Bitcoinâs Proof-of-Work mechanism is energy intensive, exceeding the electricity consumption of a medium-sized country. As the adoption accelerates, it become a concern. Most studies analyzed its energy consumption, emissions, and price in isolation. This study examines the relationship between the energy consumption and energy mix of Bitcoin and its market performance, moderated by quality of regulation, using a time-series of secondary data from reputable resources e.g. Cambridge Bitcoin Electricity Consumption Index,, the Worldwide Governance Indicators, etc. Regression analyses are employed to test the hypotheses. Eight of nine null hypotheses failed to reject. However, energy consumption was found to have a significant positive relationship with market return. It is, however, likely that this finding captures shared underlying drivers of Bitcoinâs price and its energy consumption, as well as possible reverse causality. Energy mix was found to have no significant effect on the three alternative outcomes, aligned with the fungibility of Bitcoin. Furthermore, regulatory was found not to significantly moderate also likely due to the narrow variation in the Indonesiaâs scores during the study period. The study identified that markets do not reward sustainable mining with a market premium, implying that the transition towards renewable-powered mining in Indonesia requires more policy intervention.
We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.
This study investigates the dynamic relationship between network activity and transaction fees in the Ethereum blockchain by analysing the interaction between Gas Used and Gas Price through a multivariate time series model. The objective is to determine whether variations in network demand influence short-term gas price fluctuations. Daily data of Gas Used and Gas Price were transformed into different logarithmic forms to ensure stationarity. The Augmented DickeyâFuller test confirmed that both variables are stationary at the five percent significance level, with ADF statistics of â6.21 for Îlog (Gas Used) and â7.12 for Îlog (Gas Price), and p-values below 0.001. The Vector Autoregression model was estimated with an optimal lag length of fourteen days, selected using the Akaike Information Criterion, reflecting the persistence of network and fee dynamics. The results of the Granger causality test indicate a unidirectional causal relationship from Gas Used to Gas Price, with an F-statistic of 3.72 and a p-value of 0.018, suggesting that fluctuations in network demand significantly precede changes in gas pricing. The reverse direction is not significant, with an F-statistic of 1.26 and a p-value of 0.28, indicating that transaction fees do not predict network activity. The impulse response analysis shows that a one standard deviation shock in Gas Used increases Gas Price for two to three days before returning to equilibrium, while shocks in Gas Price have minimal effects on Gas Used. These findings confirm that Ethereumâs fee market operates primarily as a demand-driven mechanism were congestion and transaction volume shape short-term gas price movements.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and TodaâYamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
Sarim Zia, Saleha Qureshi, Muhammad Zulfiqar, Arfa Ijaz
The paper discusses the economic and infrastructural challenges preventing the adoption of Electric Vehicles (EVs) in Pakistan.It focuses on key factors such as affordability, consumer preferences, and the overall readiness of the market.Based on a segment-wise comparison, the analysis reveals that four-wheeler EVs carry an initial price premium of 20 to 64 percent over internal combustion engine (ICE) vehicles, with payback periods ranging from 11 to 25 years, placing them out of reach for most middle-income consumers.In contrast, electric two-and three-wheelers-comprising more than 90 percent of registered vehicles-offer a significantly more practical and affordable pathway for mass adoption.These vehicles exhibit minimal upfront cost differences, annual operational savings exceeding PKR 62,000, and short payback periods of just 4 to 6 months, making them highly feasible in the local context.The study adopts a mixed-methods approach using national price data, vehicle registration records, and international case studies from India, Kenya, and Norway.It evaluates financing innovations such as battery leasing, concessional green loans, and carbon-credit-linked microfinance, and outlines a consumer-focused policy framework that emphasizes financial inclusion, decentralized infrastructure development, and phased implementation strategies.By aligning global lessons with Pakistan's socioeconomic and infrastructural realities, the paper offers a scalable and inclusive roadmap for accelerating EV adoption through targeted, consumer-driven solutions.
