The recession and revitalization of old industrial cities concerns urban industrial evolution and its characteristics. Based on the theory of evolutionary resilience, we developed an analytical framework for the indus...The recession and revitalization of old industrial cities concerns urban industrial evolution and its characteristics. Based on the theory of evolutionary resilience, we developed an analytical framework for the industrial structure evolution of old industrial cities, and applied the framework to a case study in Shenyang. The following conclusions are drawn. First, since 1978, Shenyang's industrial growth capacity has shown fluctuation between ‘contraction-expansion'. As the secondary industry has a much stronger expansionary and contractionary capacity for growth, this results in lacking stability leading to industrial structure transformation. Second, since 1999, the orientation towards a high-end manufacturing industry in Shenyang has weakened, and the evolution of the new and old growth path is characterized by low-end orientation. Third, since 2007, Shenyang's industrial innovation output capacity has dropped sharply which has been significantly affected by scientific and technological personnel and enterprise-owed science and technology institutions and to a less extent by R&D expenditure. We applied the resilience theory to study the industrial evolution of an old industrial city, explored new study perspectives on industrial evolution and verified the applicability of the resilience theory. This paper provides a scientific reference for understanding the recent deceleration in economic growth in the Northeast old industrial base, and for exploring new paths toward revitalization.展开更多
To better understand the resilience evolution dynamics of urban lifeline systems over extended operational periods,this study introduces a model inspired by the susceptible-infected-recovered(SIR)model,which is tradit...To better understand the resilience evolution dynamics of urban lifeline systems over extended operational periods,this study introduces a model inspired by the susceptible-infected-recovered(SIR)model,which is traditionally used to simulate population health transitions.By analyzing the mechanisms governing the performance state evolution of urban lifeline systems under disaster scenarios,integrating a disaster scenario model with resilience assessment methodologies,and comprehensively considering three key resilience components—resistance,recovery,and adaptability—we develop a system dynamics resilience-reliability(SDR-R)model.A hypothetical case study is conducted to validate the model's applicability.The results indicate that the interplay of resistance,recovery,and adaptability influences the dynamic evolution of system performance across three states:disability performance,survivability performance,and recovery performance.The model reveals a cyclical pattern in resilience enhancement,with adaptability emerging as a critical determinant.Moreover,the SDR-R model not only simulates urban lifeline performance state evolution under single disaster scenarios but also captures resilience evolution trends over long-term system operations.The case study findings reveal that resilience decreases as disaster severity intensifies,yet positive feedback from adaptability fosters resilience improvement over time.The process of resilience evolution can be divided into four distinct phases:initial impact,adaptive priming,adaptive enhancement,and threshold effect.Notably,resilience dynamics vary significantly across disaster levels.While systems exhibit high resilience under low-level disasters,resilience gradually stabilizes at a high level in medium-and high-level disaster scenarios.However,extreme disasters introduce greater fluctuations in resilience,underscoring the necessity for targeted resilience-enhancing strategies.The insights derived from this study offer methodological guidance for understanding urban lifeline resilience evolution and developing strategies to enhance system robustness.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41571152,41771179,41630749,41601124)the Key Deployment Projects of the Chinese Academy of Sciences(No.ZDBS-SSW-SQC)135 Planning and Featured Services Projects of IGA,Chinese Academy of Sciences(No.Y6H2091001)
文摘The recession and revitalization of old industrial cities concerns urban industrial evolution and its characteristics. Based on the theory of evolutionary resilience, we developed an analytical framework for the industrial structure evolution of old industrial cities, and applied the framework to a case study in Shenyang. The following conclusions are drawn. First, since 1978, Shenyang's industrial growth capacity has shown fluctuation between ‘contraction-expansion'. As the secondary industry has a much stronger expansionary and contractionary capacity for growth, this results in lacking stability leading to industrial structure transformation. Second, since 1999, the orientation towards a high-end manufacturing industry in Shenyang has weakened, and the evolution of the new and old growth path is characterized by low-end orientation. Third, since 2007, Shenyang's industrial innovation output capacity has dropped sharply which has been significantly affected by scientific and technological personnel and enterprise-owed science and technology institutions and to a less extent by R&D expenditure. We applied the resilience theory to study the industrial evolution of an old industrial city, explored new study perspectives on industrial evolution and verified the applicability of the resilience theory. This paper provides a scientific reference for understanding the recent deceleration in economic growth in the Northeast old industrial base, and for exploring new paths toward revitalization.
基金supported by the Natural Science Research Project of the Anhui Educational Committee(Grant Number 2023AH051183)the Anhui Provincial Natural Science Foundation(Grant Number 2308085QG242)the National Natural Science Foundation of China(Grant Number 52404191).
文摘To better understand the resilience evolution dynamics of urban lifeline systems over extended operational periods,this study introduces a model inspired by the susceptible-infected-recovered(SIR)model,which is traditionally used to simulate population health transitions.By analyzing the mechanisms governing the performance state evolution of urban lifeline systems under disaster scenarios,integrating a disaster scenario model with resilience assessment methodologies,and comprehensively considering three key resilience components—resistance,recovery,and adaptability—we develop a system dynamics resilience-reliability(SDR-R)model.A hypothetical case study is conducted to validate the model's applicability.The results indicate that the interplay of resistance,recovery,and adaptability influences the dynamic evolution of system performance across three states:disability performance,survivability performance,and recovery performance.The model reveals a cyclical pattern in resilience enhancement,with adaptability emerging as a critical determinant.Moreover,the SDR-R model not only simulates urban lifeline performance state evolution under single disaster scenarios but also captures resilience evolution trends over long-term system operations.The case study findings reveal that resilience decreases as disaster severity intensifies,yet positive feedback from adaptability fosters resilience improvement over time.The process of resilience evolution can be divided into four distinct phases:initial impact,adaptive priming,adaptive enhancement,and threshold effect.Notably,resilience dynamics vary significantly across disaster levels.While systems exhibit high resilience under low-level disasters,resilience gradually stabilizes at a high level in medium-and high-level disaster scenarios.However,extreme disasters introduce greater fluctuations in resilience,underscoring the necessity for targeted resilience-enhancing strategies.The insights derived from this study offer methodological guidance for understanding urban lifeline resilience evolution and developing strategies to enhance system robustness.
