Factors affecting the forest value chain resilience–a local economic perspective in five European countries

Climate change-associated disturbances such as storms, wildfires, and pest outbreaks increasingly destabilize forest systems, threatening their ecological, economic, and social functions. These disruptions impact the forest value chain (FVC) by causing fluctuations in timber supply, from a quantity and quality perspective. This study employed the operational resilience framework (ORF) to assess FVC resilience in five European case studies (CZ, HR, DE, FIN, and ESP), focusing on timber supply as a key system variable. A resilience assessment was conducted using resilience thresholds, considering sustainability from both ecological and economic perspectives. Principal component analysis (PCA) identified three predictor groups that influenced FVC resilience: wood production (WP), harvesting systems (HS), and management and silviculture (MS). Findings revealed that regions with proactive management and sufficient processing capacities (CZ, HR, and ESP) maintained relative stability despite natural disturbances, while others (DE and FIN) experienced prolonged instability due to market-driven logging practices and limited adaptive measures. The study highlighted the frequent breaching of resilience thresholds, particularly during high-volume salvage logging following disturbances such as bark beetle outbreaks, windstorms, and wildfires. The results emphasized the importance of integrating adaptive and proactive strategies to mitigate these impacts. The ORF demonstrated potential for operationalizing FVC resilience and provided guidance for improving preparedness against future disturbances.

Evaluating the Effects of Lake Urmia’s Drought on Resilience Changes in Rural Settlements

Deteriorated urban areas usually face social, economic, and environmental problems. They often struggle with issues like poverty, inadequate housing, poor public spaces, social isolation and a sense of hopelessness, limited business opportunities, and a lack of investment. These complex problems cause significant disaster resilience challenges for these areas. This article investigates the association between urban deteriorated fabric (UDF) rate and socioeconomic resilience (SER) in the neighborhoods of Tehran Metropolis. Fourteen SER variables are identified through a literature review. Exploratory factor analysis is used to transform them into fewer factors. Four factors are extracted and are labelled as economic, social, economic-demographic, and community capital resilience. Similar extracted factors are combined to obtain social and economic resilience subcomponents. Jenks' Natural Break classification method is used to classify the UDF rate into five categories. Ordinary least squares (OLS) and Geographically Weighted Regression (GWR) are used to examine the association between UDF rate (dependent variable) and SER subcomponents (independent variables). The findings of the study show that: (a) the GWR better captures spatial relationships between UDF rate and SER factors than the OLS method, (b) the relationship between DUFs and social and economic resilience is complex and not definitively one-sided, and (c) social and economic resilience can occur concurrently in DUFs, (d) neighborhoods with high UFD rates are clustered in the mid-southern parts of the Tehran city. Understanding the interplay between social and economic resilience in DUFs is crucial for developing effective strategies to promote recovery and long-term disaster resilience and sustainability.

Product diversification in agribusiness is an important strategy in increasing farmers' profits and economic stability, especially amidst the challenges of commodity price fluctuations, climate change, and dependence on one type of product. This study aims to analyze the effectiveness of product diversification in agribusiness and identify the most appropriate strategy for farmers to increase their profitability and economic resilience. Using a descriptive qualitative approach, this study was conducted in Berastagi , North Sumatra, as one of the areas with a rapidly growing agribusiness sector. Data were collected through observation, interviews, surveys, and literature studies, then analyzed using a thematic approach to understand diversification patterns and their impacts on farmers' economy. The results of the study indicate that the diversification strategies implemented in Berastagi include horizontal diversification (planting various types of commodities), vertical diversification (processing agricultural products into value-added products), and lateral diversification (development of agrotourism and livestock businesses). Diversification has been shown to increase farmers' income, reduce business risks due to price fluctuations, and expand market access. The success of this strategy is influenced by several main factors, namely access to markets, availability of capital, application of technology, and support from the government and related institutions. However, challenges such as limited capital, lack of skills in product processing, market uncertainty, and complex regulations are still obstacles in implementing diversification. Therefore, a more targeted strategy is needed, such as strengthening sustainable agricultural systems, technological innovation in processing and marketing, and collaboration between farmers, the government, and the private sector. The results of this study are expected to provide insight for farmers, policy makers, and agribusiness actors in developing more effective and sustainable diversification strategies, so as to improve farmer welfare and the resilience of the agribusiness sector as a whole.

