text:
1/acs.est.5b05989.
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Number-of-Cited-References = {{60}},
Times-Cited = {{20}},
Usage-Count-Last-180-days = {{8}},
Usage-Count-Since-2013 = {{87}},
Journal-ISO = {{Environ. Sci. Technol.}},
Doc-Delivery-Number = {{FX5TN}},
Unique-ID = {{ISI:000426143300039}},
OA = {{Green Accepted}},
DA = {{2020-08-06}},
}

@article{ ISI:000423635500055,
Author = {Antonio Lopez, Luis and Arce, Guadalupe and Kronenberg, Tobias and
   Rodrigues, Joao F. D.},
Title = {{Trade from resource-rich countries avoids the existence of a global
   pollution haven hypothesis}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{175}},
Pages = {{599-611}},
Month = {{FEB 20}},
Abstract = {{Free trade agreements are currently receiving much attention in the
   media and politics. One important question concerning free trade is its
   effect on the environment, for example in carbon dioxide emissions. The
   goal of this paper is to provide evidence on the validity of the
   pollution haven hypothesis using a multi-regional input-output approach
   for six regions and the rest of the world as a whole. Our findings
   indicate that in the period from 1995 to 2009, international trade has
   allowed the global economy to reduce its overall CO2 emissions, compared
   to a hypothetical situation without international trade. The total
   amount of emissions saved was 15.06 Gt in the period under
   consideration. However, not all of the seven regions in our model have
   been able to reduce their CO2 emissions through trade. Global value
   chains have led to China becoming a pollution haven for other regions
   and its exports have increased world emissions to 1.28 Gt CO2 in 2008.
   However, what allows a net saving of emissions on a global scale is the
   supply of energy and natural resources from a set of peripheral
   economies, which in our geographical categorization are mostly
   integrated in the region of the rest of the world. (C) 2017 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 = {{Lopez, LA (Corresponding Author), Univ Castilla La Mancha, Fac Ciencias Econ \& Empresariales, Plaza Univ 2, Albacete 02071, Spain.
   Antonio Lopez, Luis; Arce, Guadalupe, Univ Castilla La Mancha, Fac Ciencias Econ \& Empresariales, Plaza Univ 2, Albacete 02071, Spain.
   Kronenberg, Tobias, Bochum Univ Appl Sci, Lennershofstr 140, D-44801 Bochum, Germany.
   Rodrigues, Joao F. D., Leiden Univ, Inst Environm Sci CML, Einsteinweg 2, NL-2333 CC Leiden, Netherlands.}},
DOI = {{10.1016/j.jclepro.2017.12.056}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Pollution haven hypothesis; CO2 emissions; International trade;
   Multiregional input-output analysis}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; INTERNATIONAL-TRADE; CO2 EMISSIONS; EMBODIED
   EMISSIONS; CARBON; CONSUMPTION; CHINA; PERSPECTIVE; PATTERNS; LEONTIEF}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{Luis.LSantiago@uclm.es
   Guadalupe.Arce@uclm.es
   tobias.kronenberg@hs-bochum.de
   j.rodrigues@cml.leidenuniv.nl}},
ResearcherID-Numbers = {{RODRIGUES, JOAO/AAC-6993-2019
   Lopez, LUIS ANTONIO L.A./S-4183-2018
   }},
ORCID-Numbers = {{Lopez, LUIS ANTONIO L.A./0000-0002-9733-7106
   ARCE GONZALEZ, GUADALUPE/0000-0003-3499-072X}},
Funding-Acknowledgement = {{Spanish Ministry of Education, Culture and Sports through the National
   FPU Program {[}FPU 13/05869]; University of Castilla-La Mancha
   (D.O.C.M.)}},
Funding-Text = {{Luis A. Lopez thanks the grant for research stays in foreign
   institutions, funded by the University of Castilla-La Mancha (D.O.C.M.
