Yulei Xie

Prof. Yulei Xie

Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, 510006, China

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2021 in Science of The Total Environment
Verified
Journal Article
Closed access
WXWenhao Xu
Wenhao Xu
Yulei XieYulei Xie
Yulei Xie
YCYanpeng Cai
Yanpeng Cai
LJLing Ji
Ling Ji
BWBishi Wang
Bishi Wang
ZYZhifeng Yang
Zhifeng Yang

1 November 2021 in Journal of Environmental Management
Verified
Journal Article
Closed access
WXWenhao Xu
Wenhao Xu
Yulei XieYulei Xie
Yulei Xie
DXDehong Xia
Dehong Xia
Ling JiLing Ji
Ling Ji
Guohe HuangGuohe Huang
Guohe Huang

3 October 2015 in Stochastic Environmental Research and Risk Assessment
Verified
Journal Article
Closed access
PDF file
Yulei XieYulei Xie
Yulei Xie
DXD. H. Xia
D. H. Xia
Guohe HuangGuohe Huang
Guohe Huang
WLWei Li
Wei Li
YXY. Xu
Y. Xu

12 August 2022 in Journal of Environmental Management
Verified
Journal Article
Closed access
ZLZhiwei Luo
Zhiwei Luo
Ling JiLing Ji
Ling Ji
Yulei XieYulei Xie
Yulei Xie
Liang ZhaiLiang Zhai
Liang Zhai
YCYanpeng Cai
Yanpeng Cai

9 June 2017 in Water
Verified
Journal Article
Open access
PDF file
Ling JiLing Ji
Ling Ji
PSPing Sun
Ping Sun
QMQiang Ma
Qiang Ma
NJNa Jiang
Na Jiang
Guohe HuangGuohe Huang
Guohe Huang
Yulei XieYulei Xie
Yulei Xie
In this paper, an inexact two-stage stochastic programming model was developed for supporting regional water resource allocation management under uncertainties. The proposed model is an integrated framework of interval parameter programming and two-stage stochastic programming, which can tackle uncertain parameters expressed as interval values with probability distribution information. The proposed model was successfully applied to a typical heavy industrial city suffering water shortage in the east of China, Tianjin. The uncertainties of future water demand were taken into account through generating a set of representative scenarios. The results indicated that different scenarios would affect the water distribution patterns, water shortages, total benefits and system cost. The results revealed that the transferred water from Luan River and Changjiang River would still be the main water resource for each water user. Besides, nearly all water demand in planning horizon would be guaranteed through the reasonable dispatch except under high demand level scenario, in which a small proportion of water requirement in agricultural, municipal and environmental sectors would not be satisfied. The developed method could be used by environmental managers to identify the optimal water supply plan from multiple sources to different end-user sectors under system uncertainties.

3 September 2018 in Sustainability
Verified
Journal Article
Open access
PDF file
Yulei XieYulei Xie
Yulei Xie
Ling JiLing Ji
Ling Ji
BZBeibei Zhang
Beibei Zhang
Guohe HuangGuohe Huang
Guohe Huang
This study attempts to characterize the literature related to input–output analysis between 1990–2017 through bibliometric analysis technology based on the Science Citation Index and Social Sciences Citation Index databases. By means of bibliometric tools, this paper provides deep insights on the patterns of these articles, the most influential works and authors, and the emerging research topics. The results imply that China and the United States (USA) are the leading countries in terms of publication output. The Chinese Academy of Sciences is the most productive research institution, followed by Beijing Normal University and the University of Sydney. The Journal of Cleaner Production, Ecological Economics, and Energy Policy are the top mainstream journals in the input–output analysis-related field. Based on network analysis, this paper also discovers the hidden collaboration patterns and interrelations of countries, institutions, and authors. The bibliographic coupling and keywords concurrence networks are adopted to illustrate the input–output analysis evolution over time, and identify the current key research hotspots. The obtained results will help scientific researchers better understand the research status and frontier trends in this field, permit researchers to know the current research interests in the input–output analysis field, and provide useful information for further investigation and publication strategies.