This study assessed households’ resilience to climate change-induced shocks in Dinki watershed, northcentral highlands of Ethiopia. The data were collected through a cross-sectional survey conducted on 288 households, three focus group discussions, and 15 key informant interviews. The Climate Resilience Index (CRI) based on the three resilience capacities (absorptive, adaptive and transformative) frame was used to measure households’ resilience to climate change-induced shocks on an agro-ecological unit of analysis. A principal component analysis (PCA) and multiple regression analysis were used to identify determinant factors and indicators to households’ resilience, respectively. Findings indicate that the indexed scores of major components clearly differentiated the study communities in terms of their agro-ecological zones. Specifically, the absorptive capacity (0.495) was the leading contributing factor to resilience followed by adaptive (0.449) and transformative (0.387) capacities. Likewise, the Midland was relatively more resilient with a mean index value of 0.461. Both the PCA and multiple regression analysis indicated that access to and use of livelihood resources, such as farmlands and livestock holdings, diversity of income sources, infrastructure and social capital were determinants of households’ resilience. In general, it might be due to their exposure to recurrent shocks coupled with limited adaptive capacities including underdeveloped public services, poor livelihood diversification practices, among others, the study communities showed minimal resilience capacity with a mean score of 0.44. Thus, in addition to short-term buffering strategies, intervention priority focusing on both adaptive and transformative capacities, particularly focusing on most vulnerable localities and constrained livelihood strategies, would contribute to ensuring long-term resilience in the study communities.

The article examines adaptive strategic management as a key approach for ensuring the resilience and competitiveness of agricultural enterprises in the post-crisis period. Agricultural enterprises face challenges due to economic instability, supply chain disruptions, and external shocks such as conflicts and global crises. Traditional strategic management methods, focused on stability, prove insufficient in such uncertain conditions. Enterprises must adopt flexible strategies to respond to rapid changes and ensure long-term sustainability. Key elements of adaptive strategic management include flexibility in planning, innovative technologies, risk management, resource optimization, and ecological sustainability. The role of digitalization, including ERP systems, IoT, satellite monitoring, and Big Data analytics, is highlighted as crucial for optimizing processes, enhancing productivity, and reducing costs. These technologies improve decision-making, increase resource efficiency, and enhance market adaptation. The study explores post-crisis adaptation strategies such as production diversification, technological innovation, market forecasting, and sustainable farming. Diversification reduces market dependency, while precision agriculture enhances productivity and minimizes risks. Market analysis tools help anticipate price fluctuations and optimize sales. Sustainable practices, including eco-friendly methods and efficient logistics, support ecological and financial stability. Financial stability and market diversification are identified as crucial for resilience. Strengthening financial foundations enables enterprises to withstand volatility and invest in recovery and growth. Efficient logistics and supply chain management are essential for cost reduction and timely distribution. The article concludes that adaptive strategic management is vital for agricultural enterprises. By integrating innovation, financial stability, and sustainability, they can recover from crises and strengthen their competitive position in an unpredictable global market.

Economic resilience is crucial for urban sustainability as it ensures stability and growth in the face of external shocks, promotes social cohesion and inclusivity, fosters environmental sustainability, and enhances cities’ adaptability to future challenges. This study expands the conventional perspective on economic resilience beyond the context of shocks, focusing on the inherent resilience of regional economic systems. A novel method for quantifying economic resilience is introduced, emphasizing system sensitivity and adaptability. Using Chinese prefecture-level city data and an econometric model, we empirically examine how Fintech, a major digital transition in current urban systems, affects economic resilience. The findings reveal that Fintech has a substantial positive effect on economic resilience, primarily through the upgrading of industrial structures and technological innovation. Furthermore, there is significant regional heterogeneity in the impact of Fintech on economic resilience, with more pronounced contributions in the east, central, and western regions of China, as opposed to the northeast. Additionally, the impact of Fintech on economic resilience is more substantial in large-scale cities. The promotion of economic resilience through digital transformation serves as a potent risk prevention measure. Understanding the role of economic resilience in urban systems holds valuable implications for countries worldwide.

