Xiuli Liu

Prof. Xiuli Liu


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

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

Representative Publications

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11 November 2023 in Journal of Cleaner Production
Verified
Journal Article
Closed access
XLXiuli Liu
Xiuli Liu
DVDuc Vu
Duc Vu
SPSandun C. Perera
Sandun C. Perera
GWGuofeng Wang
Guofeng Wang
RXRui Xiong
Rui Xiong

28 May 2024 in Environment, Development and Sustainability
Verified
Journal Article
Closed access
PDF file
XLXiuli Liu
Xiuli Liu
XJXinchi Jia
Xinchi Jia
KLKe Lyu
Ke Lyu
PGPibin Guo
Pibin Guo
JSJun Shen
Jun Shen
GWGuofeng Wang
Guofeng Wang

LNLei Nie
Lei Nie
PCPurong Chen
Purong Chen
XLXiuli Liu
Xiuli Liu
QSQinqin Shi
Qinqin Shi
JZJing Zhang
Jing Zhang
Although the literature has studied the direction and extent of the effect of green finance on industrial-structure optimization, quantitative analysis of the coupling coordination and spatial–temporal differences between green finance and industrial structure is relatively scarce. Therefore, in this paper, we built the theoretical framework of the coupling coordination relationship between green finance and industrial-structure optimization, and then we used the coupling coordination degree and geographic detector model to investigate the spatial–temporal evolution characteristics and influencing factors of the coupling coordination between the two based on the panel data of 31 provinces from 2012 to 2019. The results show that China’s green finance and industrial-structure optimization have basically reached the primary coupling, and the coupling coordination degree is from 0.40 to 0.43, which shows a “W”-type fluctuation trend of recovery. The regional gap of the coupling coordination degree firstly decreased and then increased, showing a change law of “agglomeration, equilibrium and agglomeration”. In the spatial dimension, the high-level coordination region showed an increasing trend of “fragmentation” fluctuation, while the low-level coordination region concentrated in the central and western regions with a tendency of “low value locking”. The trend surface showed a spatial characteristic of “high in the north and low in the south–high in the east and west and low in the middle”. We also found that the dependence of foreign trade and technological innovation are the main factors affecting the coupling coordination degree, and the interaction between government support and human capital synergistic is the crucial channel for the coevolution of green finance and industrial structure to promote green and low-carbon development.

XLXiuli Liu
Xiuli Liu
RXrui xiong
rui xiong
PGPibin Guo
Pibin Guo
LNLei Nie
Lei Nie
QSQinqin Shi
Qinqin Shi
JCJing Cui
Jing Cui
Research on the Yellow River Basin’s virtual water is not only beneficial for rational water resource regulation and allocation, but it is also a crucial means of relieving the pressures of a shortage of water resources. The water stress index and pull coefficient have been introduced to calculate the implied virtual water from intraregional and interregional trade in the Yellow River Basin on the basis of a multi-regional input–output model; a systematic study of virtual water flow has been conducted. The analysis illustrated that: (1) Agriculture is the leading sector in terms of virtual water input and output among all provinces in the Yellow River Basin, which explains the high usage. Therefore, it is important to note that the agricultural sector needs to improve its water efficiency. In addition to agriculture, virtual water is mainly exported through supply companies in the upper reaches; the middle reaches mainly output services and the transportation industry, and the lower reaches mainly output to the manufacturing industry. Significant differences exist in the pull coefficients of the same sectors in different provinces (regions). The average pull coefficients of the manufacturing, mining, and construction industries are large, so it is necessary to formulate stricter water use policies. (2) The whole basin is in a state of virtual net water input, that is, throughout the region. The Henan, Shandong, Shanxi, Shaanxi, and Qinghai Provinces, which are relatively short of water, import virtual water to relieve local water pressures. However, in the Gansu Province and the Ningxia Autonomous Region, where water resources are not abundant, continuous virtual water output will exacerbate the local resource shortage. (3) The Yellow River Basin’s virtual water resources have obvious geographical distribution characteristics. The cross-provincial trade volume in the downstream area is high; the virtual water trade volume in the upstream area is low, as it is in the midstream and downstream areas; the trade relationship is insufficient. The Henan and Shandong Provinces are located in the dominant flow direction of Yellow River Basin’s virtual water, while Gansu and Inner Mongolia are at the major water sources. Trade exchanges between the midstream and downstream and the upstream should be strengthened. Therefore, the utilization of water resources should be planned nationwide to reduce water pressures, and policymakers should improve the performance of agricultural water use within the Yellow River Basin and change the main trade industries according to the resource advantages and water resources situation of each of them.

20 May 2024 in Sustainability
Verified
Journal Article
Open access
PDF file
XLXiuli Liu
Xiuli Liu
JCJing Cui
Jing Cui
Yanrui WuYanrui Wu
Yanrui Wu
Xiaohang  YueXiaohang Yue
Xiaohang Yue
JSJun Shen
Jun Shen
PGPibin Guo
Pibin Guo
Green finance has been widely acknowledged as a pivotal instrument for mitigating carbon emissions. However, few studies have focused on the role of maturity mismatches in promoting carbon emission reduction through green finance. This study aims to develop a composite criterion for green finance and examine the mechanism of how green finance affects carbon emissions via the new perspective of maturity mismatch. It is accomplished by applying a two-way fixed effects model which incorporates provincial data spanning from 2010 to 2020. The empirical evidence suggests green finance plays a significant role in carbon emission reduction, a result that remains robust even after undergoing other tests such as using instrumental variables and alternating econometric models. Furthermore, this effect is particularly pronounced in regions with high degrees of green finance and low energy consumption. Mechanism analysis documents that green finance reduces carbon emissions by addressing maturity mismatch issues faced by green enterprises. Further research finds that green finance can promote the synergy of pollution and carbon reduction; in particular, the effect of maturity mismatch on SO2 reduction is more obvious. Consequently, this study offers practical recommendations for governments, financial institutions, and other relevant policymakers to further propel the advancement of green finance.

28 November 2022 in Sustainability
Verified
Journal Article
Open access
PDF file
XCxiaoyan cao
xiaoyan cao
JJJizong Jiao
Jizong Jiao
XLXiuli Liu
Xiuli Liu
WZWanyang Zhu
Wanyang Zhu
HWHaoran Wang
Haoran Wang
JLJingtao Lu
Jingtao Lu
With the expansion of the social economy and adjustment of environmental policies, particularly with the onset of development policies for the western region, ecosystems in the arid areas of Northwest China have undergone profound changes. This study collected soil, topographical, climate, and nighttime light data to develop a set of ecological vulnerability assessment indexes based on the background ecological characteristics of the arid areas of Northwest China. The spatiotemporal evolution of ecological carrying capacity was analyzed by our team using Spatial Principal Component Analysis (SPCA) in 2000, 2007, 2012, and 2018 to construct an ecological security pattern. The results revealed that the ecological carrying capacities of the arid areas in the northwest were primarily weak, albeit decreasing, while those areas with strong carrying capacities were increasing. In terms of spatial distribution, the ecological carrying capacities of the Hexi, Northern Xinjiang, and Western Inner Mongolia regions were on the rise, while those of the Southern Xinjiang region were declining. The Minimum Cumulative Resistance (MCR) model was used to extract 51 road-type, river-type, and green corridors with a total length of 7285.43 km. A total of 71 nodes representing important patches, wet rivers, and ecologically fragile areas were extracted. According to the calculated results, the arid region of the northwest was divided into 16 ecological security patterns, which were optimized according to changes in their ecological carrying capacities.