Search Results (76)

Eco-migration (EM) constitutes a specialized form of migration aimed at enhancing living environments and alleviating ecological pressure. Nevertheless, large-scale external migration has intensified habitat fragmentation (HF) in resettlement areas. This paper takes the Shule River Resettlement Project (SRRP) as a case, based on the China Land Cover Dataset (CLCD) data of the resettlement area from 1996 to 2020, using the Landscape Pattern Index (LPI) and the land use transfer matrix (LTM) to clearly define the stages of migration and the types of resettlement areas and to quantitative explore how EM affects HF. The results show that (1) EM accelerates the transformation of natural habitats (NHs) to artificial habitats (AHs) and shows the characteristics of sudden changes in the initial stage (1996–2002), with stability in the middle stage (2002–2006) and late stage (2007–2010) and dramatic changes in the post-migration stage (2011–2020). In IS, MS, LS, and PS, AH increased by 26.145 km², 21.573 km², 22.656 km², and 16.983 km², respectively, while NH changed by 73.116 km², −21.575 km², −22.655 km², −121.82 km², and −213.454 km², respectively. The more dispersed the resettlement areas are the more obvious the expansion of AH will be, indicating that the resettlement methods for migrants have a significant effect on habitat changes. (2) During the resettlement process, the total number of plaques (NP), edge density (ED), diversity (SHDI), and dominance index (SHEI) all continued to increase, while the contagion index (C) and aggregation index (AI) continued to decline, indicating that the habitat is transforming towards fragmentation, diversification, and complexity. Compared with large-scale migration bases (LMBs), both small-scale migration bases (SMBs), and scattered migration settlement points (SMSPs) exhibit a higher degree of HF, which reflects how the scale of migration influences the extent of habitat fragmentation. While NHs are experiencing increasing fragmentation, AHs tend to show a decreasing trend in fragmentation. Ecological migrants play a dual role: they contribute to the alteration and fragmentation of natural habitat patterns, while simultaneously promoting the formation and continuity of artificial habitat structures. This study offers valuable practical insights and cautionary lessons for the resettlement of ecological migrants. Full article

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

In this paper, we propose a new and fairly general network-based contagion dynamics model framework. In the model framework, each node in the network can be in one of multiple secure (or good) and infected (or bad) states. We characterize the dynamics of our model framework, by presenting the following: (i) a sufficient condition under which the dynamics are globally asymptotically stable; (ii) a sufficient condition under which the dynamics are locally asymptotically stable; and (iii) a sufficient condition for the persistence of bad states. Finally, we implemented three operations on the transition diagram. These three operations can help eliminate the bad states and help the model achieve the stability conditions. Full article

Over the past decade, the rapid growth of supply chain finance (SCF) in developing countries has made it a key profit driver for commercial banks and financial firms. In parallel, financial risk control in SCF has attracted more and more attention from financial service providers and has gained research momentum in recent years. This study analyzes the contagion mechanism of SCF-related risks faced by commercial banks through examining SCF network topology. First, this study uses complex network theory to integrate an SEIR epidemic model (Susceptible–Exposed–Infectious–Recovered) into financial risk management. The model simulates how financial risks spread in supply chain finance (SCF) under banks’ strategic, tactical, or operational interventions. Then, some key points for financial risk control from the perspective of commercial banks are obtained by investigating the risk stability threshold of the financial network of SCF and its stability. Numerical simulations show that effective interventions—such as strengthening loan guarantees to reduce the number of exposed firms—significantly curb risk transmission by restricting its scope and shortening its duration. This research provides commercial banks with a quantitative framework to analyze risk propagation and actionable strategies to optimize SCF risk control, enhancing financial system stability and offering practical guidance for preventing systemic risks. Full article

