
   Chen, Jiepeng; Wang, Xin; Wang, Chunzai; Xie, Qiang; Chen, Sheng, Chinese Acad Sci, State Key Lab Trop Oceanog, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China.
   Wang, Xin; Xie, Qiang, Qingdao Natl Lab Marine Sci \& Technol, Lab Reg Oceanog \& Numer Modeling, Qingdao, Peoples R China.
   Zhou, Wen, City Univ Hong Kong, Guy Carpenter Asia Pacific Climate Impact Ctr, Sch Energy \& Environm, Hong Kong, Hong Kong, Peoples R China.
   Xie, Qiang, Chinese Acad Sci, Inst Deep Sea Sci \& Engn, Sanya, Peoples R China.
   Li, Gang, Xichang Satellite Launch Ctr, Xichang, Peoples R China.}},
DOI = {{10.1175/JCLI-D-17-0827.1}},
ISSN = {{0894-8755}},
EISSN = {{1520-0442}},
Keywords = {{Atmosphere-ocean interaction; Atmospheric circulation; El Nino;
   Rainbands; Rainfall; Sea surface temperature}},
Keywords-Plus = {{EAST ASIAN TELECONNECTION; SEA-SURFACE TEMPERATURE; INTERANNUAL
   VARIABILITY; SUMMER RAINFALL; ANOMALOUS ANTICYCLONE; GLOBAL
   PRECIPITATION; DIFFERENT IMPACTS; PACIFIC CLIMATE; OSCILLATION;
   CIRCULATION}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{wangxin@scsio.ac.cn}},
ResearcherID-Numbers = {{Wang, Chunzai/C-9712-2009
   Wang, Xin/B-4624-2012
   Zhou, Wen/C-3750-2012}},
ORCID-Numbers = {{Wang, Chunzai/0000-0002-7611-0308
   Wang, Xin/0000-0002-7323-2289
   Zhou, Wen/0000-0002-3297-4841}},
Funding-Acknowledgement = {{Strategic Priority Research Program of the Chinese Academy of
   SciencesChinese Academy of Sciences {[}XDA11010403]; CAS/SAFEA
   International Partnership Program for Creative Research TeamsChinese
   Academy of Sciences; National Natural Science Foundation of
   ChinaNational Natural Science Foundation of China {[}41506004, 41422601,
   41376025, 41675062, 41530530, 41731173]; Guangdong Natural Science
   FoundationNational Natural Science Foundation of Guangdong Province
   {[}2016A030310113]; project of the Guy Carpenter Asia-Pacific Climate
   Impact Centre {[}9360126]; Independent Research Project Program of State
   Key Laboratory of Tropical Oceanography {[}LTOZZ1702]; Leading Talents
   of Guangdong Province Program; Pioneer Hundred Talents Program of the
   Chinese Academy of Sciences; National Program on Global Change and
   Air-Sea Interaction {[}GASI-IPOVAI-04]}},
Funding-Text = {{This work was jointly supported by the Strategic Priority Research
   Program of the Chinese Academy of Sciences (Grant XDA11010403), the
   CAS/SAFEA International Partnership Program for Creative Research Teams,
   the National Natural Science Foundation of China (Grants 41506004,
   41422601, 41376025, 41675062, and 41530530), the Guangdong Natural
   Science Foundation (Grant 2016A030310113), the project of the Guy
   Carpenter Asia-Pacific Climate Impact Centre (Grant 9360126), and the
   Independent Research Project Program of State Key Laboratory of Tropical
   Oceanography (LTOZZ1702). Author C. Wang acknowledges the support of the
   National Natural Science Foundation of China (41731173), the Leading
   Talents of Guangdong Province Program, the Pioneer Hundred Talents
   Program of the Chinese Academy of Sciences, and the National Program on
   Global Change and Air-Sea Interaction (GASI-IPOVAI-04). The authors
   gratefully acknowledge the use of the High-Performance Computing Center
   (HPCC) for all numeric simulations and data analysis at the South China
   Sea Institute of Oceanology, Chinese Academy of Sciences. The authors
   declare no competing interests.}},
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Number-of-Cited-References = {{62}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{28}},
Journal-ISO = {{J. Clim.}},
Doc-Delivery-Number = {{GO3TQ}},
Unique-ID = {{ISI:000439919300006}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000447556200008,
Author = {Jin, Chunhan and Liu, Jian and Wang, Bin and Wang, Zhiyuan and Yan, Mi},
Title = {{A Centennial Episode of Weak East Asian Summer Monsoon in the Midst of
   the Medieval Warming}},
Journal = {{PALEOCEANOGRAPHY AND PALEOCLIMATOLOGY}},
Year = {{2018}},
Volume = {{33}},
Number = {{9}},
Pages = {{1035-1048}},
Month = {{SEP}},
Abstract = {{Recent paleoproxy evidence has suggested that there is a centennial
   episode of weaker East Asian summer monsoon (EASM) within the Medieval
   Warm Period, in which the EASM is generally strong. This study aims to
   explore the causes of this phenomenon and determine what controls the
   centennial variability of the EASM. With the Community Earth System
   Model, a suite of control and forced experiments were conducted for the
   past 2,000years. The model simulates a warm period over EA (East Asia)
   from CE 801-1250 with a generally increased summer mean precipitation
