Do Fintech, Tokenization, and Blockchain Capabilities Matter for Sustainable Investment? Evidence from a Cross-Country Analysis

Do Fintech, Tokenization, and Blockchain Capabilities Matter for Sustainable Investment? 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Evidence from a Cross-Country Analysis Rashid Khalil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9103370/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates how blockchain-related capabilities; digital infrastructure, regulatory readiness, fintech adoption, and tokenization; affect sustainable investment across countries. Though blockchain and fintech are increasingly positioned as enablers of sustainability, empirical cross-country insights remain limited. Using a balanced panel of 27 developed and emerging economies from 2017 to 2024, this study applies fixed-effects panel regression and cluster analysis to examine the association between digital financial innovation and sustainable investment size. The results reveal that digital infrastructure and fintech adoption are consistently and positively associated with sustainable investment, underscoring the role of technological readiness and innovation ecosystems in scaling ESG finance. Conversely, regulatory readiness and tokenization adoption show statistically significant but negative associations, reflecting transitional frictions and institutional adjustment costs during early adoption phases. Cluster analysis identifies three distinct country groups; leaders, followers, and laggards; based on blockchain and tokenization readiness, revealing heterogeneous development patterns. These findings suggest that blockchain’s role in sustainable finance is contingent on broader institutional and digital contexts. The study contributes to the sustainable finance literature by integrating tokenization into ESG investment analysis and highlighting the structural conditions necessary for blockchain-enabled finance to succeed. Policy implications emphasize the need for supportive digital infrastructure, adaptive regulation, and integrated fintech strategies to realize the sustainability potential of emerging financial technologies. Blockchain Fintech Tokenization Innovation Sustainable investment Digital infrastructure ESG finance Figures Figure 1 Figure 2 Figure 3 1. Introduction Sustainable investment has become a cornerstone of modern financial ecosystems, driven by heightened awareness of climate risks, social inequality, and the pursuit of long-term economic resilience. Institutional investors, governments, and capital markets are increasingly embedding environmental, social, and governance (ESG) criteria into financial decision-making frameworks [ 1 , 2 ]. Despite this growing emphasis, achieving scale in sustainable finance remains challenging due to persisting issues of transparency, trust, and cross-border capital mobilization [ 3 ]. Concurrently, blockchain technology is gaining traction as a developing infrastructure capable of reshaping financial intermediation. With its decentralized architecture, immutable ledgers, and programmable contracts, blockchain holds significant promise for enhancing ESG verification, fund traceability, and impact reporting [ 4 , 5 ]. These attributes are especially valuable in sustainable finance, where credibility of ESG claims and environmental impact measurement are central to investor confidence [ 6 ]. Recent academic interest has increasingly focused on the convergence of blockchain and sustainable investment. Studies have highlighted blockchain’s potential to facilitate green bond issuance [ 7 ], optimize carbon markets [ 8 ], and improve ESG disclosure through enhanced data verifiability [ 9 ]. However, the extant literature remains fragmented and is often dominated by case studies or conceptual explorations with limited empirical generalizability. Cross-country empirical evidence on how blockchain and related digital capabilities influence sustainable investment outcomes remains notably scarce. It is critical to recognize that blockchain adoption is not a standalone phenomenon. Its success in driving sustainable finance hinges on several complementary capabilities. Robust digital infrastructure enables scalable blockchain solutions and real-time data processing. Regulatory readiness fosters innovation while ensuring stability and investor protection. Fintech adoption plays an intermediary role, translating technological potential into accessible financial products. Tokenization, as a novel financial mechanism, enables fractionalization and liquidity of sustainable assets, enhancing market participation and monitoring [ 10 , 11 ]. These interdependencies suggest that the impact of blockchain capabilities on sustainable investment is likely to be heterogeneous and context dependent. In this light, the present study empirically investigates the role of blockchain-related capabilities in shaping sustainable investment outcomes across 27 countries from 2017 to 2024. The study focuses on four key enablers: digital infrastructure, regulatory readiness, fintech adoption, and tokenization, while controlling macro-institutional and market characteristics such as financial market development, institutional quality, economic development, and carbon pricing mechanisms. A fixed-effects panel regression model is employed to address unobserved heterogeneity and temporal trends, complemented by cluster analysis to reveal country-specific development paths. 2. Literature and Hypotheses Development 2.1. Blockchain Technology and Sustainable Finance Blockchain technology has increasingly been recognized as a foundational infrastructure capable of reshaping financial markets through the core features of decentralization, immutability, and transparency. By facilitating secure peer-to-peer transactions, tamper-proof record-keeping, and real-time settlement, blockchain offers mechanisms to mitigate information asymmetries and enhance trust among market participants [12,13. These characteristics are particularly relevant in the context of sustainable finance, where integrity, traceability, and accountability of environmental, social, and governance (ESG) claims are critical for mitigating greenwashing risks and fostering investor confidence. In the sustainable finance domain, blockchain enables real-time verification of ESG data, improves supply-chain transparency, and supports the tokenization of sustainable assets such as green bonds and carbon credits [ 14 , 15 ]. Through smart contracts, it can automate impact-linked disbursements and compliance enforcement, thereby increasing the efficiency and effectiveness of green financial instruments [ 16 ]. These capabilities contribute to a more trustworthy financial ecosystem and promote the alignment of capital flows with long-term sustainability objectives. However, the deployment of blockchain in sustainable finance is not an inherent development unless supported by complementary institutional and technological conditions. Digital infrastructure is needed to host blockchain platforms and facilitate real-time data flows. Regulatory frameworks must balance innovation and oversight to reduce legal uncertainties and prevent market fragmentation [ 17 ]. Additionally, the maturity of fintech ecosystems determines whether blockchain functionalities are accessible to end-users through digital platforms, while tokenization enables the fractionalization and tradability of sustainable assets, thereby enhancing liquidity and participation [ 18 ]. Current empirical and theoretical research underscores the potential of blockchain to address structural inefficiencies in ESG investing but also cautions against over-reliance on technology without systemic readiness. For instance, studies have shown that blockchain's effectiveness varies depending on governance quality, digital adoption levels, and sector-specific applications [ 19 , 20 ]. As such, blockchain should be viewed not as a panacea but as an enabler whose contribution to sustainable investment is mediated by broader institutional and digital ecosystems. Consistent with prior reviews highlighting blockchain’s innovative role in sustainable finance ecosystems [ 21 , 22 ], the current study positions blockchain as a foundational enabler of ESG innovation. 2.2. Digital Infrastructure and Sustainable Investment Digital infrastructure serves as a fundamental enabler for deploying blockchain-based financial ecosystems and fostering innovation in sustainable finance. It encompasses broadband internet penetration, mobile and digital payment systems, cloud computing capacity, and access to information and communication technologies (ICTs). These elements collectively reduce transaction costs, improve data availability, and enable real-time financial transactions, thus creating a conducive environment for sustainable digital finance [ 3 , 23 ]. In countries with well-developed digital infrastructure, financial institutions and investors can leverage integrated platforms for ESG data analytics, digital green bond issuance, and decentralized finance (DeFi) solutions for environmental projects. For example, mobile banking and e-wallets enhance accessibility for retail investors seeking ESG-aligned products, while APIs and open data protocols facilitate integration between financial service providers and ESG verification platforms [ 24 ] (Bazarbash & Beaton, 2020). Such connectivity accelerates capital flows toward sustainable assets, particularly in emerging markets where traditional financial intermediation is constrained [ 25 ] (Demirgüç-Kunt et al., 2020). From a theoretical standpoint, improved digital infrastructure reduces frictions in capital allocation and enhances market completeness, allowing investors to better integrate sustainability preferences into portfolio decisions [ 26 ]. (Beck, Lin, & Qian, 2022). Furthermore, digital channels improve investor reach and reduce informational asymmetries, enhancing market discipline and demand for authentic ESG performance. As reported by Chiu and Wong [ 27 ] (2021), digitally enabled financial systems also exhibit higher responsiveness to climate risk disclosures, suggesting a reinforcing loop between infrastructure quality and sustainable investment adoption. Several empirical studies have substantiated the positive relationship between digitalization and sustainability outcomes. For instance, countries with higher ICT readiness exhibit greater sustainable development financing, whereas digital infrastructure significantly moderates the effect of financial development on green innovation [ 27 , 28 ]. These findings support the notion that digital ecosystems act as multipliers for sustainability-driven capital flows and investment innovation. Similar findings have been observed in the e-governance domain, where digital infrastructure enhances institutional performance and trust in digital systems, reinforcing its foundational role in technology-enabled transformation [ 29 ]. Accordingly, this study posits the following hypothesis: H1: Digital infrastructure is significantly associated with sustainable investment size. 2.3. Blockchain Regulatory Readiness and Sustainable Investment The maturity of regulatory frameworks is a pivotal determinant of how blockchain innovations integrate with sustainable finance ecosystems. Regulatory readiness refers to the presence of coherent, adaptive, and enforceable legal structures that govern the deployment of blockchain technologies in financial markets. When effectively designed, such frameworks reduce legal uncertainty, enhance investor protection, and mitigate systemic risks factors essential for facilitating trust in emerging technologies and fostering innovation in green finance [ 30 , 31 ]. In the context of sustainable investment, regulatory readiness can support the issuance of blockchain-based green bonds, ensure compliance in carbon trading platforms, and establish standards for ESG tokenization and disclosure. For instance, sandbox regimes and pilot frameworks introduced in countries like Singapore, Switzerland, and the UK have enabled blockchain applications to develop under supervised conditions, stimulating the creation of sustainable financial products [ 32 , 33 ]. However, the benefits of regulatory clarity may come with short-term trade-offs. Overly prescriptive or prematurely rigid regulations can limit experimentation, raise compliance burdens, and deter early-stage blockchain startups from entering sustainable finance markets [ 34 ]. In many jurisdictions, the evolving nature of digital asset laws; combined with gaps in ESG taxonomy, creates regulatory ambiguity, which slows down market adoption and cross-border collaboration [ 27 ]. Moreover, transitional frictions may arise when regulators attempt to balance innovation with financial stability. As new tokenization models emerge for ESG assets, questions around custody, taxation, investor rights, and jurisdictional accountability complicate the regulatory landscape [ 35 ]. These institutional adjustment costs can suppress the short-run scalability of blockchain-based sustainable finance solutions, even when long-run benefits are expected. Empirical studies reveal mixed results. For example, Anagnostopoulos [ 36 ] finds that excessive regulatory intervention can delay blockchain adoption in ESG markets, whereas moderate and flexible frameworks catalyze innovation without compromising oversight. Similarly, de la Rosa & Stankovic [ 37 ] argue that sustainable finance regulation often lags behind technological capabilities, creating a disconnect between ESG goals and digital finance implementation. Considering this duality, the relationship between blockchain regulatory readiness and sustainable investment is hypothesized to be significant but potentially negative in the short term due to transitional costs and regulatory inertia. H2: Regulatory readiness is significantly associated with sustainable investment size. 2.4. Fintech Adoption and Sustainable Investment Fintech adoption represents the maturity and dynamism of a country's digital financial ecosystem, encompassing innovations in digital payments, peer-to-peer (P2P) lending, robo-advisory services, crowdfunding platforms, and decentralized financial applications (DeFi). These tools have revolutionized the delivery of financial services by reducing reliance on traditional intermediaries, expanding financial access, and lowering participation thresholds; conditions vital for the democratization of sustainable investment [ 38 , 39 ]. In the context of sustainable finance, fintech acts as a gateway between blockchain infrastructure and end users, enabling seamless interaction with green investment products. Crowdfunding platforms, for example, have been instrumental in financing clean energy and community-led environmental projects [ 40 ]. Similarly, algorithmic investment platforms that integrate ESG screening criteria allow retail and institutional investors to allocate capital in alignment with sustainability goals [ 41 ]. Fintech solutions can also enable more dynamic pricing and risk management in carbon trading, impact investing, and ESG-linked lending markets [ 36 ]. Theoretically, fintech supports the transmission of blockchain capabilities into practical financial services, reinforcing the infrastructure-application-user adoption chain. By reducing information asymmetry, improving transaction efficiency, and lowering search and verification costs, fintech tools contribute to allocative efficiency in sustainable capital markets [ 42 ]. Moreover, by expanding access to financial services among underbanked populations and SMEs, fintech fosters inclusive green finance, thereby aligning with global sustainability and SDG financing agendas [ 43 ]. Recent empirical studies confirm the catalytic role of fintech in sustainable investment. For instance, Li and Yu [ 44 ] find that countries with higher fintech penetration exhibit significantly greater issuance of green bonds and ESG funds. Similarly, Zhang et al. [ 45 ] argued that fintech development positively moderates the relationship between institutional quality and sustainable investment flows in emerging markets. These findings suggest that fintech is not merely a channel but a critical enabler of sustainable finance innovation. Accordingly, the following hypothesis is proposed: H3: Fintech adoption is significantly associated with sustainable investment size. 2.5. Tokenization Adoption and Sustainable Investment Tokenization involves converting rights to a real-world asset into a digital token that is issued, transferred, and stored on a blockchain platform. In the context of sustainable finance, tokenization presents a novel mechanism for enabling fractional ownership of green infrastructure projects, carbon credits, and ESG-compliant securities, thereby enhancing liquidity, investor accessibility, and transparency [ 46 – 47 ]. By lowering the minimum capital required to participate, tokenization can significantly democratize access to sustainable investment opportunities and widen the investor base, particularly among retail participants and small institutional investors. Moreover, tokenization enhances traceability and real-time monitoring of environmental outcomes through smart contracts and immutable ledgers, facilitating a more accountable system of sustainability-linked finance [ 48 ]. It also allows for more efficient impact reporting, ESG claim verification, and secondary market trading of illiquid green assets, such as solar farms or reforestation credits [ 49 ]. These features align well with broader goals of green financial innovation and contribute to enhanced market efficiency and credibility. However, despite its theoretical promise, tokenization remains in an early phase of adoption across most jurisdictions. Real-world implementation faces a range of frictions: limited legal and regulatory clarity around tokenized asset ownership, lack of interoperability across platforms, and challenges related to custody, taxation, and investor protection [ 50 ]. Many tokenized ESG assets are currently limited to pilot projects or sandbox regimes, particularly in the EU, Asia-Pacific, and the Middle East, where institutional learning curves and fragmented digital infrastructure pose hurdles to scalability [ 51 – 52 ]. Furthermore, while tokenization can improve liquidity under ideal conditions, its effectiveness depends on active participation, secondary market depth, and confidence in valuation mechanisms, elements not yet fully developed in most green asset classes. Recent empirical studies indicate that tokenization has a nonlinear and stage-dependent relationship with sustainable investment outcomes: early-stage adoption may initially disrupt traditional capital flows due to market uncertainty, before generating positive outcomes at later stages when infrastructure, regulation, and market trust align [ 53 – 54 ]. Given this duality of potential and constraint, the following hypothesis is proposed: H4: Tokenization adoption is significantly associated with sustainable investment size. 2.6. Conceptual Framework Building upon the preceding theoretical foundations, this study proposes a conceptual framework that links blockchain-related capabilities with sustainable investment outcomes. The model integrates four key independent variables; digital infrastructure, regulatory readiness, fintech adoption, and tokenization adoption; as determinants of sustainable investment size. These factors reflect both technological readiness and institutional maturity, capturing the multifaceted environment necessary for blockchain-enabled financial innovation to thrive. From this perspective, blockchain technology serves as an enabling infrastructure, whose efficacy in advancing sustainable finance depends on the interplay among digital connectivity, regulatory governance, financial service innovation, and asset digitization. While each of these dimensions has independent significance, their joint contribution shapes a country’s capacity to support scalable and credible sustainable investment solutions. Note Figure 1 visually summarizes the hypothesized relationships. Arrows represent the direct associations between each capability and the dependent variable, sustainable investment size, as posited in hypotheses H1-H4. As shown in Fig. 1 , the proposed framework assumes that countries with advanced digital infrastructure and robust fintech ecosystems are better positioned to translate blockchain capabilities into practical, user-facing sustainable finance products. Besides, the effect of regulatory readiness is likely conditional, potentially enabling long-term institutional support while introducing transitional frictions in early phases of adoption. Moreover, Tokenization may expand access and liquidity, yet its short-term effects could be constrained by legal, technical, and market immaturity. This framework guides the empirical strategy employed in the study. By testing the hypotheses derived (H1-H4), the analysis explores how differences in blockchain-related capabilities influence sustainable investment outcomes across varying developmental and regulatory contexts. 