System dynamics modelling for improving urban resilience in Beijing, China

Li, Guijun; Kou, Chenhuan; Wang, Yongsheng; Hongtao, Yang

doi:10.1016/j.resconrec.2020.104954

Cited by 85 publications

(42 citation statements)

References 46 publications

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“…Similarly, authors such as Scholten et al ( 2019 ) and Jain et al ( 2017 ) argued that SCRes relies heavily on AC, which mitigates the impact of unexpected events through the proactive identification of strategies enabling the supply chain to adapt aftershock, regaining or even enhancing its initial situation. Li et al ( 2020 ) consider adaptive planning and adaptive response are vital elements in building SCRes to pick up and recover from outbreaks under urban context. Further, Kochan and Nowicki ( 2018 ) supported the positive impact of AC on building SCRes through minimizing the likelihood of confronting non-routine events, withstand the propagation of shocks through holding control over organization and processes, and responding by proactive plans to absorb the shock and rebuild the robust state of the operations in the supply chain.…”

Section: Research Framework and Hypotheses Developmentmentioning

confidence: 99%

“…The majority of the suppliers at the lower tiers of the supply chain may not have access to the skilled resources and technological sophistication needed for developing AI capabilities or may possess inconsistent data or incompatible interface standards. The cooperation and value of information sharing amongst the supply chain partners are highly critical to mitigating the risks (Li et al 2020). The level of collaboration between the supply chain partners contributes to supply chain resilience, benefiting supply chain operations in risk management (Yen and Zeng 2011).…”

Section: Implications For Practitionersmentioning

confidence: 99%

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Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

et al. 2021

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Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

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Section: Research Framework and Hypotheses Developmentmentioning

confidence: 99%

Section: Implications For Practitionersmentioning

confidence: 99%

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

et al. 2021

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“…By linking risk management and strengthening resilience, the protection of critical infrastructure is extended by the repressive part, i.e., the reaction to already occurring adverse events (NIAC 2009). However, current approaches in various areas focus only on assessing and strengthening critical infrastructure resilience (Alizadeh and Sharifi 2020;Argyroudis et al 2020;Quitana et al 2020;Li et al 2020;Shandiz et al 2020) even though resilience goes beyond traditional risk management (Petersen et al 2020). Therefore, it can be stated that there is currently no appropriate managerial tool that would explicitly deal with preventive measures for the protection of critical infrastructure elements.…”

Section: Discussionmentioning

confidence: 99%

“…For this reason, in recent years, particular attention has been paid to research into methods for assessing and strengthening critical infrastructure resilience, as resilience goes beyond traditional risk management (Petersen et al 2020). This fact is evidenced, in particular, by articles dealing with resilience in the context of entire cities or urban areas (Chen et al 2020;Li et al 2020;Lu et al 2020;Rehak et al 2019) of their transport systems (Argyroudis et al 2020;Machado-León and Goodchild 2017;Dvorak et al 2017) or individual urban networks (Alizadeh and Sharifi 2020;Liu and Song 2020;Quitana et al 2020;Shandiz et al 2020). These approaches are realistically applicable and contribute to strengthening the resilience of critical infrastructure, but do not allow for the early indication of resilience disruption.…”

Section: Introductionmentioning

confidence: 99%

Managerial Decision Making in Indicating a Disruption of Critical Infrastructure Element Resilience

Šplíchalová

Patrman

Kotalova

et al. 2020

Administrative Sciences

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Managerial decision making is an integral process used in public and private organizations. Critical infrastructure entities are a strategically significant group dependent on the quality of decision-making processes. They aim to provide services necessary to ensure state security and to satisfy basic human needs. The quality of decision making is an important factor in the management of these entities. The quality level is determined by many factors, the key of which is risk management. For this reason, it is necessary for the operators to minimize risks affecting the elements of the critical infrastructure through which these services are provided. Risk management is commonly used for this purpose, making it possible to assess and manage these risks. However, there is a specific group of threats that affects the resilience of these elements. The indication of these threats is not possible through common risk management. Therefore, it is necessary to develop specific scenarios of negative impacts and procedures for assessing their impact on the resilience of elements of the critical infrastructure. To this end, this conceptual article introduces an entirely new managerial decision-making process for indicating the resilience of critical infrastructure elements.

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“…Their research perspectives have focused on public management [16], disaster prevention and mitigation [17,18], planning guidelines [19,20], industrial development [21,22], and supporting infrastructure construction [23,24]. The purpose of the current study is mainly focused on the classification and evaluation of resilience [25,26] and its optimization strategy [27,28], and the research ideas of previous studies show the development from theoretical construction to practical guidance.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Pattern and Influence Mechanism of Cultivated Land System Resilience: Case from China

Lyu

Wang

Niu

et al. 2021

Land

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The study of cultivated land systems from the perspective of resilience is of great significance for the innovation of the research paradigm of cultivated land use and the rational utilization and protection of cultivated land. This study aims to explain the theoretical connotations of cultivated land system resilience (CLSR), construct an evaluation system and zoning rules for CLSR, and take 30 provinces of China as case study areas to explore the influencing factors of CLSR, so as to provide a reliable governance plan for the sustainable development of cultivated land. The results show that: (1) CLSR refers to a sustainable development ability that CLS—by adjusting the structure and scale of internal elements—absorbs and adapts to internal and external disturbances and shocks to the maximum possible extent, abandons the original inapplicable state, creates a new recovery path, achieves a new balance, and avoids system recession. (2) The overall CLSR of the 30 provinces showed an upward trend, and the degree of polarization of the distribution pattern was gradually intensified and experienced a transition process from “leading by resource and ecological resilience—equilibrium of each resilience—leading by production and scale structural resilience”. (3) In the north, east, and south coastal areas of China, CLSR mainly consists of the major evolution areas and the stable development areas; the potential excitation areas of CLSR are mainly concentrated in the central and western regions of China; the CLSR-sensitive lag areas and degraded vulnerable areas are mainly distributed in the northwest and southwest of China. (4) Water resource endowment has a strong influence on CLSR, while social economy mainly influences CLSR through ‘economic foundation-superstructures’ and ‘economic development-factor agglomeration’. (5) According to the different CLSR zones, CLSR was strengthened mainly from the aspects of driving factor agglomeration, building factor free-flow systems, and multi-means support.

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System dynamics modelling for improving urban resilience in Beijing, China

Cited by 85 publications

References 46 publications

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Managerial Decision Making in Indicating a Disruption of Critical Infrastructure Element Resilience

Spatio-Temporal Pattern and Influence Mechanism of Cultivated Land System Resilience: Case from China

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