Purpose The purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states. Design/methodology/approach This study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability. Findings Key findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones. Originality/value This study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.
This study investigates the asymmetric impacts of Bitcoin prices on Bitcoin energy consumption. Two series are shown to be chaotic and non-linear using the BDS Independence test. To take into consideration this nonlinearity, we employed the QNARDL model as a traditional technique and Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) as non-conventional approaches to study the link between Bitcoin energy usage and Bitcoin prices. Referring to QNARDL estimates, results show that the relationship between Bitcoin energy use and prices is asymmetric. Additionally, results demonstrate that changes in Bitcoin prices have a considerable effect, both short- and long-run, on energy consumption. As a result, any upsurge in the price of Bitcoin leads to an immediate boost in energy use. Furthermore, the short-term drop in Bitcoin values causes an increase in energy use. However, higher Bitcoin prices reduce energy use in the long run. Otherwise, every decline in Bitcoin prices leads to a long-term reduction in energy use. In addition, the performance metrics and convergence of the cost function provide evidence that the XGBoost model dominates the SVM model in terms of Bitcoin energy consumption forecasting. In addition, we analyze the effectiveness of several modeling approaches and discover that the XGBoost model (MSE: 0.52%; RMSE: 0.72 and R2: 96%) outperforms SVM (MSE: 4.89; RMSE: 2.21 and R2: 75%) in predicting. Results indicate that the forecast of Bitcoin energy consumption is more influenced by positive shocks to Bitcoin prices than negative shocks. This study gives insights into the policies that should be implemented, such as increasing the sustainable capacity, efficiency, and flexibility of mining operations, which would allow for the reduction of the negative impacts of Bitcoin price shocks on energy consumption.
Syed Muhammad Danish, Aroosa Hameed, Ali Ranjha, Gautam Srivastava · 5 authors
The increased charging demand resulting from the rapid development of electric vehicles (EVs) poses various challenges to the stable operation of the distribution network and smart grid. Due to the stochastic EV charging behaviour, the high charging demand at the charging stations (CSs) elevates the load curve which may lead to a spatially imbalanced load demand. As such, forecasting the highly stochastic EV charging load considering an individual EV's unique charging behaviour can result in maintaining the safe operation of the grid and distribution network. Therefore, in this work, we propose Block-FeDL, a blockchain-based Federated Learning (FL) approach for EV charging load forecasting considering the private and sensitive charging information of each EV user. Thereafter, we use a Bidirectional Long Short Term Memory (BiLSTM) model within the FeDL for predicting the EV charging load patterns at each client. Moreover, instead of using a centralized server for global model aggregation, we use blockchain technology, where the model aggregation is performed in a decentralized manner and the local model parameters shared by the FL clients can be validated and securely recorded. Lastly, the results show that the Block-FeDL outperform the second-best baseline method by 95%, 96% and 77% in terms of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for forecasting the EV charging load.
Abstract This paper extends the climate literature by examining the feasibility of integrating sub‐national governance into global club governance for mitigating CO 2 emissions. Global climate clubs become an argument for having separate bundles of emission targets and incentive mechanisms in the form of opportunities for climate finance and technology sharing among the club members. An exploratory analysis is important to examine the role of import and export taxes and other channels, such as the clean development mechanism, in meeting the objective of nonmember countries to join the club. The crux, however, is how, after determining national‐level quotas, the mitigation responsibilities are shared with subnational entities. We propose a design of a carbon entry tax at the subnational level, namely states, districts, and municipalities. The carbon entry tax uses the nighttime luminosity data published by NASA as a measure of carbon, which constitutes the tax base. The carbon entry tax serves as a fiscal instrument of decarbonization in a decentralized framework.
