Abstract: The global payments landscape is undergoing a structural transformation driven by the convergence of Digital Finance (DF) technologies and Artificial Intelligence (AI). This integration marks a shift from isolated digital payment systems toward interconnected, intelligent, and highly automated financial infrastructures. AI functions as the core intelligence layer across digital rails - including Distributed Ledger Technology (DLT), Central Bank Digital Currencies (CBDCs), stable coins, and mobile networks - optimizing payment routing, enabling real - time fraud detection, and automating compliance obligations such as AML / KYC. The result is enhanced straight-through processing rates exceeding 99%, reduced cross - border transaction frictions, improved liquidity management, and democratized access to enterprise - grade payment capabilities through API - enabled FinTech platforms. However, rapid adoption introduces new systemic challenges, including algorithmic bias, data privacy vulnerabilities, explains ability concerns, and heightened third - party concentration risks. Emerging regulatory frameworks increasingly emphasize transparency, governance, and explainable AI (XAI), as evidenced in supervisory innovations such as the BIS Project Noor. While digital - AI convergence improves efficiency and fosters financial inclusion, uneven technological capacity risks widening the digital divide without deliberate inclusive design and shared digital infrastructure. This study synthesizes global trends, technological architectures, governance models, and strategic imperatives underpinning AI - enabled payment ecosystems. It highlights a future defined by programmable finance, real - time cross - border rails, intelligent automation, and collaborative regulatory innovation - establishing the foundations for secure, ethical, and scalable digital financial systems worldwide. Keywords: Digital Finance, Artificial Intelligence, Global Payment Systems, Block Chain, Distributed Ledger Technology, Cross - Border Payments, CBDCs, AI Governance, Explainable AI (XAI), Regtech, Straight - Through Processing, Financial Inclusion, Programmable Money, Fintech Infrastructure
This article reviews how blockchain-native finance is reshaping financial intermediation and how the next wave of digital finance is likely to be influenced by large language models (LLMs) and quantum finance research. Building on recent work on decentralized finance, blockchain implementation, supply chain finance, and emerging FinTech architectures, the study develops an integrated analytical framework that connects three layers of change: programmable settlement, intelligent decision support, and frontier computational finance. Rather than treating DeFi, blockchain-based supply chain finance, LLM applications, and quantum finance as isolated topics, the review shows that they form a continuous innovation trajectory with shared challenges in governance, interoperability, data quality, risk modeling, and institutional trust. The paper synthesizes prior findings, compares major technical and managerial mechanisms, and proposes a research agenda for resilient, explainable, and regulation-aware financial innovation. The results suggest that blockchain creates a credible record and execution layer, LLMs expand interpretive and operational intelligence, and quantum finance may eventually widen the solution space for complex risk-pricing and portfolio problems. The article concludes with practical implications for platform designers, regulators, and industry managers.
The amount of international capital invested in sustainability-focused investments and decentralized financial technologies has been growing fast. Thus, this research focuses on the transmission of volatility and optimal portfolio composition among decentralized finance (DeFi) assets, S&P renewable energy and technology market indices, and conventional energy commodities for the period from March 15, 2018, to August 30, 2024. The sample period was divided into three sub-periods to examine the impact of COVID-19, which increased in parallel with the adoption of DeFi and a focus on sustainability: pre-COVID, during-COVID, and post-COVID. This research utilizes the Diebold-Yilmaz and Baruník-Křehlík techniques for time-and frequency-domain analyses, and the Dynamic Conditional Correlation model for portfolio optimization. First, the findings reveal that DeFi tokens (sustainable markets) (brown investments) display moderate (high) (very low) internal connectedness. Second, DeFi tokens demonstrate very low volatility connectedness with both sustainable and brown markets, which suggests strong diversification effects. Third, volatility connectedness among sustainable markets and conventional energy commodities is equally low. Fourth, sustainable markets (conventional energy commodities) make the highest (lowest) contribution to total volatility connectedness, and they operate as net transmitters (receivers) of volatility. Moreover, the total volatility connectedness is 33.7%, which is relatively low, suggesting significant opportunities for diversification of investment portfolios. Furthermore, the outcomes for optimal portfolio weights present greater allocations to green markets compared to conventional energy commodities and DeFi assets, revealing an escalating global transition toward sustainability. Additionally, COVID-19 significantly influenced volatility transmissions and portfolio allocations.
