Yim Ling Siu

Dr. Yim Ling Siu

orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

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

Representative Publications

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1 November 2014 in Renewable and Sustainable Energy Reviews
Verified
Journal Article
Open access
KFKuishuang Feng
Kuishuang Feng
KHKlaus Hubacek
Klaus Hubacek
YSYim Ling Siu
Yim Ling Siu
XLXin Li
Xin Li

27 June 2012 in Journal of Industrial Ecology
Verified
Journal Article
Closed access
PDF file
KFKuishuang Feng
Kuishuang Feng
YSYim Ling Siu
Yim Ling Siu
DGDabo Guan
Dabo Guan
KHKlaus Hubacek
Klaus Hubacek

3 March 2016 in Natural Hazards and Earth System Sciences
Verified
Journal Article
Open access
PDF file
Baoyin LiuBaoyin Liu
Baoyin Liu
YSYim Ling Siu
Yim Ling Siu
GMGordon Mitchell
Gordon Mitchell
This paper develops a systematic hazard interaction classification based on the geophysical environment that natural hazards arise from – the hazard-forming environment. According to their contribution to natural hazards, geophysical environmental factors in the hazard-forming environment were categorized into two types. The first are relatively stable factors which construct the precondition for the occurrence of natural hazards, whilst the second are trigger factors, which determine the frequency and magnitude of hazards. Different combinations of geophysical environmental factors induce different hazards. Based on these geophysical environmental factors for some major hazards, the stable factors are used to identify which kinds of natural hazards influence a given area, and trigger factors are used to classify the relationships between these hazards into four types: independent, mutex, parallel and series relationships. This classification helps to ensure all possible hazard interactions among different hazards are considered in multi-hazard risk assessment. This can effectively fill the gap in current multi-hazard risk assessment methods which to date only consider domino effects. In addition, based on this classification, the probability and magnitude of multiple interacting natural hazards occurring together can be calculated. Hence, the developed hazard interaction classification provides a useful tool to facilitate improved multi-hazard risk assessment.

9 February 2018 in Water
Verified
Journal Article
Open access
PDF file
MHMaochuan Hu
Maochuan Hu
XZXingqi Zhang
Xingqi Zhang
YSYim Ling Siu
Yim Ling Siu
YLYU LI
YU LI
KTKenji Tanaka
Kenji Tanaka
YXYoupeng Xu
Youpeng Xu
It is important to evaluate the effectiveness of permeable pavements on flood mitigation at different spatial scales for their effective application, for example, sponge city construction in China. This study evaluated the effectiveness of three types of permeable pavements (i.e., permeable asphalts (PA), permeable concretes (PC), and permeable interlocking concrete pavers (PICP)) on flood mitigation at a community scale in China using a hydrological model. In addition, the effects of clogging and initial water content in permeable pavements on flood mitigation performance were assessed. The results indicated that in 12 scenarios, permeable pavements reduced total surface runoff by 1–40% and peak flow by 7–43%, respectively. The hydrological performance of permeable pavements was limited by clogging and initial water content. Clogging resulted in the effectiveness on total surface runoff reduction and peak flow reduction being decreased by 62–92% and 37–65%, respectively. By increasing initial water content at the beginning of the simulation, the effectiveness of total runoff reduction and peak flow reduction decreased by 57–85% and 37–67%, respectively. Overall, among the three types of permeable pavements, PC without clogging had the best performance in terms of flood mitigation, and PICP was the least prone to being clogged. Our findings demonstrate that both the type and the maintenance of permeable pavements have significant effects on their performance in the flood mitigation.

30 December 2010 in Water
Verified
Journal Article
Open access
PDF file
KFKuishuang Feng
Kuishuang Feng
KHKlaus Hubacek
Klaus Hubacek
JMJan Minx
Jan Minx
YSYim Ling Siu
Yim Ling Siu
ACAshok Chapagain
Ashok Chapagain
JBJohn Barrett
John Barrett
The Water Footprint, as an indicator of water consumption has become increasingly popular for analyzing environmental issues associated with the use of water resources in the global supply chain of consumer goods. This is particularly relevant for countries like the UK, which increasingly rely on products produced elsewhere in the world and thus impose pressures on foreign water resources. Existing studies calculating water footprints are mostly based on process analysis, and results are mainly available at the national level. The current paper assesses the domestic and foreign water requirements for UK final consumption by applying an environmentally extended multi-regional input-output model in combination with geo-demographic consumer segmentation data. This approach allows us to calculate water footprints (both direct and indirect) for different products as well as different geographies within the UK. We distinguished between production and consumption footprints where the former is the total water consumed from the UK domestic water resources by the production activities in the UK and the latter is the total water consumed from both domestic and global water resources to satisfy the UK domestic final consumption. The results show that the production water footprint is 439 m3/cap/year, 85% of which is for the final consumption in the UK itself. The average consumption water footprint of the UK is more than three times bigger than the UK production water footprint in 2006. About half of the UK consumption water footprints were associated with imports from Non-OECD countries (many of which are water-scarce), while around 19% were from EU-OECD countries, and only 3% from Non-EU-OECD countries. We find that the water footprint differs considerably across sub-national geographies in the UK, and the differences are as big as 273 m3/cap/year for the internal water footprint and 802 m3/cap/year for the external water footprint. Our results suggest that this is mainly explained by differences in the average income level across the UK. We argue that the information provided by our model at different spatial scales can be very useful for informing integrated water supply and demand side management.