3. Data and Methodology 3.1. Sample and Data Sources This study constructs a balanced panel dataset of 27 countries across five economic regions; Developed Economies, European Union, Asia-Pacific, Gulf Cooperation Council (GCC), and Emerging Economies; covering the period 2017 to 2024. The sample includes countries with demonstrable activity in blockchain development, fintech diffusion, and sustainable finance initiatives, and was selected based on the availability of consistent cross-country data across key indicators. The inclusion of GCC countries reflects both their growing interest in blockchain initiatives and the broader macroeconomic policy environment influencing investment capacity [ 55 ]. The temporal range captures the rapid evolution of blockchain-related capabilities following 2016 and aligns with the scaling up of sustainable investment practices and tokenization pilots in the post-COP21 regulatory environment. Table 1 presents the regional and economic composition of the sample, while Table 2 summarizes variable definitions and data sources. Table 1 Sample Composition by Country, Region, and Economy Category Region Country Economy Category Income Group Carbon Market Presence Developed Economies Switzerland Developed High Income Yes United States Developed High Income Yes United Kingdom Developed High Income Yes Germany Developed High Income Yes Canada Developed High Income Yes Australia Developed High Income Yes European Union Netherlands Developed High Income Yes France Developed High Income Yes Spain Developed High Income Yes Italy Developed High Income Yes Sweden Developed High Income Yes Denmark Developed High Income Yes Asia-Pacific Singapore Developed High Income Yes Japan Developed High Income No South Korea Developed High Income Yes China Emerging Upper-Middle Income Yes Hong Kong Developed High Income Yes Malaysia Emerging Upper-Middle Income No GCC United Arab Emirates Emerging High Income Yes Saudi Arabia Emerging High Income No Qatar Emerging High Income No Bahrain Emerging High Income Yes Emerging Economies India Emerging Lower-Middle Income No Brazil Emerging Upper-Middle Income Yes South Africa Emerging Upper-Middle Income Yes Indonesia Emerging Upper-Middle Income No Mexico Emerging Upper-Middle Income Yes Source: Author’s work All country-level indicators are derived from publicly accessible and internationally recognized data repositories. Principal sources include the World Bank’s Digital Adoption Index and World Development Indicators, the IMF Financial Access Survey, the World Governance Indicators (WGI), the Global Sustainable Investment Review (GSIR), and the OECD and BIS policy databases on fintech and tokenization. Additional data on regulatory readiness and tokenization activity were sourced from national financial authorities and regulatory disclosures. Data across countries were harmonized using standardized measurement techniques and normalized where necessary to ensure comparability across jurisdictions and time periods. Table 2 Variable Classification, Definitions, Measurement, and Data Sources Variable Role Variable Symbol Measurement Expected Sign Data Source Dependent Variable Sustainable Investment Size SIS Sustainable investment assets as a percentage of GDP, incorporating ESG bond issuance and carbon market participation - Global Sustainable Investment Review; World Bank Independent Variable Digital Infrastructure DIG Composite index capturing internet penetration, digital payments usage, and ICT access (0–100 scale) + World Bank Digital Adoption Index; ITU Regulatory Readiness REG Index reflecting the presence and maturity of blockchain and fintech regulatory frameworks (0–100 scale) + World Bank; OECD fintech and blockchain policy reports Fintech Adoption FIN Measure of fintech ecosystem depth and user adoption, including digital financial services usage (0–100 scale) + World Bank; IMF Financial Access Survey Tokenization Adoption Index TOK Ordinal index capturing the extent of real-world asset tokenization (0 = none; 1 = pilot; 2 = active; 3 = institutionalized) + BIS; national regulatory authorities and public disclosures Control Variable Financial Market Development FMD Domestic credit to private sector as a percentage of GDP + World Bank World Development Indicators Institutional Quality INST Regulatory quality index ranging from − 2.5 (weak) to + 2.5 (strong) + World Governance Indicators (WGI) Economic Development GDPpc Natural logarithm of GDP per capita (constant USD) + World Bank World Development Indicators Dummy Variable Carbon Market Presence CARB Binary indicator (1 = emissions trading system or carbon pricing mechanism in place; 0 = otherwise) + World Bank Carbon Pricing Dashboard Source: Author’s work 3.2. Variable Measurement 3.2.1. Dependent Variable The dependent variable, Sustainable Investment Size (SIS), serves as the central measure of a country’s sustainable finance depth. It is computed as the ratio of sustainable investment assets to gross domestic product (GDP), integrating ESG-aligned fund flows, green and social bond issuance, and verified carbon market participation. This indicator captures the breadth and systemic importance of sustainable finance, rather than focusing solely on niche segments or isolated market instruments. The use of this macro-financial perspective aligns with prior studies examining ESG investment scale in cross-country contexts [ 38 ]. 3.2.2. Independent Variables To examine the influence of blockchain-related capabilities on sustainable finance development, the empirical model incorporates four key independent variables. Digital Infrastructure (DIG) represents a country’s technological capacity to support blockchain-based financial platforms. It is operationalized as a composite index covering internet penetration, digital payments usage, and access to information and communication technology, with values scaled from 0 to 100. Regulatory Readiness (REG) captures the maturity and scope of blockchain and fintech regulation, based on the presence of licensing frameworks, supervisory oversight, and innovation sandboxes. This index also follows a normalized 0-100 scale to reflect variation in legal and institutional development across countries. The Fintech Adoption (FIN) variable reflects the breadth and depth of digital financial services within each country. It encompasses indicators of mobile money usage, alternative lending volumes, and the adoption of automated investment tools. Higher values reflect stronger integration of financial innovation into mainstream capital allocation mechanisms. Tokenization Adoption (TOK), a central innovation proxy in this study, is measured as an ordinal variable ranging from 0 (no observable tokenization) to 3 (institutionalized adoption), capturing the progressive evolution of tokenized real asset markets across countries and years. 3.2.3. Control Variables To isolate the effects of these technological and regulatory dimensions, the model includes several structural control variables. Financial Market Development (FMD), expressed as domestic credit to the private sector relative to GDP, accounts for differences in financial system depth. Institutional Quality (INST) is proxied by the regulatory quality sub-index from the World Governance Indicators, representing the robustness of governance systems. Economic Development is captured by the natural logarithm of GDP per capita in constant USD, providing a scale-sensitive measure of national income. Additionally, Carbon Market Presence (CARB) is included as a binary control (1 = yes, 0 = no), accounting for the influence of national carbon pricing frameworks on sustainable investment flows. These variables jointly form the basis of the panel regression analysis, allowing for an empirical test of the hypotheses developed in Section 2 and facilitating a multi-dimensional examination of blockchain readiness and its impact on sustainable investment. The operationalization strategy ensures compatibility with previous empirical models in sustainable finance and digital policy research [ 42 , 56 , 30 ]. 3.3. Empirical Model To empirically investigate the influence of blockchain-related capabilities on sustainable investment outcomes, this study employs a fixed-effects panel regression model that accounts for both cross-country heterogeneity and time-specific global factors. The model specification is designed to capture the structural and institutional factors that may condition the relationship between blockchain ecosystem maturity and sustainable finance development. The core econometric model is expressed as follows: SIS it ​=​ α + β 1 ​DIG it​ + β 2​ REG it ​ + β 3 ​FIN it​ + β 4 ​TOK it ​ + β 5​ FMD it​ + β 6 ​INST it ​ + β 7 ​ln(GDPpc it ​ ) + β 8 ​CARB it ​+ µ i ​ + λ t ​ + ε it​​ where i , indexes coun t ries and t indexes years. The dependent variable SIS it​ represents the sustainable investment size in country i at time t , measured as the ratio of ESG-aligned financial assets to GDP. The key explanatory variables include: DIG it​ for digital infrastructure, REG it ​ for regulatory readiness, FIN it ​ for fintech adoption, and TOK it ​ for tokenization adoption level. Control variables include financial market development (FMD it )​, institutional quality (INST it ​), economic development captured by the natural logarithm of GDP per capita [In(GDPpc it ​)], and carbon market presence (CARB it )​. The model includes country fixed effects ( µ i )​ to control for time-invariant national characteristics, such as legal traditions, political culture, and structural economic conditions, that may influence both blockchain deployment and sustainable investment independently. It also incorporates year fixed effects ( λ t )​ to absorb global shocks, common macroeconomic trends, or international regulatory shifts that could affect all countries simultaneously, such as the Paris Agreement compliance push or COVID-19-related policy interventions. The inclusion of these fixed effects ensures that the estimated coefficients reflect within-country variation over time, thereby mitigating bias from omitted variables that are constant across countries or common across time periods. The error term ϵ it captures idiosyncratic shocks unaccounted for by the included covariates. This fixed-effects framework has been widely adopted in cross-country policy and finance studies where both structural and temporal factors may shape institutional change and market evolution [ 57 , 31 ]. To ensure robust inference, standard errors are clustered at the country level to account for serial correlation in the error structure. 3.4. Estimation Strategy To estimate the relationship between blockchain-related capabilities and sustainable investment size, this study applies an ordinary least squares (OLS) estimation with country and year fixed effects. This specification is well-suited for panel data where unobserved time-invariant country characteristics, such as legal infrastructure, governance traditions, or macroeconomic fundamentals, may correlate with both the explanatory variables and the dependent variable. The inclusion of year fixed effects accounts for global shocks and macro-trends, such as international climate policy changes or major fintech adoption waves, that could uniformly influence all countries within the panel. Standard errors are clustered at the country level to address issues of heteroskedasticity and serial correlation, a common practice in empirical studies using macro panel data. This approach improves inference robustness by accounting for within-country error dependence across time periods. Before estimating the model, descriptive statistics and pairwise correlation analysis are conducted to assess the distributional properties of the variables and to evaluate potential multicollinearity. While moderate to high correlations are observed among some blockchain-related indicators, these associations are theoretically expected due to the intertwined nature of digital infrastructure, regulatory maturity, and fintech development. The fixed-effects structure, along with robustness checks introduced in later sections, helps address concerns regarding omitted variable bias and overlapping variance. In addition to the regression analysis, the study incorporates a cluster analysis to group countries based on their levels of blockchain and tokenization readiness. This unsupervised learning technique complements the econometric model by enabling a structural typology of countries, highlighting differences in the technological and institutional pathways through which blockchain influences sustainable finance. The classification enables comparative insights into heterogeneity in adoption patterns across developed, emerging, and transitional economies. All statistical estimations and data visualizations are implemented using Python (version 3.x), utilizing established scientific libraries including pandas, NumPy, statsmodels, and scikit-learn. This approach ensures transparency and reproducibility of empirical results, aligning with open science practices increasingly adopted in the field of sustainable finance. 4. Data Analysis and Findings 4.1. Descriptive Analysis Table 3 presents the descriptive statistics for all variables used in the analysis over the 2017–2024 period. The dependent variable, Sustainable Investment Size (SIS), exhibits a mean value of 2.81% of GDP, with a minimum of 2.00% and a maximum of 3.75%, reflecting substantial variation in the scale of sustainable finance across countries. This degree of dispersion underscores the heterogeneous nature of ESG investment activity globally and supports the panel structure of the dataset for econometric analysis. Table 3 Descriptive Statistics of Key Variables. Variable Mean Std. Dev. Min Max Sustainable Investment Size (SIS) 2.810 0.556 2.000 3.750 Digital Infrastructure (DIG) 77.231 10.349 57.000 95.500 Regulatory Readiness (REG) 72.565 10.433 53.000 93.500 Fintech Adoption (FIN) 72.306 10.079 52.000 91.500 Tokenization Adoption Index (TOK) 2.231 0.842 0.000 3.000 Financial Market Development (FMD) 132.407 40.217 55.000 190.000 Institutional Quality (INST) 1.076 0.703 −0.300 2.050 Economic Development (ln GDP per capita) 10.531 0.898 7.940 11.440 Carbon Market Presence (CARB) 0.778 0.417 0.000 1.000 Source: Author’s calculations The blockchain-related variables also demonstrate meaningful variation. Digital Infrastructure (DIG) has a mean of 77.23, ranging from 57.00 to 95.50, indicating that most sample countries possess moderate to high levels of technological readiness. Regulatory Readiness (REG) and Fintech Adoption (FIN) show similar patterns, with means of 72.57 and 72.31, respectively, and standard deviations exceeding 10 points, reflecting differentiated institutional maturity and digital finance adoption. Tokenization Adoption (TOK), measured on a 0–3 ordinal scale, has a mean of 2.23 and a wider relative spread (standard deviation of 0.84), consistent with its emerging nature and varying stages of experimentation across jurisdictions. Among the control variables, Financial Market Development (FMD) shows significant variation, with values ranging from 55.00 to 190.00 (as a percentage of GDP), indicating wide disparities in financial sector depth. Institutional Quality (INST) spans from − 0.30 to 2.05, with a mean of 1.08, capturing differences in governance and regulatory effectiveness. The natural log of GDP per capita exhibits a mean of 10.53, consistent with a sample that includes both advanced and emerging economies. Lastly, Carbon Market Presence (CARB), a binary variable, shows that approximately 78% of the country-year observations involve a functioning carbon pricing mechanism or emissions trading system. Together, the descriptive statistics confirm the presence of both cross-sectional and temporal heterogeneity in the dataset, which is essential for identifying meaningful relationships between blockchain readiness and sustainable investment. The variation observed across technological, institutional, and economic indicators provides a strong empirical foundation for testing the proposed hypotheses (H1-H4) in subsequent regression analysis. 