This study examines the daily top 100 cryptocurrencies on Uniswap V3. It denoises the correlation coefficient matrix of cryptocurrencies by using sliding window techniques and random matrix theory. Further, this study constructs a time-varying correlation network of cryptocurrencies under different thresholds based on complex network methods and analyzes the Uniswap V3 network’s time-varying topological properties and risk contagion intensity of Uniswap V3. The study findings suggest the presence of random noise on the Uniswap V3 cryptocurrency market. The strength of connection relationships in cryptocurrency networks varies at different thresholds. With a low threshold, the cryptocurrency network shows high average degree and average clustering coefficient, indicating a small-world effect. Conversely, at a high threshold, the cryptocurrency network appears relatively sparse. Moreover, the Uniswap V3 cryptocurrency network demonstrates heterogeneity. Additionally, cryptocurrency networks exhibit diverse local time-varying characteristics depending on the thresholds. Notably, with a low threshold, the local time-varying characteristics of the network become more stable. Furthermore, risk contagion analysis reveals that WETH (Wrapped Ether) exhibits the highest contagion intensity, indicating its predominant role in propagating risks across the Uniswap V3 network. The novelty of this study lies in its capture of time-varying characteristics in decentralized exchange network topologies, unveiling dynamic evolution patterns in cryptocurrency correlation structures. Full article

Trolling behavior is not simply a result of ‘bad actors’, an individual trait, or a linguistic phenomenon, but emerges from complex contagious social dynamics. This study uses formal concepts from information theory and complexity science to study it as such. The data comprised over 13 million Reddit comments, which were classified as troll or non-troll messages using the BERT model, fine-tuned with a human coding set. We derive the unique, minimally complex, and maximally predictive model from statistical mechanics, i.e., ε-machines and transducers, and can distinguish which aspects of trolling behaviors are both self-motivated and socially induced. While the vast majority of self-driven dynamics are like flipping a coin (86.3%), when social contagion is considered, most users (95.6%) show complex hidden multiple-state patterns. Within this complexity, trolling follows predictable transitions, with, for example, a 76% probability of remaining in a trolling state once it is reached. We find that replying to a trolling comment significantly increases the likelihood of switching to a trolling state or staying in it (72%). Besides being a showcase for the use of information-theoretic measures from dynamic systems theory to conceptualize human dynamics, our findings suggest that users and platform designers should go beyond calling out and removing trolls, but foster and design environments that discourage the dynamics leading to the emergence of trolling behavior. Full article

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

With the continuous integration of globalization and financial markets, the linkage of global financial risks has increased significantly. This study examines the risk spillover effects and transmission dynamics among the financial markets in G20 countries, which together represent over 80% of global GDP. With increasing globalization and the interconnectedness of financial markets, understanding risk transmission mechanisms has become critical for effective risk management. Previous research has primarily focused on price volatility to measure financial risks, often overlooking other critical dimensions such as liquidity, credit, and operational risks. This paper addresses this gap by utilizing the vector autoregressive (VAR) model to explore the spillover effects and the temporal and spatial characteristics of risk transmission. Specifically, we employ global and local Moran indices to analyze spatial dependencies across markets. Our findings reveal that the risk linkages among the G20 financial markets exhibit significant time-varying characteristics, with spatial risk distribution showing weaker dispersion. By constructing a comprehensive financial risk index system and applying a network-based spillover analysis, this study enhances the measurement of financial market risk and uncovers the complex transmission pathways between sub-markets and countries. These results not only deepen our understanding of global financial market dynamics but also provide valuable insights for the design of effective cross-border financial regulatory policies. The study’s contributions lie in enriching the empirical literature on multi-dimensional financial risks, advancing policy formulation by identifying key risk transmission channels, and supporting international risk management strategies through the detection and mitigation of potential contagion effects. Full article

