Journal of Risk Analysis and Crisis Response

Beyond the Seawall: Social Capital and Resilience Grants in Massachusetts Coastal Towns

Margarida Soares Rodrigues — 2026-03-31

As climate change intensifies, governments fund risk mitigation and recovery in vulnerable coastal communities. This study investigates the factors influencing grant acquisition using an original dataset of nearly 60 Massachusetts coastal towns, supplemented by 10 in-depth interviews with stakeholders in the field. Employing a mixed-methods approach, combining quantitative regression analysis and qualitative insights, we examine correlations with state-level climate grant activity. Controlling for geographic, demographic, bonding social capital, and voter turnout variables, our regression analysis reveals two significant predictors of grant acquisition: the demographic makeup of the community and bridging social capital. These findings suggest that having a greater proportion of minority population as well reduced access to external resources drive grant allocations. This research offers actionable recommendations for local communities, NGOs, and policymakers seeking to engage with residents facing the consequences of climate change.

Building Consumer Trust in Fintech: The Influence of Digital Risk, Technology Dependence, and Cybersecurity

Anggun Anggita Kinasih Sunowo Putri — 2026-03-31

The growth of fintech services in emerging markets has accelerated financial inclusion while simultaneously introducing digital risks that may compromise consumer trust. This study investigates how perceived digital risks, technology dependence, and cybersecurity perceptions influence user trust in fintech platforms. Drawing from the Technology Acceptance Model (TAM) and Perceived Risk Theory, the research analyses responses from Indonesian fintech users using Structural Equation Modelling (SEM) via AMOS. The data were collected in May 2025 through an online survey. Out of 300 questionnaires distributed, 284 were returned and 250 were valid for analysis. The findings indicate that emerging risks and technology dependence significantly shape both cybersecurity perceptions and trust in digital financial platforms. Additionally, cybersecurity itself exerts a direct and statistically significant influence on consumer trust. Hypothesis testing results confirm that all proposed hypotheses (H1–H7) are supported, indicating that trust in fintech is simultaneously shaped by user perceptions of risk, technology dependence, and cybersecurity. These results suggest that trust in fintech is driven by both user perceptions of risk and the perceived strength of platform-level security systems. Practically, the study urges fintech providers to adopt transparent and user-centric security strategies, and encourages regulators to prioritize digital literacy and robust cybersecurity governance in the rapidly evolving fintech landscape.

The Impact of Patient Capital on the High-Quality Development of Enterprises in Strategic Emerging Industries

Fang-fang Ji — 2026-03-31

The high-quality development of enterprises in strategic emerging industries plays a crucial role in achieving high-quality economic development. As a form of long-term strategic investment adhering to the principles of value investing, patient capital is emerging as a key driver of high-quality development in these enterprises. Based on data from 743 listed enterprises in China’s strategic emerging industries from 2014 to 2023, this study examines the differential effects of patient capital on the high-quality development of these enterprises. It further tests the mediating roles of digital-green transformation synergy, information asymmetry, and financing constraints, as well as the moderating role of artificial intelligence applications. The results indicate: First, patient capital plays a significant role in promoting the high-quality development of enterprises in strategic emerging industries; second, patient capital promotes the high-quality development of these enterprises by enhancing the level of synergy in digital and green transformation, alleviating information asymmetry, and easing financing constraints. Third, the application of artificial intelligence enhances the positive impact of patient capital on the high-quality development of enterprises in strategic emerging industries; fourth, the impact of patient capital on the high-quality development of enterprises in strategic emerging industries exhibits distinct differences across regional heterogeneity and industrial characteristics. Analysis of regional heterogeneity reveals that enterprises in the central region are more sensitive to patient capital in terms of high-quality development, while an analysis of industrial heterogeneity reveals that the effects of two distinct forms of patient capital—stable equity and relationship-based debt—are more pronounced in promoting high-quality development in the new energy vehicle industry, energy conservation and environmental protection industry, biotechnology industry, new materials industry, and next-generation information technology industry. Compared to relationship-based debt, stable equity significantly promotes high-quality development in the high-end equipment manufacturing and new energy industries. This study provides theoretical foundations and policy implications for leveraging patient capital to drive high-quality development in strategic emerging industries.

Artificial Intelligence: A Blessing or a Curse for Climate Action (SDG 13)? The Moderating Roles of Governance Quality and Digital Infrastructure

Partha Acharjee — 2026-03-31

This study examines the dynamic relationship between the adoption of artificial intelligence (AI) and carbon dioxide (CO2) emissions, focusing on the moderating roles of governance quality (GQI) and digital infrastructure (DII) across 104 countries from 2000 to 2023. Using two-step system GMM and two-stage least squares (2SLS) estimations, the findings reveal that AI, while enhancing innovation and productivity, currently contributes to higher CO2 emissions, particularly in economies with weak governance and underdeveloped digital ecosystems. Strong institutional quality and advanced digital infrastructure significantly mitigate this effect, suggesting that GQI and DII are critical for realizing AI’s potential as a sustainable technology. The results further reveal pronounced heterogeneity across energy-efficient and energy-inefficient countries as well as low-AI and high-AI stages, indicating that the environmental impact of AI is weaker in settings characterized by higher energy efficiency and early-stage AI diffusion, but stronger in energy-inefficient and AI-advanced contexts. These findings underscore the context-dependent nature of AI’s environmental outcomes and highlight the importance of governance-driven digital transformation for achieving sustainable growth.

Machine Learning Approaches for Detecting Irregular Financial Activities in China

Zhong-Qiang Zhou — 2026-03-31

Irregular financial activities (IFAs) pose serious challenges to regulators, especially in China where high-profile scams have highlighted gaps in oversight. This study develops a machine learning framework to identify such risks using a dataset of 540 financial cases from 2014 to 2024. Activities are classified as irregular or normal, and the performance of 18 algorithms—including traditional machine learning, ensemble methods, and deep learning models—is compared. Ensemble learning models demonstrate superior performance in detecting IFAs, balancing high accuracy with practical applicability. In particular, Bagging and LightGBM achieve the highest accuracy and robust F1-scores among all tested methods. These findings offer novel insights and technical tools for early warning of IFAs, contributing to the literature on financial risk detection and informing policy design. This comparison is among the first systematic evaluations of diverse machine learning algorithms for IFA detection in China, bridging a gap in the literature on regulatory technology and risk management. The proposed approach provides regulators with real-time, data-driven tools to identify irregularities before substantial losses occur.

A Review of the Impact Effect of Tax Credit Rating System on Enterprises in China

Chang Guo — 2026-03-31

In order to promote the theoretical research and practical development of tax credit rating system, this paper summarizes the research status of the impact of tax credit rating system on enterprises in China. At present, scholars’ research on the impact of China’s tax credit rating system on enterprises mainly focuses on the impact of tax credit rating system on enterprise business activities, the impact of tax credit rating system on enterprise business performance, and the impact of tax credit rating system on capital market. Overall, scholars have conducted extensive research on the impact of tax credit rating system on enterprise business activities, while research on the impact of tax credit rating system on enterprise business performance is relatively weak. In particular, the research on the impact of the tax credit rating system on the capital market is relatively weak. Future research can focus on the impact of tax credit rating system on the M&A activities of listed companies, the impact of tax credit rating system on the dividend strategy of listed companies, the synergy between tax credit rating and third-party credit rating and the internal credit rating of commercial banks.