4.2. Correlation Analysis Table 4 reports on the pairwise correlation coefficients among the key variables. The dependent variable, Sustainable Investment Size (SIS), is positively correlated with all four blockchain-related indicators, offering preliminary evidence of their association with sustainable finance outcomes. Notably, Tokenization Adoption (TOK) shows the strongest positive correlation with SIS (r = 0.679), suggesting that countries actively piloting or institutionalizing tokenization frameworks tend to host larger ESG-aligned investment volumes. This finding is consistent with theoretical expectations that tokenization enhances capital accessibility, liquidity, and investor participation in green finance. Moderate positive correlations are also observed between SIS and the broader digital finance variables: Digital Infrastructure (DIG) (r = 0.284), Regulatory Readiness (REG) (r = 0.281), and Fintech Adoption (FIN) (r = 0.285). These results lend preliminary directional support to hypotheses H1 through H3, suggesting that digital and regulatory readiness may complement the scaling of sustainable financial instruments, although further testing is required within the multivariate framework. Table 4 Pairwise Correlation Matrix Variable SIS DIG REG FIN TOK FMD INST ln(GDPpc) CARB SIS 1.000 DIG 0.284 1.000 REG 0.281 0.990 1.000 FIN 0.285 0.978 0.992 1.000 TOK 0.679 0.663 0.663 0.672 1.000 FMD 0.196 0.777 0.750 0.718 0.398 1.000 INST 0.206 0.922 0.908 0.898 0.554 0.854 1.000 ln (GDPpc) 0.217 0.855 0.837 0.825 0.493 0.805 0.939 1.000 CARB 0.050 0.413 0.418 0.366 0.227 0.421 0.369 0.370 1.000 Source: Author’s calculations As anticipated, the explanatory variables DIG, REG, and FIN are strongly intercorrelated (r > 0.97), reflecting their interconnected role in shaping the enabling environment for blockchain-based finance. Similarly, Institutional Quality (INST) and GDP per capita (ln(GDPpc)) are strongly correlated with each other (r = 0.939) and with the digital readiness variables, as higher-income economies often exhibit stronger institutions and deeper digital ecosystems. These relationships reflect conceptual proximity and co-evolution within digital finance ecosystems, as also observed in prior studies on technological complementarities in financial infrastructure development [ 58 , 42 ]. Carbon Market Presence (CARB) shows weaker correlations with the other variables, as expected for a binary policy indicator. Despite these high intercorrelations, multicollinearity is not expected to bias estimation results due to two key design features: the use of country fixed effects, which control for time-invariant country characteristics, and subsequent robustness diagnostics, which assess variance inflation and specification stability. These safeguards ensure the reliability of coefficient estimates in the regression analysis. In sum, the correlation results align with the theoretical framework established in Section 2 and provide preliminary empirical justification for the inclusion of the blockchain-related variables in the panel regression model. They suggest that technological readiness, regulatory maturity, and financial innovation are not only conceptually but also statistically associated with sustainable investment dynamics across countries. 4.3. Panel Regression Results Table 5 presents the results of the panel regression model estimated with country and year fixed effects, with standard errors clustered at the country level. The overall model fit is high, with an R² of 0.978 and adjusted R² of 0.973, indicating that the explanatory variables collectively explain a substantial portion of the variation in sustainable investment size across countries and over time. The coefficient for Digital Infrastructure (DIG) is positive and statistically significant at the 1% level (β = 0.031; t = 10.426), suggesting that improvements in digital infrastructure are strongly associated with an expansion in sustainable investment as a share of GDP. This result confirms H1, highlighting the role of digital readiness in enabling blockchain-based platforms and ESG-related financial services. The presence of robust ICT infrastructure, widespread internet penetration, and digital payment systems appears to reduce frictions in sustainable capital allocation. Table 5 Blockchain, Tokenization, and Sustainable Investment: Panel Regression Results (Country FE + Year FE) Variable Coefficient (Clustered Std. Error) t-stat Sig. Digital Infrastructure (DIG) 0.031 (0.003) 10.426 *** Regulatory Readiness (REG) −0.025 (0.003) −9.771 *** Fintech Adoption (FIN) 0.029 (0.002) 13.490 *** Tokenization Adoption (TOK) −0.114 (0.047) −2.420 ** Financial Market Development (FMD) 0.001 (0.000) 3.808 *** Institutional Quality (INST) −0.202 (0.015) −13.173 *** Economic Development (ln GDP per capita) −0.024 (0.009) −2.662 *** Carbon Market Presence (CARB) −0.073 (0.003) −23.252 *** Source: Author’s calculations Similarly, Fintech Adoption (FIN) is also positively associated with sustainable investment (β = 0.029; t = 13.490), providing empirical support to H3. This indicates that digitally mature financial ecosystems are more capable of converting blockchain and technological advancements into scalable ESG investment products. These findings are consistent with recent studies showing that fintech innovations enhance investor inclusion and facilitate ESG-aligned capital flows through more efficient financial intermediation [ 57 , 42 , 59 ]. In contrast, Regulatory Readiness (REG) shows a negative and statistically significant relationship with sustainable investment (β = -0.025; t = -9.771), consistent with H2. While regulatory frameworks are necessary for ensuring investor protection and legal certainty, overly rigid or transitional regimes may raise compliance costs and create short-term barriers to innovation. Similarly, Tokenization Adoption (TOK) is also negatively associated with sustainable investment (β = -0.114; t = − 2.420; p < 0.05), supporting H4. These results suggest that although tokenization holds long-term promise for democratizing green assets, its early-stage implementation may be constrained by legal uncertainties, institutional learning curves, and limited market depth. Similar transitional frictions have been observed in cross-country studies examining green fintech regulation and tokenization ecosystems [ 60 – 61 ]. Among the control variables, Financial Market Development (FMD) is positively and significantly associated with sustainable investment (β = 0.001; t = 3.808), reaffirming the role of financial depth in facilitating ESG-related capital flows. Conversely, Institutional Quality (INST) (β = -0.202; t = -13.173) and Economic Development (ln GDP per capita) (β = -0.024; t = -2.662) exhibit negative coefficients. These results may reflect within-country temporal dynamics, such as policy realignment or ESG rebalancing, rather than a lack of institutional or economic maturity per se. Interestingly, Carbon Market Presence (CARB) is negatively associated with sustainable investment (β = -0.073; t = -23.252), indicating that the existence of carbon pricing mechanisms does not automatically translate into larger ESG investment volumes and may even reflect policy inefficiencies or lagged implementation impacts. Overall, the panel regression results confirm that blockchain-related capabilities, particularly digital infrastructure and fintech adoption, serve as significant enablers of sustainable investment. However, their positive effects may be moderated by transitional constraints in regulatory and tokenization domains, particularly in jurisdictions undergoing early-stage ecosystem development. 4.4. Robustness Checks To analyse the robustness of the main regression findings, additional model specifications are estimated and presented in Table 6 . Model A excludes Carbon Market Presence (CARB), while Model B excludes Institutional Quality (INST) to evaluate whether the core results are sensitive to these control variables. Both models maintain the use of country and year fixed effects and cluster standard errors at the country level. In Model A, the coefficient for Digital Infrastructure (DIG) remains positive and significant (β = 0.030; p < 0.01), consistent with the baseline result and supporting H1. Fintech Adoption (FIN) also maintains a strong positive relationship (β = 0.038; p < 0.01), further supporting H3. Regulatory Readiness (REG) and Tokenization Adoption (TOK) continue to show negative and statistically significant coefficients (β = −0.034; p < 0.01 and β = −0.114; p < 0.05, respectively), which affirms the findings for H2 and H4 even after excluding policy-specific variables. Table 6 Robustness Checks Using Alternative Model Specifications. Model Variable Coefficient (Clustered Std. Error) Sig. Model A: Excluding CARB Digital Infrastructure (DIG) 0.030 (0.003) *** Regulatory Readiness (REG) −0.034 (0.002) *** Fintech Adoption (FIN) 0.038 (0.002) *** Tokenization Adoption (TOK) −0.114 (0.047) ** Financial Market Development (FMD) 0.001 (0.000) *** Institutional Quality (INST) −0.202 (0.015) *** Economic Development (ln GDP per capita) −0.023 (0.009) ** Model B: Excluding INST Digital Infrastructure (DIG) 0.019 (0.004) *** Regulatory Readiness (REG) −0.019 (0.003) *** Fintech Adoption (FIN) 0.028 (0.002) *** Tokenization Adoption (TOK) −0.114 (0.047) ** Financial Market Development (FMD) −0.002 (0.000) *** Economic Development (ln GDP per capita) 0.028 (0.013) ** Carbon Market Presence (CARB) −0.073 (0.003) *** Source: Author’s calculations In Model B, when Institutional Quality is excluded, the signs and statistical significance of all blockchain-related predictors remain intact. DIG shows a slightly lower coefficient (β = 0.019; p < 0.01), yet still significant, while FIN remains strong and positive (β = 0.028; p < 0.01). REG continues to demonstrate a negative and significant association (β = −0.019; p < 0.01), and TOK again retains its negative sign (β = -0.114; p < 0.05), reinforcing the conclusion that early-stage tokenization may entail institutional frictions or transitional inefficiencies. The variation in control variables slightly affects the magnitude of coefficients, but not their signs or significance levels. For instance, the FMD variable switches from positive (β = 0.001) in Model A to negative (β = -0.002) in Model B, yet the blockchain-related variables remain stable. The Economic Development (ln GDP per capita) variable changes sign between models, reflecting possible collinearity with the dropped controls, but this does not influence the core findings. Overall, these results confirm that the associations identified in the main regression, especially the positive roles of digital infrastructure and fintech, and the transitional constraints linked to regulation and tokenization, are robust to alternate specifications (Table 7 ). Table 7 Summary of Hypotheses and Empirical Results. Hypothesis Variable Theoretical Link Literature Insight Observed Sign Statistical Significance Empirical Support H1 Digital Infrastructure (DIG) Technology readiness → Sustainable Investment Size (SIS) Enhances ESG access and digital finance integration Positive (+) *** p < 0.01 Supported H2 Regulatory Readiness (REG) Institutional conditions ↔ SIS May introduce short-run compliance frictions during transition Negative (−) *** p < 0.01 Supported H3 Fintech Adoption (FIN) Fintech maturity → SIS Facilitates democratized and data-driven sustainable investing Positive (+) *** p < 0.01 Supported H4 Tokenization Adoption (TOK) Innovation stage ↔ SIS Tokenization in early phases may entail institutional and market adjustment costs Negative (−) ** p < 0.05 Supported Source: Author’s calculations Notes: SIS = Sustainable Investment Size (dependent variable). Directions: “→” indicates theoretical direction; “↔” denotes potentially bidirectional or context-dependent influence. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.10. 4.5. Cluster Analysis To complement the regression analysis and explore structural patterns across countries, cluster analysis is conducted using k-means classification (k = 3), based on country-level averages of four blockchain-related indicators: Digital Infrastructure (DIG), Regulatory Readiness (REG), Fintech Adoption (FIN), and Tokenization Adoption (TOK) for the period 2017–2024. The clustering algorithm groups countries into three distinct categories; Leaders, Followers, and Laggards; according to their relative blockchain readiness and tokenization development. Table 8 presents the resulting classification. The Leaders cluster includes high-income countries from the Asia-Pacific (e.g., Singapore, Hong Kong), European Union (e.g., France, Netherlands, Sweden), GCC (UAE), and major developed economies such as the United States, United Kingdom, and Germany. These countries are characterized by high scores in digital infrastructure (mean DIG = 86.44), regulatory maturity (REG = 81.67), fintech adoption (FIN = 80.83), and institutionalized tokenization (TOK = 2.71), with a mean sustainable investment size (SIS) of 2.88% of GDP. Table 8 Cluster Classification of Countries Based on Blockchain and Tokenization Readiness Cluster Countries Cluster Profile (Mean Values) Leaders Australia, Canada, Denmark, France, Germany, Hong Kong, Netherlands, Singapore, Sweden, Switzerland, UAE, United Kingdom, United States DIG = 86.44; REG = 81.67; FIN = 80.83 TOK = 2.71; SIS = 2.88 Followers Bahrain, China, Italy, Japan, Malaysia, Qatar, Saudi Arabia, South Korea, Spain DIG = 72.64; REG = 68.08; FIN = 68.42 TOK = 1.96; SIS = 2.88 Laggards Brazil, India, Indonesia, Mexico, South Africa DIG = 61.55; REG = 56.95; FIN = 57.15 TOK = 1.48; SIS = 2.52 Source: Author’s calculations The Followers cluster consists of countries such as South Korea, Japan, China, Malaysia, and select GCC states like Qatar and Saudi Arabia. These countries demonstrate moderate blockchain capabilities (DIG = 72.64; REG = 68.08; FIN = 68.42), emerging tokenization initiatives (TOK = 1.96), and a comparable sustainable investment size to Leaders (SIS = 2.88). This suggests that tokenization and regulatory conditions may act as transitional factors in scaling sustainable finance. The Laggards cluster includes large emerging economies, Brazil, India, Mexico, Indonesia, and South Africa, that exhibit relatively lower technological and institutional readiness (DIG = 61.55; REG = 56.95; FIN = 57.15) and less developed tokenization practices (TOK = 1.48). These countries record a smaller sustainable investment size (SIS = 2.52), highlighting potential barriers to scaling blockchain-enabled sustainable finance. The heatmap in Fig. 2 displays country-level averages of the four blockchain-related indicators (DIG, REG, FIN, TOK) over 2017–2024. Figure 2 visually summarizes the blockchain-related indicators across countries, showing notable heterogeneity in digital infrastructure, regulatory maturity, fintech adoption, and tokenization. The heatmap highlights the concentration of high scores among the leaders, particularly in Western Europe, North America, and parts of Asia. Figure 3 plots the standardized digital infrastructure and regulatory readiness scores, revealing a clear grouping of countries. Cluster visualization is based on standardized averages of Digital Infrastructure and Regulatory Readiness. Color coding reflects the three clusters: leaders (green), followers (yellow), and laggards (purple). The top-right quadrant reflects blockchain leaders with high technological and regulatory capacity, while countries in the bottom-left demonstrate lower levels of both dimensions, aligning with the laggard profile. The follower countries fall near the center, confirming a transitional position. The cluster results reinforce the regression findings by providing structural evidence that blockchain and tokenization capabilities are systematically associated with differences in sustainable investment performance across countries. Leaders with higher readiness consistently exhibit larger SIS values, validating H1-H4 in a non-parametric framework. The follower group reflects countries progressing toward institutional maturity but facing regulatory or technological bottlenecks. Laggards demonstrate the importance of foundational infrastructure for unlocking sustainable finance potential. 5. Discussion and Policy Implications 5.1. Discussion of Findings This study examined how blockchain-related capabilities influence sustainable investment across countries, offering new empirical insights into the technological, institutional, and financial foundations of blockchain-enabled sustainable finance. The panel regression and cluster analyses reveal that the effect of blockchain is not uniform, but context-dependent; shaped by the interplay of digital infrastructure, fintech adoption, regulatory maturity, and tokenization development. First, the robust positive association between digital infrastructure and sustainable investment underscores the importance of digital readiness as a prerequisite for blockchain-based financial innovation. This finding aligns with prior studies suggesting that high internet penetration, ICT access, and digital payment infrastructure reduce transaction frictions and improve market accessibility [ 62 – 63 ]. Blockchain-based solutions, such as ESG data verification and decentralized finance (DeFi), require the foundation of a well-developed digital ecosystem to scale effectively. Hence, blockchain amplifies the benefits of existing digital networks rather than substituting them. Second, fintech adoption emerges as a key intermediary that translates blockchain capabilities into tangible sustainable investment outcomes. As shown in prior literature, fintech platforms improve financial inclusion, streamline capital allocation, and foster innovation in ESG financial products [ 64 – 65 ]. Our findings suggest that fintech acts as a gateway through which blockchain solutions, such as tokenized green bonds or smart contract-based investments, reach broader investor segments. This result reinforces the idea that blockchain and fintech operate synergistically rather than in isolation [ 66 ]. Fintech ecosystems that integrate advanced analytics and AI tools further enable responsive and inclusive sustainable finance platforms by leveraging real-time information and behavioral signals [ 67 ]. This finding aligns with ESG-augmented investment literature emphasizing innovation and financial value generation across Asian economies [ 68 ], further confirming the relevance of fintech ecosystems in supporting sustainable capital formation. In contrast, regulatory readiness is negatively associated with sustainable investment during the study period. While this may appear counterintuitive, it is consistent with the notion of transitional frictions [ 30 , 69 ]. Regulatory tightening, compliance complexity, and legal uncertainty can inhibit experimentation and raise costs for emerging blockchain-based financial instruments. As such, overly rigid or rapidly evolving regulatory frameworks may constrain early-stage sustainable finance applications of blockchain, even if they promote long-term market integrity. Similarly, the negative association of tokenization adoption with sustainable investment highlights the early-stage and experimental nature of tokenization in many countries. Tokenization initiatives may face legal ambiguity, infrastructure fragmentation, or insufficient market demand, leading to a temporary disconnect between technological potential and measurable investment outcomes [ 70 – 71 ]. Over time, as tokenization ecosystems mature and regulatory clarity improves, their contribution to sustainable finance may increase. The cluster analysis supports and contextualizes these findings by identifying structural groupings. Countries in the “leader” cluster exhibit advanced digital and regulatory capacity, high fintech adoption, and more institutionalized tokenization efforts, translating into larger sustainable investment markets. “Follower” and “laggard” countries, on the other hand, face structural gaps that hinder the effective scaling of blockchain-based ESG finance. 