In response to growing environmental, economic, and social demands for more accountable carbon markets, there is increasing recognition of the limitations inherent in traditional carbon credit management systems. These systems often rely on centralized authorities, making them prone to inefficiencies such as high administrative costs, slow verification processes, and limited accessibility for small-scale participants. Moreover, issues like double counting, lack of transparency, and data tampering continue to undermine stakeholder trust and market integrity. A decentralized approach powered by blockchain technology offers a transformative alternative by ensuring transparency, immutability, and automation through the use of smart contracts. On blockchain platforms such as Polygon, carbon credits can be tokenized using the ERC-721 standard, where each credit becomes a unique, verifiable non-fungible token (NFT). These tokens allow precise tracking of credit ownership, issuance, transfers, and retirement (burning), effectively eliminating the need for intermediaries and reducing verification costs. This decentralized model not only enhances operational efficiency but also democratizes participation in the carbon economy, enabling even smaller stakeholders to engage meaningfully. By combining environmental accountability with digital innovation and economic inclusivity, blockchain-based carbon credit platforms represent a significant advancement toward more sustainable, transparent, and equitable global climate and resource governance systems.
This study investigates the impact of sustainability-related uncertainty (SRU)—captured via the Sustainability-related Uncertainty Index in equal-weighted (ESGUI_EQ) and GDP-weighted (ESGUI_GDP) forms—on the volatility of green financial assets, focusing on decentralized finance (DeFi) protocols and Environmental, Social, and Governance (ESG)-focused Exchange-Traded Funds (ETFs). Employing a fuzzy logic framework, complemented by 3D surface visualization, Rule Viewer analysis, diagnostic validation, and Granger causality tests, the study uncovers non-linear, asymmetric, and time-varying responses of these assets to sustainability ambiguity. Empirical results reveal a structural divergence: DeFi protocols amplify volatility due to fragmented governance, speculative investor behavior, and sensitivity to policy-driven signals, often exhibiting bidirectional predictive feedback with SRU, whereas ESG ETFs maintain stability through diversification, regulatory oversight, and rigorous ESG screening, primarily absorbing sustainability shocks. These findings extend sustainable finance theory by integrating governance, technology, and policy dimensions, and illustrate the value of fuzzy logic combined with Granger causality in modeling complex, ambiguous markets. From a practical standpoint, the study provides actionable guidance for investors, fund managers, and policymakers, emphasizing the importance of technology-informed governance, standardized ESG disclosures, regulatory sandboxes, and continuous monitoring of SRU.
Stephen Bishibura Erick, Bonamax Mbasa, Kulwa Mang’ana
This study conducts a comprehensive bibliometric analysis of scholarly research on green economy and sustainable finance from 2014 to 2024. Drawing upon a dataset of 692 peer-reviewed publications indexed in Scopus and analysed using the Bibliometrix R package, the study maps the field’s intellectual landscape, thematic development, and collaborative networks. The findings reveal a consistent increase in scientific output, with a pronounced surge in publications after 2018. This growth trend aligns with global policy milestones such as the Paris Agreement, the European Union [EU] Sustainable Finance Action Plan, and the proliferation of Environmental, Social, and Governance [ESG] integration and green bonds. China emerges as the most productive country, while institutions such as Jiangsu University, the Southwestern University of Finance and Economics, and the Lebanese American University lead in publication volume and collaboration intensity. Keyword co-occurrence and thematic mapping identify dominant themes related to green finance, environmental sustainability, ESG frameworks, and renewable energy, alongside emerging topics like climate risk disclosure and transition finance. Conceptual and co-word network analyses further reveal the interdisciplinary integration of finance, economics, policy, and environmental science. The study also demonstrates the growing decentralization of institutional influence and the rise of both North–South and South–South collaborations. These findings offer valuable insights into the evolving structure of research in sustainable finance and inform future academic inquiry and policy development.