   18 diciembre de 2012). Guadalupe Arce also thanks the Spanish Ministry
   of Education, Culture and Sports through the National FPU Program (grant
   ref. FPU 13/05869).}},
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Number-of-Cited-References = {{70}},
Times-Cited = {{24}},
Usage-Count-Last-180-days = {{7}},
Usage-Count-Since-2013 = {{38}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{FU1UR}},
Unique-ID = {{ISI:000423635500055}},
DA = {{2020-08-06}},
}

@article{ ISI:000424180100017,
Author = {Zhao, Lin and Dai, Aiguo and Dong, Bo},
Title = {{Changes in global vegetation activity and its driving factors during
   1982-2013}},
Journal = {{AGRICULTURAL AND FOREST METEOROLOGY}},
Year = {{2018}},
Volume = {{249}},
Pages = {{198-209}},
Month = {{FEB 15}},
Abstract = {{Vegetation activity plays a crucial role in the global carbon cycle and
   climate. Many studies have examined recent changes in vegetation growth
   and the associated local climatic drivers. They revealed a global
   greening trend during the recent decades. However, few studies have
   analyzed how remote oceanic conditions affect land vegetation growth
   through atmospheric teleconnection, and the causes of the recent
   greening needs further investigation. In this study, we investigate the
   spatio-temporal variations (including trends) of vegetation activity
   using satellite data of growing-season normalized difference vegetation
   index (NDVIgs), and examine their relationship to local and remote
   climate oscillations and external anthropogenic forcing by statistical
   means. As expected, there is an increasing trend in global-mean NDVIgs
   from 1982-2013, with significant greening over Europe and many other
   land areas. NDVIgs is temperature-limited at northern high-latitudes,
   but water-limited in arid and semi-arid regions, and radiation-limited
   in the Amazon and eastern and southern Asia. Globally, El Nifio-Southern
   Oscillation (ENSO) is the leading climatic driver of interannual
   variability of NDVIgs, especially over southern and eastern Africa,
   eastern Australia, northeastern Asia, and northern South America.
   Consistent with previous modeling studies, a regression-based
   attribution analysis suggests that historical anthropogenic forcing
   (mainly increases in greenhouse gases) explains about two thirds of the
   NDVIgs trend from 1982 to 2013, with the rest coming mainly from the
   Atlantic Multi-decadal Oscillation (AMO). Contributions to the recent
   NDVIgs trend from ENSO and Pacific decadal variability and Artctic
   Oscillation appear to be small.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Dai, AG (Corresponding Author), SUNY Albany, Univ Albany, Dept Atmospher \& Environm Sci, Albany, NY 12222 USA.
   Zhao, Lin, Wuhan Univ, Sch Resource \& Environm Sci, Wuhan, Hubei, Peoples R China.
   Dai, Aiguo; Dong, Bo, SUNY Albany, Univ Albany, Dept Atmospher \& Environm Sci, Albany, NY 12222 USA.}},
DOI = {{10.1016/j.agrformet.2017.11.013}},
ISSN = {{0168-1923}},
EISSN = {{1873-2240}},
Keywords = {{NDVI; Vegetation activity; Climatic oscillation; External forcing; Trend
   attribution}},
Keywords-Plus = {{NINO-SOUTHERN-OSCILLATION; NET PRIMARY PRODUCTION; PRECIPITATION
   PATTERNS; CLIMATE VARIABILITY; ARCTIC OSCILLATION; GREENING TREND;
   DROUGHT; ENSO; NDVI; GREENNESS}},
Research-Areas = {{Agriculture; Forestry; Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Agronomy; Forestry; Meteorology \& Atmospheric Sciences}},
Author-Email = {{adai@albany.edu}},
ResearcherID-Numbers = {{Dai, Aiguo/Y-2443-2019
   }},
ORCID-Numbers = {{zhao, lin/0000-0001-9106-3047}},
