Abstract This study examines the return connectedness between decentralized finance (DeFi)âs and the Association of Southeast Asian Nations (ASEAN) stock markets using the quantile vector autoregressive framework, which allows us to investigate the connectedness at conditional quantiles. Our sample includes four major DeFiâs and six ASEAN stock markets, spanning from March 2018 to December 2022. The static results indicate a moderate level of return transmission between the system at mean and median quantile. This propagation increases substantially under extreme market conditions, establishing an asymmetric transmission across quantiles. Despite being a relatively new asset class, DeFi dominates the equity market and acts as the primary shock transmitter to the system in most instances. The dynamic analysis reveals that total system connectedness fluctuates over time and quantiles. The total system connectedness peaked during the COVID-19 and the RussiaâUkraine conflict period, indicating the impact of global events on system transmission. The optimal weight and hedge ratio estimated using the DCC-GARCH model indicate that DeFi is beneficial for portfolio construction and risk management. The rising trend in dynamic optimal weight and hedge ratio during the COVID-19 pandemic demonstrates that investors should decrease their investments in DeFi and increase hedging costs. Therefore, portfolio managers and investors should readjust their portfolio allocation in a timely manner according to different market states to build additional effective hedging and diversification strategies to avoid large losses and to reduce portfolio risk exposure.
B J Santiyano, V F Tanty, J Hariwinarta, Shinta Amalina Hazrati Havidz
Abstract This study investigated the effects of macroeconomic and environmental factors on Bitcoin return. The analysis included 31 lower-income countries selected based on their GDP rankings from 2012 to 2022. Using 341 observations, we applied Feasible Generalized Least Squares (FGLS) and One-step System Generalized Method of Moments (GMM) to evaluate the effects of Gross Domestic Product (GDP), Inflation Rate (INF), CO2 Emissions (CO2E), Climate Policy Uncertainty (CPU), Global Price Energy Index (GPEI), and Cambridge Bitcoin Electricity Consumption Index (CBECI) on Bitcoin return. The findings revealed a significant negative relationship between CO2E and Bitcoin return, reflecting investor concerns about Bitcoinâs environmental impact. CBECI showed a significant positive effect, suggesting that higher electricity consumption associated with mining activities led to increased Bitcoin return. CPU was positively associated with Bitcoin return, indicating that investors might have viewed Bitcoin as a hedge against climate policy uncertainties. GPEI had a significant negative effect, demonstrating Bitcoinâs sensitivity to global energy price fluctuations. GDP and INF showed no significant effect on Bitcoin return, suggesting that macroeconomic indicators did not significantly impact Bitcoinâs value in emerging markets. These results offered essential guidance for investors and policymakers in emerging markets.
The increasing importance of emerging digital assets has created a multifaceted environmental challenge and opportunity. This paper explores one form of emerging digital asset, cryptocurrency (crypto), and the causal factors that drive its environmental impact. While cryptocurrency makes up a relatively small proportion of overall global electricity consumption (0.6 percent) and carbon dioxide equivalent emissions (0.2 percent), the electricity demand associated with its mining operations is growing rapidly. Its emissions profile surpasses that of traditional banking by a wide margin and, when compared on a climate damages per unit price basis, also exceeds that of gold mining by an order of magnitude. A scenario analysis reveals that cryptocurrency is set on an unsustainable path. The paper concludes with recommendations that include efforts to improve cryptocurrency's environmental performance and enable migration toward greater adoption of efficient algorithmic approaches; increased transparency of cryptocurrency mining operations through monitoring and reporting frameworks to promote grid stability and decarbonization; and investment that prioritizes the use of cryptocurrency for everyone's benefit.
This study explores how disruptive technologies, and financial innovations can strengthen climate and sustainable finance by addressing persistent structural, institutional, and social barriers. Using a systematic literature review, the research analyzes academic and policy sources to explore the integration of tools such as blockchain, artificial intelligence, and decentralized finance into climate finance frameworks. The central hypothesis is that these innovations enhance the transparency, accessibility, and effectiveness of climate finance, particularly in developing economies. Findings suggest that innovation can improve fund traceability, stakeholder inclusion, and project evaluation, contributing to more equitable and resilient financing mechanisms. However, technological adoption faces limitations related to regulatory gaps, technical capacity, and institutional resistance. The articleâs originality lies in linking disruptive innovation to climate justice and proposing a framework for more just and efficient financial architecture. It contributes to climate policy, finance, and development debates by bridging technological potential with sustainability goals.