The visitor economy is responsible for a substantial percentage of the global carbon footprint. The mechanisms used to decarbonize it are insufficient, and the industry is relying on carbon trading with substandard credits that allow businesses to outsource the responsibility to decarbonize. We aim to transform carbon markets, help finance climate investments, and support decarbonization strategies. We identify and define the problem, outline the components and their interactions, and develop a conceptual model to transform carbon markets. The new, blockchain-based Carbon Tokenomics Model rolls out a decentralized database to store, trade, and manage carbon credits, with the goal of enabling sustainable climate finance investment. We outline the criteria needed for an industry-wide carbon calculator. We explain the process needed to increase rigor in climate investments in the visitor economy and introduce a delegated Proof of Commitment consensus mechanism. Our inclusive and transparent model illustrates how to reduce transaction costs and how to build consumer and industry trust, generating much-needed investments for decarbonization.
He Peng, Yao Sun, Jianli Hao, Chunjiang An · 5 authors
Ground transportation, which includes the road and rail sectors, is a major source of greenhouse gas (GHG) emissions. An emissions trading system (ETS) is one of the environmental policies for controlling carbon emissions from fossil fuel combustion during transport activities. To investigate the status of existing ETS interventions on ground transportation emissions, a comprehensive literature review is conducted, including policy evaluation and comparison, scheme optimization and design, and specific transport behavior interventions. The results show that existing upstream policies are insufficient in stringency and effectiveness, while policy implementation for downstream interventions remains limited. To address these shortcomings, this study proposes a downstream ground transportation emissions trading system (GTETS), incorporating the Internet of Things and blockchain technology. As well as road and rail transportation emissions, the system includes the emissions-related activities of individuals and transportation companies, and its Web3-based technologies provide effective and efficient monitoring, reporting, and verification.
Thobekile Qabhobho, Cwayita Mpuku, Izunna Anyikwa, Andrew Phiri
since the onset of the cOViD-19 pandemic, african currencies, cryptocurrencies, and commodity markets have undergone significant fluctuations, displaying fat-tail properties that lies at the outer ends of the normal probability curve.the recent Russia-Ukraine war has further disrupted these markets, generating considerable interest among academics and practitioners.Our study delves into tail-end returns and volatility connectedness between Bitcoin, crude oil, gold, and four african currencies amidst the cOViD-19 and Russia-Ukraine war.employing a quantile vector autoregressive (QVaR) approach, we analyze tail-end spillover effects between markets from 4 november 2019, to 7 september 2022.Our findings reveal heightened connectedness at the quantile ends of co-movements, with left-tail spillovers being more pronounced for returns, while right-tail spillovers dominate for volatility.Bitcoin, and to a lesser extent gold and oil, emerge as effective tail-ended hedges for the egyptian Pound and nigerian naira but not for other african currencies like the algerian Dinar and south african Rand.consequently, users of egyptian and nigerian currencies in international financial markets can seek hedging opportunities in traditional cryptocurrencies and commodities during recent Black swan events, unlike those using south african and algerian currencies.additionally, our results suggest limited diversification benefits associated with (i) currencies linked to oil-exporting or oil-importing countries, (ii) currencies linked to shariah-compliant financial systems, but do indicate diversification benefits in high-inflation environments.these findings hold relevance for investors seeking improved hedging strategies against african currency risk and for african policymakers aiming to enhance intra-continental trade, foreign direct investment, and cross-border business expansions.
We analyze the hedging feature of gold against inflation by analyzing the factors affecting gold prices for the post-2013 period, including the tapering process in the United States. Our results show that especially demand for gold Exchange Traded Funds (ETFs) and US 10-year bond rates are effective on gold prices in this period. Inflation has no statistically significant effect on gold prices over the sample period; however, in the subperiod, excluding 2014–2019, inflation has a statistically significant positive impact on gold prices. We conclude that gold does provide a partial hedge against inflation as an investment tool, at least for the recent period. Furthermore, our analysis of Bitcoin’s effect on gold prices starting in the second half of 2016 shows no statistically significant relationship.
This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration.
