Resilience Metrics Research Articles

Climate-based measures of supply chain resilience

PurposeSupply chains are a complex but integral part of the food distribution system with unique vulnerabilities, as agricultural production is a function of biological processes and food goods are perishable necessities. Various shocks, including pandemics, geopolitical conflicts and extreme weather events, can cause disruptions to the food supply chain. International trade often plays an adaptive role in mitigating the effects of these shocks as it allows for a market-oriented redistribution of resources that can mitigate the impacts of localized shortages and surpluses.Design/methodology/approachWith this in mind, our goal is to combine information on weather shocks and trade flows to propose novel supply chain resilience metrics focusing on key weather drivers in over 50 countries. We focus on the role of extreme heat (degree days above 29°C) for maize, soybeans and rice, but the approach is general enough to be widely applied to any combination of crops, trade partners and weather/climate variables.FindingsWe focus on the role of extreme heat (degree days above 29°C) for maize, soybeans and rice, but the approach is general enough to be widely applied to any combination of crops and weather/climate variables. Leveraging globally gridded temperature data, we estimate the metrics for the United States and find a heterogeneous range of resilience across crops and risk dimensions. In addition, we provide a detailed look at the spatial correlations with the US and its historical trade partners and find evidence that these metrics could (potentially) be enhanced via strategic trade relationships.Research limitations/implicationsLeveraging globally gridded temperature data, we estimate the metrics for the United States of America and China to demonstrate differences that might arise from a net-exporter versus net-importer perspective. Our results suggest that these metrics can be useful for disentangling the resilience a country faces between its own internal supply chain versus its participation in other countries’ supply chains.Practical implicationsSince these metrics are a combination of exogenous spatial correlations of weather shocks and endogenous trade patterns, we also discuss how they can be adjusted via strategic trade relationships to enhance resiliency.Originality/valueOur results provide pertinent insights to US policymakers promoting export expansion under climate change (USDA FAS, 2024). Moreover, the metrics provided here are focused on climate resiliency and thus could be an important component of strategic trade decisions given the recent concerns between the US and Mexico centered around GM maize (Beckman et al., 2024) and the seemingly improving US–India agricultural trade relationship.

A resilience assessment framework for slopes considering multiple performance indicators

The performance assessment of slopes during various disturbances is important to ensure the safe service of slopes. Due to the four characteristics of slope engineering, it is difficult for existing resilience evaluation models to accurately reflect slope performance. To address this problem, a resilience evaluation model applicable to slopes was developed in this study. In this model, a typical slope performance curve is proposed according to the characteristics of slope engineering and the calculation formula of slope resilience metric is derived. In order to facilitate engineering application, this study proposes a slope resilience assessment framework with the evaluation model as the core. In the resilience assessment framework, a multi-indicator system for slope performance was established to cover the slope components, and the CRITIC theory was used to combine the subjective and objective weights for each indicator. Finally, the proposed slope resilience assessment framework was applied to a slope case under the influence of continuous excavation and rainfall. The results show that the proposed resilience assessment framework can more accurately reflect the performance variations of slopes during disturbance and recovery processes than the existing model, and the resilience metric is 23.4% higher than that of the existing model. This study was the first attempt to establish a resilience assessment framework applicable to slopes, which informs subsequent research.

Motif‐based resiliency assessment for cyber‐physical power systems under various hazards

AbstractCyber‐physical power systems (CPPS) are integral to meeting society's demand for secure, sustainable, affordable and resilient critical networks and services. Given the convergence of decarbonising, heating, cooling, and transportation networks onto cyber‐physical power systems (CPPS), this takes on increased significance. This paper introduces an innovative approach to the open challenge of how we evaluate CPPS resilience, presenting the use of network motifs and Monte Carlo simulations. We demonstrate how our methodology enables a comprehensive analysis of CPPS by capturing the interdependence between cyber and physical networks and by accounting for inherent uncertainties in cyber and physical components. Specifically, this method incorporates the dynamic interplay between the physical and cyber networks, presenting a time‐dependent motif‐based resilience metric. This metric evaluates CPPS performance in maintaining critical loads during and after diverse extreme events in cyber and/or physical layers. The resilience status of the system is determined using the prevalence of 4‐node motifs within the system's network, offering valuable redundant paths for critical load supply. The study models a variety of natural events, including earthquakes, windstorms, and tornadoes, along with cyber‐attacks while accounting for their inherent uncertainties using Monte Carlo simulation. The proposed approach is demonstrated through two test CPPS, specifically the IEEE 14‐bus and IEEE 30‐bus test systems, affirming its effectiveness in quantifying CPPS resilience. By comprehensively addressing system dynamics, interdependencies, and uncertainties, the proposed technique advances our understanding of CPPS and supports resilient system design.