Funding-Acknowledgement = {{U.S. National Science FoundationNational Science Foundation (NSF)
   {[}AGS-1353740]; U.S. Department of Energy's Office of ScienceUnited
   States Department of Energy (DOE) {[}DE-SC0012602]; U.S. National
   Oceanic and Atmospheric AdministrationNational Oceanic Atmospheric Admin
   (NOAA) - USA {[}NA15OAR4310086]; National Natural Science Foundation of
   ChinaNational Natural Science Foundation of China {[}41301586]; China
   Postdoctoral Science FoundationChina Postdoctoral Science Foundation
   {[}2014T70731]; China Scholarship Council (CSC)China Scholarship Council}},
Funding-Text = {{A. Dai acknowledges the funding support from the U.S. National Science
   Foundation (Grant \#AGS-1353740), U.S. Department of Energy's Office of
   Science (Award \#DE-SC0012602), and U.S. National Oceanic and
   Atmospheric Administration (Award \#NA15OAR4310086). This study is also
   supported by National Natural Science Foundation of China (Grant
   \#41301586) and China Postdoctoral Science Foundation (Grants
   \#2014T70731). This work was conducted during a visit by L. Zhao to the
   University at Albany supported by the China Scholarship Council (CSC).}},
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Number-of-Cited-References = {{82}},
Times-Cited = {{34}},
Usage-Count-Last-180-days = {{10}},
Usage-Count-Since-2013 = {{103}},
Journal-ISO = {{Agric. For. Meteorol.}},
Doc-Delivery-Number = {{FU9MN}},
Unique-ID = {{ISI:000424180100017}},
DA = {{2020-08-06}},
}

@article{ ISI:000426413400034,
Author = {Ouyang, Xiaoling and Gao, Beiying and Du, Kerui and Du, Gang},
Title = {{Industrial sectors' energy rebound effect: An empirical study of Yangtze
   River Delta urban agglomeration}},
Journal = {{ENERGY}},
Year = {{2018}},
Volume = {{145}},
Pages = {{408-416}},
Month = {{FEB 15}},
Abstract = {{As the most prosperous area and one of China's major economic centers,
   Yangtze River Delta urban agglomeration (YRDUA) shows the largest
   concentration of adjacent metropolitan areas in the world. Its energy
   conservation and emissions reduction efforts are critical for developing
   a low carbon economy in China. Based on panel data of 14 cities from
   2003 to 2013, this study estimates the rebound effect's magnitude in
   YRDUA's industrial sectors using dynamic ordinary least squares (DOLS)
   and seemingly unrelated regression (SUR) methods. The empirical results
   are as follows: (1) Significant substitute relationships exist between
   energy and capital factors and between energy and labor factors. (2) The
   own price elasticity of labor is the most elastic, followed by those of
   energy and capital. (3) The rebound effect is approximately 40.04\%.
   Evidence suggests that promoting financial development, conducting
   structural reform in the supply side, and establishing reasonable
   controls of industrial growth and scale expansion are conducive for
   energy conservation and pollution alleviation of YRDUA's industrial
   sectors. (C) 2018 Elsevier Ltd. All rights reserved.}},
Publisher = {{PERGAMON-ELSEVIER SCIENCE LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Du, KR (Corresponding Author), Shandong Univ, Ctr Econ Res, Jinan 250100, Shandong, Peoples R China.
   Du, G (Corresponding Author), East China Normal Univ, Fac Econ \& Management, Sch Business \& Adm, Shanghai 200062, Peoples R China.
   Ouyang, Xiaoling; Gao, Beiying, East China Normal Univ, Fac Econ \& Management, Sch Econ, Dept Econ, Shanghai 200062, Peoples R China.
   Du, Kerui, Shandong Univ, Ctr Econ Res, Jinan 250100, Shandong, Peoples R China.