5.2. Policy Implications The empirical results generate important policy insights for governments, regulators, and financial actors seeking to harness blockchain technologies for advancing sustainable finance. First, investment in digital infrastructure should be a strategic priority. Expanding broadband access, secure data platforms, and digital ID systems will enable countries to adopt blockchain applications more effectively. These efforts form the technical backbone for ESG transparency, traceable green finance, and tokenized asset platforms [ 27 ]. Second, fostering fintech ecosystems is critical. Governments can promote fintech growth through regulatory innovation hubs, digital sandbox environments, and open banking policies. Fintech firms help translate blockchain potential into market-ready ESG solutions and increase investor participation in sustainable finance [ 32 ]. Third, regulatory policy must be adaptive and phased. Policymakers should avoid excessive rigidity in early stages of blockchain or tokenization regulations. Instead, proportionate and iterative regulatory strategies, such as pilot licenses and graduated compliance schemes, can reduce transitional frictions and build long-term resilience [ 30 ]. Fourth, tokenization development should be supported through legal modernization and cross-sector collaboration. Clarifying rules around digital asset ownership, custody, settlement, and interoperability can reduce uncertainty and foster the integration of tokenized ESG assets into mainstream markets [ 71 – 72 ]. Finally, global coordination will be essential. Blockchain-enabled finance is inherently cross-border. International organizations, such as the Financial Stability Board (FSB), BIS, and IMF, should work with national authorities to harmonize standards, share best practices, and ensure regulatory coherence across jurisdictions. 5.3. Implications for Sustainable Finance Strategy From a strategic perspective, blockchain should be viewed not as a standalone fix but as an enabling infrastructure that depends on supportive conditions, digital, institutional, and financial. Sustainable finance strategies must integrate blockchain deployment with broader digital and ESG financial system development. For governments, this means designing holistic strategies that align sustainability goals with digital innovation, including digital finance roadmaps and ESG-focused fintech support. For private sector actors, especially financial institutions and asset managers, the implication is to embed blockchain solutions within ESG frameworks, ensuring interoperability, scalability, and investor trust. Ultimately, the transition to blockchain-enabled sustainable finance will require cross-disciplinary collaboration, phased institutional change, and deliberate investment in infrastructure and governance capacity. Only then can blockchain’s promise for credible, transparent, and inclusive ESG finance be fully realized. 6. Conclusion This study examined the relationship between blockchain-related capabilities and sustainable investment using a balanced panel of 27 countries over the period 2017–2024. By integrating digital infrastructure, regulatory readiness, fintech adoption, and tokenization adoption into a unified empirical framework, the analysis provides new cross-country evidence on how blockchain-enabled ecosystems shape sustainable finance outcomes. The results demonstrate that blockchain’s influence on sustainable investment is contingent, context-dependent, and significantly shaped by a country's broader technological and institutional ecosystem. Digital infrastructure and fintech adoption emerge as robust and consistent enablers of sustainable investment, affirming that technological readiness and digitally mature financial systems play a critical role in amplifying blockchain's potential. These findings are consistent with earlier literature emphasizing the enabling role of innovation ecosystems in financial inclusion and ESG investing [ 68 ]. Countries with advanced digital connectivity and strong fintech ecosystems are better positioned to translate blockchain potential into scalable, accessible, and credible sustainable finance solutions. In contrast, the negative association observed between regulatory readiness and sustainable investment suggests the presence of short-term transitional frictions. While regulation is essential for market stability, overly stringent or rapidly evolving frameworks can constrain innovation and experimentation in the early phases of blockchain deployment. This finding aligns with prior discussions on the trade-offs between regulatory oversight and innovation flexibility [ 57 ]. Similarly, adoption of tokenization is negatively associated with sustainable investment in the short run, possibly due to early-stage implementation hurdles, legal uncertainties, and underdeveloped market infrastructure [ 22 ]. This study contributes to sustainable finance and digital innovation literature in several ways. First, it advances empirical research on blockchain and ESG finance by moving beyond conceptual or country-specific evidence to a robust, large-scale, cross-country analysis. Second, it explicitly integrates tokenization into the sustainable finance discourse, offering empirical insights into its early-stage impact, an area largely unexplored in current literature. Third, by combining panel regression with cluster analysis, the study reveals heterogeneous country groupings and validates that the evolution of blockchain-enabled sustainable finance is uneven and path-dependent. From a practical perspective, the findings emphasize that blockchain should be viewed as an enabling infrastructure within a broader digital finance strategy, not a standalone fix. Policymakers and market actors should adopt integrated strategies that align digital infrastructure development, fintech ecosystem strengthening, and regulatory adaptability. Investments in foundational digital public goods, such as broadband networks and digital payment systems, are essential to unlock the potential of blockchain in ESG-oriented finance [ 29 ]. Despite its contributions, the study is subject to several limitations. The use of aggregate sustainable investment measures may obscure variation in ESG asset types or sectors. The tokenization indicator, while novel, captures only the stage of adoption rather than transactional volume or liquidity. Future research should explore more granular, asset-level data and investigate dynamic or nonlinear interactions between blockchain adoption and sustainable investment, especially as tokenized markets and legal clarity evolve. Emerging areas such as AI-enabled blockchain analytics and real-time ESG impact measurement, using sentiment and unstructured data [ 67 ], may also offer promising directions. Overall, this study offers timely, data-driven insights into the evolving nexus between blockchain, fintech, and sustainable finance. It provides a foundation for future research and policy efforts at the intersection of digital innovation, capital markets, and sustainability transition. Declarations Funding The author received no financial support for the research, authorship, and/or publication of this article. Availability of data and material The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval Not applicable Consent to participate Not applicable Consent to publish Not applicable Artificial intelligence declarations The author/contributor declares commitment to the ethical use of artificial intelligence. The author pledges to uphold the principles of fairness, transparency, accountability, and inclusivity in the development and deployment of AI technologies. Data availability statement The data that supports the findings of this study are available from the corresponding author upon reasonable request. Disclosure statement The author reported no potential conflict of interest. Credit authorship contribution statement Rashid Khalil: Methodology, Data curation, Conceptualization, Writing, Review & editing, Validation, Supervision, Software, Investigation, Formal analysis. 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Sustainability , 14 (6), 3125. https://doi.org/10.3390/su14063125 Sami, H., Bekri, M., & Ghoul, S. E. (2023). ESG tokenization and the future of impact investing. Financ Res Lett , 56 , 104188. https://doi.org/10.1016/j.frl.2023.104188 Weber, A., Maurer, R., & Krahnen, J. P. (2023). Regulating the token economy: Challenges for sustainable asset markets. J Bank Regul , 24 (1), 55–72. https://doi.org/10.1057/s41261-022-00195-6 Board, F. S. (2023). Tokenization and financial stability: Risks, use cases, and regulatory frameworks . FSB. Broby, P., & Paul, S. (2022). Tokenization and digital asset markets: Legal, regulatory, and market perspectives . Palgrave Macmillan. Capozza, C., & Ruester, S. (2023). The economics of asset tokenization: Efficiency gains or new frictions? Energy Economics , 126 , 106861. https://doi.org/10.1016/j.eneco.2023.106861 Zhao, Y., Liu, M., & Tan, L. (2024). Tokenization and the evolution of ESG investing: Evidence from global pilots. Technol Forecast Soc Change , 198 , 122981. https://doi.org/10.1016/j.techfore.2023.122981 Khalil, R., & Pandow, B. (2020). Influence of fiscal policy on GDP: An empirical study of GCC countries. J Public Aff , 20 (4), e2101. http://dx.doi.org/10.21511/imfi.17(3).2020.24\ OECD, Global outlook on financing for sustainable development 2023: Towards a more resilient and inclusive global financial architecture , Organisation for Economic Co-operation and Development, Paris (2023). https://doi.org/10.1787/9f59f0e3-en Beck, T., Lin, C., & Ma, Y. (2020). Financial development and institutional change: Evidence from cross-country panel data. J Int Money Financ , 108 , 102187. https://doi.org/10.1016/j.jimonfin.2020.102187 Beck, T., Lin, C., & Ma, Y. (2020). Technological complementarities and financial infrastructure development. World Development , 134 , 105044. https://doi.org/10.1016/j.worlddev.2020.105044 Shanaev, S., Ghimire, B., & Belousova, J. (2022). Fintech and socially responsible investing: Empirical evidence from global markets. Journal Of International Financial Markets, Institutions And Money , 78 , 101555. https://doi.org/10.1016/j.intfin.2022.101555 Ghosh, S., & Vukovic, A. (2023). Regulation and green fintech: A cross-country assessment. Financ Res Lett , 54 , 104814. https://doi.org/10.1016/j.frl.2023.104814 Arnaboldi, F., Rossignoli, B., & Azzalini, C. (2022). Sustainable finance and digital transformation: Regulatory challenges and opportunities. Journal Of Cleaner Production , 340 , 130739. https://doi.org/10.1016/j.jclepro.2022.130739 Narayan, P. K., Rehman, M. U., & Ullah, S. (2022). Financial technology and green investment. Energy Economics , 105 , 105762. https://doi.org/10.1016/j.eneco.2022.105762 Chen, H., Ho, K. C., Zhang, M., & Zhang, Q. (2021). Digital finance, environmental regulation and green innovation: Evidence from cross-country data. Technol Forecast Soc Change , 170 , 120880. https://doi.org/10.1016/j.techfore.2021.120880 Bömer, M., & Maxin, H. (2021). FinTech and sustainable finance: The role of digital financial services in ESG investing, Sustainability 13 (15) 8472. https://doi.org/10.3390/su13158472 Laidroo, L., & Avarmaa, M. (2021). FinTech in sustainable finance: Evidence from the European market. Sustainability , 13 (6), 3181. https://doi.org/10.3390/su13063181 Wamba, S. F., Kala Kamdjoug, J. R., Tchatchouang, C. E., & Wanko (2020). Blockchain technology and the sustainable investment decision process. Technol Forecast Soc Change , 158 , 120160. https://doi.org/10.1016/j.techfore.2020.120160 Khalil, R. (2026). AI-driven sentiment analysis in financial markets: Using transformer-based models and social media signals for stock market predictions. J Model Manag , 19 (1), 1–25. https://doi.org/10.1108/JM2-08-2025-0415 Khalil, M. A., Khalil, R., & Khalil, M. K. (2024). Environmental, social and governance (ESG)-augmented investments in innovation and firms’ value: A fixed-effects panel regression of Asian economies. China Financ Rev Int . https://doi.org/10.1108/CFRI-05-2022-0067 Omarova, S. T. (2021). De-financialization and the future of finance. Wash Univ Law Rev , 98 (6), 1251–1289. Tapscott, D., & Tapscott, A. (2019). Blockchain revolution: How the technology behind bitcoin and other cryptocurrencies is changing money, business, and the world . Penguin. Allen, H., Berg, J., Goring, S., Klebeck, J., Smith, M., & Teigland, T. (2022). FinTech, regulatory sandboxes, and innovation . World Bank Group. Bank for International Settlements. (2023). Annual economic report 2023 . BIS. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Arrows represent the direct associations between each capability and the dependent variable, sustainable investment size, as posited in hypotheses H1-H4.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9103370/v1/3f343447d9b1c08836763b58.jpg"},{"id":104814589,"identity":"433542a9-8ea5-472b-aca7-e545d215becd","added_by":"auto","created_at":"2026-03-17 13:13:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32714,"visible":true,"origin":"","legend":"\u003cp\u003eBlockchain Readiness, Tokenization, and Sustainable Investment (Country Averages)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9103370/v1/36a2c734ada88d74896d1210.jpg"},{"id":104814590,"identity":"da5a9484-a88d-4554-ba8a-1fe1dcddb7c4","added_by":"auto","created_at":"2026-03-17 13:13:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18923,"visible":true,"origin":"","legend":"\u003cp\u003eCountry Clusters Based on Blockchain and Tokenization Readiness\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9103370/v1/24c35a708e58ffe66ab56c30.jpg"},{"id":104835065,"identity":"017bb15d-116d-468f-9d1b-80fb3d1c1877","added_by":"auto","created_at":"2026-03-17 17:39:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1786115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9103370/v1/38834ca2-6953-423a-aafa-d66b23e306f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do Fintech, Tokenization, and Blockchain Capabilities Matter for Sustainable Investment? Evidence from a Cross-Country Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSustainable investment has become a cornerstone of modern financial ecosystems, driven by heightened awareness of climate risks, social inequality, and the pursuit of long-term economic resilience. Institutional investors, governments, and capital markets are increasingly embedding environmental, social, and governance (ESG) criteria into financial decision-making frameworks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite this growing emphasis, achieving scale in sustainable finance remains challenging due to persisting issues of transparency, trust, and cross-border capital mobilization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConcurrently, blockchain technology is gaining traction as a developing infrastructure capable of reshaping financial intermediation. With its decentralized architecture, immutable ledgers, and programmable contracts, blockchain holds significant promise for enhancing ESG verification, fund traceability, and impact reporting [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These attributes are especially valuable in sustainable finance, where credibility of ESG claims and environmental impact measurement are central to investor confidence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent academic interest has increasingly focused on the convergence of blockchain and sustainable investment. Studies have highlighted blockchain\u0026rsquo;s potential to facilitate green bond issuance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], optimize carbon markets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and improve ESG disclosure through enhanced data verifiability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the extant literature remains fragmented and is often dominated by case studies or conceptual explorations with limited empirical generalizability. Cross-country empirical evidence on how blockchain and related digital capabilities influence sustainable investment outcomes remains notably scarce.\u003c/p\u003e \u003cp\u003eIt is critical to recognize that blockchain adoption is not a standalone phenomenon. Its success in driving sustainable finance hinges on several complementary capabilities. Robust digital infrastructure enables scalable blockchain solutions and real-time data processing. Regulatory readiness fosters innovation while ensuring stability and investor protection. Fintech adoption plays an intermediary role, translating technological potential into accessible financial products. Tokenization, as a novel financial mechanism, enables fractionalization and liquidity of sustainable assets, enhancing market participation and monitoring [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These interdependencies suggest that the impact of blockchain capabilities on sustainable investment is likely to be heterogeneous and context dependent.\u003c/p\u003e \u003cp\u003eIn this light, the present study empirically investigates the role of blockchain-related capabilities in shaping sustainable investment outcomes across 27 countries from 2017 to 2024. The study focuses on four key enablers: digital infrastructure, regulatory readiness, fintech adoption, and tokenization, while controlling macro-institutional and market characteristics such as financial market development, institutional quality, economic development, and carbon pricing mechanisms. A fixed-effects panel regression model is employed to address unobserved heterogeneity and temporal trends, complemented by cluster analysis to reveal country-specific development paths.