Asad Mujeeb, Jamiu O. Oladigbolu, Mutiu Shola Bakare, Abduljelil Atima Ibrahim
As the global push for carbon neutrality accelerates, energy efficiency has become essential for sustainable development, especially for nations like Nigeria that face rising energy demands and significant environmental challenges. This study explores how integrating energy efficiency with carbon neutrality can support Nigeria's strategic energy goals while offering global lessons for other countries facing similar challenges, focusing on key sectors, including industry, transport, and power generation. The study systematically examines the impacts of renewable energy (RE) technologies, like solar, wind, and hydropowerâalongside policy reforms, technological innovations, and demand-side management strategies to advance energy efficiency in Nigeria. Key findings include the identification of strategic policy frameworks, technological solutions, and the transformative role of green hydrogen in decarbonizing hard-to-electrify sectors. The study also emphasizes the importance of international climate finance, decentralized RE systems like solar mini-grids for improving energy access, and economic opportunities for job creation in the RE sector. Furthermore, it highlights the need for behavioral changes, community engagement, and consistent policy implementation to address infrastructure gaps and drive energy efficiency goals. The novelty of this research lies in its scenario-based analysis of Nigeria's low-carbon transition, detailing both the opportunities and challenges, such as policy inconsistencies, infrastructure deficits, and financial constraints. The findings stress the importance of international collaboration, technological advancements, and targeted investments to overcome these challenges. By offering actionable insights and strategic recommendations, this study provides a roadmap for policymakers, industry stakeholders, and researchers to drive Nigeria towards a sustainable, carbon-neutral future by 2050.
This study presents a comprehensive ten-year (2015â2024) evaluation of renewable energy development in Cameroon, emphasizing its intersection with Sustainable Development Goals (SDGs) and broader cross-sectoral development outcomes. Combining time-series analysis of national capacity data, policy content evaluation, and SDG-aligned simulation modeling, the paper assesses both technical and institutional trajectories of the energy transition. Key findings reveal a substantial increase in off-grid installations in underserved regions and a notable rise in grid-connected solar capacityâfrom 0 MW in 2015 to 63 MW by 2024âdriven largely by post-2017 policy decentralization. Hydropower remains the dominant source, but the solar sector exhibited accelerated growth, contributing to enhanced rural electrification and public health infrastructure, with 27 % of rural health institutions now electrified. The renewable energy sector generated an estimated 3500 new jobs over the decade. An SDG alignment index applied across five targets indicates moderate but uneven progress, particularly for Goals 7 (affordable and clean energy), 3 (good health and well-being), and 13 (climate action). Scenario-based simulations underscore that policies promoting decentralized innovation and integrated energy planning significantly enhance rural energy access and socio-economic resilience. However, persistent financing barriers and institutional fragmentation constrain broader impact. The study offers a replicable analytical framework for data-driven, SDG-oriented assessment of energy transitions in Sub-Saharan Africa, contributing actionable insights for sustainable energy policy design in low-resource contexts. ⢠Renewable energy in Cameroon grew steadily between 2015 and 2024. ⢠Off-grid solar access expanded, boosting rural electrification progress. ⢠Policy reforms accelerated decentralized energy access and regulation. ⢠RE growth improved health, jobs, and equity across sectors. ⢠A roadmap aligns RE planning with SDG targets for Cameroon.
This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investorsâ reactions to information arrival in a âprimary market.â Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.
With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO2 emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities.
This deliverable examines political economy barriers to climate policy through sectoral entry points that can make transitions more just, feasible, and developmentcompatible. It focuses on three areas where governance and politics strongly shape outcomes: coal transition strategies, carbon pricing, and international finance. Across these domains, the report draws on seven peer-reviewed studies and working papers to highlight five strategic entry points: i) decentralized just transition planning, ii) clean industrial development, iii) revenue recycling with social protection, iv) strategic framing and coalition building for carbon taxes, and v) equity-focused international finance reforms. Coal transitions are shown to depend on domestic contexts. Comparative analysis of 12 coal-relevant countries reveals six distinct clusters of political economy dynamics, ranging from civil society-driven transition in South Africa to contested transition pathways in India and Indonesia. Case studies stress the need for regionally tailored approaches. Carbon pricing is politically viable when embedded in broader fiscal or political agendas. Evidence from 46 global policy attempts underlines the role of coalitions, leadership, and framing co-benefits. Microsimulations for 16 Latin American and Caribbean countries show regressive impacts, with many highly affected households lacking social protection. The international finance analysis assesses the G7 pledge and Clean Energy Transition Partnership, tracking shifts in public finance for energy across income groups. While fossil fuel support has declined, clean energy funding has not risen proportionally, remains loan-heavy, and is concentrated in wealthier nations. Low-income countries receive negligible concessional flows, while G7 members continue expanding domestic fossil infrastructure. The study recommends embedding distributive justice into finance governance, scaling grant-based clean energy support for the Global South and aligning domestic actions with international commitments.
