Resilience Planning Research Articles

An interpretable machine learning approach for predicting the capacity and failure mode of reinforced concrete columns

During seismic events, reinforced concrete (RC) columns play a crucial role in maintaining buildings’ structural integrity. This motivated engineers and practitioners to search for key parameters that influence the load-carrying capacity and failure mechanisms of such columns. However, the complexity and nonlinearity of seismic effects along with the intricate nature of RC columns as a composite system challenge the capabilities of analytical and empirical approaches to accurately capture the response of RC columns. Subsequently, the present study utilizes Machine Learning (ML) techniques to identify the failure modes and predict the corresponding capacities of RC columns based on both their geometrical and material properties. Decision trees and different ensemble methods were employed to predict both the columns’ failure mode and ultimate capacity. A multivariate dataset consisting of 486 cyclically loaded rectangular and circular columns was used to develop and validate the models. In addition, different embedded variable selection techniques were employed to evaluate the significance of input parameters in predicting the performance of columns. Moreover, partial dependence plots and accumulated local effects were employed to uncover the interrelationships between the input features and the modelled outputs. The developed models yielded an average accuracy of 90% and 95% for predicting the failure mode and ultimate capacity of RC columns, respectively. Given such high accuracy, it can be inferred that, ML techniques have the potential to provide efficient and reliable prediction tools to support seismic design and assessment decisions - mitigating seismic risks and empowering resilience planning in the face of extreme events.

Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Impact of Droughts on Served Drinking Water Disparities in California, 2007-2020.

Objectives. To quantify the impact of droughts on drinking water arsenic and nitrate levels provided by community water systems (CWSs) in California and to assess whether this effect varies across sociodemographic subgroups. Methods. I integrated CWS characteristics, drought records, sociodemographic data, and regulatory drinking water samples (n = 83 317) from 2378 water systems serving 34.8 million residents from 2007 to 2020. I analyzed differential drought effects using fixed-effect regression analyses that cumulatively accounted for CWS-level trends, income, and agricultural measures. Results. CWSs serving majority Latino/a communities show persistently higher and more variable drinking water nitrate levels. Drought increased nitrate concentrations in majority Latino/a communities, with the effect doubling for CWSs with more than 75% Latino/a populations served. Arsenic concentrations in surface sources also increased during drought for all groups. Differential effects are driven by very small (< 500) and privately owned systems. Conclusions. Impending droughts driven by climate change may further increase drinking water disparities and arsenic threats. This underscores the critical need to address existing inequities in climate resilience planning and grant making. (Am J Public Health. 2024;114(9):935-945. https://doi.org/10.2105/AJPH.2024.307758).

Reforms of the Financial System in the European Union –Towards a New Legal-Institutional Order

The combination of the legal documents including the 2021-2027 EU long-term budget, NextGenerationEU, andthe Own Resources Decision (3) (Decision of 14 December 2020 on the system of own resources of the EuropeanUnion, repealing Council Decision 2014/335/EU, Euratom of 26 May 2014 on the system of own resources of theEuropean Union) has been praised as a groundbreaking achievement and political success of the EU authorities,one whose importance is no lesser than that of the establishment of the EU’s common market. The package hasmarked a powerful turn in EU’s politics, in that it helps move the debts of the countries receiving funds fromNextGenerationEU as well as within National Recovery and Resilience Plans (NRRPs) outside their national budgetarysystems. As is emphasised in a number of EU documents, the increases in EU Member States’ deficits anddebts have been due to the financial and health crisis. However, how or in what ways the new own resources andnew outside-Treaty financial engineering might contribute to the funding and eventual repayment of NextGenerationEUdebt, has not been explained.In parallel, the aforementioned success has given the basis for further action in view of in-EU integration, as expressedin the project to amend EU Treaties. Work on the amendment will continue during the present EuropeanParliament’s term. Analysis of the directions of change in the Treaty provisions covering EU fiscal and monetarypolicies and the gradually remoulded model of EU funding form t he core of this present paper.