   Du, Gang, East China Normal Univ, Fac Econ \& Management, Sch Business \& Adm, Shanghai 200062, Peoples R China.}},
DOI = {{10.1016/j.energy.2018.01.009}},
ISSN = {{0360-5442}},
EISSN = {{1873-6785}},
Keywords = {{Energy rebound effect; Energy substitution; Dynamic ordinary least
   square; Yangtze River Delta; Urban agglomeration}},
Keywords-Plus = {{RESIDENTIAL ELECTRICITY CONSUMPTION; INPUT-OUTPUT-ANALYSIS;
   ECONOMY-WIDE; PASSENGER TRANSPORT; EFFICIENCY IMPROVEMENT; CHINA;
   HOUSEHOLDS; COINTEGRATION; PERFORMANCE; COUNTRIES}},
Research-Areas = {{Thermodynamics; Energy \& Fuels}},
Web-of-Science-Categories  = {{Thermodynamics; Energy \& Fuels}},
Author-Email = {{kerrydu@sdu.edu.cn
   gdu@dbm.ecnu.edu.cn}},
ORCID-Numbers = {{Du, Kerui/0000-0001-7388-7018}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71603084, 71603148, 71472065]; Social Sciences
   Research; Ministry of Education of ChinaMinistry of Education, China
   {[}16YJC790078, 14YJC630026]; Shandong Provincial Natural Science
   Foundation, ChinaNatural Science Foundation of Shandong Province
   {[}ZR2016GB10]; Shanghai Planning Office of Philosophy and Social
   Science {[}2016EJB003]; Fundamental Research Funds for the Central
   UniversitiesFundamental Research Funds for the Central Universities
   {[}2017ECNU-HWFW033]; Key projects of Shanghai soft science Research
   Program {[}17692107000]; Shanghai Pujiang ProgramShanghai Pujiang
   Program {[}14PJC027]}},
Funding-Text = {{We thank three anonymous reviewers for their valuable comments and
   suggestions which led to an improved version of this paper. This study
   was supported by the National Natural Science Foundation of China (Grant
   Nos. 71603084, 71603148, 71472065); Social Sciences Research, supported
   by the Ministry of Education of China (Grant Nos. 16YJC790078,
   14YJC630026); Shandong Provincial Natural Science Foundation, China
   (ZR2016GB10); Social Sciences Research, supported by the Shanghai
   Planning Office of Philosophy and Social Science (Grant No. 2016EJB003);
   Fundamental Research Funds for the Central Universities
   (2017ECNU-HWFW033); Key projects of Shanghai soft science Research
   Program (17692107000); and Shanghai Pujiang Program (14PJC027).}},
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Number-of-Cited-References = {{80}},
Times-Cited = {{24}},
Usage-Count-Last-180-days = {{7}},
Usage-Count-Since-2013 = {{47}},
Journal-ISO = {{Energy}},
Doc-Delivery-Number = {{FX9JB}},
Unique-ID = {{ISI:000426413400034}},
DA = {{2020-08-06}},
}

@article{ ISI:000424996300036,
Author = {Jones, T. R. and Roberts, W. H. G. and Steig, E. J. and Cuffey, K. M.
   and Markle, B. R. and White, J. W. C.},
Title = {{Southern Hemisphere climate variability forced by Northern Hemisphere
   ice-sheet topography}},
Journal = {{NATURE}},
Year = {{2018}},
Volume = {{554}},
Number = {{7692}},
Pages = {{351+}},
Month = {{FEB 15}},
Abstract = {{The presence of large Northern Hemisphere ice sheets and reduced
   greenhouse gas concentrations during the Last Glacial Maximum
   fundamentally altered global ocean-atmosphere climate dynamics(1). Model
   simulations and palaeoclimate records suggest that glacial boundary
   conditions affected the El Nino-Southern Oscillation(2,3), a dominant
   source of short-term global climate variability. Yet little is known
   about changes in short-term climate variability at mid-to high
   latitudes. Here we use a high-resolution water isotope record from West
   Antarctica to demonstrate that interannual to decadal climate
   variability at high southern latitudes was almost twice as large at the
   Last Glacial Maximum as during the ensuing Holocene epoch (the past
   11,700 years). Climate model simulations indicate that this increased
   variability reflects an increase in the teleconnection strength between
   the tropical Pacific and West Antarctica, owing to a shift in the mean
   location of tropical convection. This shift, in turn, can be attributed
   to the influence of topography and albedo of the North American ice
   sheets on atmospheric circulation. As the planet deglaciated, the
   largest and most abrupt decline in teleconnection strength occurred
   between approximately 16,000 years and 15,000 years ago, followed by a
   slower decline into the early Holocene.}},
Publisher = {{NATURE PUBLISHING GROUP}},
Address = {{MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Jones, TR (Corresponding Author), Univ Colorado, Inst Arctic \& Alpine Res, Boulder, CO 80309 USA.