Leonardo Soares dos Santos, Ana Paula Neutzling Gomes, P Rupino
The widespread adoption of distributed energy resources poses challenges to the operation and management of electricity grids. The intrinsic characteristics of such resources, such as variability and dispatchability, require increased flexibility in power systems. Demand-side flexibility is expected to play a significant role in future power systems, necessitating a more active role for consumers and prosumers in the energy system. To effectively operationalize flexibility and accommodate the growth of distributed generation, there is an urgent need for active and automated local management of energy resources alongside local transactions and energy exchanges. Technologies like blockchain and smart contracts offer significant potential for facilitating energy transactions within decentralized systems, mainly due to their capacity to facilitate secure microtransactions over time. Although their potential is recognized and review works exist, a detailed understanding of their characteristics and functionalities is lacking, which is critical for the deployment of those technologies. In this regard, the authors utilize the Prisma protocol to conduct a comprehensive analysis of current developments, identify primary innovative contract functionalities, quantify their utilization, and uncover potential gaps in their application in local energy transactions. They were analyzed 197 smart contracts, where 179 indicated at least one functionality. The findings suggest that most of these functionalities focus on energy transactions without details. They were identified and characterized in terms of the type of blockchain on which these smart contracts were developed. The conclusions show that they primarily work on a private Ethereum, promoting transactions between two peers in real-time and in the day ahead. The study culminates in an inclusive conclusion that spans the range of smart contract functionalities across different aspects of blockchain technology and temporal trade dynamics. This analysis reveals a significant gap in the transaction approach involving multiple sellers and buyers, underscoring the need for further exploration. This gap presents an exciting opportunity for future research and development in energy management, particularly in the context of blockchain's potential to facilitate local energy transactions. ⢠Review of smart contracts for energy trading based on 127 reviewed articles. ⢠Presentation of the smart contract's functionalities for energy trading. ⢠Critical features overview of reviewed energy trading platforms. ⢠Identification of the challenges in applying smart contracts in energy transactions. ⢠Recommendations to consider when implementing smart contracts for energy trading.
Efficient smart contract implementation affects gas fees required for deployment and invocation, contributing to the usability and sustainability of blockchain applications that rely on smart contracts. Optimizing smart contract codes is crucial to curb the continued rise of costs related to deploying and invocating smart contracts. This work proposes an extended version of static smart contract optimizer to reduce unnecessary gas fees caused by inefficient smart contract code implementation. Thirty open-licensed Ethereum smart contract codes in the Solidity programming language are included for optimization using the proposed static optimizer. The results show a decrease of 11,447 gas for deployment and 25 for invocation. Additional optimization using the Solidity compiler optimizer reveals a further gas reduction of 9,331 for deployment. Although there was a slight gas increase of 23 during invocation. These findings demonstrate the contribution of the proposed static optimizer in optimizing code implementation for Solidity smart contracts in terms of deployment and invocation. In addition to the gas reductions, the functionalities of the optimized smart contracts remain the same.
Urban areas in the Global South are at the forefront of the climate crisis, contributing over 70% of global CO2 emissions while lacking access to intelligent, transparent, and equitable carbon governance systems. Existing carbon markets, plagued by opacity, centralization, and static MRV (Monitoring, Reporting, and Verification) practices, are inadequate for dynamically managing decentralized, sectoral emissions in rapidly evolving megacities. This research proposes a novel, AI-powered carbon market intelligence framework that integrates cutting-edge technologies: Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), blockchain-enabled smart contracts, federated learning (FL), digital twins, and explainable AI (SHAP, LIME). The system is modular, privacy-preserving, and designed for real-time urban-scale decarbonization, adaptive policymaking, and citizen-level participation. Using Dhaka, Bangladesh, a climate-vulnerable megacity, as the primary use case, and Nairobi as a secondary scalability testbed, this study simulates a comprehensive pipeline: IoT sensors stream data to digital twins; AI models forecast emissions and carbon prices; smart contracts trigger transparent offset issuance; and federated models ensure localized learning without compromising data sovereignty. The system achieves high predictive accuracy (R2 > 0.92), 27.6% emission reductions in waste-energy sectors, and 12.3% gains in offset ROI over static baselines. Smart contract execution remains under 4.5 seconds, with negligible energy use under Proof-of-Stake blockchain. The explainability layer enhances stakeholder trust and policy interpretability, while gamified P2P carbon trading and participatory digital twins democratize climate action. The framework aligns with global instruments, including the UNFCCC Enhanced Transparency Framework, Article 6 mechanisms, Verra and Gold Standard protocols, and ICAOâs CORSIA, positioning it for integration into national and voluntary carbon markets. Ethical safeguards address algorithmic bias, data privacy, system resilience, and governance decentralization via DAOs. A full AI sustainability audit quantifies environmental trade-offs, demonstrating that avoided emissions exceed compute footprints by orders of magnitude. This paper delivers the first end-to-end, federated-AI and blockchain-driven carbon governance system for urban infrastructures in the Global South. It enables a paradigm shift toward real-time, transparent, and just carbon markets, offering a scalable blueprint for Net Zero-aligned smart cities worldwide. The proposed architecture not only advances scientific frontiers but also lays the groundwork for high-impact funding, policy integration, and global replication.