As an emerging economic form, the digital economy is a crucial force in promoting high-quality economic development, resolving regional development incoherence, and improving the level of urban resilience. In this paper, the urban resilience indicator system is composed of four dimensions: social resilience, economic resilience, infrastructure resilience, and ecological resilience. Meanwhile, the digital economy indicator system is composed of five dimensions: Internet penetration rate, number of Internet-related employees, Internet-related output, number of mobile Internet users, and digital financial inclusion development. The development level was measured using the entropy value method and principal component analysis. On this basis, the impact of the digital economy on the urban resilience of 185 prefecture-level cities in China from 2011 to 2019 was analyzed using a benchmark regression model, spatial econometric model, and mediating effect model. This study shows the following: (1) The development of the digital economy has a positive impact on improving urban resilience. (2) The digital economy affects urban resilience with positive spatial spillover effects, and the temporal heterogeneity and heterogeneity of external openness levels are significant, while the regional heterogeneity is not significant. (3) The digital economy can improve urban resilience through the intermediary role of technological innovation. In the future, we should strengthen digital construction, deeply integrate the relationship between the digital economy, technological innovation, and urban resilience, give full play to the engine role of the digital economy, and further promote the enhancement of sustainable urban development.

In the event of disasters such as droughts, floods, and landslides, social sectors including housing, education, and social protection are the most affected. Here, we present a project that incorporates the school system as a vulnerable sector to water insecurity and a tool to promote resilience. In this sense, we adopt the concept of water security defined by the United Nations (UN), including the availability of water to support socioeconomic development, the preservation of aquatic ecosystems, and the ability to withstand a reasonable amount of risk from floods and droughts. Planning for the supply and use of water at the national level should be based on the four elements of water security. This project is contextualized at the Brazilian National Observatory for Adaptive Water Security and Management (ONSEAdapta) (https://onseadapta.org/en/elementor-642/). Given the importance of schools, the objective of this project is to propose a conceptual framework to incorporate school resilience as a response to water-related disasters and adaptive management. The proposed methodology is divided into two approaches. First, a top-down approach is proposed to collect data from the annual school census of Brazilian schools that is provided at school level by the Anisio Teixeira National Institute of Educational Research and Studies (INEP) and water security data from the National Water and Sanitation Agency of Brazil (ANA). Second, a bottom-up approach is proposed to survey educators and members of the school community to depict how water security is incorporated into schools, what initiatives promote the participation of school and society, and the main implications for reducing disaster risk, building capacity, and increasing disaster resilience. In Brazil, according to the 2022 school census, there were 184,331 schools that accommodated 22% of the Brazilian population (~47 million students). To propose the concept of school resilience as a dimension of water security, we located and diagnosed the number of schools that are in water insecurity by combining the Brazilian water security index (ISH) with the georeferenced map of Brazilian schools. Using the ISH that combines human, ecosystemic, economic, and resilience dimensions, we identified that 11.93, 14.40, 16.04 million students are under minimum to low, medium, and high to maximum water security, respectively. This analysis unveils that almost 28% of Brazilian students are below a low level of water security. These students come from preschool, elementary and secondary education in rural and urban areas. We conceptualize the assessment of school resilience using a comprehensive framework that considers infrastructure, level of water insecurity, impacts on school, emergency preparedness, and community involvement. To foster community involvement and scientific contributions, the next step is the creation of an online platform to promote citizen science, collect data, and engage with educators. By fostering participatory citizenship education in schools, this project aims to create a resilient and well-informed community capable of mitigating the impact of disasters and contributing to general water security and adaptive management.