   over the northern EA; however, during CE 980-1100, the EASM is
   significantly weaker. On the multi-decadal to centennial time scale, a
   strong EASM is associated with a La Nina-like Indo-Pacific warming, and
   a weak EASM with the El Nino-like conditions. This sea surface
   temperature anomaly pattern represents the leading EOF (empirical
   orthogonal function) mode of centennial sea surface temperature
   variations, which are primarily forced by solar radiation and volcanic
   activity. In contrast, land use/land cover and greenhouse gases as well
   as internal dynamics play a negligible role. The weak EASM during CE
   980-1100 is attributed to the relatively low solar radiation, which
   leads to a prevailing El Nino-like Indo-Pacific cooling with the
   strongest cooling occurring in the equatorial western Pacific. The
   suppressed convection over the equatorial western Pacific directly
   induces a Philippine Sea anticyclone anomaly, which increases the
   southern China precipitation, while suppressing Philippine Sea
   precipitation. The latter excites a meridional teleconnection that
   induces anomalous northerly winds and dry conditions over northern
   China.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Liu, J (Corresponding Author), Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China.
   Liu, J (Corresponding Author), Nanjing Normal Univ, State Key Lab Geog Environm Evolut, Jiangsu Prov Cultivat Base, Nanjing, Jiangsu, Peoples R China.
   Liu, J (Corresponding Author), Nanjing Normal Univ, Sch Geog Sci, Nanjing, Jiangsu, Peoples R China.
   Liu, J (Corresponding Author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China.
   Liu, J (Corresponding Author), Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab Numer Simulat Large Scale Co, Nanjing, Jiangsu, Peoples R China.
   Jin, Chunhan; Liu, Jian; Wang, Zhiyuan; Yan, Mi, Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China.
   Jin, Chunhan; Liu, Jian; Wang, Zhiyuan; Yan, Mi, Nanjing Normal Univ, State Key Lab Geog Environm Evolut, Jiangsu Prov Cultivat Base, Nanjing, Jiangsu, Peoples R China.
   Jin, Chunhan; Liu, Jian; Wang, Zhiyuan; Yan, Mi, Nanjing Normal Univ, Sch Geog Sci, Nanjing, Jiangsu, Peoples R China.
   Jin, Chunhan; Liu, Jian; Wang, Zhiyuan; Yan, Mi, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China.
   Liu, Jian, Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab Numer Simulat Large Scale Co, Nanjing, Jiangsu, Peoples R China.
   Wang, Bin, Nanjing Univ Informat Sci \& Technol, Earth Syst Modeling Ctr, Nanjing, Jiangsu, Peoples R China.
   Wang, Bin, Univ Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USA.}},
DOI = {{10.1029/2017PA003264}},
ISSN = {{2572-4517}},
EISSN = {{2572-4525}},
Keywords-Plus = {{PRECIPITATION VARIABILITY; INTERANNUAL VARIABILITY; PAST MILLENNIUM;
   SCALE CHANGES; ICE-AGE; CLIMATE; PACIFIC; SIMULATION; OSCILLATION;
   IMPACT}},
Research-Areas = {{Geology; Oceanography; Paleontology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary; Oceanography; Paleontology}},
Author-Email = {{jliu@njnu.edu.cn}},
ORCID-Numbers = {{Liu, Jian/0000-0001-5772-7065
   Yan, Mi/0000-0003-4730-3781}},
Funding-Acknowledgement = {{National Key Research and Development Program of China
   {[}2016YFA0600401]; National Natural Science Foundation of ChinaNational
   Natural Science Foundation of China {[}41420104002, 41371209, 41671197,
   41501210]; Natural Science Foundation of Jiangsu Higher Education
   Institutions {[}14KJA170002]; Program of Innovative Research Team of
   Jiangsu Higher Education Institutions of China; Priority Academic
   Program Development of Jiangsu Higher Education Institutions
   {[}164320H116]}},
Funding-Text = {{We thank Liu Jianbao for providing us with historical records. Thanks
   for Wang carrying out those six experiments used in this paper. This
   data set is stored on the server of the Nanjing Normal University, and
   the data used in this paper have been published
   (http://dky.njnu.edu.cn/data/data.html). This work was supported by the
   National Key Research and Development Program of China (2016YFA0600401),
   National Natural Science Foundation of China (41420104002, 41371209,
   41671197 and 41501210), Natural Science Foundation of Jiangsu Higher
   Education Institutions (14KJA170002), Program of Innovative Research
   Team of Jiangsu Higher Education Institutions of China, and Priority
   Academic Program Development of Jiangsu Higher Education Institutions
   (164320H116). This is the ESMC publication No 220.}},
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   Zhang PZ, 2008, SCIENCE, V322, P940, DOI 10.1126/science.1163965.