A community resilience index for place-based actionable metrics.

Community resilience measurement to natural hazards is becoming increasingly relevant due to the growth of federal programs and local and state resilience offices in the United States. This study introduces a methodology to co-produce an actionable resilience metric to measure locally relevant and modifiable indicators of community resilience for the state of South Carolina. The "actionable" metrics, based on the Baseline Resilience Indicators for Communities (BRIC) index, are calculated at the county and tract scale and then compared to "conventional" versions of BRIC. Actionable BRICs perform better in reliability testing than conventional BRICs. Correlations across the two scales of BRIC construction show a stronger relationship between the actionable BRICs than conventional, though all are highly correlated. When mapped, actionable BRIC shows a shifted region of low resilience in the state when compared to conventional BRIC, suggesting that actionable and conventional BRICs are distinct. Scale differences show dissimilar drivers of resilience, with county-level resilience driven by community, social, and environmental resilience and tract-level resilience driven by social and institutional resilience. Actionable tract-level BRIC appears to be the best representation of modifiable resilience for South Carolina, but it comes with trade-offs, including calculation complexity and changing geographies over time. Regardless of scale, the resulting actionable indices offer a useful tracking mechanism for the state resilience office and highlight the importance of integrating top-down and bottom-up resilience perspectives to consider local drivers of resilience. The resulting methodology can be replicated in other states and localities to produce actionable and locally relevant resilience metrics.

Adaptation of plantations to drought events in arid and semi-arid regions: Evidence from tree resilience

Increasing drought frequency and severity pose a significant risk to global forest plantations, particularly in arid and semi-arid regions. Developing drought-adaptive plantations is particularly important for vegetation restoration in the context of climate warming. Despite the importance of drought response and adaptation, systematic research on these aspects for major tree species remains limited. Here, we collected 467 tree ring cores from eight paired sites of Robinia pseudoacacia L. (a broad-leaved species) and Pinus tabulaeformis C. (a coniferous species) plantations across a typical semi-arid region of the Loess Plateau (LP) to discern the climate-growth patterns of both species using linear mixed models. We employed hypothesis testing to analyze the differences in tree resilience metrics, including drought resistance and recovery ability, and linear mixed models combined with partial least squares path analysis to clarify the driving factors of tree resilience between R. pseudoacacia and P. tabulaeformis. Our findings demonstrated that Palmer Drought Severity Indices is the key limiting climate factor, without spatial variation, on the growth of R. pseudoacacia and P. tabulaeformis, explaining 47.7 % and 69.8 % of the variables effect percentage, respectively. Compared to P. tabulaeformis, R. pseudoacacia is more limited by atmospheric drought stress and soil moisture, with 11.0 % and 17.9 % of the explained effect percentage, respectively. R. pseudoacacia exhibited lower drought resistance ability, evidenced by a lower resistance (the average value is 0.62) and a higher average growth reduction (0.20) than P. tabulaeformis (0.85 and 0.17, respectively). In contrast, R. pseudoacacia had higher drought recovery ability, evidenced by a higher recovery (the average value is 1.19), a shorter recovery period (1.0), and a faster average recovery rate (0.14), than P. tabulaeformis (1.07, 2.0, and 0.08, respectively). Importantly, all tree resilience metrics for both species exhibited spatial consistency across the LP. Tree characteristics had a stronger effect on drought resistance and recovery ability in R. pseudoacacia than in P. tabulaeformis. Soil properties improved R. pseudoacacia drought resistance ability and reduced its drought recovery ability, but had weaker effects on P. tabulaeformis. Additionally, tree size and age strongly influence tree resilience in R. pseudoacacia through preceding year tree growth of drought events and climate sensitivity. These findings underscore the complementary drought resilience of broad-leaved and coniferous species, emphasizing the necessity for adaptive plantations and diversified tree species plantations as a strategic response to the anticipated increase in drought frequency and severity in arid and semi-arid regions.