Stochastic Resilience in Operations Management Using Ito Diffusion and Gamma Catastrophe Processes

This study introduces a novel Ito diffusion model for operations management, addressing the challenge of maintaining resilience in supply chains and production networks against unpredictable disruptions. The model incorporates a general catastrophe process with a low occurrence rate, using stochastic methods to represent disruption magnitudes as gamma distribution variables. It provides an analytical framework detailing the process's mean, variance, and sample path. Applying this model across various operational scenarios demonstrates its practical significance. By examining the impacts of disruptions on operational efficiency, the model offers insights into disruption dynamics, crucial for resilience planning and risk mitigation. The findings enhance logistics networks' resilience and efficiency, aiding decision-makers in navigating disruptions. This research presents a practical tool for decision-making in operations management and sets the stage for future research with complex variables and emerging technologies to enhance predictive strength in a dynamic environment.

Mitigating operational greenhouse gas emissions in ageing residential buildings using an Urban Digital Twin dashboard

With the increasing stock of ageing infrastructure and resource constraints in Singapore, related risks and carbon emissions can be mitigated through long-term resilience planning, automated building inspection, and effective maintenance. Sustainable actions are needed to maintain Singapore's ageing infrastructure. Hence, a state-of-the-art control and management system is required in the form of smart city digital tools. We introduce an Urban Digital Twin (UDT)—GHG App for decision-makers in Singapore's operational building greenhouse gas (GHG) emission mitigation and decarbonisation initiatives. Based on multiple-criteria decision analysis (MCDA), a Potential for Intervention (PFI) map was created to rejuvenate the building system. Decision-makers can use this map to prioritise the rejuvenation of low-carbon building systems in the built environment. A heat map of the PFI results highlights which buildings need urgent rejuvenation based on critical parameters. The GHG App utilises this method to generate maps and enables users to modify parameter weights based on their priorities, automatically updating the map. Users can plan an intervention for buildings with higher PFI values once the map is generated. The GHG App provides interactive data visualisation of 119,872 features representing Singapore's built environment, including the context size of 6,785 existing residential buildings modelled and used to demonstrate the analysis results. Our research findings can contribute to the development of standards for accounting for operational GHG emissions, setting emission limits, and planning decarbonisation in the built environment sector.

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning.

Institutional Quality in Green and Digital Transition of EU Regions – A Recovery and Resilience Analysis

AbstractThis paper assesses the National Recovery and Resilience Plans (NRRPs) of EU member states and regions to uncover commonalities and differences between green and digital transitions, focusing on the role of institutions, among additional socio‐economic drivers, in modeling them. To that end, relevant indicators have been assembled, and several econometric models have been developed and tested to evaluate institutional performance in relation to green and digital transformations. The study reveals discrepancies in the two explored transition fields and highlights the power of institutional factors in boosting them. Specifically, the findings demonstrate that the green transition in EU regions is positively associated with variables such as life expectancy, institutional quality, tertiary education attainment, and small and medium enterprises (SMEs) with innovative activities, while the fruits of digitalization are mainly allied to population with higher studies, core creative class employment, accountability of institutions, and innovative SMEs. These insights offer valuable guidance for decision‐makers to draw lessons from high‐performing or successful regions and strategically assign resources. This includes paying attention to regional financial allocations and their alignment with territorial planning and long‐term policies.