   Zhang RH, 1996, J METEOROL SOC JPN, V74, P49, DOI 10.2151/jmsj1965.74.1\_49.
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   Zhou XJ, 2011, CHINESE SCI BULL, V56, P3003, DOI 10.1007/s11434-011-4651-4.}},
Number-of-Cited-References = {{64}},
Times-Cited = {{0}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{20}},
Journal-ISO = {{Paleoceanogr. Paleoclimatology}},
Doc-Delivery-Number = {{GX2NO}},
Unique-ID = {{ISI:000447556200008}},
DA = {{2020-08-06}},
}

@article{ ISI:000442778000010,
Author = {Mattioli, Matheus C. and Campos, Alessandro T. and Yanagi Junior,
   Tadayuki and Marin, Diego B. and Eugenio, Tony M. C. and Costa Junior,
   V, Jose E.},
Title = {{Energy analysis of broiler chicken production system with darkhouse
   installation}},
Journal = {{REVISTA BRASILEIRA DE ENGENHARIA AGRICOLA E AMBIENTAL}},
Year = {{2018}},
Volume = {{22}},
Number = {{9}},
Pages = {{648-652}},
Month = {{SEP}},
Abstract = {{The objective of this study was to evaluate the sustainability of a
   broiler chicken production system, with confinement in Darkhouse sheds,
   by energetic analysis. Energy flows of a complete production cycle, 56
   days, were considered, of which the energy inputs and outputs were
   computed in direct and indirect forms, by estimating the energy coming
   from the quantity of feed, water, machinery and equipment, human labor,
   sheds, silos, among other components of the productive system,
   multiplied by their respective energetic coefficients. The total energy
   demand of a system in a production cycle for the production of 1 kg of
   live chicken and its energy efficiency coefficient were determined. The
   studied system had energy conversion coefficient of 95\%, demonstrating
   that the production model has high energy conversion efficiency and fits
   a sustainable model. On average, 37.55 MJ kg(-1) of live chicken were
   necessary. The main limiting point of the system corresponded to the
   energy consumed in the form of feed, with 75\% of the total direct
   energy.}},
Publisher = {{UNIV FEDERAL CAMPINA GRANDE}},
Address = {{RUA APRIGIO VELOSO 882, BODO CONGO, CAMPINA GRANDE PB, 58109 090, BRAZIL}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Mattioli, MC (Corresponding Author), Univ Fed Lavras, Programa Posgrad Engn Agr, Lavras, MG, Brazil.
   Mattioli, Matheus C.; Marin, Diego B., Univ Fed Lavras, Programa Posgrad Engn Agr, Lavras, MG, Brazil.
   Campos, Alessandro T.; Yanagi Junior, Tadayuki, Univ Fed Lavras, Dept Engn, Lavras, MG, Brazil.
   Eugenio, Tony M. C., Univ Fed Lavras, Programa Posgrad Engn Biomat, Lavras, MG, Brazil.