Brain metabolic resilience in Alzheimer's disease: A predictor of cognitive decline and conversion to dementia

BackgroundBrain atrophy measured by structural imaging has been used to quantify resilience against neurodegeneration in Alzheimer's disease. Considering glucose hypometabolism is another marker of neurodegeneration, we quantified metabolic resilience (MR) based on Fluorodeoxyglucose positron emission tomography (FDG PET) and investigated its clinical implications. MethodsWe quantified the MR and other resilience metrics, including brain resilience (BR) and cognitive resilience (CR), using partial least squares path modeling from the ADNI database. A linear mixed-effects model and a Cox proportional hazards model were used to identify the impact of each resilience on longitudinal cognitive function and conversion to dementia, respectively. ResultsA total of 848 participants were included in this study. All resilience metrics (CR, BR, and MR) were associated with slower cognitive decline. Results from the ANOVA test, AIC and BIC values showed that the additional inclusion of MR improved the performances of the linear mixed effect models. In survival analysis, all resilience variables were negatively associated with the risk of conversion to dementia. In line with the results of the linear mixed effects models, the additional inclusion of MR into the models with different resilience variables increased the C-index. ConclusionRelative preservation of brain glucose metabolism is a valuable predictor of future cognitive decline and conversion to dementia, adding value to existing resilience metrics. While the utility of FDG PET in clinical settings is limited by cost and accessibility, it might have potential usefulness as a prognostic marker, especially in a context of resilience.

Leveraging a Novel Phenotype to Identify Complex Traits and Biological Pathways Associated with Resilience to Alzheimer’s Disease

AbstractBackgroundPrevious models of resilience to Alzheimer’s Disease (AD) have relied on cross‐sectional designs and inclusion of measures of neuropathology. Here, we present a novel modeling approach incorporating longitudinal data and the use of APOE and higher order interaction terms to approximate neuropathological resilience, vastly increasing participant diversity and statistical power. We validate this approach and report novel genetic associations with neuropathological resilience.MethodLeveraging 20,513 participants (Table 1), we explored the genetics of resilience metrics using mixed‐effects regressions across ancestries. We conducted GWAS on four resilience metrics: two in a “silver” model, which benefit from a larger sample size and did not include neuropathology at autopsy, and two in a “gold” model, which incorporate neuropathology at autopsy. These models were built in all diagnoses and separately in cognitively unimpaired (CU) at baseline participants. Silver‐gold meta‐analyses were conducted to maximize statistical power. Models adjusted for age, sex, APOE ε4 allele count, presence of the APOE ε2 allele and all covariate interactions with interval (years from baseline). Residual cognitive resilience was quantified from residuals in three cognitive domains (memory, executive function, and language). Post‐GWAS analyses included gene and pathway tests using MAGMA and genetic correlation with 65 traits using GNOVA.ResultSilver and gold resilience metrics were highly correlated (0.77‐0.88) and showed positive genetic covariance (0.01‐0.13). We identified negative correlations of resilience with multiple sclerosis and neuropsychiatric traits, including Tourette syndrome and schizophrenia (all PFDR<2.6×10−2; Figure 1). Furthermore, we observed three significant pathway associations with resilience: metabolism of amino acids and derivatives in the silver all participants meta‐analysis, negative regulation of transforming growth factor beta production in the silver CU at baseline meta‐analysis, and the SARS pathway in the gold CU at baseline meta‐analysis (all PFDR<5.0×10−2). Finally, we identified a locus on chromosome 17 associated with resilience that approached genome‐wide significance (top SNP: rs757022, MAF = 0.18, b = 0.08, P = 1.1×10−7; Figure 2).ConclusionUsing novel, longitudinal modeling approaches, we identified multiple biological pathways and complex traits associated with resilience to AD as well as genetic loci approaching genome‐wide significance. Our findings indicate that silver standard modeling approaches can complement current gold standard methods to accelerate genetic discovery.