This study aims to examine the development and structure of global research on Sharia finance through a bibliometric analysis of publications indexed in the Scopus database from 2010 to 2024. Using bibliometric techniques and visualization tools such as VOSviewer, this study analyzes publication trends, collaboration networks among authors, institutions, and countries, as well as the thematic evolution of research topics in the field of Islamic finance. The results indicate that research on Sharia finance has grown significantly during the observed period, reflecting the increasing global importance of Islamic financial systems. The collaboration analysis shows that several key authors and institutions play central roles in connecting different research groups, while countries such as Indonesia, Malaysia, Saudi Arabia, the United Kingdom, and the United States emerge as important contributors to the global research network. Keyword co-occurrence analysis reveals that dominant themes include Islamic banking, Sharia compliance, financial institutions, and Islamic law. At the same time, emerging topics such as financial technology (fintech), blockchain, decentralized finance, and financial inclusion indicate a shift toward digital transformation and innovation in Islamic financial services. Furthermore, themes related to sustainable development, ESG, and waqf highlight the growing integration of Islamic finance with broader sustainability and ethical finance agendas. This study provides a comprehensive overview of the intellectual structure, collaboration patterns, and emerging research trends in Sharia finance, offering valuable insights for future academic research and policy development in the global Islamic financial industry.
This paper introduces CarbonLedgerProof (CLP), a novel cryptographic traceability algorithm designed to connect asset-level emissions data with financial statement estimates for enhanced Environmental, Social, and Governance (ESG) assurance and impairment testing. The proposed CLP algorithm bridges the gap between carbon emissions reporting and the financial implications of environmental risks, ensuring transparency and traceability across asset portfolios. By integrating blockchain technology and zero-knowledge proofs (ZKPs), CLP offers a secure and efficient way to validate emissions data against financial estimates, addressing challenges in ESG data integrity and providing an automated framework for impairment testing in the context of sustainability. In comparison to existing algorithms such as GreenLedger, CarbonProof, ESG-Chain, and a Traditional Audit (TradAudit) baseline. CLP demonstrates superior performance in terms of scalability, data integrity, and computational efficiency. Through an extensive experimental evaluation, we showcase CLP's ability to significantly reduce verification time and enhance the accuracy of ESG assurance processes. The results indicate that CLP outperforms traditional methods in integrating emissions data into financial systems, offering an innovative approach for real-time emissions monitoring and risk assessment. This paper concludes by proposing CLP as a transformative tool for corporate ESG reporting, with practical implications for financial institutions, auditors, and regulators seeking to streamline the integration of carbon data into decision-making frameworks.
Sustainable finance models are most often built for contexts characterized by institutional stability, effective governance, and functioning capital markets. In fragile states, such conditions are often absent. This paper revisits sustainable finance through the case of Lebanon, where the post-2019 financial collapse rendered conventional instruments, such as ESG frameworks, green bonds, and sustainability-linked loans, difficult to implement and contextually irrelevant. Drawing on literature regarding sustainable finance, degrowth and post-growth economics, and the political economy of fragility, the paper proposes a conceptual framework for Agile Sustainable Finance: a model that explains how financial practices oriented towards sustainability can persist despite institutional collapse with agility operating as the mediating capability. The model positions agility as the central capability enabling households, firms, and communities to reorganize financial life amid institutional erosion, liquidity shortages, and involuntary degrowth. It highlights how informal credit systems, remittances, community financing, and decentralized energy solutions become essential tools for resilience and ecological sufficiency in collapsed economies. By reframing finance as a mechanism for survival, redistribution, and basic sustainability rather than growth, this conceptual study offers a theoretical model that bridges domains that rarely intersect: sustainable finance and fragile-state dynamics.