   Costa Junior, Jose E., V, Univ Fed Lavras, Programa Posgrad Engn Florestal, Lavras, MG, Brazil.}},
DOI = {{10.1590/1807-1929/agriambi.v22n9p648-652}},
ISSN = {{1807-1929}},
Keywords = {{sustainability; input-output analysis; livestock buildings and
   environment; aviculture; broiler chicken facilities}},
Keywords-Plus = {{SWINE PRODUCTION SYSTEM; EFFICIENCY; FARMS; DAIRY}},
Research-Areas = {{Agriculture}},
Web-of-Science-Categories  = {{Agricultural Engineering}},
Author-Email = {{matheus\_mattioli@yahoo.com.br
   campos@deg.ufla.br
   yanagi@deg.ufla.br
   db.marin@hotmail.com
   tony.matheus@hotmail.com
   jevcjunior@gmail.com}},
ResearcherID-Numbers = {{MATTIOLI, MATHEUS CAMPOS/AAU-5030-2020
   Campos, Alessandro Torres/F-8316-2012
   }},
ORCID-Numbers = {{MATTIOLI, MATHEUS CAMPOS/0000-0001-7769-3392
   Campos, Alessandro Torres/0000-0002-4653-9453
   Marin, Diego/0000-0001-7526-0825}},
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Number-of-Cited-References = {{18}},
Times-Cited = {{1}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{5}},
Journal-ISO = {{Rev. Bras. Eng. Agric. Ambient.}},
Doc-Delivery-Number = {{GR6PI}},
Unique-ID = {{ISI:000442778000010}},
OA = {{DOAJ Gold}},
DA = {{2020-08-06}},
}

@article{ ISI:000436494400134,
Author = {Anyah, R. O. and Forootan, E. and Awange, J. L. and Khaki, M.},
Title = {{Understanding linkages between global climate indices and terrestrial
   water storage changes over Africa using GRACE products}},
Journal = {{SCIENCE OF THE TOTAL ENVIRONMENT}},
Year = {{2018}},
Volume = {{635}},
Pages = {{1405-1416}},
Month = {{SEP 1}},
Abstract = {{Africa, a continent endowed with huge water resources that sustain its
   agricultural activities is increasingly coming under threat from impacts
   of climate extremes (droughts and floods), which puts the very precious
   water resource into jeopardy. Understanding the relationship between
   climate variability and water storage over the continent, therefore, is
   paramount in order to inform future water management strategies. This
   study employs Gravity Recovery And Climate Experiment (GRACE) satellite
   data and the higher order (fourth order cumulant) statistical
   independent component analysis (ICA) method to study the relationship
   between terrestrial water storage (TWS) changes and five global
   climate-teleconnection indices; El Nino-Southern Oscillation (ENSO),
   North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO),
   Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over
   Africa for the period 2003-2014. Pearson correlation analysis is applied
   to extract the connections between these climate indices (CIs) and TWS,
   from which some known strong CI-rainfall relationships (e.g., over
   equatorial eastern Africa) are found. Results indicate unique
   linear-relationships and regions that exhibit strong linkages between
   CIs and TWS. Moreover, unique regions having strong CI-TWS connections
   that are completely different from the typical ENSO-rainfall connections
   over eastern and southern Africa are also identified. Furthermore, the
   results indicate that the first dominant independent components (IC) of
   the CIs are linked to NAO, and are characterized by significant
   reductions of TWS over southern Africa. The second dominant ICs are
   associated with IOD and are characterized by significant increases in
   TWS over equatorial eastern Africa, while the combined ENSO and MJO are
   apparently linked to the third ICs, which are also associated with
   significant increase in TWS changes over both southern Africa, as well
   as equatorial eastern Africa. (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 = {{Awange, JL (Corresponding Author), Curtin Univ, Dicipline Spatial Sci, Sch Earth \& Planetary Sci, Perth, WA, Australia.
   Anyah, R. O., Univ Connecticut, Dept Nat Resources \& Environm, Mansfield, PA USA.
   Forootan, E., Cardiff Univ, Sch Earth \& Ocean Sci, Cardiff, S Glam, Wales.
   Awange, J. L.; Khaki, M., Curtin Univ, Dicipline Spatial Sci, Sch Earth \& Planetary Sci, Perth, WA, Australia.}},
DOI = {{10.1016/j.scitotenv.2018.04.159}},
ISSN = {{0048-9697}},
EISSN = {{1879-1026}},
Keywords = {{Africa; Terrestrial water storage (TWS); Climate indices; GRACE; ENSO;
   IOD; NAO; MJO; QBO; Climate-TWS hotspots}},
Keywords-Plus = {{WEST-AFRICA; DROUGHT CHARACTERIZATION; SATELLITE-OBSERVATIONS; DATA
   ASSIMILATION; INDIAN-OCEAN; GREATER HORN; EAST-AFRICA; RAINFALL;
   VARIABILITY; BASIN}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Environmental Sciences}},
Author-Email = {{J.awange@curtin.edu.au}},
ResearcherID-Numbers = {{Khaki, Mehdi/AAP-6010-2020
   }},
ORCID-Numbers = {{Awange, Joseph/0000-0003-3533-613X
   Khaki, Mehdi/0000-0002-3718-2843
   Forootan, Ehsan/0000-0003-3055-041X}},
Funding-Acknowledgement = {{US National Science FoundationNational Science Foundation (NSF)
   {[}AGS-1305043]; Alexander Von Humboldt foundationAlexander von Humboldt
   Foundation; Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany;
   Japan Society of Promotion of Science (JSPS)Ministry of Education,
   Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for
   the Promotion of Science; Brazilian Science without Borders
   Program/CAPES {[}88881.068057/2014-01]; UFPE, Brazil; German Aerospace
   Center (DLR)Helmholtz AssociationGerman Aerospace Centre (DLR) {[}50 LZ
   1402]; WASM/TIGeR research fellowship from Curtin University; Curtin
   University}},
Funding-Text = {{R. Anyah was supported by US National Science Foundation through

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