Multiple resiliency metrics reveal complementary drivers of ecosystem persistence: An application to kelp forest systems.

Human-caused global change produces biotic and abiotic conditions that increase the uncertainty and risk of failure of restoration efforts. A focus of managing for resiliency, that is, the ability of the system to respond to disturbance, has the potential to reduce this uncertainty and risk. However, identifying what drives resiliency might depend on how one measures it. An example of a system where identifying how the drivers of different aspects of resiliency can inform restoration under climate change is the northern coast of California, where kelp experienced a decline in coverage of over 95% due to the combination of an intense marine heat wave and the functional extinction of the primary predator of the kelp-grazing purple sea urchin, the sunflower sea star. Although restoration efforts focused on urchin removal and kelp reintroduction in this system are ongoing, the question of how to increase the resiliency of this system to future marine heat waves remains open. In this paper, we introduce a dynamical model that describes a tritrophic food chain of kelp, purple urchins, and a purple urchin predator such as the sunflower sea star. We run a global sensitivity analysis of three different resiliency metrics (recovery likelihood, recovery rate, and resistance to disturbance) of the kelp forest to identify their ecological drivers. We find that each metric depends the most on a unique set of drivers: Recovery likelihood depends the most on live and drift kelp production, recovery rate depends the most on urchin production and feedbacks that determine urchin grazing on live kelp, and resistance depends the most on feedbacks that determine predator consumption of urchins. Therefore, an understanding of the potential role of predator reintroduction or recovery in kelp systems relies on a comprehensive approach to measuring resiliency.

A higher tissue fraction of parenchyma in secondary xylem supports growth recovery of angiosperm trees after drought

Abstract Resilience to drought represents an important focus for trees during climate change, with the aim of predicting the resistance and recovery of species worldwide. Previous studies mainly linked tree growth resilience to plant functional traits that are related to resource acquisition and investment. Here, we investigate a potential link between resilience and the amount of parenchyma tissue in wood, a multifunctional tissue that may provide various physiological benefits to drought‐related mechanisms. We compiled a global tree‐ring data set to evaluate the relationship between growth resistance, resilience or recovery from drought and xylem parenchyma tissue fractions of 50 angiosperms, which was complemented with a local study of nine species from Mt. Tiantong in China. We also assessed the influence of climate and phylogeny on these relationships. We found that growth recovery after drought was positively related to the fractions of total parenchyma locally and globally. This association remained statistically significant when accounting for the effects of climate and phylogeny. No other associations between parenchyma fractions and growth resilience metrics were statistically significant. Our results suggest that drought recovery of angiosperm trees is affected by the amount of parenchyma. Incorporating xylem parenchyma fraction with other traits, such as hydraulic traits, could therefore enhance our comprehension of how various angiosperm tree species will respond to future droughts. Further studies should focus on unravelling the physiological roles of xylem parenchyma fraction. Read the free Plain Language Summary for this article on the Journal blog.

Analyzing and predicting the urbanization and ecological resilience coupling and coordination of Guangdong Province

Taking 21 cities in Guangdong Province as the research object, this study quantitatively analyzes the spatiotemporal evolution of urbanization and ecological environment coupling from 2000 to 2020 using a coupling coordination model, and predicts the coordinated development of urbanization and ecological resilience coupling from 2000 to 2020 using an LSTM deep learning model. Analysis of the geographical data conveys the following observations: (1) Remotely sensed night-time luminosity data indicate a precipitous escalation in the urbanization quotient within Guangdong, with the Pearl River Delta, featuring Guangzhou and Shenzhen as hubs, exhibiting high levels of nocturnal radiance, in stark contrast to the lower and more dispersed illumination in the province’s northern latitudes. (2) Indices of ecological resilience suggest a progressive enhancement across Guangdong, with Maoming exhibiting the utmost resilience, succeeded by Jiangmen, Dongguan, Shenzhen, and Zhongshan in terms of ecological robustness. (3) The synergistic relationship between urbanization dynamics and ecological resilience metrics in Guangdong has been incrementally enhanced, with Qingyuan and Shaoguan exemplifying optimal coupling and coordination, while Zhongshan has experienced the most pronounced increment in these interdependencies. (4) The proportional advancement of urbanization and ecological resilience in the province is approaching a theoretical equilibrium state. (5) Projections based on spatiotemporal models forecast a gradual upgrade in the urban-ecological systems’ coupling and coordination coefficients from 2021 to 2025, with a general trend of incremental progression among the majority of the province’s urban centers towards a moderate level of integrated development.

