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.
Chi Keung Marco Lau, Alaa M. Soliman, Dongna Zhang
This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages.
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.
Syed Muhammad Danish, Kaiwen Zhang, Fatima Amara, Juan Carlos Oviedo Cepeda · 6 authors
Climate change is a major issue that has disastrous impacts on the environment through different causes like the greenhouse gas (GHG) emission. Many energy utilities around the world intend to reduce GHG emissions by promoting different systems including carbon emission trading (CET), renewable energy certificates (RECs), and tradable white certificates (TWCs). However, these systems are centralized, highly regulated, and operationally expensive and do not meet transparency, trust and security requirements. Accordingly, GHG emission reduction schemes are gradually moving towards blockchain-based solutions due to their underpinning characteristics including decentralization, transparency, anonymity, and trust (independent from third parties). This paper performs a comprehensive investigation into the blockchain technology, deployed for GHG emission reduction plans. It explores existing blockchain solutions along with their associated challenges to effectively uncover their potentials. As a result, this study suggests possible lines of research for future enhancements of blockchain systems particularly their incorporation in GHG emission reduction.
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.
Non-fungible tokens (NFTs) are digital assets that represent ownership of a particular item or can represent in-game items or virtual real estate. They are exclusive and limited in quantity, and their ability to be modified and controlled is what makes digital assets so valuable. The objective of the initiative is to develop interactive and immersive gaming experiences by utilizing the unique capabilities of NFTs. This concept is made possible through the use of smart contracts, which decentralize the ownership of NFTs and increase their desirability. The endeavor entails the creation of two online games employing NFTs. “Obstacle Assault” is a side-scrolling game in which players guide a character through obstacles and adversaries to reach the level&s;s conclusion. The game will use NFTs to depict weapons, armor, and power-ups that can be purchased and used to improve the player&s;s character. “Turtle Sidestep” is a puzzle game in which players must guide a turtle through a succession of obstacles in order to reach the level&s;s conclusion. NFTs will be used to depict virtual real estate in this game, allowing players to purchase and own specific locations within the game world.
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.
As the demand for renewable energy grows, there is a need for efficient and transparent mechanisms to facilitate renewable energy trading. This research presents a novel Renewable Energy Trading Platform (RETP) that leverages blockchain technology to enable secure and decentralized energy trading. The platform utilizes distributed ledger technology to record and verify energy transactions, ensuring transparency and immutability. Smart contracts are employed to automate trade execution and settlement, eliminating the need for intermediaries and reducing transaction costs. The RETP integrates renewable energy data from various sources, such as smart meters and IoT devices, to enable accurate tracking and verification of energy production and consumption. Through the implementation of the RETP, energy producers can directly sell excess renewable energy to consumers, promoting the adoption of green energy and enhancing energy grid efficiency. A comprehensive evaluation of the platform demonstrates its efficiency, scalability, and security. The proposed RETP has the potential to revolutionize the renewable energy market by fostering peer-to-peer energy trading, empowering energy communities, and accelerating the transition to a sustainable energy future.