Amro Saleem Alamaren, Korhan K. Gökmenoğlu, Nigar Taşpınar
Abstract This study investigates the volatility spillover and connectedness networks among renewable energy sources (Biofuel, Fuel cell, Geothermal, Solar), green bonds, and cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin) in the U.S. market. To accomplish this objective, we analyzed data from November 15, 2017, to May 31, 2024, via the methods introduced by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Baruník and Křehlík (J Financ Econometr 16:271 296, 2018). Our findings reveal that major global disruptions—including the COVID-19 pandemic, the Russia–Ukraine war, the collapse of Silicon Valley Bank, and the Credit Suisse crisis—have intensified volatility spillovers and financial contagion across markets, exacerbating their outcomes. The findings suggest that the effectiveness of green finance depends on its allocation across these sectors, highlighting the importance of examining each sector to understand the success of these financial initiatives. The influence of COVID-19 on the U.S. economy has increased transmission risk across markets. Renewable energy is less volatile than green bonds and cryptocurrencies are, with these indices reacting more quickly to short-term shocks. Investors should focus on short-term impacts to manage market risk effectively. By providing insights into how financial shocks propagate across sectors, emphasizing the need for a sector-specific approach to assessing financial sustainability, and underscoring the importance of short-term risk management strategies, this research offers valuable contributions to decision-makers and investors.
Multi-chain deployment has become a mainstream strategy for U.S.-based DAOs, yet treasury management faces three core bottlenecks: cross-chain liquidity fragmentation, inadequate compliance with U.S. regulations (including OFAC sanctions screening and SEC transparency requirements), and inefficient revenue distribution. Leveraging the incubation practices of over 12 U.S. DAOs (via daos.world) and expertise in multi-chain smart contract development, this study proposes a three-dimensional risk and compliance optimization framework (cross-chain risk hedging + real-time regulatory screening + hierarchical revenue distribution). Empirical testing on 8 U.S. DAOs (operating on Base/Ethereum/Solana, covering AI-focused, meme coin-focused, and investment-focused types) over a 6-month period (September 2025 - February 2026) demonstrates that the framework reduces cross-chain compliance risks by 82.3% (OFAC violation rate drops from 18.0% to 3.2%), increases the annualized treasury return rate by 17.6% (from 4.2% to 5.04%), lowers cross-chain transaction costs by 28.5% (average Gas fee decreases from $12.8 to $9.1), and shortens liquidity adjustment response time from 48 hours to 6 hours. Integrating U.S. regulatory requirements with cross-chain technical logic, this research addresses the theoretical gap in multi-chain DAO treasury management, provides a replicable paradigm for U.S. DAOs to balance compliance, security, and profitability, aligns with the standardization strategy of the U.S. Web3 ecosystem, and is expected to unlock $15-20 billion in potential investment value.
Emergence of blockchain technology has disrupted a number of economic sectors, particularly financial institutions, with significant effects on their operations. This paper investigates the impact of asset tokenization on the issuance and trading process of financial assets, specifically bonds. It examines the effect of tokenizing the High Yield Bond on the Ethereum blockchain across two key dimensions: On costs, a comparative cost-benefit analysis is conducted before and after tokenization, and on green sustainability, through a comparative analysis on the carbon footprint of the bond before and after Ethereum's merge to proof of stake. The results show that Tokenization improves cost-savings, and it promotes a greener, more sustainable approach when using the Ethereum blockchain post-transition to proof of stake.
Sustainable Development Goal 7 (SDG-7) seeks universal access to affordable, reliable, and modern energy by 2030, yet progress remains uneven and structurally constrained. Despite declining renewable energy costs, around 685 million people lack electricity and more than 2 billion depend on traditional biomass for cooking. This review moves beyond descriptive assessments by providing a systematic, decision-oriented synthesis of SDG-7 pathways. Using a replicable PRISMA-informed protocol, it integrates peer-reviewed studies and authoritative international datasets published between 2015 and 2025. Centralized, decentralized, and hybrid energy systems are evaluated in terms of technical maturity, affordability, governance feasibility, and socio-environmental impacts. A structured barrier-to-intervention framework identifies context-specific challenges, including intermittency, financing risk, institutional capacity, infrastructure gaps, and climatic and geopolitical exposure, alongside viable technological and policy responses. Comparative case studies from India, Sub-Saharan Africa, Southeast Asia, and Latin America explain divergent outcomes of similar technologies across institutional and market contexts, and development pathways globally.