\u003c/p\u003e"},{"header":"2. Literature and Hypotheses Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Blockchain Technology and Sustainable Finance\u003c/h2\u003e \u003cp\u003eBlockchain technology has increasingly been recognized as a foundational infrastructure capable of reshaping financial markets through the core features of decentralization, immutability, and transparency. By facilitating secure peer-to-peer transactions, tamper-proof record-keeping, and real-time settlement, blockchain offers mechanisms to mitigate information asymmetries and enhance trust among market participants [12,13. These characteristics are particularly relevant in the context of sustainable finance, where integrity, traceability, and accountability of environmental, social, and governance (ESG) claims are critical for mitigating greenwashing risks and fostering investor confidence.\u003c/p\u003e \u003cp\u003eIn the sustainable finance domain, blockchain enables real-time verification of ESG data, improves supply-chain transparency, and supports the tokenization of sustainable assets such as green bonds and carbon credits [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Through smart contracts, it can automate impact-linked disbursements and compliance enforcement, thereby increasing the efficiency and effectiveness of green financial instruments [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These capabilities contribute to a more trustworthy financial ecosystem and promote the alignment of capital flows with long-term sustainability objectives.\u003c/p\u003e \u003cp\u003eHowever, the deployment of blockchain in sustainable finance is not an inherent development unless supported by complementary institutional and technological conditions. Digital infrastructure is needed to host blockchain platforms and facilitate real-time data flows. Regulatory frameworks must balance innovation and oversight to reduce legal uncertainties and prevent market fragmentation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, the maturity of fintech ecosystems determines whether blockchain functionalities are accessible to end-users through digital platforms, while tokenization enables the fractionalization and tradability of sustainable assets, thereby enhancing liquidity and participation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent empirical and theoretical research underscores the potential of blockchain to address structural inefficiencies in ESG investing but also cautions against over-reliance on technology without systemic readiness. For instance, studies have shown that blockchain's effectiveness varies depending on governance quality, digital adoption levels, and sector-specific applications [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As such, blockchain should be viewed not as a panacea but as an enabler whose contribution to sustainable investment is mediated by broader institutional and digital ecosystems. Consistent with prior reviews highlighting blockchain\u0026rsquo;s innovative role in sustainable finance ecosystems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the current study positions blockchain as a foundational enabler of ESG innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Digital Infrastructure and Sustainable Investment\u003c/h2\u003e \u003cp\u003eDigital infrastructure serves as a fundamental enabler for deploying blockchain-based financial ecosystems and fostering innovation in sustainable finance. It encompasses broadband internet penetration, mobile and digital payment systems, cloud computing capacity, and access to information and communication technologies (ICTs). These elements collectively reduce transaction costs, improve data availability, and enable real-time financial transactions, thus creating a conducive environment for sustainable digital finance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn countries with well-developed digital infrastructure, financial institutions and investors can leverage integrated platforms for ESG data analytics, digital green bond issuance, and decentralized finance (DeFi) solutions for environmental projects. For example, mobile banking and e-wallets enhance accessibility for retail investors seeking ESG-aligned products, while APIs and open data protocols facilitate integration between financial service providers and ESG verification platforms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (Bazarbash \u0026amp; Beaton, 2020). Such connectivity accelerates capital flows toward sustainable assets, particularly in emerging markets where traditional financial intermediation is constrained [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (Demirg\u0026uuml;\u0026ccedil;-Kunt et al., 2020).\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, improved digital infrastructure reduces frictions in capital allocation and enhances market completeness, allowing investors to better integrate sustainability preferences into portfolio decisions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. (Beck, Lin, \u0026amp; Qian, 2022). Furthermore, digital channels improve investor reach and reduce informational asymmetries, enhancing market discipline and demand for authentic ESG performance. As reported by Chiu and Wong [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] (2021), digitally enabled financial systems also exhibit higher responsiveness to climate risk disclosures, suggesting a reinforcing loop between infrastructure quality and sustainable investment adoption.\u003c/p\u003e \u003cp\u003eSeveral empirical studies have substantiated the positive relationship between digitalization and sustainability outcomes. For instance, countries with higher ICT readiness exhibit greater sustainable development financing, whereas digital infrastructure significantly moderates the effect of financial development on green innovation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings support the notion that digital ecosystems act as multipliers for sustainability-driven capital flows and investment innovation. Similar findings have been observed in the e-governance domain, where digital infrastructure enhances institutional performance and trust in digital systems, reinforcing its foundational role in technology-enabled transformation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, this study posits the following hypothesis:\u003c/p\u003e \u003cp\u003eH1: Digital infrastructure is significantly associated with sustainable investment size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Blockchain Regulatory Readiness and Sustainable Investment\u003c/h2\u003e \u003cp\u003eThe maturity of regulatory frameworks is a pivotal determinant of how blockchain innovations integrate with sustainable finance ecosystems. Regulatory readiness refers to the presence of coherent, adaptive, and enforceable legal structures that govern the deployment of blockchain technologies in financial markets. When effectively designed, such frameworks reduce legal uncertainty, enhance investor protection, and mitigate systemic risks factors essential for facilitating trust in emerging technologies and fostering innovation in green finance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of sustainable investment, regulatory readiness can support the issuance of blockchain-based green bonds, ensure compliance in carbon trading platforms, and establish standards for ESG tokenization and disclosure. For instance, sandbox regimes and pilot frameworks introduced in countries like Singapore, Switzerland, and the UK have enabled blockchain applications to develop under supervised conditions, stimulating the creation of sustainable financial products [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the benefits of regulatory clarity may come with short-term trade-offs. Overly prescriptive or prematurely rigid regulations can limit experimentation, raise compliance burdens, and deter early-stage blockchain startups from entering sustainable finance markets [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In many jurisdictions, the evolving nature of digital asset laws; combined with gaps in ESG taxonomy, creates regulatory ambiguity, which slows down market adoption and cross-border collaboration [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, transitional frictions may arise when regulators attempt to balance innovation with financial stability. As new tokenization models emerge for ESG assets, questions around custody, taxation, investor rights, and jurisdictional accountability complicate the regulatory landscape [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These institutional adjustment costs can suppress the short-run scalability of blockchain-based sustainable finance solutions, even when long-run benefits are expected.\u003c/p\u003e \u003cp\u003eEmpirical studies reveal mixed results. For example, Anagnostopoulos [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] finds that excessive regulatory intervention can delay blockchain adoption in ESG markets, whereas moderate and flexible frameworks catalyze innovation without compromising oversight. Similarly, de la Rosa \u0026amp; Stankovic [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] argue that sustainable finance regulation often lags behind technological capabilities, creating a disconnect between ESG goals and digital finance implementation.\u003c/p\u003e \u003cp\u003eConsidering this duality, the relationship between blockchain regulatory readiness and sustainable investment is hypothesized to be significant but potentially negative in the short term due to transitional costs and regulatory inertia.\u003c/p\u003e \u003cp\u003eH2: Regulatory readiness is significantly associated with sustainable investment size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Fintech Adoption and Sustainable Investment\u003c/h2\u003e \u003cp\u003eFintech adoption represents the maturity and dynamism of a country's digital financial ecosystem, encompassing innovations in digital payments, peer-to-peer (P2P) lending, robo-advisory services, crowdfunding platforms, and decentralized financial applications (DeFi). These tools have revolutionized the delivery of financial services by reducing reliance on traditional intermediaries, expanding financial access, and lowering participation thresholds; conditions vital for the democratization of sustainable investment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of sustainable finance, fintech acts as a gateway between blockchain infrastructure and end users, enabling seamless interaction with green investment products. Crowdfunding platforms, for example, have been instrumental in financing clean energy and community-led environmental projects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, algorithmic investment platforms that integrate ESG screening criteria allow retail and institutional investors to allocate capital in alignment with sustainability goals [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Fintech solutions can also enable more dynamic pricing and risk management in carbon trading, impact investing, and ESG-linked lending markets [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTheoretically, fintech supports the transmission of blockchain capabilities into practical financial services, reinforcing the infrastructure-application-user adoption chain. By reducing information asymmetry, improving transaction efficiency, and lowering search and verification costs, fintech tools contribute to allocative efficiency in sustainable capital markets [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, by expanding access to financial services among underbanked populations and SMEs, fintech fosters inclusive green finance, thereby aligning with global sustainability and SDG financing agendas [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent empirical studies confirm the catalytic role of fintech in sustainable investment. For instance, Li and Yu [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] find that countries with higher fintech penetration exhibit significantly greater issuance of green bonds and ESG funds. Similarly, Zhang et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] argued that fintech development positively moderates the relationship between institutional quality and sustainable investment flows in emerging markets. These findings suggest that fintech is not merely a channel but a critical enabler of sustainable finance innovation.\u003c/p\u003e \u003cp\u003eAccordingly, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH3: Fintech adoption is significantly associated with sustainable investment size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Tokenization Adoption and Sustainable Investment\u003c/h2\u003e \u003cp\u003eTokenization involves converting rights to a real-world asset into a digital token that is issued, transferred, and stored on a blockchain platform. In the context of sustainable finance, tokenization presents a novel mechanism for enabling fractional ownership of green infrastructure projects, carbon credits, and ESG-compliant securities, thereby enhancing liquidity, investor accessibility, and transparency [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. By lowering the minimum capital required to participate, tokenization can significantly democratize access to sustainable investment opportunities and widen the investor base, particularly among retail participants and small institutional investors.\u003c/p\u003e \u003cp\u003eMoreover, tokenization enhances traceability and real-time monitoring of environmental outcomes through smart contracts and immutable ledgers, facilitating a more accountable system of sustainability-linked finance [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It also allows for more efficient impact reporting, ESG claim verification, and secondary market trading of illiquid green assets, such as solar farms or reforestation credits [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These features align well with broader goals of green financial innovation and contribute to enhanced market efficiency and credibility.\u003c/p\u003e \u003cp\u003eHowever, despite its theoretical promise, tokenization remains in an early phase of adoption across most jurisdictions. Real-world implementation faces a range of frictions: limited legal and regulatory clarity around tokenized asset ownership, lack of interoperability across platforms, and challenges related to custody, taxation, and investor protection [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Many tokenized ESG assets are currently limited to pilot projects or sandbox regimes, particularly in the EU, Asia-Pacific, and the Middle East, where institutional learning curves and fragmented digital infrastructure pose hurdles to scalability [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, while tokenization can improve liquidity under ideal conditions, its effectiveness depends on active participation, secondary market depth, and confidence in valuation mechanisms, elements not yet fully developed in most green asset classes. Recent empirical studies indicate that tokenization has a nonlinear and stage-dependent relationship with sustainable investment outcomes: early-stage adoption may initially disrupt traditional capital flows due to market uncertainty, before generating positive outcomes at later stages when infrastructure, regulation, and market trust align [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven this duality of potential and constraint, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH4: Tokenization adoption is significantly associated with sustainable investment size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Conceptual Framework\u003c/h2\u003e \u003cp\u003eBuilding upon the preceding theoretical foundations, this study proposes a conceptual framework that links blockchain-related capabilities with sustainable investment outcomes. The model integrates four key independent variables; digital infrastructure, regulatory readiness, fintech adoption, and tokenization adoption; as determinants of sustainable investment size. These factors reflect both technological readiness and institutional maturity, capturing the multifaceted environment necessary for blockchain-enabled financial innovation to thrive.\u003c/p\u003e \u003cp\u003eFrom this perspective, blockchain technology serves as an enabling infrastructure, whose efficacy in advancing sustainable finance depends on the interplay among digital connectivity, regulatory governance, financial service innovation, and asset digitization. While each of these dimensions has independent significance, their joint contribution shapes a country\u0026rsquo;s capacity to support scalable and credible sustainable investment solutions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visually summarizes the hypothesized relationships. Arrows represent the direct associations between each capability and the dependent variable, sustainable investment size, as posited in hypotheses H1-H4.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the proposed framework assumes that countries with advanced digital infrastructure and robust fintech ecosystems are better positioned to translate blockchain capabilities into practical, user-facing sustainable finance products. Besides, the effect of regulatory readiness is likely conditional, potentially enabling long-term institutional support while introducing transitional frictions in early phases of adoption. Moreover, Tokenization may expand access and liquidity, yet its short-term effects could be constrained by legal, technical, and market immaturity. This framework guides the empirical strategy employed in the study. By testing the hypotheses derived (H1-H4), the analysis explores how differences in blockchain-related capabilities influence sustainable investment outcomes across varying developmental and regulatory contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample and Data Sources\u003c/h2\u003e \u003cp\u003eThis study constructs a balanced panel dataset of 27 countries across five economic regions; Developed Economies, European Union, Asia-Pacific, Gulf Cooperation Council (GCC), and Emerging Economies; covering the period 2017 to 2024. The sample includes countries with demonstrable activity in blockchain development, fintech diffusion, and sustainable finance initiatives, and was selected based on the availability of consistent cross-country data across key indicators. The inclusion of GCC countries reflects both their growing interest in blockchain initiatives and the broader macroeconomic policy environment influencing investment capacity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The temporal range captures the rapid evolution of blockchain-related capabilities following 2016 and aligns with the scaling up of sustainable investment practices and tokenization pilots in the post-COP21 regulatory environment. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the regional and economic composition of the sample, while Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes variable definitions and data sources.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Composition by Country, Region, and Economy Category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEconomy Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncome Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarbon Market Presence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeveloped Economies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEuropean Union\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsia-Pacific\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Arab Emirates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQatar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBahrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmerging Economies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmerging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper-Middle Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s work\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll country-level indicators are derived from publicly accessible and internationally recognized data repositories. Principal sources include the World Bank\u0026rsquo;s Digital Adoption Index and World Development Indicators, the IMF Financial Access Survey, the World Governance Indicators (WGI), the Global Sustainable Investment Review (GSIR), and the OECD and BIS policy databases on fintech and tokenization. Additional data on regulatory readiness and tokenization activity were sourced from national financial authorities and regulatory disclosures. Data across countries were harmonized using standardized measurement techniques and normalized where necessary to ensure comparability across jurisdictions and time periods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Classification, Definitions, Measurement, and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpected Sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSustainable Investment Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSustainable investment assets as a percentage of GDP, incorporating ESG bond issuance and carbon market participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlobal Sustainable Investment Review; World Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite index capturing internet penetration, digital payments usage, and ICT access (0\u0026ndash;100 scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank Digital Adoption Index; ITU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory Readiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndex reflecting the presence and maturity of blockchain and fintech regulatory frameworks (0\u0026ndash;100 scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank; OECD fintech and blockchain policy reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFintech Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasure of fintech ecosystem depth and user adoption, including digital financial services usage (0\u0026ndash;100 scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank; IMF Financial Access Survey\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTokenization Adoption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTOK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrdinal index capturing the extent of real-world asset tokenization (0\u0026thinsp;=\u0026thinsp;none; 1\u0026thinsp;=\u0026thinsp;pilot; 2\u0026thinsp;=\u0026thinsp;active; 3\u0026thinsp;=\u0026thinsp;institutionalized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBIS; national regulatory authorities and public disclosures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial Market Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDomestic credit to private sector as a percentage of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank World Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulatory quality index ranging from \u0026minus;\u0026thinsp;2.5 (weak) to +\u0026thinsp;2.5 (strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Governance Indicators (WGI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDPpc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural logarithm of GDP per capita (constant USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank World Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDummy Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon Market Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary indicator (1\u0026thinsp;=\u0026thinsp;emissions trading system or carbon pricing mechanism in place; 0\u0026thinsp;=\u0026thinsp;otherwise)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank Carbon Pricing Dashboard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author\u0026rsquo;s work\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variable Measurement\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Dependent Variable\u003c/h2\u003e \u003cp\u003eThe dependent variable, Sustainable Investment Size (SIS), serves as the central measure of a country\u0026rsquo;s sustainable finance depth. It is computed as the ratio of sustainable investment assets to gross domestic product (GDP), integrating ESG-aligned fund flows, green and social bond issuance, and verified carbon market participation. This indicator captures the breadth and systemic importance of sustainable finance, rather than focusing solely on niche segments or isolated market instruments. The use of this macro-financial perspective aligns with prior studies examining ESG investment scale in cross-country contexts [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Independent Variables\u003c/h2\u003e \u003cp\u003eTo examine the influence of blockchain-related capabilities on sustainable finance development, the empirical model incorporates four key independent variables. Digital Infrastructure (DIG) represents a country\u0026rsquo;s technological capacity to support blockchain-based financial platforms. It is operationalized as a composite index covering internet penetration, digital payments usage, and access to information and communication technology, with values scaled from 0 to 100. Regulatory Readiness (REG) captures the maturity and scope of blockchain and fintech regulation, based on the presence of licensing frameworks, supervisory oversight, and innovation sandboxes. This index also follows a normalized 0-100 scale to reflect variation in legal and institutional development across countries.\u003c/p\u003e \u003cp\u003eThe Fintech Adoption (FIN) variable reflects the breadth and depth of digital financial services within each country. It encompasses indicators of mobile money usage, alternative lending volumes, and the adoption of automated investment tools. Higher values reflect stronger integration of financial innovation into mainstream capital allocation mechanisms. Tokenization Adoption (TOK), a central innovation proxy in this study, is measured as an ordinal variable ranging from 0 (no observable tokenization) to 3 (institutionalized adoption), capturing the progressive evolution of tokenized real asset markets across countries and years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Control Variables\u003c/h2\u003e \u003cp\u003eTo isolate the effects of these technological and regulatory dimensions, the model includes several structural control variables. Financial Market Development (FMD), expressed as domestic credit to the private sector relative to GDP, accounts for differences in financial system depth. Institutional Quality (INST) is proxied by the regulatory quality sub-index from the World Governance Indicators, representing the robustness of governance systems. Economic Development is captured by the natural logarithm of GDP per capita in constant USD, providing a scale-sensitive measure of national income. Additionally, Carbon Market Presence (CARB) is included as a binary control (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), accounting for the influence of national carbon pricing frameworks on sustainable investment flows.\u003c/p\u003e \u003cp\u003eThese variables jointly form the basis of the panel regression analysis, allowing for an empirical test of the hypotheses developed in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and facilitating a multi-dimensional examination of blockchain readiness and its impact on sustainable investment. The operationalization strategy ensures compatibility with previous empirical models in sustainable finance and digital policy research [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Empirical Model\u003c/h2\u003e \u003cp\u003eTo empirically investigate the influence of blockchain-related capabilities on sustainable investment outcomes, this study employs a fixed-effects panel regression model that accounts for both cross-country heterogeneity and time-specific global factors. The model specification is designed to capture the structural and institutional factors that may condition the relationship between blockchain ecosystem maturity and sustainable finance development.\u003c/p\u003e \u003cp\u003eThe core econometric model is expressed as follows:\u003c/p\u003e \u003cp\u003eSIS\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​=​\u003cem\u003eα\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e​DIG\u003csub\u003e\u003cem\u003eit​\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2​\u003c/sub\u003eREG\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​ + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e​FIN\u003csub\u003e\u003cem\u003eit​\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e​TOK\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​ + β\u003c/em\u003e\u003csub\u003e5​\u003c/sub\u003eFMD\u003csub\u003e\u003cem\u003eit​\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e​INST\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​ + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e​ln(GDPpc\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e​CARB\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​+ \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e​ + λ\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e​ + \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit​​\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e, indexes coun\u003cem\u003et\u003c/em\u003eries and \u003cem\u003et\u003c/em\u003e indexes years. The dependent variable SIS\u003csub\u003e\u003cem\u003eit​\u003c/em\u003e\u003c/sub\u003e represents the sustainable investment size in country \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e, measured as the ratio of ESG-aligned financial assets to GDP. The key explanatory variables include: DIG\u003csub\u003e\u003cem\u003eit​\u003c/em\u003e\u003c/sub\u003e for digital infrastructure, REG\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​ for regulatory readiness, FIN\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​ for fintech adoption, and TOK\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​ for tokenization adoption level. Control variables include financial market development (FMD\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e)​, institutional quality (INST\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​), economic development captured by the natural logarithm of GDP per capita [In(GDPpc\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e​)], and carbon market presence (CARB\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e)​.\u003c/p\u003e \u003cp\u003eThe model includes country fixed effects (\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e)​ to control for time-invariant national characteristics, such as legal traditions, political culture, and structural economic conditions, that may influence both blockchain deployment and sustainable investment independently. It also incorporates year fixed effects (\u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e)​ to absorb global shocks, common macroeconomic trends, or international regulatory shifts that could affect all countries simultaneously, such as the Paris Agreement compliance push or COVID-19-related policy interventions.\u003c/p\u003e \u003cp\u003eThe inclusion of these fixed effects ensures that the estimated coefficients reflect within-country variation over time, thereby mitigating bias from omitted variables that are constant across countries or common across time periods. The error term ϵ\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e captures idiosyncratic shocks unaccounted for by the included covariates. This fixed-effects framework has been widely adopted in cross-country policy and finance studies where both structural and temporal factors may shape institutional change and market evolution [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To ensure robust inference, standard errors are clustered at the country level to account for serial correlation in the error structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Estimation Strategy\u003c/h2\u003e \u003cp\u003eTo estimate the relationship between blockchain-related capabilities and sustainable investment size, this study applies an ordinary least squares (OLS) estimation with country and year fixed effects. This specification is well-suited for panel data where unobserved time-invariant country characteristics, such as legal infrastructure, governance traditions, or macroeconomic fundamentals, may correlate with both the explanatory variables and the dependent variable. The inclusion of year fixed effects accounts for global shocks and macro-trends, such as international climate policy changes or major fintech adoption waves, that could uniformly influence all countries within the panel.\u003c/p\u003e \u003cp\u003eStandard errors are clustered at the country level to address issues of heteroskedasticity and serial correlation, a common practice in empirical studies using macro panel data. This approach improves inference robustness by accounting for within-country error dependence across time periods. Before estimating the model, descriptive statistics and pairwise correlation analysis are conducted to assess the distributional properties of the variables and to evaluate potential multicollinearity. While moderate to high correlations are observed among some blockchain-related indicators, these associations are theoretically expected due to the intertwined nature of digital infrastructure, regulatory maturity, and fintech development. The fixed-effects structure, along with robustness checks introduced in later sections, helps address concerns regarding omitted variable bias and overlapping variance.\u003c/p\u003e \u003cp\u003eIn addition to the regression analysis, the study incorporates a cluster analysis to group countries based on their levels of blockchain and tokenization readiness. This unsupervised learning technique complements the econometric model by enabling a structural typology of countries, highlighting differences in the technological and institutional pathways through which blockchain influences sustainable finance. The classification enables comparative insights into heterogeneity in adoption patterns across developed, emerging, and transitional economies. All statistical estimations and data visualizations are implemented using Python (version 3.x), utilizing established scientific libraries including pandas, NumPy, statsmodels, and scikit-learn. This approach ensures transparency and reproducibility of empirical results, aligning with open science practices increasingly adopted in the field of sustainable finance.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data Analysis and Findings","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the descriptive statistics for all variables used in the analysis over the 2017\u0026ndash;2024 period. The dependent variable, Sustainable Investment Size (SIS), exhibits a mean value of 2.81% of GDP, with a minimum of 2.00% and a maximum of 3.75%, reflecting substantial variation in the scale of sustainable finance across countries. This degree of dispersion underscores the heterogeneous nature of ESG investment activity globally and supports the panel structure of the dataset for econometric analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Key Variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable Investment Size (SIS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Infrastructure (DIG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Readiness (REG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFintech Adoption (FIN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTokenization Adoption Index (TOK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Market Development (FMD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional Quality (INST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Development (ln GDP per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Market Presence (CARB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe blockchain-related variables also demonstrate meaningful variation. Digital Infrastructure (DIG) has a mean of 77.23, ranging from 57.00 to 95.50, indicating that most sample countries possess moderate to high levels of technological readiness. Regulatory Readiness (REG) and Fintech Adoption (FIN) show similar patterns, with means of 72.57 and 72.31, respectively, and standard deviations exceeding 10 points, reflecting differentiated institutional maturity and digital finance adoption. Tokenization Adoption (TOK), measured on a 0\u0026ndash;3 ordinal scale, has a mean of 2.23 and a wider relative spread (standard deviation of 0.84), consistent with its emerging nature and varying stages of experimentation across jurisdictions.\u003c/p\u003e \u003cp\u003eAmong the control variables, Financial Market Development (FMD) shows significant variation, with values ranging from 55.00 to 190.00 (as a percentage of GDP), indicating wide disparities in financial sector depth. Institutional Quality (INST) spans from \u0026minus;\u0026thinsp;0.30 to 2.05, with a mean of 1.08, capturing differences in governance and regulatory effectiveness. The natural log of GDP per capita exhibits a mean of 10.53, consistent with a sample that includes both advanced and emerging economies. Lastly, Carbon Market Presence (CARB), a binary variable, shows that approximately 78% of the country-year observations involve a functioning carbon pricing mechanism or emissions trading system.