Resilience evaluation of active and passive redundancy using carbon-hydrogen-oxygen symbiosis network – An integrated and post-design analysis

Carbon-hydrogen-oxygen symbiosis network, which is an eco-industrial park design method, along with atomic targeting provides a different avenue to approach effluents whereby effluents from industries could be gathered and redistributed to the respective interceptors to produce products. Resilience of an eco-industrial park is of particular interest due to the fluctuating economy whereby commitment of participants would be into question due to downturn in the economy. Resilience index provides a metric to evaluate the resilience of an eco-industrial park by assigning a value between 0 and 1. Resilience of an eco-industrial park could be improved by either having active redundancy such as extra process lines or oversizing the process lines. However, passive redundancy such as storage and buffer tanks also play a role in improving resilience of an eco-industrial park. This work aims to develop a method for resilience evaluation of a carbon-hydrogen-oxygen symbiosis network as well as comparing active and passive redundancy in resilience improvement of an eco-industrial park. Besides, the resilience metric is also incorporated into the carbon-hydrogen-oxygen symbiosis network model to compare the differences between post-design and integrated approach. The results obtained showed that while incorporation of both active and passive redundancy on all species would maximise the resilience index, partial combination of both could achieve a similar result. Besides, post-design and integrated approach would yield similar resilience index when interceptors of carbon-hydrogen-oxygen symbiosis network is limited. When more degrees of freedom are given to the number of interceptors, the results of post-design would not differ much. On the other hand, the results from integrated approach would select extra interceptors. Primarily, species which have not been compensated in the network would be selected to allow for redirection of flow when disruption occurs.

Resilience Metrics for Socio-Ecological and Socio-Technical Systems: A Scoping Review

An increased interest in the resilience of complex socio-ecological and socio-technical systems has led to a variety of metrics being proposed. An overview of these metrics and their underlying concepts would support identifying useful metrics for applications in science and engineering. This study undertakes a scoping review of resilience metrics for systems straddling the societal, ecological, and technical domains to determine how resilience has been measured, the conceptual differences between the proposed approaches, and how they align with the domains of their case studies. We find that a wide variety of resilience metrics have been proposed in the literature. Conceptually, ten different quantification approaches were identified. Four different disturbance types were observed, including sudden, continuous, multiple, and abruptly ending disturbances. Surprisingly, there is no strong pattern regarding socio-ecological systems being studied using the “ecological resilience” concept and socio-technical systems being studied using the “engineering resilience” concept. As a result, we recommend that researchers use multiple resilience metrics in the same study, ideally following different conceptual approaches, and compare the resulting insights. Furthermore, the used metrics should be mathematically defined, the included variables explained and their units provided, and the chosen functional form justified.

Larger and older church forests promote bird species richness, diversity and resilience across northwest Ethiopia's deforested landscapes