Resilient planning pathways to community resilience to tsunami in Chile

There is growing urgency to develop resilient coastal communities worldwide due to an increase in destructive events. Many resilience assessment models, tools, and frameworks are being developed to guide planners and policy makers for planning resilient communities. However, many lack a multidimensional approach, while others disregard regional variations in indicators' importance, and neglect an adequate community involvement level. In turn, these factors hinder the development of a ‘Resilient Planning Pathway’ (RPP), or a set of planning strategies that consider key local environmental attributes and socio-cultural capacities. The RPP approach has been used in the context of vulnerable groups and urban and landscape planning worldwide. The objective of this study is to identify key resilience aspects to develop RPPs in Chilean coastal communities exposed to tsunami hazards. We used a cross-sectional participatory method with public servants and coastal communities which identifies regional variations in resilience dimensions, in order to define challenges for planners and policy makers. A systematic international literature review in Atlas.Ti revealed 78 resilience composite indicators and six resilience dimensions for tsunami hazards in Chile. Composite indicators were evaluated according to their importance and experience by 134 public servants within 42 coastal communities, revealing the aspects that guide the current development of RPPs. Public servants' responses are similar among macrozones and suggest that RPP development has focused on the physical, socio-cultural, and institutional dimensions, neglecting the ecological and economic dimensions. In contrast, the community opinions from 10 representative coastal settlements collected in 29 focus groups indicated that the importance of composite indicators varies among macrozones and with regards to the public servants' responses. This finding suggests commonalities and differences among macrozones regarding the challenges in developing Chilean coastal communities' RPPs. We believe that our methodological approach could help generate similar assessments in other world zones.

Quantification of interactions among agricultural drought indices within Köppen–Geiger climate zones in Bangladesh

Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.

Understanding Multi-Hazard Interactions and Impacts on Small-Island Communities: Insights from the Active Volcano Island of Ternate, Indonesia

Drawing on a case study from Ternate Island, a densely populated volcanic island in Eastern Indonesia, this research illustrates how multi-hazards and extreme weather events are likely to compound and cascade, with serious consequences for sustainable development in small island context. At the heart of Ternate Island sits the active Gamalama volcano, posing a constant eruption threat. Its location within the Ring of Fire further exposes the island to the risks of tsunamis and earthquakes. Additionally, the island’s physical features make it highly susceptible to flooding, landslides, and windstorms. Rapid urbanization has led to significant coastal alterations, increasing exposure to hazards. Ternate’s small-island characteristics include limited resources, few evacuation options, vulnerable infrastructure, and inadequate resilience planning. Combining GIS multi-hazard mapping with a structured survey in 60 villages in Ternate, this case study investigates the multi-hazard exposure faced by the local population and land coverage. The findings suggest significant gaps between village chiefs’ perceptions of the types of hazards and the multi-hazard assessment in each village. Out of 60 village chiefs surveyed, 42 (70%) are aware of earthquake risks, 17 (28%) recognize tsunami threats, and 39 see volcanoes as a danger. GIS assessments show that earthquakes could impact all villages, tsunamis could affect 46 villages (77%), and volcanoes could threaten 39 villages. The hazard map indicates that 32 villages are at risk of flash floods and 37 are at risk of landslides, and extreme weather could affect all villages. Additionally, 42 coastal villages on Ternate Island face potential extreme wave and abrasion disasters, but only 18 chiefs acknowledge extreme weather as a threat. The paper argues that addressing the cognitive biases reflected in the perceptions of community leaders requires transdisciplinary dialogue and engagement.

Spatial clusters for demand and supply of early childhood education and care services in Italy

In recent years, concern has increased about affordable and high-quality early childhood education and care (ECEC) services. Such services can help balance work and family life, increase workforce participation, promote gender equality, and increase fertility rates. ECEC services are then essential, especially for countries facing ultralow fertility rates like Italy. To face these challenges, the Italian government has included an unprecedented investment in the recovery and resilience plan financed with Next Generations EU funds to increase the supply of ECEC services and make them more equitably distributed across the country. However, socio-economic policies need a comprehensive understanding of demand dynamics and the intricate interplay between supply and demand of such services. Therefore, in this article, we propose a novel analysis of the complex combination of supply and demand of Italian ECEC services. We explore patterns of this combination in terms of groups of spatial areas sharing similar structures of service coverage and public expenditure rates in ECEC, as well as other socio-demographic and economic factors such as female employment, education, and grandparent rates. Our empirical findings confirm how Italy is characterized by several distinct territorial models and how some are shared across multiple regions. Current empirical findings suggest a lack of uniform governance across the Italian territory. Policymakers can use these results to plan, discuss, and implement area-specific welfare strategies regarding ECEC services.