This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression (QQR), wavelet cross-quantilogram (WCQ), and advanced portfolio optimization strategies, our analysis captures asymmetric dependence, tail risks, and time-frequency dynamics from January 2015 to December 2024. Our results show that BTC consistently has strong hedging potential at lower quantiles, particularly during short-term stress, whereas GOLD and GBOND offer greater stability over medium- and long-term horizons. Conditional expected shortfall (CES) and extreme downside correlation (EDC) analyses highlight BTCâs resilience to extreme downside risks, whereas GOLD and GBOND serve primarily as long-term defensive assets. Portfolio optimization confirms BTCâs critical role in diversification under minimum correlation and connectedness strategies, and GBOND dominates variance-minimizing portfolios. These findings offer practical guidance for constructing robust, adaptive portfolios under geopolitical uncertainty.
This study aims to demystify the link between Bitcoin pricing and the associated electricity costs, constituting the most significant cost in mining Bitcoin. The article revisits the typical Cost-price (electricity consumption -Bitcoin price) relationship in the context of Bitcoin. The research question is answered using the Nonlinear Autoregressive Distributed Lag (NARDL) Model complemented with Multiple Breakpoints Least Squares Regression (MBLSR). The study analyzes monthly data from various sources from March 2017 to September 2023 and is segregated into four different regimes. The convergence in both techniques provides rigour and robustness to the results. The findings reveal the asymmetric relationship where Bitcoin's energy consumption does not increase significantly with a positive change in Bitcoin Price. This behaviour is counterintuitive given that electricity consumption is expected to increase in a high price period because of more profit margins. The flooding of accumulated Bitcoins by the miners in high price periods may be a contributing reason for no significant increase in the electricity consumption in mining Bitcoins. Conversely, the fall in Bitcoin prices will reduce the energy consumed by the Bitcoin Network conforming to the anticipated pattern. This behaviour is in stark contradiction to the Law of Supply and is well explained by the Bitcoin miners' operational strategy in the Boom and Recession period. Relying on the asymmetric behaviour, investors can revamp their strategy to make profits in the market. In addition, findings suggest policymakers try to limit credit accessibility to miners in the bust to reduce the colossal energy consumption of Bitcoin.
This study investigates the global adoption of Bitcoin by analyzing its price elasticity of demand (PED) across 46 countries or regions, with a focus on the interplay between economic, regulatory, and technological factors. Utilizing robust econometric techniques, including Huber regression, the research identifies significant variations in Bitcoin demand elasticity between developed and developing economies. The findings reveal that developed economies exhibit a mix of elastic and inelastic demand, driven by market maturity and discretionary consumption, while developing economies predominantly demonstrate inelastic demand, reflecting necessity-driven adoption amidst economic constraints. Key determinants of adoption include regulatory frameworks, such as legality, taxation, and anti-money laundering measures, alongside technological readiness indicators like blockchain infrastructure and internet penetration. These results underscore the critical influence of non-price factors on Bitcoinâs adoption dynamics and provide valuable insights for policymakers, investors, and industry stakeholders aiming to balance innovation with market stability. By offering a nuanced understanding of Bitcoin demand, this research contributes to the broader discourse on cryptocurrency adoption and its socioeconomic implications.