Currently, such factors as the high dependence of the economy on the oil sector, instability of world prices for raw materials, insufficient level of technological development of the agroindustrial complex of Kazakhstan, as well as a low degree of diversification of agricultural production have a negative impact on the sustainable economic modernization of Kazakhstan. The goal is to analyze the role of agriculture in the process of multidisciplinary transformation of the economic model of the republic and determine its contribution to reducing the direction of raw materials. The agricultural sector, which has significant potential for ensuring financial balance, the growth of regional initiatives and strengthening the country's food security, was chosen as the object of scientific research. Methods - linear regression was used to assess the impact of the specific weight of the agricultural sector on the formation of an effective resource system, as well as the Herfindal index in order to quantify the level of market concentration of economic processes. The analysis is based on official statistical data characterizing the dynamics of the main economic and economic indicators for 2010-2022. The results of the obtained data showed that an increase in the share of the rural segment in the macroeconomics of the state has a statistically significant negative impact on the market structure, contributing to its reduction and more productive production activities. To strengthen the position of the agro-industrial complex, comprehensive support is needed: effective state mechanisms, the introduction of innovative technologies, the popularization of agritourism and cooperative movement, the modernization of infrastructure, the organization of new branches of crop production and animal husbandry, the inclusion of productive types, varieties and hybrids of agricultural crops in crop rotation, the expansion of the scale of the processing industry, material and technical equipment and product sales systems. The results obtained made it possible to draw the following key conclusions - diversification of production is a promising direction for increasing the economic activity of agricultural enterprises, which ensures an increase in product selection, profit and profit from sales.

New urbanization construction and city economic resilience-based on multi-period DID tests for 278 cities

In today's dynamic and complex business environment, organizations are increasingly turning to artificial intelligence (AI) to gain a competitive edge in strategic management. This abstract delves into the various applications of AI in strategic management, highlighting its transformative impact on decision-making processes, resource allocation, risk management, and overall organizational performance. One of the key areas where AI is revolutionizing strategic management is in decision-making. AI-powered algorithms analyze vast amounts of data, identify patterns, and generate actionable insights to support strategic decision-making processes. These insights enable organizations to make data-driven decisions that are aligned with their long-term objectives and market trends, leading to improved performance and competitive advantage. Furthermore, AI plays a crucial role in optimizing resource allocation within organizations. By leveraging predictive analytics and machine learning algorithms, AI systems can forecast demand, optimize inventory levels, and allocate resources efficiently across different departments or projects. This helps organizations minimize costs, maximize utilization, and enhance overall operational efficiency. Another significant application of AI in strategic management is in risk management. AI-powered risk assessment models can analyze historical data, identify potential risks, and predict future threats to the organization. This proactive approach allows companies to implement mitigation strategies, reduce vulnerabilities, and enhance resilience in the face of uncertainties, thereby safeguarding their long-term sustainability. Moreover, AI-driven technologies such as natural language processing (NLP) and sentiment analysis are transforming how organizations gather and analyze market intelligence. By monitoring social media, news articles, and customer feedback, AI systems can extract valuable insights about market trends, competitor activities, and customer preferences. This real-time information empowers organizations to adapt their strategies swiftly, capitalize on emerging opportunities, and stay ahead in competitive markets. Keywords: Artificial Intelligence, management, technological innovation

New ventures' strategic growth patterns

Urban resilience reduces depressive symptoms among middle-aged and elderly adults: A multidimensional analysis based on China longitudinal healthy longevity survey

Extensive damages of natural disasters have made resilience a focus of disaster management plans in order to limit damages. The aim of this study was a comparative evaluation of social and economic resilience in Bam and Rudbar. This applied research attempted to quantify and compare different dimensions of social and economic resilience in Bam and Rudbar with a descriptive-analytical method. Cochran’s formula determined the sample size as 330 households from both cities (a total of 660 households). The indicators of social and economic resilience were identified from the literature, and then data were collected through a field study using questionnaires. Data were analysed using multiple linear regression and feed-forward multilayer perceptron artificial neural network. Results denoted that several resilient-related socio-economic features were significantly different for Bam and Rudbar cities, such as the number of earthquakes experienced, length of stay in current neighbourhood and mean individual and household income. Mean social and economic resilience scores were significantly higher for Rudbar (216.3 ± 33.4 and 30.6 ± 7.3) compared to Bam (193 ± 26.5 and 29.4 ± 7.07) (p < 0.05). In addition, linear regression indicated that an increase in education level of the household head, length of stay in current neighbourhood and household income could result in an increase in social and economic resilience of the households under study. Neural network analysis revealed that social capital and employment recovery are the most and least effective factors, respectively, in both cities. In the population under study, social component, namely, social capital, was the most important determinant of resilience.