Resiliency planning of distribution network using of active distribution network partitioning

Today, micro-grids (MGs) include all kinds of energy storage systems (ESSs), wind turbines (WTs), photovoltaic (PV), combined heat and power (CHP), etc., also demand response are active on the demand side. In this paper, single-level robust methods for partitioning and planning the active distribution network (ADN) into several MGs are presented. According to the desired purpose, the objective function of the model is investment costs minimization for installing the capacity of distributed generations (DGs) and switches, the activity of responsive loads based on the forecast of the generation of non- DG, losses and the risk of the load points of the costumers. On the other hand, maximizing the income from the MGs energy sales to the upstream grid and the technical constraints include optimal power flow (OPF) equations. The mentioned problem is a complex nonlinear model, and therefore, the improved genetic algorithm (GA) is used. In order to validate the efficiency, the improved method has been used on a 25-bus ADN including five switches. The simulation results obtained from the case studies prove the fact that the use of the retrofitted model increases the investment costs of the MG, especially in the case of an island operation, in contrast to the active presence of responsive loads that significantly reduce costs.

Exploring the Role of Destination Resilience Plans in Promoting Tourism: A Literature Review

The study investigates the critical importance of destination resilience in enhancing tourism, particularly in the wake of challenges such as the COVID-19 pandemic. The primary aim of the research is to analyze how effective governance, stakeholder engagement, resource management, and risk assessment contribute to the resilience of tourist destinations and micro, small, and medium enterprises (MSMEs). To achieve this, the study employs a comprehensive literature review methodology, synthesizing existing research on destination resilience strategies and their implications for tourism promotion. The findings reveal that destination resilience plans significantly impact tourism performance, stakeholder participation, and sustainable development. Additionally, the study highlights the necessity of integrating innovative practices and technology to bolster the resilience of tourism sectors. In conclusion, this research provides valuable insights for destination management organizations, policymakers, and industry researchers, emphasizing the need for strategic planning and collaboration to foster long-term sustainability and resilience in tourism. The study serves as a foundation for future investigations and policy-making in the realm of sustainable tourism, addressing existing gaps in the literature and offering practical recommendations for enhancing destination resilience.

Plan evaluation for heat resilience: complementary methods to comprehensively assess heat planning in Tempe and Tucson, Arizona

Abstract Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities’ progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

Technology as a solution to the supply chain problems in the United States: What more can be done?

The study examined the critical role of technology in addressing supply chain challenges in the United States, with a focus on identifying gaps and exploring opportunities for improvement. Through a comprehensive analysis of existing literature and empirical studies, the research uncovered key supply chain challenges faced by firms in the U.S., including issues related to counterfeit goods, logistics inefficiencies, environmental concerns, inaccurate demand forecasting, and cyber-security threats. Also, the study examined the role of emerging technologies such as blockchain, artificial intelligence, Internet of Things, big data analytics, and machine learning in enhancing supply chain resilience and agility. This study highlighted the contributions of these technologies in promoting transparency, traceability, sustainability, and efficiency across various aspects of supply chain management. By synthesizing insights from diverse sources and datasets, the study provided valuable recommendations and strategies for leveraging technology to address supply chain challenges effectively. These recommendations covers areas such as investment in advanced technologies, collaborative data sharing platforms, integration of sustainability metrics, predictive analytics for risk management, and supply chain resilience planning.