\u003c/p\u003e \u003cp\u003eTogether, the descriptive statistics confirm the presence of both cross-sectional and temporal heterogeneity in the dataset, which is essential for identifying meaningful relationships between blockchain readiness and sustainable investment. The variation observed across technological, institutional, and economic indicators provides a strong empirical foundation for testing the proposed hypotheses (H1-H4) in subsequent regression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Correlation Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports on the pairwise correlation coefficients among the key variables. The dependent variable, Sustainable Investment Size (SIS), is positively correlated with all four blockchain-related indicators, offering preliminary evidence of their association with sustainable finance outcomes. Notably, Tokenization Adoption (TOK) shows the strongest positive correlation with SIS (r\u0026thinsp;=\u0026thinsp;0.679), suggesting that countries actively piloting or institutionalizing tokenization frameworks tend to host larger ESG-aligned investment volumes. This finding is consistent with theoretical expectations that tokenization enhances capital accessibility, liquidity, and investor participation in green finance.\u003c/p\u003e \u003cp\u003eModerate positive correlations are also observed between SIS and the broader digital finance variables: Digital Infrastructure (DIG) (r\u0026thinsp;=\u0026thinsp;0.284), Regulatory Readiness (REG) (r\u0026thinsp;=\u0026thinsp;0.281), and Fintech Adoption (FIN) (r\u0026thinsp;=\u0026thinsp;0.285). These results lend preliminary directional support to hypotheses H1 through H3, suggesting that digital and regulatory readiness may complement the scaling of sustainable financial instruments, although further testing is required within the multivariate framework.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise Correlation Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTOK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eINST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eln(GDPpc)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCARB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln (GDPpc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs anticipated, the explanatory variables DIG, REG, and FIN are strongly intercorrelated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.97), reflecting their interconnected role in shaping the enabling environment for blockchain-based finance. Similarly, Institutional Quality (INST) and GDP per capita (ln(GDPpc)) are strongly correlated with each other (r\u0026thinsp;=\u0026thinsp;0.939) and with the digital readiness variables, as higher-income economies often exhibit stronger institutions and deeper digital ecosystems. These relationships reflect conceptual proximity and co-evolution within digital finance ecosystems, as also observed in prior studies on technological complementarities in financial infrastructure development [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Carbon Market Presence (CARB) shows weaker correlations with the other variables, as expected for a binary policy indicator.\u003c/p\u003e \u003cp\u003eDespite these high intercorrelations, multicollinearity is not expected to bias estimation results due to two key design features: the use of country fixed effects, which control for time-invariant country characteristics, and subsequent robustness diagnostics, which assess variance inflation and specification stability. These safeguards ensure the reliability of coefficient estimates in the regression analysis. In sum, the correlation results align with the theoretical framework established in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and provide preliminary empirical justification for the inclusion of the blockchain-related variables in the panel regression model. They suggest that technological readiness, regulatory maturity, and financial innovation are not only conceptually but also statistically associated with sustainable investment dynamics across countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Panel Regression Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the panel regression model estimated with country and year fixed effects, with standard errors clustered at the country level. The overall model fit is high, with an R\u0026sup2; of 0.978 and adjusted R\u0026sup2; of 0.973, indicating that the explanatory variables collectively explain a substantial portion of the variation in sustainable investment size across countries and over time.\u003c/p\u003e \u003cp\u003eThe coefficient for Digital Infrastructure (DIG) is positive and statistically significant at the 1% level (β\u0026thinsp;=\u0026thinsp;0.031; t\u0026thinsp;=\u0026thinsp;10.426), suggesting that improvements in digital infrastructure are strongly associated with an expansion in sustainable investment as a share of GDP. This result confirms H1, highlighting the role of digital readiness in enabling blockchain-based platforms and ESG-related financial services. The presence of robust ICT infrastructure, widespread internet penetration, and digital payment systems appears to reduce frictions in sustainable capital allocation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBlockchain, Tokenization, and Sustainable Investment: Panel Regression Results (Country FE\u0026thinsp;+\u0026thinsp;Year FE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Clustered Std. Error)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Infrastructure (DIG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Readiness (REG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;9.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFintech Adoption (FIN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTokenization Adoption (TOK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Market Development (FMD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional Quality (INST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;13.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Development (ln GDP per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Market Presence (CARB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;23.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly, Fintech Adoption (FIN) is also positively associated with sustainable investment (β\u0026thinsp;=\u0026thinsp;0.029; t\u0026thinsp;=\u0026thinsp;13.490), providing empirical support to H3. This indicates that digitally mature financial ecosystems are more capable of converting blockchain and technological advancements into scalable ESG investment products. These findings are consistent with recent studies showing that fintech innovations enhance investor inclusion and facilitate ESG-aligned capital flows through more efficient financial intermediation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, Regulatory Readiness (REG) shows a negative and statistically significant relationship with sustainable investment (β = -0.025; t = -9.771), consistent with H2. While regulatory frameworks are necessary for ensuring investor protection and legal certainty, overly rigid or transitional regimes may raise compliance costs and create short-term barriers to innovation. Similarly, Tokenization Adoption (TOK) is also negatively associated with sustainable investment (β = -0.114; t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.420; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting H4. These results suggest that although tokenization holds long-term promise for democratizing green assets, its early-stage implementation may be constrained by legal uncertainties, institutional learning curves, and limited market depth. Similar transitional frictions have been observed in cross-country studies examining green fintech regulation and tokenization ecosystems [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the control variables, Financial Market Development (FMD) is positively and significantly associated with sustainable investment (β\u0026thinsp;=\u0026thinsp;0.001; t\u0026thinsp;=\u0026thinsp;3.808), reaffirming the role of financial depth in facilitating ESG-related capital flows. Conversely, Institutional Quality (INST) (β = -0.202; t = -13.173) and Economic Development (ln GDP per capita) (β = -0.024; t = -2.662) exhibit negative coefficients. These results may reflect within-country temporal dynamics, such as policy realignment or ESG rebalancing, rather than a lack of institutional or economic maturity per se. Interestingly, Carbon Market Presence (CARB) is negatively associated with sustainable investment (β = -0.073; t = -23.252), indicating that the existence of carbon pricing mechanisms does not automatically translate into larger ESG investment volumes and may even reflect policy inefficiencies or lagged implementation impacts.\u003c/p\u003e \u003cp\u003eOverall, the panel regression results confirm that blockchain-related capabilities, particularly digital infrastructure and fintech adoption, serve as significant enablers of sustainable investment. However, their positive effects may be moderated by transitional constraints in regulatory and tokenization domains, particularly in jurisdictions undergoing early-stage ecosystem development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Robustness Checks\u003c/h2\u003e \u003cp\u003eTo analyse the robustness of the main regression findings, additional model specifications are estimated and presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Model A excludes Carbon Market Presence (CARB), while Model B excludes Institutional Quality (INST) to evaluate whether the core results are sensitive to these control variables. Both models maintain the use of country and year fixed effects and cluster standard errors at the country level.\u003c/p\u003e \u003cp\u003eIn Model A, the coefficient for Digital Infrastructure (DIG) remains positive and significant (β\u0026thinsp;=\u0026thinsp;0.030; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), consistent with the baseline result and supporting H1. Fintech Adoption (FIN) also maintains a strong positive relationship (β\u0026thinsp;=\u0026thinsp;0.038; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), further supporting H3. Regulatory Readiness (REG) and Tokenization Adoption (TOK) continue to show negative and statistically significant coefficients (β = \u0026minus;0.034; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and β = \u0026minus;0.114; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively), which affirms the findings for H2 and H4 even after excluding policy-specific variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Checks Using Alternative Model Specifications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Clustered Std. Error)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel A: Excluding CARB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Infrastructure (DIG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory Readiness (REG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFintech Adoption (FIN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTokenization Adoption (TOK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial Market Development (FMD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional Quality (INST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Development (ln GDP per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel B: Excluding INST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Infrastructure (DIG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory Readiness (REG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFintech Adoption (FIN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTokenization Adoption (TOK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial Market Development (FMD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Development (ln GDP per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon Market Presence (CARB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Model B, when Institutional Quality is excluded, the signs and statistical significance of all blockchain-related predictors remain intact. DIG shows a slightly lower coefficient (β\u0026thinsp;=\u0026thinsp;0.019; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), yet still significant, while FIN remains strong and positive (β\u0026thinsp;=\u0026thinsp;0.028; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). REG continues to demonstrate a negative and significant association (β = \u0026minus;0.019; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and TOK again retains its negative sign (β = -0.114; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), reinforcing the conclusion that early-stage tokenization may entail institutional frictions or transitional inefficiencies.\u003c/p\u003e \u003cp\u003eThe variation in control variables slightly affects the magnitude of coefficients, but not their signs or significance levels. For instance, the FMD variable switches from positive (β\u0026thinsp;=\u0026thinsp;0.001) in Model A to negative (β = -0.002) in Model B, yet the blockchain-related variables remain stable. The Economic Development (ln GDP per capita) variable changes sign between models, reflecting possible collinearity with the dropped controls, but this does not influence the core findings.\u003c/p\u003e \u003cp\u003eOverall, these results confirm that the associations identified in the main regression, especially the positive roles of digital infrastructure and fintech, and the transitional constraints linked to regulation and tokenization, are robust to alternate specifications (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Hypotheses and Empirical Results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTheoretical Link\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLiterature Insight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved Sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistical Significance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEmpirical Support\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Infrastructure (DIG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology readiness \u0026rarr; Sustainable Investment Size (SIS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnhances ESG access and digital finance integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory Readiness (REG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitutional conditions \u0026harr; SIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMay introduce short-run compliance frictions during transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative (\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFintech Adoption (FIN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFintech maturity \u0026rarr; SIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitates democratized and data-driven sustainable investing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTokenization Adoption (TOK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInnovation stage \u0026harr; SIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTokenization in early phases may entail institutional and market adjustment costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative (\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSIS\u0026thinsp;=\u0026thinsp;Sustainable Investment Size (dependent variable).\u003c/p\u003e \u003cp\u003eDirections: \u0026ldquo;\u0026rarr;\u0026rdquo; indicates theoretical direction; \u0026ldquo;\u0026harr;\u0026rdquo; denotes potentially bidirectional or context-dependent influence.\u003c/p\u003e \u003cp\u003eSignificance levels: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Cluster Analysis\u003c/h2\u003e \u003cp\u003eTo complement the regression analysis and explore structural patterns across countries, cluster analysis is conducted using k-means classification (k\u0026thinsp;=\u0026thinsp;3), based on country-level averages of four blockchain-related indicators: Digital Infrastructure (DIG), Regulatory Readiness (REG), Fintech Adoption (FIN), and Tokenization Adoption (TOK) for the period 2017\u0026ndash;2024. The clustering algorithm groups countries into three distinct categories; Leaders, Followers, and Laggards; according to their relative blockchain readiness and tokenization development.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the resulting classification. The Leaders cluster includes high-income countries from the Asia-Pacific (e.g., Singapore, Hong Kong), European Union (e.g., France, Netherlands, Sweden), GCC (UAE), and major developed economies such as the United States, United Kingdom, and Germany. These countries are characterized by high scores in digital infrastructure (mean DIG\u0026thinsp;=\u0026thinsp;86.44), regulatory maturity (REG\u0026thinsp;=\u0026thinsp;81.67), fintech adoption (FIN\u0026thinsp;=\u0026thinsp;80.83), and institutionalized tokenization (TOK\u0026thinsp;=\u0026thinsp;2.71), with a mean sustainable investment size (SIS) of 2.88% of GDP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster Classification of Countries Based on Blockchain and Tokenization Readiness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster Profile (Mean Values)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeaders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia, Canada, Denmark, France, Germany,\u003c/p\u003e \u003cp\u003eHong Kong, Netherlands, Singapore, Sweden,\u003c/p\u003e \u003cp\u003eSwitzerland, UAE, United Kingdom, United States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIG\u0026thinsp;=\u0026thinsp;86.44; REG\u0026thinsp;=\u0026thinsp;81.67; FIN\u0026thinsp;=\u0026thinsp;80.83\u003c/p\u003e \u003cp\u003eTOK\u0026thinsp;=\u0026thinsp;2.71; SIS\u0026thinsp;=\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollowers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBahrain, China, Italy, Japan, Malaysia,\u003c/p\u003e \u003cp\u003eQatar, Saudi Arabia, South Korea, Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIG\u0026thinsp;=\u0026thinsp;72.64; REG\u0026thinsp;=\u0026thinsp;68.08; FIN\u0026thinsp;=\u0026thinsp;68.42\u003c/p\u003e \u003cp\u003eTOK\u0026thinsp;=\u0026thinsp;1.96; SIS\u0026thinsp;=\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaggards\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil, India, Indonesia, Mexico, South Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIG\u0026thinsp;=\u0026thinsp;61.55; REG\u0026thinsp;=\u0026thinsp;56.95; FIN\u0026thinsp;=\u0026thinsp;57.15\u003c/p\u003e \u003cp\u003eTOK\u0026thinsp;=\u0026thinsp;1.48; SIS\u0026thinsp;=\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Author\u0026rsquo;s calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Followers cluster consists of countries such as South Korea, Japan, China, Malaysia, and select GCC states like Qatar and Saudi Arabia. These countries demonstrate moderate blockchain capabilities (DIG\u0026thinsp;=\u0026thinsp;72.64; REG\u0026thinsp;=\u0026thinsp;68.08; FIN\u0026thinsp;=\u0026thinsp;68.42), emerging tokenization initiatives (TOK\u0026thinsp;=\u0026thinsp;1.96), and a comparable sustainable investment size to Leaders (SIS\u0026thinsp;=\u0026thinsp;2.88). This suggests that tokenization and regulatory conditions may act as transitional factors in scaling sustainable finance.