   Jones, T. R.; White, J. W. C., Univ Colorado, Inst Arctic \& Alpine Res, Boulder, CO 80309 USA.
   Roberts, W. H. G., Univ Bristol, Sch Geog Sci, Bristol Res Initiat Dynam Global Environm, Bristol BS8 1SS, Avon, England.
   Roberts, W. H. G., Univ Bristol, Sch Geog Sci, Bristol Glaciol Ctr, Bristol BS8 1SS, Avon, England.
   Steig, E. J.; Markle, B. R., Univ Washington, Dept Earth \& Space Sci, Seattle, WA 98195 USA.
   Cuffey, K. M., Univ Calif Berkeley, Dept Geog, Berkeley, CA 94720 USA.
   White, J. W. C., Univ Colorado, Dept Geol Sci, Boulder, CO 80309 USA.}},
DOI = {{10.1038/nature24669}},
ISSN = {{0028-0836}},
EISSN = {{1476-4687}},
Keywords-Plus = {{LAST GLACIAL MAXIMUM; HIGH-LATITUDE CLIMATE; WEST ANTARCTICA; SEA-LEVEL;
   TROPICAL PACIFIC; EL-NINO; ENSO TELECONNECTION; ISOTOPIC DIFFUSION; LATE
   PLEISTOCENE; HEINRICH EVENTS}},
Research-Areas = {{Science \& Technology - Other Topics}},
Web-of-Science-Categories  = {{Multidisciplinary Sciences}},
Author-Email = {{tyler.jones@colorado.edu}},
ResearcherID-Numbers = {{Markle, Bradley R/I-6132-2019
   White, James W.C./A-7845-2009
   }},
ORCID-Numbers = {{White, James W.C./0000-0001-6041-4684
   Roberts, William/0000-0002-8249-1901
   Markle, Bradley/0000-0002-2282-6546}},
Funding-Acknowledgement = {{US National Science Foundation (NSF)National Science Foundation (NSF)
   {[}0537593, 0537661, 0537930, 0539232, 1043092, 1043167, 1043518,
   1142166]; NSFNational Science Foundation (NSF) {[}0230396, 0440817,
   0944266, 0944348]; NSF Division of Polar Programs; Leverhulme
   TrustLeverhulme Trust}},
Funding-Text = {{This work was supported by US National Science Foundation (NSF) grants
   0537593, 0537661, 0537930, 0539232, 1043092, 1043167, 1043518 and
   1142166. Field and logistical activities were managed by the WAIS Divide
   Science Coordination Office at the Desert Research Institute, USA, and
   the University of New Hampshire, USA (NSF grants 0230396, 0440817,
   0944266 and 0944348). The NSF Division of Polar Programs funded the Ice
   Drilling Program Office (IDPO), the Ice Drilling Design and Operations
   (IDDO) group, the National Ice Core Laboratory (NICL), the Antarctic
   Support Contractor, and the 109th New York Air National Guard. W.H.G.R.