This study has been undertaken in the burgeoning intersection of financial technology (Fintech) and Environmental, Social, and Governance (ESG) paradigms, a domain that serves the purpose of redefining capital allocation in the 21st century. The research investigates the "Digital-Sustainability Convergence" theory, which posits that digital innovations serve the purpose of democratizing green finance and enhancing transparency. However, a critical review of the literature reveals a phenomenon termed the "Green Mirage," where the digital representation of sustainability obscures a lack of tangible ecological impact. Utilizing a bibliometric analysis based on VOS viewer logic, this paper examines a dataset of academic literature from 2015 to 2025. The findings indicate that while publication volume is on a rise, particularly in China and the United Kingdom, the intellectual structure is fragmented. The analysis identifies a significant gap between technological implementation—such as blockchain and artificial intelligence (AI)—and genuine sustainability outcomes. It is important to note that concepts like "token washing" and "digital greenwashing" have emerged as pivotal retention factors for critical scholarship, suggesting that the sector faces an important challenge in aligning "proof of stake" with "proof of impact." The study concludes that while Fintech serves the purpose of mobilizing retail capital, with 81.5% of investors considering ESG factors, the prevalence of managerial myopia and data asymmetry poses a challenge for the integrity of the ecosystem. Thus, it is important that regulators and practitioners move beyond symbolic compliance to address the structural disconnects identified.
Clean energy transitions increasingly depend on the ability of small and medium-sized enterprises (SMEs) to access capital on terms that allow them to compete with large, vertically integrated incumbents. At a macro level, clean energy finance has evolved from subsidy-heavy public funding toward blended models combining private capital, risk-sharing instruments, and performance-based incentives. These structures aim to lower the cost of capital, correct market failures, and accelerate diffusion of renewable technologies across national energy systems. However, capital markets continue to privilege scale, balance-sheet strength, and long operating histories, creating persistent financing asymmetries that disadvantage smaller firms. This study situates clean energy financing within broader frameworks of financial inclusion, industrial competitiveness, and energy market liberalization. It examines how innovative financing architectures such as blended finance vehicles, green credit guarantees, pay-as-you-save schemes, revenue-backed project finance, and aggregated procurement platforms reshape risk allocation and margin dynamics. By reducing upfront capital requirements, smoothing cash flows, and improving bankability, these models enable SMEs to price energy products and services competitively while maintaining sustainable margins. Narrowing to the national context, the analysis highlights how policy design, regulatory certainty, and domestic financial infrastructure determine whether financing innovations translate into real competitive parity. Case-informed synthesis shows that when concessional capital is strategically deployed to crowd in commercial lenders, small enterprises can achieve cost structures comparable to larger incumbents, expand market share, and drive decentralized energy adoption. The findings underscore that clean energy competition is not solely a technological challenge, but a financial architecture problem, where well-designed financing models are decisive in leveling margins and unlocking inclusive energy-led growth at national scale under diverse regulatory and macroeconomic conditions globally relevant insights.
Water scarcity represents one of the most critical challenges confronting arid and semi-arid regions, particularly under the intensifying pressures of climate change. In desert environments, limited freshwater availability constrains public health, food security, and socio-economic development, while traditional funding mechanisms often prove inadequate for scaling sustainable water infrastructure. This study examines the potential of decentralized finance (DeFi) bonds, combined with desalination and atmospheric water harvesting technologies, as an innovative financing and delivery model for enhancing water resilience in desert regions. The research adopts a qualitative, exploratory case study approach, drawing on a structured review of academic and policy literature, documented blockchain-based water initiatives, and a conceptual financial analysis of DeFi bond mechanisms. The OikosNomos.world (ONW) initiative is examined as the primary case study, with attention to its proposed deployment of solar-powered desalination systems, boreholes, and atmospheric water harvesting infrastructure. The analysis indicates that existing desalination and water harvesting technologies are technically viable in arid environments, particularly when integrated with renewable energy systems. Furthermore, blockchain-enabled DeFi bonds demonstrate potential to enhance transparency, automate fund allocation through smart contracts, and attract global impact-oriented capital beyond traditional grant-based models. However, the study also identifies key challenges, including regulatory uncertainty, governance complexity, infrastructure constraints, and the need for sustained community engagement. The paper concludes that while DeFi-financed water infrastructure is not a standalone solution to water scarcity, its strategic integration with proven water technologies and inclusive governance models offers a scalable and transparent pathway for strengthening desert resilience. Future empirical research and pilot deployments are required to validate financial performance, adoption outcomes, and long-term socio-environmental impacts.