Birds are vital bioindicators of biodiversity and ecosystem health, but their persistence and ecological roles are under severe threat from human activities in the Lake Tana Basin, Ethiopia. Rapid land and forest degradation driven by agricultural expansion, urbanization, and the rising demand for fuelwood imperil avian community structure and ecosystem functions in this region. However, amidst this ecological challenge, church forests have emerged as crucial sanctuaries. Despite their small size and isolation from each other by mainly agricultural land-use, these church forests provide indispensable ecosystem services and play a pivotal role in preserving biodiversity. Understanding the significance of church forests for avian biodiversity and the factors influencing bird species richness and community composition is paramount. To this end, a comprehensive survey was conducted in 24 church forests between November and December 2018. Data collection involved both point counts utilizing Audiomoth acoustic loggers and transect surveys. The study revealed a total of 131 bird species in the surveyed church forests, including endemic and threatened species. These birds exhibited diverse foraging habits and functional traits. Notably, church forest size and canopy cover were positively associated with species richness, highlighting the importance of preserving and potentially expanding these forested areas. Furthermore, the research identified key factors driving the similarity of bird communities in church forests, notably forest surface area, canopy cover, and altitude. Importantly, resilience metrics demonstrated significant positive relationships with species richness, emphasizing the conservation value of these unique ecosystems. Our study underscores the critical role of church forests in safeguarding avian biodiversity in the Lake Tana Basin, and our findings advocate for conservation efforts aimed at preserving and enhancing the region's church forests, as their continued degradation could jeopardize both ecological and socio-economic potential in this network of remnant forests.

A Decision Support Methodology for Seismic Design Requirements of Buildings to Achieve Community-Level Resilience Metrics

A Decision Support Methodology for Seismic Design Requirements of Buildings to Achieve Community-Level Resilience Metrics

Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events

Climate change leads to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data present new opportunities to monitor and investigate road traffic changes during adverse weather. This study utilizes two types of crowdsourced data: passively collected location data (PCLD) from mobile devices and actively collected user reports (ACUR) from the WAZE app, to examine the impact of floods, winter storms, and fog on road traffic. Three metrics derived from PCLD—speed change, event duration, and area under the curve (AUC)—along with an additional metric from ACUR, user-perceived severity, are utilized to assess link-level traffic resilience. All metrics are correlated with road characteristics such as functional class, number of lanes, divider types, road geometry, and historical traffic, to analyze underlying relationships and make forecasting. Using the Dallas–Fort Worth (DFW) metroplex in 2022 as a case, the study finds that overall, winter storms have the most substantial impact on road traffic, followed by floods and fog. Travel demand metrics, such as POI visits and traffic volume, experience a more significant decrease than traffic speed. The impact of weather also varies by road characteristics, with higher-class roads with greater volume and higher speed experiencing more substantial changes. Road geometry, such as minimal altitude and slopes, also significantly influences road traffic resilience under floods but not during winter storms. Moreover, the study reveals that ACUR may not accurately reflect the actual impact during extreme weather events, as few users are able to travel outside to actively act as “sensors”. In sum, this study evaluates a set of crowdsourced data for assessing link-level traffic resilience, designs various resilience metrics, and examines how they vary across road attributes and weather events, which are essential for guiding disaster preparedness, response, and recovery in road transportation systems.

Predictive Resilience Modeling Using Statistical Regression Methods

Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on a single domain. This paper introduces three methods to model system performance and resilience metrics, which are applicable to various engineering and social science domains. These models utilize reliability engineering techniques, including bathtub-shaped functions, mixture distributions, and regression analysis incorporating event intensity covariates. Historical U.S. job loss data during recessions are used to evaluate these approaches’ predictive accuracy. This study computes goodness-of-fit measures, confidence intervals, and resilience metrics. The results show that bathtub-shaped functions and mixture distributions accurately predict curves possessing V, U, L, and J shapes but struggle with W and K shapes involving multiple disruptions or sudden performance drops. In contrast, covariate-based models effectively track all curve types, including complex W and K shapes, like the successive shocks in the 1980 U.S. recession and the sharp decline in the 2020 U.S. recession. These models achieve a high predictive accuracy for future performance and resilience metrics, evidenced by the low sum of square errors and high adjusted coefficients of determination.