   was funded by a Leverhulme Trust Research Project Grant. All HadCM3
   model simulations were carried out using the computational facilities of
   the Advanced Computing Research Centre, University of Bristol
   (http://www.bris.ac.uk/acrc/). We thank P.J. Valdes and J.S. Singarayer
   for providing their model simulations, as well as the groups that
   provided climate model data as part of the PMIP2/3.}},
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Number-of-Cited-References = {{63}},
Times-Cited = {{19}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{50}},
Journal-ISO = {{Nature}},
Doc-Delivery-Number = {{FW0PD}},
Unique-ID = {{ISI:000424996300036}},
DA = {{2020-08-06}},
}

@article{ ISI:000425078000004,
Author = {Hao, Yan and Zhang, Menghui and Zhang, Yan and Fu, Chenling and Lu,
   Zhongming},
Title = {{Multi-scale analysis of the energy metabolic processes in the
   Beijing-Tianjin-Hebei (Jing-Jin-Ji) urban agglomeration}},
Journal = {{ECOLOGICAL MODELLING}},
Year = {{2018}},
Volume = {{369}},
Pages = {{66-76}},
Month = {{FEB 10}},
Abstract = {{The area of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region is about
   2.25\% of China land area. But the energy consumption and carbon
   emission of Jing-Jin-Ji region account for more than 10\% of China's
   total consumption and emissions. To reduce the region's energy
   consumption and carbon emission, we studied energy flow process in this
   region. The previous research on energy flow is concentrated on a single
   scale, with little multi-scale integration. In this study, based on
   framework of urban metabolism, we used ecological network analysis
   method to study the region's energy metabolism and ecological
   relationship among nodes at two scales: in 3-node and 13-node models of
   the agglomeration. The integral (direct + indirect) energy consumption
   in the 3-node model first increased, then decreased, from 2002 to 2010;
   consumption in the 13-node model first decreased, then increased. The
   direct and integral energy flows showed that Hebei provided the most
   energy to Beijing in the 3-node network; in the 13-node network,
   Tangshan provided the most energy to the other 12 cities. In terms of
   the ecological relationships among the nodes, the 13-node model showed
   proportionally more competition relationships than the 3-node model. The
   relationships among Beijing, Tianjin, and Hebei were dominated by
   exploitation and control. Based on the analysis of flow and
   relationship, the importance of multi-scale model research is verified,
   which serves different research objectives and helps researchers choose
   the appropriate scale model under different data acquisition precision
   and research depth. (C) 2017 Elsevier B.V. All rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhang, Y (Corresponding Author), Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat \& Pollut Con, Xinjiekouwai St 19, Beijing 100875, Peoples R China.
   Hao, Yan; Zhang, Menghui; Zhang, Yan; Fu, Chenling; Lu, Zhongming, Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat \& Pollut Con, Xinjiekouwai St 19, Beijing 100875, Peoples R China.}},
DOI = {{10.1016/j.ecolmodel.2017.12.012}},
ISSN = {{0304-3800}},
EISSN = {{1872-7026}},
Keywords = {{Urban energy metabolism; Ecological network analysis; Multi-scale
   analysis; Urban agglomeration; Jing-Jin-Ji}},
Keywords-Plus = {{ECOLOGICAL NETWORK ANALYSIS; INPUT-OUTPUT-ANALYSIS; VIRTUAL WATER TRADE;
   SOCIETAL METABOLISM; INTERNATIONAL-TRADE; CARBON FOOTPRINT; DOMESTIC
   TRADE; FLOW-ANALYSIS; CHINA; CONSUMPTION}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Ecology}},
Author-Email = {{yzhang@bnu.edu.cn}},
ResearcherID-Numbers = {{Lu, Zhongming/M-8292-2018
   Lu, Zhongming/S-2757-2019}},
ORCID-Numbers = {{Lu, Zhongming/0000-0002-4151-5065
   Lu, Zhongming/0000-0002-4151-5065}},
Funding-Acknowledgement = {{National Key Research and Development Program of China
   {[}2016YFC0503005]; Fund for Innovative Research Group of the National
   Natural Science Foundation of ChinaNational Natural Science Foundation
   of China {[}51721093]; National Natural Science Foundation of
   ChinaNational Natural Science Foundation of China {[}41571521,
   51661125010]; Fundamental Research Funds for the Central
   UniversitiesFundamental Research Funds for the Central Universities
   {[}2015NT01]}},
Funding-Text = {{This work was supported by the National Key Research and Development
   Program of China (no. 2016YFC0503005), by the Fund for Innovative
   Research Group of the National Natural Science Foundation of China (no.
   51721093), by the National Natural Science Foundation of China (no.
   41571521), by Funds for International Cooperation and Exchanges of the
   National Natural Science Foundation of China (no.51661125010), and by
   the Fundamental Research Funds for the Central Universities (no.