Research background: In todayâs digital age, traditional environmental, social, and governance (ESG) development paths are gradually facing challenges, including from digital technologies. In particular, the potential roles of artificial intelligence (AI), cloud computing (CC), and blockchain (BC) in the ESG market have not been fully explored. Purpose of the article: This study explores the deep integration of digital technology and ESG by evaluating the correlation and spillover effects among AI, CC, BC, and eight global ESG indices. Methods: This study explores the spillovers between AI, CC, BC, and eight global ESG indices by cross-quantilogram and quantile time-frequency connectedness approaches. Findings & value addition: The lower quantile of ESG returns has a weak positive (strong negative) correlation with the lower (upper) quantile of digital technology. Next, the spillover effects vary with time, frequency, and quantile levels. Meanwhile, the North America and Asia-Pacific developed ESG indices serve as the transmitter and receiver of spillover effects, respectively. Furthermore, the dependence between digital technology and ESG returns is insignificant before the COVID-19 crisis but increases after it. This quantile-dependent asymmetry fundamentally challenges linear assumptions prevalent in current ESG-technology integration theories. Overall, this study contributes by integrating AI, CC, BC, and ESG into a unified framework, and analyzing their interaction mechanisms. Furthermore, it dynamically analyzes the asymmetry over long and short-term horizons, and highlights the hedging role of digital technology in stabilizing ESG markets. Moreover, we provide novel insights about the interconnectedness between these markets, offering valuable guidance on risk management. Consequently, regulators should urgently explore the development of digital asset-based ESG derivatives as targeted risk mitigation tools. Positioned at the cutting-edge, this work sets a methodological benchmark for analyzing non-linear, frequency-sensitive interdependencies within the rapidly evolving ESG-digital nexus, transforming the theoretical framework from static linearities to dynamic non-linearities. Finally, this study proposes some reasonable suggestions, including raising risk awareness, promoting digital transformation, building integration and innovation platforms, and leveraging ESGâs diffusion role.
Mahsa Bashari, Saleh Ghavidel, Mehdi Fathabadi, Masoud Soufimajidpour
This study examines the environmental impact of cryptocurrency mining, specifically its contribution to CO2 emissions , in nine countries that account for 90% of global mining: the United States, China, Russia, Canada, Germany , Malaysia, Kazakhstan, Ireland, and Iran. Utilizing monthly panel data from 2019 to 2022 across nine countries and applying both pooled and fixed effects econometric techniques, the analysis reveals that âenergy intensityâ (the amount of energy used to produce a unit of GDP), as a moderator variable, influences the effect of cryptocurrency mining on CO2 emissions. Specifically, in countries where the annual energy intensity growth rate is greater than â 6 % , cryptocurrency mining tends to result in higher CO 2 emissions. Conversely, in countries with a growth rate of energy intensity below -6%, cryptocurrency mining results in lower CO2 emissions. The findings indicate that all nine countries experience a positive impact on CO2 emissions, albeit to varying degrees. The countries are categorized into three groups based on their performance: underperformers (Russia, the United States, Canada), neutral-effect countries (Iran, Kazakhstan, China), and positive performers (Ireland, Germany, Malaysia). This research underscores the urgent need for sustainable practices in cryptocurrency mining to mitigate its environmental effects.
This study examines the dynamic connectedness between Bitcoin and various financial assets, including the stock market, gold, oil, bonds, and exchange rates, as well as explores portfolio strategies involving these assets. The study covers the period from January 2, 2015, to March 1, 2024. The quantile connectedness approach and portfolio strategies are utilized in the analysis. The findings are as follows: Intermarket volatility spillover significantly increases under extreme conditions. Bitcoin emerges as a transmitter during bullish markets and acts as a receiver in bearish and normal market conditions. Gold serves as a receiver in extreme conditions and a transmitter in normal conditions. Unlike gold, oil acts as a transmitter under extreme conditions and functions as a receiver under normal conditions. Among the fundamental markets, the stock market is the most significant shock transmitter. In risk-mitigating portfolios, the proportion of Bitcoin is low, while the proportions of gold and the dollar index are high. Bitcoin has been found to have low hedging properties. <br />Implications for Central European Audience: Since the emergence of Bitcoin in 2008, the cryptocurrency market has developed rapidly. Bitcoin and cryptocurrencies have come to occupy an important place in financial markets in terms of value and volume. Bitcoin can affect portfolio management in the financial system in terms of diversification, hedging, risk management, portfolio strategies, and linkages between financial assets. This study investigates the linkages, hedging and portfolio strategies between Bitcoin and the stock market, gold, oil, bond and exchange rate markets. The results of the study are important for portfolio managers, risk managers, financial analysts and economic managers.