Fintech enterprises operate at the intersection of rapid technological innovation and stringent regulatory oversight, creating a complex organizational challenge. This review systematically examines organizational restructuring strategies that enable fintech firms to balance innovation and compliance. Drawing on the concepts of ambidexterity and contingency theory, the paper analyzes functional, divisional, matrix, and networked structures, highlighting their respective advantages and limitations for fostering innovation and ensuring regulatory adherence. Cross-functional teams, hybrid models, and embedded compliance practices emerge as key enablers for achieving dual objectives. The synthesis provides practical guidance for managers seeking to design adaptable organizational architectures, while also offering theoretical contributions to the literature on innovation management and regulatory alignment. Future research directions include cross-country comparisons, longitudinal studies, and exploration of emerging fintech models such as decentralized finance platforms.
The article substantiates the critical inadequacy of traditional static risk assessment methods (specifically, VaR and standard deviation) for analyzing the effectiveness of integrating Decentralized Finance (DeFi) assets into investment portfolios. It is proven that the returns of DeFi assets are characterized by a non-normal distribution with pronounced «fat tails», which creates a significant risk of underestimating catastrophic losses. The purpose of the study is to develop and theoretically substantiate a methodology for evaluating the effectiveness of DeFi platforms in diversifying investment portfolios. The methodological gap between the requirements of the volatile DeFi market and the limitations of classical financial models is investigated, particularly in the areas of controlling Tail Risk and the dynamic nature of correlational dependence, which critically increases during market shocks (the «correlation-to-one» effect). A four-stage methodology is proposed, which includes the theoretical integration of Conditional Value-at-Risk (CVaR) as a basic coherent measure of extreme risk and a developed algorithm for proactive diversification management based on the DCC-GARCH model. This made it possible to calculate the Optimal Dynamic Hedging Weight, necessary for the daily adjustment of the portfolio structure to prevent the loss of the diversification effect. The comprehensive methodology developed provides a complete cycle of proactive risk management and offers a clear algorithm for making decisions about the structure of an investment portfolio. The scientific and practical significance of the research lies in formulating methodological recommendations and evaluation criteria that ensure a transition from static analysis to proactive risk management in investment activities. The developed methodology provides a toolkit for making informed decisions regarding the optimal share of DeFi assets in a portfolio, combining return maximization with extreme risk minimization. The application of this methodology is beneficial for investors, financial analysts, quantitative strategists, and hedge fund managers working with high-risk and innovative asset classes that require advanced risk control tools.
The article substantiates the critical inadequacy of traditional static risk assessment methods (specifically, VaR and standard deviation) for analyzing the effectiveness of integrating Decentralized Finance (DeFi) assets into investment portfolios. It is proven that the returns of DeFi assets are characterized by a non-normal distribution with pronounced «fat tails», which creates a significant risk of underestimating catastrophic losses. The purpose of the study is to develop and theoretically substantiate a methodology for evaluating the effectiveness of DeFi platforms in diversifying investment portfolios. The methodological gap between the requirements of the volatile DeFi market and the limitations of classical financial models is investigated, particularly in the areas of controlling Tail Risk and the dynamic nature of correlational dependence, which critically increases during market shocks (the «correlation-to-one» effect). A four-stage methodology is proposed, which includes the theoretical integration of Conditional Value-at-Risk (CVaR) as a basic coherent measure of extreme risk and a developed algorithm for proactive diversification management based on the DCC-GARCH model. This made it possible to calculate the Optimal Dynamic Hedging Weight, necessary for the daily adjustment of the portfolio structure to prevent the loss of the diversification effect. The comprehensive methodology developed provides a complete cycle of proactive risk management and offers a clear algorithm for making decisions about the structure of an investment portfolio. The scientific and practical significance of the research lies in formulating methodological recommendations and evaluation criteria that ensure a transition from static analysis to proactive risk management in investment activities. The developed methodology provides a toolkit for making informed decisions regarding the optimal share of DeFi assets in a portfolio, combining return maximization with extreme risk minimization. The application of this methodology is beneficial for investors, financial analysts, quantitative strategists, and hedge fund managers working with high-risk and innovative asset classes that require advanced risk control tools.
