Search Results (23)

The modern electric power-distribution grid is increasingly integrating various components, including distributed sources of renewable energy, electric vehicles (EVs), and Bitcoin-mining operations. This shift signals a transformation in energy management and consumption. The growing presence of solar and wind energy contributes to a more diversified and sustainable energy mix, while the incorporation of EVs advances the pursuit of sustainable transportation. However, the addition of Bitcoin-mining operations introduces new complexities, raising concerns over energy consumption and grid stability. To address these challenges, this study conducted 24-h load-flow analyses on a power system that integrates intermittent renewable sources, Bitcoin-mining farms, and EVs, considering the variability in power demand. The analysis examined changes in bus voltage and power factor throughout the day using a Matlab/Simulink 2016b program. Simulation results indicate that bus voltages remained relatively stable despite the fluctuations in the generation of renewable energy and load variations. However, as the penetration of distributed generation of renewable energy increased, power factors exhibited a significant decline, dropping as low as 0.59 at certain buses due to increased injection of reactive power. At 13:00, during the period of peak generation of solar energy and high EV demand, voltage levels increased by up to 1.1 p.u., while power factors deteriorated significantly. This study highlights the importance of limiting the production of reactive power from local renewable sources under high-production conditions to sustain power factor stability. The findings emphasize the importance of detecting unfavorable system conditions and implementing safeguards to ensure reliable resource management in the evolving landscape of electric power-distribution grids. Full article

Improvements in quality of life, new technologies and population growth have significantly increased energy consumption in Brazil and around the world. The Paris Agreement aims to limit global warming and promote sustainable development, making green hydrogen a fundamental option for industrial decarbonization. Green hydrogen, produced through the electrolysis of water using renewable energy, is gaining traction as a solution to reducing carbon emissions, with the global hydrogen market expected to grow substantially. This study applies the ${\rho }_{DCCA}$ method to evaluate the cross-correlation between the green hydrogen market and various financial assets, including the URTH ETF, Bitcoin, oil futures, and commodities, revealing some strong positive correlations. It highlights the interconnection of the green hydrogen market with developed financial markets and digital currencies. The cross-correlation between the green hydrogen market and the index representing global financial markets presented a value close to 0.7 for small and large time scales, indicating a strong cross-correlation. The green hydrogen market and Bitcoin also presented a cross-correlation value of 0.4. This study provides valuable information for investors and policymakers, especially those concerned with achieving sustainability goals and environmental-social governance compliance and seeking green assets to protect and diversify various traditional investments. Full article

This article delves into the transformative impact of blockchain technology on enhancing transaction quality and efficiency. Since the emergence of blockchain alongside Bitcoin in 2008, its decentralised and transparent nature has significantly improved transaction speed, security, and cost efficiency. These advancements have solidified blockchain as a foundational innovation in financial services. The paper examines critical milestones in blockchain, including Bitcoin, Ethereum, and Binance Coin (BNB), and their role in reshaping global finance by automating processes and reducing reliance on intermediaries. Additionally, the study evaluates blockchain’s impact on quality management, particularly emphasising how its immutable ledger system enhances the reliability and transparency of financial transactions. Despite challenges such as scalability, energy consumption, and regulatory hurdles, the potential for blockchain to redefine transaction quality in financial services is evident. This research contributes to the growing body of literature by integrating blockchain technology and traditional quality management systems, providing a comprehensive perspective on how the two domains influence one another. The findings underscore blockchain’s ability to drive innovation in financial services while addressing security, efficiency, and operational quality concerns. Full article

Blockchain has several unique features: data integrity, security, privacy, and immutability. For this reason, it is considered one of the most promising new technologies for a wide range of applications. Initially prominent in cryptocurrencies such as Bitcoin, its applications have expanded into areas such as the Internet of Things. However, integrating blockchain into IoT systems is challenging due to the limited computing and storage capabilities of IoT devices. Efficient blockchain mining requires lightweight hash functions that balance computational complexity with resource constraints. In this study, we employed a structured methodology to evaluate hash functions for blockchain–IoT systems. Initially, a survey is conducted to identify the most commonly used hash functions in such environments. Also, this study identifies and evaluates a lightweight hash function, designated as HashLEA, for integration within blockchain-based IoT systems. Subsequently, these functions are implemented and evaluated using software coded in C and Node.js, thereby ensuring compatibility and practical applicability. Performance metrics, including software efficiency, hardware implementation, energy consumption, and security assessments, were conducted and analyzed. Ultimately, the most suitable hash functions, including HashLEA for blockchain–IoT applications, are discussed, striking a balance between computational efficiency and robust cryptographic properties. Also, the HashLEA hash function is implemented on a Raspberry Pi 4 with an ARM processor to assess its performance in a real-world blockchain–IoT environment. HashLEA successfully passes security tests, achieving a near-ideal avalanche effect, uniform hash distribution, and low standard deviation. It has been shown to demonstrate superior execution time performance, processing 100 KB messages in 0.157 ms and 10 MB messages in 15.48 ms, which represents a significant improvement in execution time over other alternatives such as Scrypt, X11, and Skein. Full article