This study conducts a comprehensive bibliometric analysis of the use of agent-based modeling (ABM) in finance and banking, aiming to uncover how this methodology has evolved over the past two decades. It addresses the following overarching question: How has ABM contributed to the development of financial research in terms of trends, key contributors, and thematic directions? The relevance of this topic is based on the growing complexity of financial systems and the limitations of traditional models in capturing dynamic interactions, contagion effects, and systemic risks. Using a refined dataset of 489 articles from the Web of Science (2000–2024), selected through a multi-step keyword and relevance screening process, we apply bibliometric techniques using R Studio (version 2024.12.1+563) and Bibliometrix (version 4.3.3). The analysis reveals stable publication growth, strong international collaborations (notably Italy, USA, and China), and core thematic areas such as risk management, market simulation, financial stability, and policy evaluation. The findings highlight both well-established and emerging research fronts, with agent-based models increasingly used to simulate real-world financial phenomena and support regulatory strategies. By mapping the intellectual structure of the field, this paper provides a solid foundation for future interdisciplinary research and practical insights for policymakers seeking innovative tools for financial supervision and decision making. Full article

The study of contagion dynamics is a well-established domain within epidemiology, where the spread of infectious diseases is modeled and analyzed. In recent years, similar methodologies have been applied to the financial sector to understand and predict the propagation of risks within banking systems better. This paper examines the application of contagion models to assessing liquidity risk in the banking sector, leveraging optimal control theory to evaluate potential interventions by central banks. Using data from the largest European banks, we simulate the impact of central bank measures on liquidity risk. By employing optimal control techniques, we construct a model capable of simulating various scenarios to evaluate the effectiveness of policy interventions in mitigating financial contagion. Our approach provides a robust framework for analyzing the systemic risk propagation within the banking network, offering qualitative insights into the contagion mechanisms and their implications for the financial and macroeconomic landscape. The model simulates three distinct scenarios, with each representing varying levels of intervention and market conditions. The results demonstrate the model’s ability to capture the intricate interactions among major European banks, reflecting the complex realities of the financial system. These findings emphasize the critical role of central bank policies in maintaining financial stability and underscore the necessity of coordinated international efforts to manage systemic risks. This analysis contributes to a broader understanding of financial contagion, offering valuable insights for policymakers and financial institutions aiming to strengthen their resilience against future crises. The data used for the parameters are historical, which may not reflect recent changes in the banking system. The model could also be improved by incorporating non-financial factors, such as the behaviors of market actors. For future research, several improvements are possible. One improvement would be to make the bank interactions more dynamic to reflect rapid market changes better. It would also be interesting to add financial crisis scenarios to test the system’s resilience. Using more up-to-date data and incorporating new regulations would help refine the model. Finally, it would be relevant to examine the impact of external events, such as geopolitical crises, on the propagation of systemic risk. In conclusion, while the model is useful, there are several avenues for improving it and making it more suitable for our current realities. Full article

Information and knowledge diffusion are important dynamical processes in complex social systems, in which the underlying topology of interactions among individuals is often modeled as networks. Recent studies have examined various information diffusion scenarios primarily focusing on the dynamics within one network; yet, relatively little scholarly attention has been paid to possible interactions among individuals beyond the focal network. Here, in this study, we account for this phenomenon by modeling the information diffusion dynamics with the involvement of independent spreaders in a susceptible–exposed–infectious–recovered contagion process. Independent spreaders receive information using latent information transmission pathways without following the links in the focal network and can spread the information to remote areas of the network not well connected to the major components. We derive the mathematics of the critical epidemic thresholds on homogeneous and heterogeneous networks as a function of the infectious rate, exposure rate, recovery rate and the activeness of independent spreaders. We present simulation results on Small World and Scale-Free complex networks, and real-world social networks of Facebook artists and physicist collaborations. The result shows that the extent to which information or knowledge can spread might be more extensive than we can explain in terms of link contagion only. In addition, these results also help to explain how the activeness of independent spreaders can affect the diffusion process of information and knowledge in complex networks, which may have implications for studies exploring other dynamical processes. Full article