\u003c/p\u003e \u003cp\u003eThe Laggards cluster includes large emerging economies, Brazil, India, Mexico, Indonesia, and South Africa, that exhibit relatively lower technological and institutional readiness (DIG\u0026thinsp;=\u0026thinsp;61.55; REG\u0026thinsp;=\u0026thinsp;56.95; FIN\u0026thinsp;=\u0026thinsp;57.15) and less developed tokenization practices (TOK\u0026thinsp;=\u0026thinsp;1.48). These countries record a smaller sustainable investment size (SIS\u0026thinsp;=\u0026thinsp;2.52), highlighting potential barriers to scaling blockchain-enabled sustainable finance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays country-level averages of the four blockchain-related indicators (DIG, REG, FIN, TOK) over 2017\u0026ndash;2024. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visually summarizes the blockchain-related indicators across countries, showing notable heterogeneity in digital infrastructure, regulatory maturity, fintech adoption, and tokenization. The heatmap highlights the concentration of high scores among the leaders, particularly in Western Europe, North America, and parts of Asia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e plots the standardized digital infrastructure and regulatory readiness scores, revealing a clear grouping of countries. Cluster visualization is based on standardized averages of Digital Infrastructure and Regulatory Readiness. Color coding reflects the three clusters: leaders (green), followers (yellow), and laggards (purple). The top-right quadrant reflects blockchain leaders with high technological and regulatory capacity, while countries in the bottom-left demonstrate lower levels of both dimensions, aligning with the laggard profile. The follower countries fall near the center, confirming a transitional position.\u003c/p\u003e \u003cp\u003eThe cluster results reinforce the regression findings by providing structural evidence that blockchain and tokenization capabilities are systematically associated with differences in sustainable investment performance across countries. Leaders with higher readiness consistently exhibit larger SIS values, validating H1-H4 in a non-parametric framework. The follower group reflects countries progressing toward institutional maturity but facing regulatory or technological bottlenecks. Laggards demonstrate the importance of foundational infrastructure for unlocking sustainable finance potential.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion and Policy Implications","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Discussion of Findings\u003c/h2\u003e \u003cp\u003eThis study examined how blockchain-related capabilities influence sustainable investment across countries, offering new empirical insights into the technological, institutional, and financial foundations of blockchain-enabled sustainable finance. The panel regression and cluster analyses reveal that the effect of blockchain is not uniform, but context-dependent; shaped by the interplay of digital infrastructure, fintech adoption, regulatory maturity, and tokenization development.\u003c/p\u003e \u003cp\u003eFirst, the robust positive association between digital infrastructure and sustainable investment underscores the importance of digital readiness as a prerequisite for blockchain-based financial innovation. This finding aligns with prior studies suggesting that high internet penetration, ICT access, and digital payment infrastructure reduce transaction frictions and improve market accessibility [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Blockchain-based solutions, such as ESG data verification and decentralized finance (DeFi), require the foundation of a well-developed digital ecosystem to scale effectively. Hence, blockchain amplifies the benefits of existing digital networks rather than substituting them.\u003c/p\u003e \u003cp\u003eSecond, fintech adoption emerges as a key intermediary that translates blockchain capabilities into tangible sustainable investment outcomes. As shown in prior literature, fintech platforms improve financial inclusion, streamline capital allocation, and foster innovation in ESG financial products [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Our findings suggest that fintech acts as a gateway through which blockchain solutions, such as tokenized green bonds or smart contract-based investments, reach broader investor segments. This result reinforces the idea that blockchain and fintech operate synergistically rather than in isolation [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Fintech ecosystems that integrate advanced analytics and AI tools further enable responsive and inclusive sustainable finance platforms by leveraging real-time information and behavioral signals [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This finding aligns with ESG-augmented investment literature emphasizing innovation and financial value generation across Asian economies [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], further confirming the relevance of fintech ecosystems in supporting sustainable capital formation.\u003c/p\u003e \u003cp\u003eIn contrast, regulatory readiness is negatively associated with sustainable investment during the study period. While this may appear counterintuitive, it is consistent with the notion of transitional frictions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Regulatory tightening, compliance complexity, and legal uncertainty can inhibit experimentation and raise costs for emerging blockchain-based financial instruments. As such, overly rigid or rapidly evolving regulatory frameworks may constrain early-stage sustainable finance applications of blockchain, even if they promote long-term market integrity.\u003c/p\u003e \u003cp\u003eSimilarly, the negative association of tokenization adoption with sustainable investment highlights the early-stage and experimental nature of tokenization in many countries. Tokenization initiatives may face legal ambiguity, infrastructure fragmentation, or insufficient market demand, leading to a temporary disconnect between technological potential and measurable investment outcomes [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Over time, as tokenization ecosystems mature and regulatory clarity improves, their contribution to sustainable finance may increase.\u003c/p\u003e \u003cp\u003eThe cluster analysis supports and contextualizes these findings by identifying structural groupings. Countries in the \u0026ldquo;leader\u0026rdquo; cluster exhibit advanced digital and regulatory capacity, high fintech adoption, and more institutionalized tokenization efforts, translating into larger sustainable investment markets. \u0026ldquo;Follower\u0026rdquo; and \u0026ldquo;laggard\u0026rdquo; countries, on the other hand, face structural gaps that hinder the effective scaling of blockchain-based ESG finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Policy Implications\u003c/h2\u003e \u003cp\u003eThe empirical results generate important policy insights for governments, regulators, and financial actors seeking to harness blockchain technologies for advancing sustainable finance.\u003c/p\u003e \u003cp\u003eFirst, investment in digital infrastructure should be a strategic priority. Expanding broadband access, secure data platforms, and digital ID systems will enable countries to adopt blockchain applications more effectively. These efforts form the technical backbone for ESG transparency, traceable green finance, and tokenized asset platforms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, fostering fintech ecosystems is critical. Governments can promote fintech growth through regulatory innovation hubs, digital sandbox environments, and open banking policies. Fintech firms help translate blockchain potential into market-ready ESG solutions and increase investor participation in sustainable finance [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, regulatory policy must be adaptive and phased. Policymakers should avoid excessive rigidity in early stages of blockchain or tokenization regulations. Instead, proportionate and iterative regulatory strategies, such as pilot licenses and graduated compliance schemes, can reduce transitional frictions and build long-term resilience [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, tokenization development should be supported through legal modernization and cross-sector collaboration. Clarifying rules around digital asset ownership, custody, settlement, and interoperability can reduce uncertainty and foster the integration of tokenized ESG assets into mainstream markets [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, global coordination will be essential. Blockchain-enabled finance is inherently cross-border. International organizations, such as the Financial Stability Board (FSB), BIS, and IMF, should work with national authorities to harmonize standards, share best practices, and ensure regulatory coherence across jurisdictions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Implications for Sustainable Finance Strategy\u003c/h2\u003e \u003cp\u003eFrom a strategic perspective, blockchain should be viewed not as a standalone fix but as an enabling infrastructure that depends on supportive conditions, digital, institutional, and financial. Sustainable finance strategies must integrate blockchain deployment with broader digital and ESG financial system development.\u003c/p\u003e \u003cp\u003eFor governments, this means designing holistic strategies that align sustainability goals with digital innovation, including digital finance roadmaps and ESG-focused fintech support. For private sector actors, especially financial institutions and asset managers, the implication is to embed blockchain solutions within ESG frameworks, ensuring interoperability, scalability, and investor trust.\u003c/p\u003e \u003cp\u003eUltimately, the transition to blockchain-enabled sustainable finance will require cross-disciplinary collaboration, phased institutional change, and deliberate investment in infrastructure and governance capacity. Only then can blockchain\u0026rsquo;s promise for credible, transparent, and inclusive ESG finance be fully realized.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examined the relationship between blockchain-related capabilities and sustainable investment using a balanced panel of 27 countries over the period 2017\u0026ndash;2024. By integrating digital infrastructure, regulatory readiness, fintech adoption, and tokenization adoption into a unified empirical framework, the analysis provides new cross-country evidence on how blockchain-enabled ecosystems shape sustainable finance outcomes. The results demonstrate that blockchain\u0026rsquo;s influence on sustainable investment is contingent, context-dependent, and significantly shaped by a country's broader technological and institutional ecosystem.\u003c/p\u003e \u003cp\u003eDigital infrastructure and fintech adoption emerge as robust and consistent enablers of sustainable investment, affirming that technological readiness and digitally mature financial systems play a critical role in amplifying blockchain's potential. These findings are consistent with earlier literature emphasizing the enabling role of innovation ecosystems in financial inclusion and ESG investing [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Countries with advanced digital connectivity and strong fintech ecosystems are better positioned to translate blockchain potential into scalable, accessible, and credible sustainable finance solutions.\u003c/p\u003e \u003cp\u003eIn contrast, the negative association observed between regulatory readiness and sustainable investment suggests the presence of short-term transitional frictions. While regulation is essential for market stability, overly stringent or rapidly evolving frameworks can constrain innovation and experimentation in the early phases of blockchain deployment. This finding aligns with prior discussions on the trade-offs between regulatory oversight and innovation flexibility [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Similarly, adoption of tokenization is negatively associated with sustainable investment in the short run, possibly due to early-stage implementation hurdles, legal uncertainties, and underdeveloped market infrastructure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study contributes to sustainable finance and digital innovation literature in several ways. First, it advances empirical research on blockchain and ESG finance by moving beyond conceptual or country-specific evidence to a robust, large-scale, cross-country analysis. Second, it explicitly integrates tokenization into the sustainable finance discourse, offering empirical insights into its early-stage impact, an area largely unexplored in current literature. Third, by combining panel regression with cluster analysis, the study reveals heterogeneous country groupings and validates that the evolution of blockchain-enabled sustainable finance is uneven and path-dependent.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, the findings emphasize that blockchain should be viewed as an enabling infrastructure within a broader digital finance strategy, not a standalone fix. Policymakers and market actors should adopt integrated strategies that align digital infrastructure development, fintech ecosystem strengthening, and regulatory adaptability. Investments in foundational digital public goods, such as broadband networks and digital payment systems, are essential to unlock the potential of blockchain in ESG-oriented finance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its contributions, the study is subject to several limitations. The use of aggregate sustainable investment measures may obscure variation in ESG asset types or sectors. The tokenization indicator, while novel, captures only the stage of adoption rather than transactional volume or liquidity. Future research should explore more granular, asset-level data and investigate dynamic or nonlinear interactions between blockchain adoption and sustainable investment, especially as tokenized markets and legal clarity evolve. Emerging areas such as AI-enabled blockchain analytics and real-time ESG impact measurement, using sentiment and unstructured data [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], may also offer promising directions.\u003c/p\u003e \u003cp\u003eOverall, this study offers timely, data-driven insights into the evolving nexus between blockchain, fintech, and sustainable finance. It provides a foundation for future research and policy efforts at the intersection of digital innovation, capital markets, and sustainability transition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial intelligence declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author/contributor declares commitment to the ethical use of artificial intelligence. The author pledges to uphold the principles of fairness, transparency, accountability, and inclusivity in the development and deployment of AI technologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author reported no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRashid Khalil: Methodology, Data curation, Conceptualization, Writing, Review \u0026amp; editing, Validation, Supervision, Software, Investigation, Formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFatemi, A., Glaum, M., \u0026amp; Kaiser, S. 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Penguin.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen, H., Berg, J., Goring, S., Klebeck, J., Smith, M., \u0026amp; Teigland, T. (2022). \u003cem\u003eFinTech, regulatory sandboxes, and innovation\u003c/em\u003e. World Bank Group.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBank for International Settlements. (2023). \u003cem\u003eAnnual economic report 2023\u003c/em\u003e. BIS.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blockchain, Fintech, Tokenization, Innovation, Sustainable investment, Digital infrastructure, ESG finance","lastPublishedDoi":"10.21203/rs.3.rs-9103370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9103370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how blockchain-related capabilities; digital infrastructure, regulatory readiness, fintech adoption, and tokenization; affect sustainable investment across countries. Though blockchain and fintech are increasingly positioned as enablers of sustainability, empirical cross-country insights remain limited. Using a balanced panel of 27 developed and emerging economies from 2017 to 2024, this study applies fixed-effects panel regression and cluster analysis to examine the association between digital financial innovation and sustainable investment size. The results reveal that digital infrastructure and fintech adoption are consistently and positively associated with sustainable investment, underscoring the role of technological readiness and innovation ecosystems in scaling ESG finance. Conversely, regulatory readiness and tokenization adoption show statistically significant but negative associations, reflecting transitional frictions and institutional adjustment costs during early adoption phases. Cluster analysis identifies three distinct country groups; leaders, followers, and laggards; based on blockchain and tokenization readiness, revealing heterogeneous development patterns. These findings suggest that blockchain\u0026rsquo;s role in sustainable finance is contingent on broader institutional and digital contexts. The study contributes to the sustainable finance literature by integrating tokenization into ESG investment analysis and highlighting the structural conditions necessary for blockchain-enabled finance to succeed. Policy implications emphasize the need for supportive digital infrastructure, adaptive regulation, and integrated fintech strategies to realize the sustainability potential of emerging financial technologies.\u003c/p\u003e","manuscriptTitle":"Do Fintech, Tokenization, and Blockchain Capabilities Matter for Sustainable Investment? Evidence from a Cross-Country Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 13:12:33","doi":"10.21203/rs.3.rs-9103370/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bb28cb2-5622-48f3-993f-ed148b0553ba","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T13:13:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 13:12:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9103370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9103370","identity":"rs-9103370","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}