Bitcoin, which has been used for 13 years, has a role in transactions and investments as a major cryptocurrency. However, as the number of users increases, Bitcoin faces difficulties, such as scalability for transaction throughput and energy-consumption problems due to the concentration of the mining pool. When Bitcoin first started to come out, it began to develop gradually through the mining of individuals. Nevertheless, as the price of the cryptocurrency gradually climbed, large mining corporation groups entered the mining competition with integrated circuit (IC) chips. Consequently, the substantial increase in power consumption is raising concerns regarding energy expenditure. This paper confirms that the verifiable random selection consensus protocol based on proof of work facilitates a fair and efficient system, enabling the participation of numerous individual miners in the mining competition while counteracting the monopolization of the hash rate by large mining corporations, thereby preserving the decentralization of mining. The protocol demonstrates the potential to mitigate substantial energy consumption. Moreover, it embodies features that create barriers to the adoption of high-energy-consuming application-specific integrated circuit equipment, significantly diminishing the principal factors contributing to extensive power utilization. Full article

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO₂ eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO₂ equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers. Full article

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO₂ emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO₂ emissions as well as causality from energy consumption to CO₂ emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech. Full article

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption. Full article

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. Full article

Bitcoin, the most valuable and energy-consuming cryptocurrency, has recently been at the center of a heated debate over its environmental impact. This controversy has caught the public’s attention, prompting us to investigate the energy consumption of Bitcoin. In this paper, we have conducted a review of the literature on various aspects of Bitcoin mining, including its mechanisms, energy consumption, mining sites, and the potential for renewable energy use. Our findings reveal that the power consumption of Bitcoin is bound to increase with the continued adoption of the proof-of-work (PoW) consensus algorithm. Nonetheless, the growing availability of affordable renewable energy sources worldwide brings hope that Bitcoin mining will shift towards cleaner energy in the near future. Full article

Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this paper broadens the perspective by evaluating energy consumption across various cryptocurrencies. We analyze nine cryptocurrency projects, chosen for their market value, technology, and data availability. These span a spectrum from pioneering to emerging digital coins, offering a holistic view of the crypto realm. To contextualize, we juxtapose the energy usage of these digital currencies with traditional payment means like Visa and Mastercard. Our analysis shows vast differences in energy use among cryptocurrencies, largely tied to their consensus algorithms. Notably, while Bitcoin stands out as highly energy-intensive, several newer digital currencies have energy footprints mirroring those of conventional payment methods. Additionally, CO₂ emissions estimation presents challenges due to variances in miner locations and regional energy sources, with potential higher emissions if concentrated in carbon-intensive regions like China. Nonetheless, the silver lining emerges as many cryptocurrencies, especially those beyond Bitcoin, register considerably lower CO₂ emissions. By moving the lens beyond Bitcoin, this paper paints a more nuanced picture of the environmental ramifications of the crypto world. Full article

Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers who intend to inflict significant infections on a power system. Simply put, an effort to phony up the consumption data of mining devices results in the furtherance of messing up the optimal energy management within the power system. Hence, this paper introduces a new cyber-attack named miner-misuse for power systems equipped by transaction tech. To overwhelm this dispute, this article also addresses an online coefficient anomaly detection approach with reliance on the reinforcement learning (RL) concept for the power system. On account of not being sufficiently aware of the system, we fulfilled the Observable Markov Decision Process (OMDP) idea in the RL mechanism in order to barricade the miner attack. The proposed method would be enhanced in an optimal and punctual way if the setting parameters were properly established in the learning procedure. So to speak, a hybrid mechanism of the optimization approach and learning structure will not only guarantee catching in the best and most far-sighted solution but also become the high converging time. To this end, this paper proposes an Intelligent Priority Selection (IPS) algorithm merging with the suggested RL method to become more punctual and optimum in the way of detecting miner attacks. Additionally, to conjure up the proposed detection approach’s effectiveness, mathematical modeling of the energy consumption of the mining devices based on the hashing rate within BFTS is provided. The uncertain fluctuation related to the needed energy of miners makes energy management unpredictable and needs to be dealt with. Hence, the unscented transformation (UT) method can obtain a high chance of precisely modeling the uncertain parameters within the system. All in all, the F-score value and successful probability of attack inferred from results revealed that the proposed anomaly detection method has the ability to identify the miner attacks as real-time-short as possible compared to other approaches. Full article