Abstract In recent decades, the rising challenges posed by climate change have prompted investors to take a keen interest in green assets and incorporate them into their portfolios to achieve optimal returns. Therefore, this article explores the static and dynamic connectedness between renewable energy stocks (solar, wind, and geothermal), green cryptocurrencies (Stellar, Nano, Cardona, and IOTA), and agricultural commodities (wheat, cocoa, coffee, corn, cotton, sugar, and soybean) using the TVP-VAR (time-varying parameter vector autoregression) framework offering novel empirical evidence for investors and portfolio managers. The connectedness is examined across two distinct sub-samples: during COVID-19 and post-COVID-19 times. Because the relevant connectedness can have implications for diversification benefits, we proceed with the computation of optimal weights, hedge ratios, and hedge effectiveness using the DCC-GARCH model. The main findings are as follows: We first find that green cryptocurrencies particularly Cardona and Stellar exhibit the highest spillovers to the network and wind energy stock has the least connectedness with the other markets. Second, the dynamic NET spillover indices reveal that cotton, cocoa, and coffee are consistently net receivers over the entire period except in the beginning of the pandemic. Third, renewable energy stocks exhibit diverse positions implying that the impact of the pandemic has varied significantly across the sectors. Finally, agricultural commodity depicts greater weights in the pandemic period under scoring the benefit of a diversified portfolio consisting of agriculture and green assets.
This study aims to explore the dynamic relationships among economic policy uncertainty (EPU), Bitcoin trading activity, and the NASDAQ index over the period from January 2, 2014, to March 21, 2023. Employing the dynamic conditional correlationâgeneralized autoregressive conditional heteroskedasticity (DCCâGARCH) model, this research reveals significant timeâvarying interdependencies between EPU and financial markets, with a specific focus on the Bitcoin sector. This paper extends the literature by examining EPUâs influence on trading volume and volatility spillovers during different market regimes and crisis events, including the COVIDâ19 pandemic. The results demonstrate that the correlations between EPU and both Bitcoin and the NASDAQ index are dynamic and sensitive to market phases, with stronger effects observed during bull markets and heightened volatility during the pandemic. The findings provide new empirical evidence on the evolving role of EPU in shaping investor behavior and financial asset comovement. This study offers practical implications for investors, regulators, and policymakers, especially in designing risk management strategies under uncertainty.
Pushpa Bandari, S K Shabana Begum, Thota Nikhitha, Shaik Jareena ¡ 5 authors
The integration of blockchain technology into green finance has emerged as a transformative approach to fostering sustainable investments, particularly in developing economies like India. This study explores how blockchain enhances transparency, accountability, and efficiency in green finance mechanisms, thereby promoting environmental sustainability. By leveraging features such as decentralization, smart contracts, and immutable records, blockchain can address challenges like fraud, inefficiency, and lack of trust in green financial transactions. The study employs a mixed-methods approach, utilizing surveys and statistical analysis to assess blockchainâs impact on green investment tracking and regulatory compliance. Findings indicate that blockchain significantly improves transparency (β = 0.42, p < 0.01) and automation (β = 0.35, p < 0.01), positively influencing green finance adoption. However, regulatory uncertainty and high implementation costs remain key barriers. The study suggests policy reforms, financial incentives, and capacity-building initiatives to enhance blockchain adoption in India's green finance sector. With strategic interventions, blockchain can serve as a catalyst for accelerating the transition toward a more sustainable and transparent financial ecosystem.