The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies.
The association between cryptocurrency and sustainability is a complex and growing topic. Given that such linkage requires a continuous investigation, this empirical research, unlike the existing literature, explores if the volatility dynamics of digital assets are driven by the changes in sustainability uncertainty. In doing so, we use a recently developed ESG-based sustainability uncertainty index (ESGUI) and examine its effect on the volatility dynamics of Bitcoin and Ethereum ETFs. Employing the mixed data sampling (MIDAS) approach shows that ESGUI exerts a negative effect on the realized volatility of cryptocurrency markets. One possible explanation for this linkage is that as sustainability-related uncertainty rises, investors tend to adopt sustainability practices and initiatives. This shift towards sustainable practices can result in more consistent and foreseeable long-term economic conditions, thereby reducing the volatility of financial markets including the digital asset class. Our analysis offers key implications to cryptocurrency investors.
Rosa Galvão, Domingos Santos Martinho, Nuno Nogueira, Rui Dias
The main objective of this study is to compare the efficiency levels, in their weak form, between sustainable cryptocurrencies such as Avalanche (AVAX), Cardano (ADA), Solana (SOL), Toncoin (TON) and Ethereum (ETH) (after 'The Merge'), which use efficient mechanisms such as proof-of-stake (PoS), and Binance Coin (BNB), Litecoin (LTC), Monero (XMR), Ripple (XRP), and Bitcoin (BTC) classified as unsustainable cryptocurrencies due to their excessive energy consumption based on proof-of-work (PoW). The analysed period was from 1 January 2023 to 10 December 2024. The Detrended Fluctuation Analysis (DFA) slopes reveal a significant impact of the 2023 Conflict on cryptocurrency dynamics, with distinct effects per asset. Sustainable cryptocurrencies (AVAX, ADA, SOL) demonstrated greater resilience, maintaining persistence with a brief reduction in long memory, reflecting their relative stability and attractiveness in uncertainty scenarios. In contrast, non-sustainable cryptocurrencies (LTC, XMR) transitioned from persistence to anti-persistence, indicating greater instability and speculation, associated with lower investor confidence. Assets such as TON (white noise) and XRP (consistent persistence) were less affected, suggesting intrinsic characteristics that confer resilience. Distinguishing between sustainability and other market factors is crucial to understand behaviours and build resilient portfolios, providing valuable insights for investors and researchers.
Jerusa Alberton, Marcelo Cabús Klötzle, Marcelo Guedes Pecly, Carlos de Lamare Bastian-Pinto
This paper examines whether the release of ESG ratings for blockchains and tokens influences investor behavior in cryptocurrency markets. In October 2021, Green Crypto Research (GCR) published the first systematic ESG ratings for digital assets, addressing growing institutional demand for sustainability information. Building on Ammann et al. (2018), who documented increased flows into high-ESG mutual funds after Morningstar’s ESG rating release, we use an Event Study methodology to analyze abnormal trading volumes before and after the GCR announcement. We find no significant increase in trading activity for highly rated blockchains or tokens, providing no evidence that investors reallocated funds toward higher-rated cryptocurrencies. These findings are relevant for investors evaluating ESG integration in digital assets, for policymakers considering sustainability disclosure in crypto markets, and for researchers studying the intersection between ESG and emerging financial technologies.
Climate resilience activities in vulnerable regions often confront problems relating to the financial opacity, corruption, and the unreliable verification of outcomes. The paper proposes a blockchain system to strengthen transparency and traceability in climate adaptation funding, particularly for green infrastructure. Using smart contracts and Internet of Things (IoT)- verified geospatial data, the system assures the real-time monitoring of performance metrics and releases funds securely upon assessed performance. A hybrid PoW/PoS consensus mechanism is suggested to enable scalability while remaining energy friendly toward resource-constrained geographies. Pilot implementation among small countries is scheduled to assess the adoption and governance. The goal is for this model to restore public faith and speed climate resilience activities in places where the traditional setup fails.