   2015NT01).}},
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Number-of-Cited-References = {{66}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{58}},
Journal-ISO = {{Ecol. Model.}},
Doc-Delivery-Number = {{FW1RV}},
Unique-ID = {{ISI:000425078000004}},
DA = {{2020-08-06}},
}

@article{ ISI:000424727100050,
Author = {Sun, Xiaoqi and An, Haizhong},
Title = {{Emergy network analysis of Chinese sectoral ecological sustainability}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{174}},
Pages = {{548-559}},
Month = {{FEB 10}},
Abstract = {{Ecological sustainability of the Chinese economy is vital for China as
   well as for the world. By integrating environmental input-output
   analysis, emergy theory, and a complex network model into one framework,
   this paper explores Chinese sectoral ecological sustainability with the
   most recent 2012 input-output table. The results show that there are
   significant differences in emergy intensities among sectors. The
   non-metal mining sector is the most emergy-intensive sector at 6.06E+17
   sej/10000 Yuan, followed by the metal mining sector. Additionally,
   output connections have a positive impact on the emergy yield ratio,
   input connections exert a negative impact on the emergy yield ratio, and
   PageRank centrality has a positive effect on the environmental loading
   ratio. This paper updates the Chinese sectoral emergy assessment,
   identifies key sectors in the Chinese ecological network, and analyzes
   the impact of network indicators on sectoral emergy performance. These
   analyses will be useful in updating the guide for adjusting industrial
   structure in China. (C) 2017 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 = {{An, HZ (Corresponding Author), China Univ Geosci, Sch Humanities \& Econ Management, Beijing 100083, Peoples R China.
   Sun, Xiaoqi; An, Haizhong, China Univ Geosci, Sch Humanities \& Econ Management, Beijing 100083, Peoples R China.
   Sun, Xiaoqi; An, Haizhong, Minist Land \& Resources, Key Lab Carrying Capac Assessment Resource \& Envi, Beijing 100083, Peoples R China.
   Sun, Xiaoqi; An, Haizhong, Minist Land \& Resources, Key Lab Strateg Studies, Beijing 100812, Peoples R China.}},
DOI = {{10.1016/j.jclepro.2017.10.228}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Emergy; Network; Chinese sectors; Input-output table; Sustainability;
   Environmental accounting}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; VIRTUAL WATER FLOWS; INTERNATIONAL-TRADE;
   EMBODIED ENERGY; CARBON EMISSIONS; CO2 EMISSIONS; INTERPROVINCIAL
   TRANSFER; INDUSTRIAL SECTORS; ECONOMY 2007; RIVER-BASIN}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{ahz369@163.com}},
ORCID-Numbers = {{An, Haizhong/0000-0002-6141-5619}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71173199]; China Scholarship CouncilChina
   Scholarship Council {[}201606400031]; Fundamental Research Funds for the
   Central UniversitiesFundamental Research Funds for the Central
   Universities {[}2-9-2016-165]}},
Funding-Text = {{We appreciate the financial supports very much from the National Natural
   Science Foundation of China (Grant No. 71173199), the China Scholarship
   Council (Grant No. 201606400031), and the Fundamental Research Funds for
   the Central Universities (Grant No.2-9-2016-165). The authors are very
   grateful to Dr. Mark Brown and Dr. Sam Arden for their excellent
   suggestions to this paper, and to the anonymous referees for insightful
   comments and suggestions to improve this paper. Xiaoqi Sun much
   appreciate the Center for Environmental Policy at the University of
   Florida for its warm academic hospitality to him for one year. Finally,
   we are thankful to American Journal Experts for the English editing
   service.}},
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Number-of-Cited-References = {{82}},
Times-Cited = {{17}},
Usage-Count-Last-180-days = {{7}},
Usage-Count-Since-2013 = {{88}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{FV6YD}},
Unique-ID = {{ISI:000424727100050}},
DA = {{2020-08-06}},
}

@article{ ISI:000425075600006,
Author = {Liu, Shangwei and Tian, Xin and Cai, Wenjia and Chen, Weiqiang and Wang,
   Yafei},
Title = {{How the transitions in iron and steel and construction material