Previous studies have found that passive social media use (PaSMU) tends to induce upward contrast, thereby affecting well-being. However, this perspective alone may overlook the mechanisms of other social comparison phenomena. This study analyzes the influence mechanism of PaSMU on subjective well-being (SWB) by categorizing social comparison into upward identification, upward contrast, downward identification, and downward contrast while incorporating social comparison orientation (SCO) as a moderating variable. This study surveyed college students who use RED (Xiaohongshu) and collected 352 valid questionnaires. A two-stage hybrid structural equation modeling (SEM)–artificial neural network (ANN) method was employed, utilizing path and mediation effect analysis to verify the moderating effect of SCO in the process of PaSMU affecting SWB. PaSMU is positively correlated with upward contrast and downward identification, both of which negatively affect SWB. Upward contrast and downward identification are associated with lower SWB, while downward comparison is positively correlated with SWB. High SCO strengthens the association between upward contrast and reduced SWB. Furthermore, upward contrast and downward identification were found to have comparable mediating effects between PaSMU and SWB. In contrast to previous studies, this research highlights that downward identification plays a comparably significant mediating role alongside upward contrast. Downward identification significantly mediates the relationship between PaSMU and SWB due to increased risk awareness, higher sensitivity to negative information among socially anxious students, emotional contagion from negative content, and anonymity that fosters an “imagined community”. Additionally, students with high SCO are more affected by idealized self-presentations and rely on upward contrasts for social feedback, lowering their SWB. This study reveals the complex correlation of PaSMU and SWB, providing new theoretical insights and practical strategies to encourage positive social media use among college students. Full article

During the COVID-19 pandemic, the role of housing in controlling the spread of the virus was limited, as policies primarily focused on short-term measures such as lockdowns and social distancing. As the pandemic recedes, a shift has occurred towards restructuring the environment to confront future health crises better. This research thoroughly evaluates existing literature and housing complexes. It recommends that future projects prioritize several key features: ample exposure to natural environments, opportunities for growing food, encouragement of casual social interactions, inclusion of communal spaces, and provision of areas for exercise to help reduce the risks of contagion and alleviate the mental health impacts on residents. Based on research conducted during and after the pandemic, current recommendations for housing often provide generalized suggestions or propose ideal layouts through diagrams. This approach can be unrealistic from both spatial and economic perspectives and fails to inspire or stimulate creativity. This paper, by contrast, reviews and analyzes historical housing projects while critically examining three case studies that have the potential to inspire future designs. The goal is to provide officials, architects, and stakeholders with a series of practical possibilities and guidelines that contribute to the post-COVID home design process by making it more health-conscious and fostering the creation of new types of neighborhoods that can significantly impact the planning of cities in California. Full article

In the context of a low-carbon economic transition and escalating uncertainties in financial markets, understanding the relationship between the long-term benefits of ESG (Environmental, Social, and Governance) investments and the stability of China’s financial markets emerges as a critical issue. This paper analyzes the risk contagion mechanisms within China’s financial system from the perspective of volatility spillovers associated with ESG investments. Initially, the study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to calculate the variance decomposition spillover index, contrasting the dynamics and risk transmission mechanisms of market volatility between portfolios composed of ESG and conventional stocks. Building upon the analysis of risk spillover relations among financial sub-markets, the study utilizes the generalized forecast error variance decomposition method to construct a complex network of financial system risk spillovers, investigating the risk contagion characteristics within both financial systems through network topology. Empirical findings indicate a significant reduction in the risk and net spillover effects of China’s financial system when ESG stock indices replace conventional stock indices, with a notable mutation in the volatility spillover network structure during extreme risk events and even more substantial changes during the COVID-19 pandemic. Furthermore, based on volatility spillover analysis, the study computes optimal weights and hedging strategies for portfolios incorporating the ESG volatility index and other market volatility indices. The conclusions of this research are instrumental for regulatory authorities in establishing early warning mechanisms and for investors in avoiding financial investment risks. Full article