Analyzing network topological characteristics of eco-industrial parks from the perspective of resilience: A case study

Li, Xiangmei; Xiao, Renbin

doi:10.1016/j.ecolind.2016.11.031

Cited by 51 publications

(31 citation statements)

References 37 publications

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“…Here, we constructed an integrated node centrality (INC 푖 ) indicator [21] by combining total node degree ( tot 푖 ) and node betweenness ( 푖 ) to detect the important nodes in land system networks:…”

Section: Identification Of Key Land Use Types and Transitionsmentioning

confidence: 99%

“…As shown in Table 2, the average node degree in the land system of Wuhan City had experienced a downward trend in the first four periods from 1990 to 2010 and then increased during 2010-2015. This indicates the land system tends to be more stable before Integrated node centrality Paddy (11) Reservoir and ponds (43) High cover grass (31) Sparse woods (23) Urban (51) Woods (21) River (41) Rural resident (52) Moderate cover grass (32) Other woods (24) Bare soil (65) Shrub (22) Other developed (53) Beach (46) Low cover grass (33) Lake 42 Table 3, the 19 land use types are divided into three major categories, that is, ecological lands, artificial lands, and other lands. Generally, the artificial land types (i.e., urban land, rural residential land, and other built-up land) are input dominated (i.e., outstrength/in-strength < 1), suggesting that these lands have more area gains than losses.…”

Section: Study Area and Datamentioning

confidence: 99%

“…However, when the important nodes are attacked, the connectivity and efficiency of the land system network decrease drastically (Figures 5(b)-5(f)). For example, the removal of paddy land area by 30% contributes to a decline in network connectivity with the remaining number of Dryland (12) Woods (21) Shrub (22) Sparse woods (23) Other woods (24) High coverage grassland (31) Moderate coverage grassland (32) Low coverage grassland (33) River (41) Lake (42) Reservoir and pond (43) River beach (46) Urban land (51) Rural residential land (52) Other built-up land (53) Wetland ( links decreasing from 289 to 178 and the network efficiency decreasing from 0.93 to 0.53 ( Figure 5(b)). Figure 6 exhibits the changing trends of the network efficiency when the area of each node decreases from 10% to 100%.…”

Section: Structural Vulnerability Assessmentmentioning

confidence: 99%

“…For example, Ouyang et al [20] introduced the complex network method in evaluating structural and functional vulnerabilities of interdependent infrastructures. Li and Xiao [21] extracted the topological structure of eco-industrial parks based on complex network methods and analyzed the resilience of the parks under different attack strategies. Regarding the applications of complex network analysis in land systems, existing studies are still in an early phase of extracting network topology [22], identifying key land use types or dominant land use conversions [16,23].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the rapidly growing interest in complex networks was triggered by the emergence of the smallworld model (1998) [32] and the scale-free networks (1999) [29]. Since then, complex network analysis has been widely applied to the investigation of real-world networks such as, for example, the World Wide Web [33], electric power grids [34], transportation systems [35], ecological networks [21], genetic regulatory networks [36], and brain networks [17].…”

Section: An Introduction To Complexmentioning

confidence: 99%

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Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach

Wang

et al. 2018

Complexity

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Rapid urbanization is responsible for the increased vulnerability of land systems and the loss of many crucial ecosystem services. Land systems are typical complex systems comprised of different land use types which interact with each other and respond to external environment processes (such as urbanization), resulting in dynamics in land systems. This work develops a methodology approach by integrating complex networks and disruptive scenarios and applies it to a case study area (Wuhan City in China) to explore the effects of urbanization on land system structural vulnerability. The land system network topologies of Wuhan City during five time periods from 1990 to 2015 are extracted. Our results reveal that (1) the urban land expands at a higher speed than the urban population in Wuhan City; (2) the period of 2005–2010 has witnessed more land area conversions from ecological lands to urban land than other periods; (3) the land system is more vulnerable to intentional attacks on nodes with higher integrated node centrality and larger land area, such as paddy, dryland, and lake; and (4) the network efficiency of the land system would decline sharply if the area shrinkage of paddy, dryland, and lake is larger than 30%, 50%, and 20%, respectively. The results provide some insights into building a resilient urban land system, such as increasing the efficiency of existing urban land and controlling the shrinkage rate of important land use types. This study contributes to existing literature on complex networks by expanding its application in land systems, which highlight the potential of complex networks to capture the complexity, dynamics, heterogeneity, and emergent phenomena in land systems.

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Section: Identification Of Key Land Use Types and Transitionsmentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

Section: Structural Vulnerability Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: An Introduction To Complexmentioning

confidence: 99%

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Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach

Wang

et al. 2018

Complexity

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Input–Output Models of Industrial Complexes

Tan

Aviso

Promentilla

et al. 2018

Lecture Notes in Management and Industrial Engineering

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Potentially useful elements (Al, Fe, Ga, Ge, U) in coal gangue: a case study in Weibei coal mining area, Shaanxi Province, northwestern China

Wang

2018

Environ Sci Pollut Res

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Coal gangue has become one of the largest industrial solid waste in China, but it is also a kind of reserve resources. Representative coal gangue samples from different coal mines (mainly in Permo-Carboniferous and Jurassic) in Weibei area in Shaanxi Province are collected, and potentially useful elements (Al, Fe, Ga, Ge, U) in coal gangue are analyzed. The results show that the reserves of Al and Fe in Chinese coal gangue are 262 million tons and 196 million tons, respectively, based on the geometric mean values of AlO (15.18%, weight percent) and FeO (6.24%, weight percent). Meanwhile, the crude reserves estimation of Ga, Ge, and U are 55,282, 6867, and 32,981 tons, respectively, based on the weighted mean contents of Ga, Ge, and U in coal gangue at 17.55, 2.18, and 10.47 mg/kg, respectively. Furthermore, Ga and Al contents in quite a large number of coal gangue mines exceed the cutoff value, which has a prospect of development and utilization from coal gangue. The policy implications from this study may include that (1) recycling of useful elements in Chinese coal gangue should be treated as an integral part of sustainable development with professional legislations and (2) establishing a basic database of coal gangue and authoritative system with relevant departments for solid waste management may effectively improve comprehensive utilization of coal gangue in the future.

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Analyzing network topological characteristics of eco-industrial parks from the perspective of resilience: A case study

Cited by 51 publications

References 37 publications

Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach

Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach

Input–Output Models of Industrial Complexes

Potentially useful elements (Al, Fe, Ga, Ge, U) in coal gangue: a case study in Weibei coal mining area, Shaanxi Province, northwestern China

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