Ămit Cali, Annabelle Lee, Barry Hayes, ClĂĄudio Lima ¡ 23 authors
The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization , leading to the emergence of Distributed Ledger Technology (DLT) â particularly blockchain â as a promising tool for enhancing transparency, security, and efficiency in modern power systems . This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services , and peer-to-peer energy trading . The modelâs rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.
The energy sector underwent a significant transformation with increasing demand for efficiency, transparency, and sustainability. The traditional or conventional system often faces several challenges, such as inefficient energy trading, a lack of transparency in renewable energy generation verification, and complex regulatory guidelines that affect its widespread adoption. Thus, blockchain technology has emerged as a potential solution to overcome these challenges, as it is known for its transparent, secure, and decentralized nature. However, despite the promising application of blockchain, its integration into the energy supply chain (ESC) is underexplored. The purpose of this research is to analyze the potential applications of blockchain technology in ESC in order to enhance efficiency, transparency, and sustainability in energy systems. The aim is to investigate the integration of blockchain with emerging technologies (such as IoTs, smart contracts, and P2P energy trading) in order to optimize energy production, distribution, and consumption. Furthermore, by comparing different blockchain platforms (like Ethereum, Solana, Hedera, and Hyperledger Fabric), this study discusses the security and scalability challenges of using blockchain in energy systems. It also examines the practical use cases of blockchain for the tokenization of RECs, dynamic energy pricing, and P2P energy trading by providing the Energy Web Foundation and Power Ledger as real-world examples. The article concludes that blockchain technology has the potential to transform ESC by enabling decentralized energy trading, which subsequently enhances transparency in energy transactions and the verification of renewable energy generation. It also identifies smart contracts and tokenization of energy assets as key parameters for dynamic pricing models and efficient trading mechanisms. However, regulatory and scalability challenges remain significant obstacles to its widespread adoption. Finally, this study provides the basis for future advancement in the adoption of blockchain technology in ESC, which offers a valuable resource for industry professionals, regulating authorities, and researchers.
The aim of this study is to reveal the dynamics between climate policy uncertainty (CPU) and S&P Global Carbon Credit Index (CARBON), S&P Cryptocurrency DeFi Index (DeFi), and WilderHill New Energy Global Innovation Index (NEX) using data from December 2017 to March 2024 in the US. Fourier Bootstrap ARDL, Fourier Bootstrap quantile causality, and KRLS methods are used in the study. The findings reveal that there is a negative relationship between the CARBON and the CPU index in the long term. Although the DeFi does not have a statistically significant effect in the long term, it reveals that it has a negative effect on the CPU index in the short term. In contrast, the NEX has a positive relationship with the CPU index in both the short and long term. Moreover, there is a U-shaped non-linear relationship between the NEX and the CPU index, which weakens in moderate climate uncertainties and strengthens again in high uncertainty. Considering the causality results, there exists a causality from CARBON to CPU in the 2nd, 3rd, and 4th quantiles, and from CPU to CARBON in the 2nd and 3rd quantiles. Additionally, there is a causality from DeFi to CPU in the 8th quantile and from CPU to DeFi in the 1st quantile. Finally, there is a causal relationship from NEX to CPU in the 2nd, 3rd, 4th, and 5th quantiles and from CPU to NEX in the 9th quantile.
ABSTRACT This research examined the connection between Bitcoin, the prominent and extensively mined cryptocurrency, and CO 2 emissions using the SVAR model. Azerbaijan, Kazakhstan, and Russia, the three main countries in the Caspian Basin that are the centre of cryptocurrency mining, were examined in terms of their primary industries. The variance decomposition analysis indicated that the Bitcoin price had the most significant explanatory role in CO 2 emissions released by Oil and Natural Gas industry in Azerbaijan. When it comes to the CO 2 emissions that were emitted by the Petroleum RefiningâManufacture of Solid Fuels and Other Energy industry, as well as Manufacturing Industries and Construction, the Bitcoin price had the most important effect in Kazakhstan. There was a significant contribution made by Bitcoin to the CO 2 emissions that were emitted by the Manufacturing Industries and Construction in Russia. The impulse response functions illustrated a strong association between Bitcoin and CO 2 emissions. However, in contrast to existing research, this relationship was found to be negative. The increase in energy usage during Bitcoin price falls can be attributed to the need to compensate for losses, particularly in the mining process. To diminish this connection, the dependence of the cryptocurrency on fossil fuels must be minimised.