A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks

Rail transit is an important means of ensuring the sustainable development of urban transportation, but disturbance events caused by natural disasters, human factors, and other influences can lead to disruptions in rail transit operations. To cope with the impact of disturbance events on urban rail transit networks, and to explore the changes in rail transit network performance and recovery strategies under the influence of disturbance events from a resilience perspective, this paper overviews the existing research on resilience assessment and recovery strategies for urban rail transit networks. Firstly, the characteristics of the urban rail transit network and the model construction method are analyzed. Secondly, on the basis of combing the connotation development of system resilience, urban rail transit network resilience is defined, while the existing resilience metrics and assessment indexes are classified and summarized. Finally, the failure scenarios and recovery strategies of urban rail transportation network are deeply studied and discussed. The research results show that urban rail transit network resilience has been widely concerned by scholars, and certain results have been achieved in three aspects of resilience connotation, resilience assessment and recovery strategy. Nevertheless, further research is needed on these aspects. We propose future research directions that involve exploring modeling methods aligned with actual network topologies, developing unified indexes for resilience assessment and focusing on resilience assessment and recovery strategies under uncertain disturbance events. The research results can provide an important reference for the resilient operation and sustainable construction of urban rail transit.

Assessing the winter indoor environment with different comfort metrics in self-built houses of hot-humid areas: Does undercooling matter for the elderly?

Thermal comfort in housing is vital for well-being. Despite hot and humid regions experiencing shorter cold periods compared to heatwaves, vulnerable populations are susceptible to threats from cold snaps. However, existing research overlooks undercooling risks in hot regions during winter. This study examines self-built houses in densely populated, hot and humid areas, mainly occupied by elderly residents. We conducted an on-site measurement recording hourly indoor air temperature, humidity, and black globe temperature during wintertime. Two thermal comfort metrics (aPMV and adaptive regression model) and two resilience metrics (SET degree-hour, Hours of Safety) were used to quantify thermal conditions during cold periods under current and future climate conditions. The results show that undercooling hours in recent years exceed 90 % of wintertime using aPMV index and adaptive regression models. Under future climate scenarios (Representative Concentration Pathways), undercooling hours only decrease by 10 %–25 % from 2020 to 2100 across all metrics. Extreme cold events in 2018 saw SET degree-hours double the thermal resilience threshold, while they pose health risks to the elderly for up to 21 days according to the HOS metrics. This study aims to identify the importance of addressing winter thermal comfort in hot and humid regions and to assess the risk posed by cold snaps to vulnerable populations. By quantifying these risks, the study contributes to better decision-making regarding hot-humid region housing thermal retrofit strategies for architects, engineers, and other stakeholders.

Impact of Board Members’ Social Capital on the Resilience of Public Companies to Exogenous Shocks

The objective of this study is to estimate the impact of board members’ social capital on firms’ market-based metrics of resilience to exogenous shocks. The social capital of directors was measured by their professional, political, and international connections. Firms’ resilience was evaluated based on their ability to resist and recover from the impact of shocks, as determined by stock market data. The data covers the period from 2007 to 2020 for over 200 Russian companies whose shares were included in the calculation of the Moscow Exchange Broad Market Index. During this period, three exogenous shocks occurred: the global financial crisis of 2008–2009, commodity price shock and sanctions in 2014–2015, and the COVID-19 pandemic in 2020. The system generalized method of moments is used to estimate the effect of directors’ connections on theability to mitigate shocks, while OLS with robust standard errors is used to reveal the influence of directors’ connections on firms’ ability to recover from shocks. The results indicate that professional connections moderated the negative impact on firms’ resistance to shocks and improved recovery speed during the global financial crisis. However, this type of connection reduced stock recovery speed after the COVID-19 crisis. Political and international connections have different effects on market-based metrics of firm resilience. It is possible that shocks of different nature require firms to leverage various forms of social capital from their directors in order to mitigate the negative effects of such shocks.

From 2008–2011 Great Recession to COVID-19 pandemic: an analysis of resilience metrics in European regions

AbstractWe aim to clarify the usefulness of different measures of economic resilience in the context of global shocks. In relation to the Great Recession, Oil& European migrant crises, and COVID-19, we compute and validate from a statistical point of view, then characterize five metrics on 317 NUTS2 regions and 21 years. ROC curves and Cox regression compare them by the capacity to predict the post-shock state. Heterogenous behavior characterizes resistance, recovery, and loss. Furthermore, the non-linear resistance performs best in future state prediction, while the composite index has the lowest efficiency as a predictor of the regions’ recovery.