regulatory impact analysis

This study addresses the lack of systematic comparative analysis in business process compliance monitoring, particularly for non-conformance checking techniques. Through a systematic literature review (SLR), process mining, compliance modeling, and qualitative comparative analysis, it maps real-world applications across domains, operational workflows, technical foundations, and result representations. The analysis identifies key implementation barriers—especially pervasive human dependence and the absence of standardized evaluation criteria. As the first structured survey framework dedicated to non-conformance checking, the study introduces a standardized, multi-dimensional evaluation framework that clarifies commonalities and distinctions across the technical landscape. It further proposes an extensible theoretical pathway and practical guidelines for automated compliance monitoring. This work provides a methodological foundation and strategic direction for both academic research and industrial deployment. (149 words)

This study addresses the quantification of the impact of different climate scenarios on expected credit losses (ECL) for financial assets. To this end, it proposes an operational framework for measuring scenario-induced impacts by leveraging existing provisioning systems within financial institutions. The approach adjusts probabilities of default to reflect climate-related shocks and integrates mappings of risk drivers with standardized exposure grouping methodologies, thereby enabling comparable scenario analyses across institutions. The framework provides both a theoretical foundation and a practical implementation pathway for regulators conducting standardized climate stress tests. It has been successfully applied in the 2024 joint climate scenario analysis conducted by the Office of the Superintendent of Financial Institutions Canada and the Autorité des marchés financiers du Québec.

This study addresses the limitation of existing regulatory comment analyses, which typically operate at a coarse granularity and thus fail to assess the actual impact of public input on specific regulatory obligations. To overcome this, the authors propose an Obligation-Level Responsiveness Auditing Framework—the first approach enabling auditable measurement of responsiveness at the level of individual obligations. Leveraging NLP techniques, the framework extracts and aligns specific obligations from proposed and final rules with corresponding public comments, while blinded human review distinguishes substantive from editorial modifications. Empirical analysis of 36 EPA rules and over 70,000 comments reveals that organizational commenters are more likely to elicit editorial adjustments than substantive changes, with commenter stance showing no significant effect. Crucially, disparities in commenters’ capacity to identify and challenge specific obligations emerge as a key determinant of equity in regulatory responsiveness.

This study addresses the lack of methodological foundations for systemic risk assessment and independent auditing under the EU’s Digital Services Act (DSA). Methodologically, it proposes the first evidence-driven compliance auditing framework, uniquely integrating legal requirements with empirical sampling theory to establish dynamic, risk-category-specific, and platform-sensitive representativeness criteria. At its core lies legally guided content sampling, augmented by stratified and temporal sampling, interdisciplinary representativeness evaluation, systemic risk classification modeling, and empirical review of compliance reporting—yielding a mixed-methods audit pathway combining qualitative and quantitative analysis. The contribution lies in overcoming key limitations of existing auditing approaches—namely, opacity and weak evidentiary chains—by empirically validating the “sampling + legal analysis” approach across diverse systemic risks, including illegal content, fundamental rights violations, democratic interference, and gender-based violence. The framework delivers an actionable, reproducible DSA compliance assessment methodology for regulators and independent auditors.

Current evaluations of AI governance proposals often fall into binary oppositions, overlooking implicit value trade-offs and lacking transparent analytical tools. This work proposes a multidimensional policy analysis framework that integrates expert interviews with computational text analysis to construct an interpretable scoring system across policy attributes, enabling cross-proposal comparison through visualization. Its novelty lies in three aspects: first, a multidimensional evaluation approach that avoids predetermined conclusions and explicitly reveals inherent trade-offs; second, a transparent hybrid methodology combining qualitative expert insights with quantitative computational validation; and third, the introduction of a domain-calibrated model as a benchmark against general-purpose large language models. The framework enables comparable, interpretable assessments of AI governance proposals across multiple attributes, allowing stakeholders to evaluate proposal relevance and coherence according to their own normative priorities.

This study addresses the challenges posed by the proliferation, complexity, and expanding scope of regulatory requirements in software engineering, which hinder their systematic integration into development processes. To tackle this issue, the paper proposes a viewpoint-centered, artifact-based approach to regulatory requirements engineering. The approach innovatively integrates viewpoint analysis with artifact modeling to develop the AM4RRE (Artifact Modeling for Regulatory Requirements Engineering) framework, which facilitates cross-functional collaboration and ensures consistency in compliance-driven design. Preliminary validation demonstrates that AM4RRE effectively bridges the gap between organizational regulatory processes and software development practices, enabling a shift from ad hoc compliance responses toward systematic integration. This foundational work paves the way for further empirical investigation into scalable and sustainable regulatory compliance in software engineering.

The EU AI Act faces challenges including the absence of systematic methodologies for legal compliance verification, heterogeneous national preparedness, and ambiguous regulatory interpretations. To address these, this study proposes the first comprehensive compliance verification framework tailored to high-risk AI systems. Structured along two dimensions—“method type” (governance vs. testing) and “assessment object” (data, model, process, product)—the framework establishes a multi-layered, lifecycle-spanning verification paradigm. It introduces a novel mapping mechanism that systematically translates legal provisions into executable verification activities, integrating compliance engineering, law-technology alignment modeling, standards-mapping matrices, and risk-informed pathway design. The framework significantly reduces regulatory uncertainty, enhances cross-border assessment consistency, and enables coordinated governance among policymakers, auditors, and developers. (149 words)

This study addresses the lack of a systematic framework for identifying critical input variables and conducting sensitivity analysis under uncertainty in complex simulations, particularly in military decision-making contexts. The authors propose a unified sensitivity analysis framework that integrates local and global methods—including variance-based, derivative-based, screening, and uncertainty quantification techniques—and strategically maps these approaches to specific decision objectives such as factor prioritization, fixing, variance reduction, and mapping. Innovatively, the framework introduces a “sensitivity audit” mechanism to enhance traceability of model assumptions and promote responsible model usage. By providing a structured guide for high-dimensional, complex simulation systems, this work significantly improves model interpretability, transparency, and the credibility of decisions derived from such models.

This study evaluates the regional environmental and economic pressures imposed by artificial intelligence data centers in the United States on electricity consumption, water resources, and land use. Integrating regional grid structures, hydrological conditions, and land-use planning, the research employs a multidimensional approach—including marginal emission factor modeling, cooling system analysis, water stress assessment, and land-use simulation—to move beyond single-facility perspectives and uncover the spatiotemporal heterogeneity of impacts. It further reveals that current policies inadequately address these externalities. While improvements in energy efficiency and operational optimization can mitigate adverse effects, their efficacy is highly contingent on system-level conditions. The study thus advocates for policy pathways that cohesively integrate grid characteristics, water availability, and land-use planning to effectively manage AI-driven infrastructure impacts.

This study examines how the U.S. Food and Drug Administration’s new food traceability rule transforms small-scale agricultural producers into uncompensated data laborers, exacerbating existing burdens related to labor, financial resources, and technological capacity. Drawing on data feminist theory—an approach newly applied to food regulatory policy analysis—the research employs qualitative coding of 1,198 public comments to systematically uncover structural inequities embedded in the rule’s implementation. The analysis identifies three core tensions: the invisible burden of data labor imposed on marginalized actors, the technical infeasibility of mandated tracking systems for small operations, and regulatory ambiguity that fuels inconsistent enforcement. These findings offer both empirical evidence and theoretical innovation to inform the development of more inclusive and equitable data governance frameworks in food safety regulation.