AI Based Urban Resilience Planning: Opportunities and Challenges

Urban areas across the globe are confronting escalating flood risks, a crisis exacerbated by the effects of climate change, which necessitates precise hyper-localized risk assessments. This research introduces the application of the AI for resilient cities model for flood risk assessment, focusing specifically on the vulnerable area of Penthakata, located in the coastal city of Puri, Odisha. The hyper-local evaluation of associated flood risks is carried out, particularly at the building level. Leveraging cutting-edge geospatial technology, deep learning methodologies, and multi-parameter analysis, this study offers valuable insights into the flood vulnerabilities in the region. Additionally, the research emphasizes the integration of technology with community volunteers and local knowledge, highlighting the essential role of grassroots-level efforts in effective disaster management. The model is one of its kind combining advanced AI technology with community engagement, the study contributes to a holistic and localized approach to strengthen adaptive capacity in the face of increasing flood risks. The findings offer a compelling case for the adoption of hyperlocal risk assessments for urban and rural areas to be better informed of flood risks, prepared for potential disasters, and implement more effective mitigative measures. Ultimately, it aims to safeguard the lives and livelihoods of vulnerable communities of various regions, offering a model for spatial environmental and community resilience.

How to Plan Urban Parks and the Surrounding Buildings to Maximize the Cooling Effect: A Case Study in Xi’an, China

Urban areas with parks tend to have the best outdoor thermal comfort in regions with high urban heat island effects during summer. This study analyzed the synergistic cooling effects of 94 urban parks and the adjacent built-up areas in six districts of Xi’an City using four cooling indicators: park cooling intensity (PCI), park cooling area (PCA), park cooling effect (PCE), and park cooling gradient (PCG). The results showed that 84 out of 94 parks exhibited significant cooling effects, with an average PCI of 1.98 °C, PCA of 51.7 ha, PCE of 6.6, and PCG of 8.2 °C/km. Correlation analyses indicated that the intrinsic park attributes, external buffer zone building height, and building density were the main factors affecting the cooling effect. The park landscape configuration, building height, and density significantly influenced the PCI and PCG, while the park shape and size were crucial for the PCA (positive) and PCE (negative). The optimal park areas for improving the thermal environment were identified as 26 ha (cooling area focus, building density &lt;13%) and 15 ha (cooling intensity focus, building height &lt;21 m, density &gt;32%). This study provides theoretical guidance for planning urban parks and the surrounding areas based on cooling effects, offering insights for future climate resilience planning.

The Russia-Ukraine conflict, soaring international energy prices, and implications for global economic policies

This study examines the economic impact of soaring international energy prices during the Russia-Ukraine conflict from February 23, 2022, to May 31, 2022. Notably, by applying a CGE model, this study offers insights into energy policies at both macroeconomic and industrial levels, emphasizing the model's utility in analyzing complex economic interactions under geopolitical stress. Findings indicate that: (1) Russia, a critical energy-producing country, faced severe economic setbacks due to sanctions, with its GDP contracting by 5.5 %, household income decreasing by 4 %, and consumer spending dropping by 3.5 %. This was accompanied by a significant reduction in domestic investment by 6 %, a decline in output by 5 %, and a decrease in societal welfare indicators. (2) Other energy-producing countries or regions, such as the Middle Eastern oil-producing countries, Australia, Canada, Mexico, and Southeast Asia, experienced economic benefits from the global energy market's “crowding-out effect.” These regions saw an increase in GDP ranging from 2 % to 4.5 %, output growth by 3 %–6 %, and household income and consumption improvements by approximately 3 %–5 %. However, these benefits were tempered by a 1 %–2.5 % decline in domestic investment due to rising local energy costs. (3) Developed and developing regions, suffered adverse impacts, including the US, UK, EU, Japan, China, South Asia, Middle Eastern non-oil-producing countries, and Africa. These regions reported a decrease in GDP by 0.5 %–3 %, a decline in household income by 2 %–4 %, and lower consumption rates by 1.5 %–3.5 %. The economic strain was further exacerbated by an inflation increase of up to 2 % across these economies. This research offers valuable insights for governments and policymakers globally to address the challenges posed by the Ukraine crisis-induced energy crisis, underscoring the need for strategic energy policy adjustments and economic resilience planning.

Impacts of Local Government Perceptions of Disaster Risks on Land Resilience Planning Implementation

Impacts of Local Government Perceptions of Disaster Risks on Land Resilience Planning Implementation