   industries impact China's CO2 emissions: Comprehensive analysis from an
   inter-sector linked perspective}},
Journal = {{APPLIED ENERGY}},
Year = {{2018}},
Volume = {{211}},
Pages = {{64-75}},
Month = {{FEB 1}},
Abstract = {{CO2 emissions mitigation in iron and steel industry (ISI) and
   construction material industry (CMI), including cement, glass, and
   ceramics materials, is crucial for the realization of CO2 emission peak
   targets in China, given their great contributions to China's emission
   structure. Great transitions have occurred in the two industries
   recently, including scale expansion, efficiency improvement, and changes
   in production and demand structures. By developing an integrated
   framework for inter-sector linkage analysis, we investigated the impact
   of recent transitions in the ISI and CMI on China's CO2 emissions
   between 1992 and 2012. Results show that the CO2 emissions from ISI and
   CMI increased by 4.2 and 6.8 times over two decades, respectively, and
   the two key sectors have significantly higher backward and forward
   linkages than average in terms of CO2 emissions. The internal efficiency
   improvement of the ISI and CMI are crucial factors curbing the rising
   CO2 emissions in these two sectors. The total CO2 intensity of the ISI
   and CMI have declined by 78\% and 68\%, separately, cumulatively
   reducing 517 Mt and 704 Mt CO2 emissions during the studied period. The
   external final demand growth and its structure changes of the ISI and
   CMI have had a significant impact on their CO2 emissions. The
   construction sector is the greatest consumer, responsible for 53\% and
   86\% emissions increase of ISI and CMI during 2002-2012, respectively.
   Emerging manufacturing and machinery also became substantial emissions
   sources, generating 536 Mt CO2 emissions in 2012 by consuming iron and
   steel products. Based on these findings, policy recommendations for CO2
   emissions mitigation in the two key sectors and their related sectors
   are also discussed.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Tian, X (Corresponding Author), Beijing Normal Univ, Beijing 100875, Peoples R China.
   Liu, Shangwei; Tian, Xin, Beijing Normal Univ, Sch Environm, Beijing 100875, Peoples R China.
   Tian, Xin, Beijing Normal Univ, State Key Lab Water Environm Simulat, Beijing 100875, Peoples R China.
   Cai, Wenjia, Tsinghua Univ, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China.
   Cai, Wenjia, Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China.
   Cai, Wenjia, JCGCS, Beijing 100875, Peoples R China.
   Chen, Weiqiang, Chinese Acad Sci, Inst Urban Environm, Key lab Urban Environm \& Hlth, Beijing 361021, Peoples R China.
   Wang, Yafei, Beijing Normal Univ, Inst Natl Accounts, Beijing 100875, Peoples R China.}},
DOI = {{10.1016/j.apenergy.2017.11.040}},
ISSN = {{0306-2619}},
EISSN = {{1872-9118}},
Keywords = {{CO2 emissions; China; Input-output analysis; Inter-sector linkage
   analysis; Iron and steel industry; Construction material industry}},
Keywords-Plus = {{CARBON-DIOXIDE EMISSIONS; HYPOTHETICAL EXTRACTION METHOD; ENERGY
   EFFICIENCY IMPROVEMENT; CEMENT INDUSTRY; DECOMPOSITION ANALYSIS;
   POLICY-MAKING; GLOBAL STEEL; REDUCTION; CONSUMPTION; POTENTIALS}},
Research-Areas = {{Energy \& Fuels; Engineering}},
Web-of-Science-Categories  = {{Energy \& Fuels; Engineering, Chemical}},
Author-Email = {{tianx@bnu.edu.cn}},
ResearcherID-Numbers = {{CAS, KLUEH/T-5743-2019
   }},
ORCID-Numbers = {{Cai, Wenjia/0000-0002-4436-512X
   Liu, Shangwei/0000-0003-4106-3959}},
Funding-Acknowledgement = {{National Key Research and Development Program {[}2016YFC0503005];
   National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71704012, 71303133]; Fundamental Research Funds
   for the Central UniversitiesFundamental Research Funds for the Central
   Universities}},
Funding-Text = {{This work was jointly supported by the National Key Research and
   Development Program (No. 2016YFC0503005), the National Natural Science
   Foundation of China (Nos. 71704012; 71303133), and the ``Fundamental
   Research Funds for the Central Universities.{''}}},
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