Renewable energy trading could be considered the next step in power trading's development. It is probable that individuals currently involved in power trading will need to upgrade their data collection, processing, and reporting systems. This article provides a comprehensive evaluation of renewable energy trading utilizing Blockchain technology. Initially, the paper examines country-specific renewable energy trading with a focus on India, China, the US, France, and Germany's renewable energy policies. Moreover, the paper presents potential renewable energy trading markets such as peer-to-peer, over the grid, and partially or fully independent microgrid's. This paper shows the appraisal of bond, commodity, derivative, and algorithm-based renewable energy trading using different Blockchain methods, including Ethereum and R3 Corda. It is find out during the renewable energy trading, proposers of bid, also include capital cost of the renewable energy power plant, salvage value after useful life of different component of renewable energy power plant. It is also find out proper trading is to be done with offering subsidies of up to 70% of the capital cost, and with a 30% viability gap finance (VGF) at this cost.
Oktay Özkan, Salah Abosedra, Arshian Sharif, Andrew Adewale Alola
Abstract The objective of this paper is to assess the dynamic volatility connectedness between fossil energy, clean energy, and major assets i.e., Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500 from September 17, 2014 to October 11, 2022. The main motivation of the study relates to examining the dynamic volatility connectedness mentioned during periods of important events such as the recent coronavirus pandemic and the Russia–Ukraine conflict which has shown the vulnerability of economic and financial assets, energy commodities, and clean energy. The novel Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) approach is employed for the investigation of the sample period mentioned. Empirical analysis reveals that both the total and net volatility connectedness between assets is time-varying. The highest connectedness among the assets is observed with the onset of the coronavirus (COVID-19) pandemic, and it increases with some important international events, such as the Russia–Ukraine conflict, the referendum of Brexit, China–US trade war, and Brexit day. On average, the result shows that 32.8% of the volatility in one asset spills over to all other assets. The DCC-GARCH results also indicate that crude oil, bonds, and Bitcoin act as almost pure volatility transmitters, whereas the Dollar index, gold, and S&P500 act as volatility receivers. On the other hand, clean energy is found neutral to external shocks until the first quarter of 2020 and after that time, it starts to behave as a volatility transmitter. Based on the obtained results, we offer some specific policy implications that are beneficial to the US economy and other countries. Graphical Abstract Dynamic volatility connectedness between fossil energy, clean energy, and major assets (Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500)
This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.
Moein Qaisari Hasan Abadi, Russell Sadeghi, Ava Hajian, Omid Shahvari · 5 authors
The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the Autoregressive Integrated Moving Average (ARIMA) algorithm has the highest accuracy compared to other algorithms.
The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.
Tianqi Jiang, Haoxiang Luo, Kun Yang, Gang Sun · 7 authors
The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the energy market.
Carbon dioxide emissions are a major cause of climate change and its negative impacts on the environment and human well-being. To address this problem, a blockchain-based decentralized system for carbon emission trading is proposed, allowing anyone to trade carbon allowances as a commodity. The proposed system leverages the advantages of blockchain technology, such as transparency, immutability, and security, to enable efficient and trustworthy transactions among peers without intermediaries. Additionally, the system provides flexibility by enabling customization of permits, represented as Non-Fungible Tokens, thereby enhancing market accessibility and engagement. A graph neural network for identity inference is introduced to infer the attributes of accounts on the blockchain, such as their type and behavior. This can help detect and prevent illegal or malicious activities on the chain, as well as understand and improve user behavior patterns and preferences. The aim is to increase market inclusiveness and diversity, reduce transaction costs and carbon price volatility, and enhance the traceability and verifiability of carbon emissions.
There is increased interest in the dynamic relationships between cryptocurrency and commodity futures. This study examines the hedging performance of four well-known commodity futures against fluctuations in Bitcoin prices. Furthermore, this study used the DCC- and ADCC-MGARCH models to estimate conditional correlations and time-varying optimal hedge ratios between the returns of copper, gas, gold, and crude oil futures, and Bitcoin. We use a rolling window method to calculate one-step-ahead time-varying optimal hedge ratios and evaluate hedging performance. The empirical results show that gas and gold have hedge benefits to Bitcoin. However, crude oil shows poor hedge performance. From the results of one-step-ahead hedge ratios, for copper and oil, we find that hedge ratios increased and hedge effectiveness improved since the COVID-19 outbreak.