Addressing the shortcomings of the Voluntary Carbon Markets (VCMs), a nascent blockchain industry has disrupted this area with an ever-growing number of projects leveraging open-source, decentralised base-layer platforms (e.g. Ethereum, Cosmos) and business-oriented decentralized applications (Dapps). Building on this emerging digital infrastructure over the Internet, community-driven decentralized autonomous organizations (DAOs) are building new socio-technical systems for decentralized finance (DeFi) and, more recently, regenerative finance (ReFi). Both areas are making their way into VCMs promising improved transparency, efficiency and greater accessibility. This paper examines the goals, scope, and intended outcomes of prominent blockchain-based ReFi projects in the VCM space. In particular, it explores the potential for commons-based outcomes emerging from peer-to-peer configurations in the VCM. Using a qualitative approach, the study analyses text-data from industry whitepapers focusing on the cases of Celo, Regen, Toucan, Klima and Moss. The findings show the ReFi ecosystem as a low-transaction-cost environment fostering open-source prototypes of peer-production for carbon accounting and trading. These innovations materialize through application interfaces operating on base-layer blockchains running smart-contracts and tokens. The tokenization of real-world assets (RWA) or rights (e.g. renewable energy generation, storage capacity, or forestry land) and the automation of operations (e.g. exchanges) via smart contracting, provides participants with new affordances for secure, bi-directional coordination in near-real time. The preliminary conclusion is that, while the ReFi organizations considered seem to be Ostrom-compliant with regard to some principles (e.g. clearly defined boundaries, procedures for making own rules, monitoring, or minimal recognition of rights) others are still ad-hoc practices or work in progress (e.g. graduated sanctions or dispute resolution mechanisms). This study contributes to the critical exploration of enhanced governance mechanisms, enabled by technological innovation, that can address climate action challenges and provide viable alternatives to traditional market-based approaches.
Oghenerume Augoye, Adekunle Adewoyin, Olugbenga Adediwin, Audu Joseph Audu
Artificial Intelligence (AI) is transforming energy financing by enhancing decision-making, optimizing investment portfolios, and improving risk assessment in sustainable infrastructure projects. This review explores the role of AI in energy financing, focusing on its applications in risk evaluation, credit scoring, investment optimization, and the development of climate-aligned financial strategies. AI-driven predictive analytics enable investors to assess the financial viability of renewable energy projects, identify high-impact opportunities, and optimize asset allocation. Additionally, AI-powered models enhance credit scoring for energy developers, facilitating access to funding for clean energy initiatives. The integration of AI with blockchain and smart contracts is also revolutionizing energy financing by ensuring transparency, reducing fraud, and automating financial transactions in sustainable projects. Furthermore, AI plays a crucial role in the management and monitoring of green bonds, improving impact assessment and ensuring accountability in climate finance. However, several challenges hinder AI-driven energy financing, including data limitations, regulatory gaps, cybersecurity risks, and potential biases in AI decision-making models. Ensuring data quality, developing ethical AI frameworks, and addressing cybersecurity concerns are essential for AIâs successful adoption in energy investment strategies. Future opportunities lie in AI-driven predictive analytics for emerging markets, enabling better financing mechanisms for off-grid and decentralized energy solutions. AI can also enhance public-private partnerships by optimizing investment structures and improving government funding allocation for renewable energy projects. As AI continues to evolve, it holds the potential to reshape energy financing, drive sustainable investments, and accelerate the transition to a low-carbon economy. This review underscores the need for collaborative efforts among policymakers, financial institutions, and technology providers to maximize AIâs potential in sustainable energy infrastructure financing while addressing its inherent challenges.
Green finance and sustainable investment strategies have gained significant attention as key mechanisms to address global environmental challenges and drive sustainable development. This study employs bibliometric analysis to explore the thematic, geographical, and temporal trends in research on green finance and sustainable investments, using data from the Scopus database. The findings reveal that central themes, such as "sustainability," "green economy," and "investments," dominate scholarly discourse, with growing emphasis on emerging topics like "green technology innovation" and "decentralized finance." China, the United Kingdom, and European nations are identified as leading contributors to research in this field, with notable collaborations across regions. However, disparities in regional representation and challenges such as inconsistent ESG frameworks and perceived financial risks hinder the adoption of green finance globally. The study highlights opportunities for harmonizing global standards, leveraging technological innovations, and expanding research in underrepresented regions. These insights provide valuable guidance for policymakers, financial institutions, and researchers aiming to enhance the effectiveness of green finance and sustainable investments.
Purpose The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining. Design/methodology/approach We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTCâCPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the dataâs inherent properties. Findings The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the âanticipatory regulatory decline effectâ, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use. Originality/value This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the âanticipatory regulatory decline effectâ and the âmining-driven regulatory surgeâ, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.