Blockchain is a peer-to-peer trustless network that keeps records of digital assets without any central authority. With the passage of time, the sustainability issue of blockchain is rising. This paper discusses two major sustainability issues of blockchain: power consumption and scalability. It discusses the challenge of power consumption by analyzing various approaches to estimating power consumption in the literature. A case study of bitcoin is presented for this purpose. The study presents a review of the growing energy consumption of bitcoin along with a solution for immersion cooling in blockchain mining. The second challenge addressed in this research is scalability. With the increase in network size, scalability issues are also increasing as the number of transactions per second is decreasing. In other words, blockchain is observing low throughput with its increase in size. The paper discusses research studies and techniques proposed in the literature. The paper then investigates how to scale blockchain for better performance. Full article

Internet of Things (IoT) is used to describe devices with sensors that connect and exchange data with other devices or systems on the Internet or other communication networks. Actually, the data not only represent the concrete things connected but also describe the abstract matters related. Therefore, it is expected to support trust on IoT since blockchain was invented so that trusted IoT could be possible or, recently, even metaverse could be imaginable. However, IoT systems are usually composed of a lot of device nodes with limited computing power. The built-in unsolved performance and energy-consumption problems in blockchain become more critical in IoT. The other problems such as finality, privacy, or scalability introduce even more complexity so that trusted IoT is still far from realization, let alone the metaverse. With general Proof of Work (GPoW), the energy consumption of Bitcoin can be reduced to less than 1 billionth and proof of PowerTimestamp (PoPT) can be constructed so that a global even ordering can be reached to conduct synchronization on distributed systems in real-time. Therefore, trusted IoT is possible. We reintroduce GPoW with more mathematic proofs so that PoPT can be optimal and describe how PoPT can be realized with simulation results, mining examples and synchronization scenario toward trusted IoT. Full article

Proof-of-Work (PoW) was the first blockchain consensus protocol discovered, followed by Proof-of-Stake and others. The disadvantage of the PoW is that it requires high energy consumption compared to other consensus protocols. Based on this weakness, some researchers proposed a lightweight blockchain technology, a modified blockchain that has a simplified algorithm but does not reduce the security factor. This lightweight blockchain is suitable for applications requiring data reliability but with limited computing resources, such as Internet of Things devices. This paper discussed and modified the SimBlock simulator as one of the existing blockchain simulators. It has a visualization tool to look further into the propagation transition of the block. Unfortunately, the existing PoW consensus on the SimBlock simulator is unable to pinpoint the actual hash computation method. Therefore, the hashing process in the SimBlock simulator was modified by including the difficulty level for finding the hash target. The purpose of including the difficulty level was to determine how long it takes to create a block. By knowing the time needed, a recommendation could be obtained for the most suitable difficulty level for a lightweight blockchain and its implementation with IoT devices. There are two options of approaches to the difficulty level referred to in this paper; finding the number of zeros that appear sequentially and are in front of a hash value (leading zero) and finding the number of zeros that appear arbitrarily (count zero). For example, the first difficulty level on a leading-zero quest has the same meaning as searching for a leading zero, the second level of difficulty is the search for the two leading zeros, etc. The block generation time on a blockchain network using the PoW consensus highly depends on the difficulty level. Block generation time and resource utility have been analyzed and compared with other blockchain simulators and existing networks, such as Ethereum and Bitcoin. The modified SimBlock simulator was tested in this experiment using 100–600 nodes, with the expected result of creating 100–1000 blocks. Based on the experiments, creating a block using leading zeros as the hash target for the first to fourth difficulty levels took less than 1 s, whereas when using count zeros (zero-count) as the target hash, it took less than 1 s for the first to fifteenth difficulty levels. Using leading zeros took approximately 237.4 s at difficulty level 7, while count-zero took approximately 633.8 s at difficulty level 19. The experiment was not continued at the next difficulty level because it required a longer compilation time. With the modifications made, the creation of a block on a blockchain network using the PoW consensus can be clearly seen. The difficulty level added to the simulator can also provide information for determining the difficulty level to be implemented on the lightweight blockchain. Full article