The blockchain has been proposed for use in various applications in the energy field. Although the blockchain has technical strengths, several obstacles affect the application of the technology in energy services. The scope of this study is to highlight and prioritise the most important barriers to such applications. The first step in this direction is specifying the potential areas of the implementation of blockchain technology in the energy sector. Two useful tools for market analysis were used: Political, Economic, Social, Technological, Legal and Environmental, PESTLE Analysis, and Strengths, Weaknesses, Opportunities and Threats, SWOT Analysis, which examine external and internal factors, respectively. Thus, a list of the most important elements hindering the incorporation of the blockchain in the energy sector was extracted. The detected barriers were classified and ranked by energy and IT experts using the multicriteria method, “Analytical Hierarchy Process for Group Decision Making”. The results reveal that legal barriers relating to the complexities of deficiencies of regulations are the most significant, while technological barriers, especially those related to security issues, are also important. Sociopolitical barriers related mainly to lack of trust in blockchain, as well as economic concerns such as high upfront costs, are less influential but should still be considered. The conclusions of the conducted research have the potential to guide market actors in their endeavours to modernise energy systems through the use of the blockchain, assisting them in designing the most appropriate market strategies.
Sahar Yousif Mohammed, Thaar Kh. Asman, Hadeel M Salih, Alaa Mohammed Mahmood
These days, we are observing a very rapid spread of the electric vehicleindustry. This means a significant increase in the data and energy exchanged betweenthese vehicles. The existing centralized approach is less secure and more vulnerableto data destruction and manipulation by intruders. Therefore, it became necessary tosearch for an alternative that provides excellent protection for this massive amountof data and energy. Although blockchain technology and cryptocurrencies are closelyassociated, they also have many other potential applications in fields including energyand sustainability, the Internet of Things (IoT), smart cities, smart mobility, andmore. In the Internet of Vehicles (IoV) idea, blockchain can provide security forelectric vehicle (EV) transactions, enabling electricity trading to be carried out ina decentralized, transparent, and secure manner. . This paper will explain the use ofblockchain in this field and how it can handle the trade of transmitted and receivedenergy between electric vehicles. The advantages of using blockchain with electriccars and how it can secure the transactions of energy trading will be shown too. Agroup of researchers in this field and the challenges that face this technology in energytrading will be discussed too; the studies will be looked at, and recommendations forinvestments and security will be made. Additionally, the future implications of variousblockchain technologies will be highlighted.
Mohammad Parhamfar, Iman Sadeghkhani, Amir Mohammad Adeli
Abstract The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem.
The task of carbon emission reduction is severe in the power industry in China under the national goal of “carbon peaking and carbon neutrality”. The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users’ utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry.
Purpose Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets. Design/methodology/approach This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set. Findings The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022. Originality/value The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
The widespread adoption of electric cars (EVs) can be attributed to their many advantages over conventional gas-powered automobiles. However, there may be difficulties in incorporating EVs into the grid due to increased energy demand and peak load. We propose a blockchain-based federated learning scheme using different linear regression algorithms for energy demand prediction for EVs. The information gathered from EVs is stored on the blockchain network. Only those with the proper credentials can decrypt the data from its encrypted storage. Data from EVs is utilized to train a machine learning model with the use of a federated learning algorithm. Each EV is used to train a model, and then the models’ parameters are distributed throughout the blockchain. Our approach is innovative in analyzing of BCFL communications overhead and latency issues, while delving deeper into its dynamics to measure and reduce communication delays to maximize system efficiency. The implementation results verify the effectiveness of our system in anticipating EVs’ energy requirements. For the training of the BCFL model, a huge real-world dataset was used from over 60,000 transactions at EV charging stations in Boulder city, Colorado. The results show that the framework is reliable, since all the models have R2values above 0.91, which indicates a high degree of accuracy in predicting energy use.