Decentralized Finance (DeFi) is a recent advancement of the cryptocurrency ecosystem, giving plenty of opportunities for financial inclusion, innovation, and growth domains by providing services such as lending, borrowing, and trading without traditional intermediaries. However, inadequate regulatory oversight and technological vulnerabilities raise pressing concerns around market manipulation, fraud, and regulatory compliance, exposing a clear research gap in effective DeFi risk management. This paper addresses this gap by proposing a utility-based framework to evaluate six leading DeFi tracking platforms—Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan—focusing on two critical metrics: transaction accuracy and real-time responsiveness. Applying a mixed methods approach that combines a quantitative survey (n = 138) with qualitative interviews (n = 12), we identified critical platform features and found significant differences across these platforms with respect to compliance features, advanced analytics, and user experience. We used a utility-based model that links accuracy and responsiveness metrics, allowing us to adjust differing priorities and risk management needs for users. The results show the need for balanced, user-centric solutions that accommodate regulatory, technological efficiency and affordability requirements. Our study contributes to the growing knowledge base by providing a structured evaluation model and empirical insights, offering clear directions for practitioners, platform developers, and policymakers aiming to strengthen the DeFi ecosystem.

This study revisits the globalisation–sustainability nexus by focusing on the divergence between formal policy commitments and realised integration. While the 2030 Agenda assumes coherence across the seventeen Sustainable Development Goals (SDGs), empirical evidence increasingly points to uneven progress and systemic trade-offs. We introduce the concept of the globalisation implementation gap, defined as the normalised difference between de jure commitments and de facto integration, and examine its impact on SDG performance. Using a panel dataset of 101 countries over the period of 2000–2023 (N = 2197), we construct gap indices based on the KOF Globalisation framework and estimate two-way fixed-effects models with Driscoll–Kraay standard errors. The results reveal a significant negative association between implementation gaps and overall SDG performance. However, this relationship is highly heterogeneous across goals. Larger gaps are strongly negatively associated with social outcomes, particularly poverty reduction (SDG 1), clean water and sanitation (SDG 6), and sustainable cities (SDG 11), while being positively associated with certain environmental outcomes, including responsible consumption (SDG 12) and apparent biodiversity protection (SDG 15)—the latter, however, is interpreted as associational only, given evidence of reverse causation in placebo lead-testing. Further analysis using panel threshold regression demonstrates that governance quality moderates this relationship non-linearly. Above a critical threshold of institutional quality, the magnitude of the negative gap–SDG association increases substantially, suggesting that stronger governance amplifies rather than buffers the association. In addition, Gaussian mixture clustering identifies three distinct country archetypes (Balanced, Informal, and Policy-Led Integrators). A diagnostic placebo lead test indicates that the SDG 15 association is associational rather than causal (likely reverse causation), and the result for SDG 15 is therefore reported with that caveat. Each is characterised by different gap structures and sustainability trade-offs. Overall, the findings shift the perspective from a uniform globalisation–SDG relationship to a goal-specific and governance-contingent framework. The study highlights the importance of aligning policy commitments with actual integration processes and provides policy-relevant insights for managing SDG trade-offs under conditions of globalisation. The headline gap–SDG association is conditional on the listwise sample of 101 countries with complete data and on Driscoll–Kraay variance estimation; sensitivity analyses show that the result attenuates under multiple imputation on the full 208-country panel and loses statistical significance under cluster-robust standard errors, while the political-channel decomposition and the governance threshold remain robust under both stress tests.

This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR₁%) and 67.5% (ETH VaR₁%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR₁% fell by 22.0% and ETH VaR₁% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ⁻ = 0.847 vs. ρ⁺ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ⁺ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets.