• Energy Research

Cryptocurrencies have gained a lot of attention in recent years, mostly due to their decentralized manner of operation and their growth in value. However, a major drawback most of them possess is their high energy consumption. Current solutions to this problem have significant l imitations: bringing back centralization and/or substituting the required energy with, e. g., storage space. This paper aims to address the problem by investigating the use of a two-level deep reinforcement learning (RL) model to design incentive policies for green mining in cryptocurrencies. This is done by modeling one such energy-intensive cryptocurrency system and creating an RL environment. Finally, by running simulations in an RL environment, we develop and test incentive policies, according to which cryptocurrency participants who primarily use renewable energy for their mining operations are more likely to add new blocks to the blockchain. Our results show that even when the green score of each crypto miner (determined by their use of green energy sources) has relatively small importance (up to 0.3) in their selection probability, miners still shift towards green mining in order to increase their chance of being picked to validate cryptocurrency transactions and receive the corresponding rewards.

The high electricity consumption of cryptocurrencies that rely on proof-of-work (PoW) consensus algorithms has raised serious environmental concerns due to its association with carbon emissions and strain on energy grids. There has been significant research into estimating the electricity consumption of PoW-based cryptocurrencies and developing alternatives to PoW. In this article, we introduce refined models to estimate the electricity consumption of two prominent alternatives: Ethereum, now utilizing proof-of-stake (PoS), and Filecoin, which employs proof-of-spacetime (PoSt). Ethereum stands as a leading blockchain platform for crafting decentralized applications, whereas Filecoin is recognized as the world's foremost decentralized data storage network. Prior studies for modeling electricity consumption have been criticized for methodological flaws and shortcomings, low-quality data, and unvalidated assumptions. We improve on this in several ways: we obtain more novel, validated data from the systems in question, extract information from existing data and research, and we improve transparency and reproducibility by clearly explaining and documenting the used methodology and explicitly stating unavoidable limitations and assumptions made. When comparing the current, most prominent models for Ethereum and Filecoin to our refined models, we find that given the wide error margins of both the refined models and the ones introduced in prior literature, the resulting average estimates are to a large extent in line with each other. The final version of the article will be published in ACM SAC 2024

There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to estimate their energy consumption accurately. Existing studies, both in academia and industry, attempt to model cryptocurrency energy consumption often based on a number of assumptions, for instance, about the hardware in use or the geographic distribution of the computing nodes. A number of these studies have already been widely criticized for their design choices and subsequent over- or under-estimation of energy use.In this study, we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain, such as social energy sciences and information systems. We first design a quality assessment framework based on existing research, and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these issues.Our goal with this article is to advance the field by promoting scientific rigor in studies focusing on blockchain energy footprint. To that end, we provide a novel set of codes of conduct for the five most widely used research methodologies: Quantitative energy modeling, literature reviews, data analysis and statistics, case studies, and experiments. We envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems' energy and environmental footprint.

Bitcoin is an electronic currency that has become increasingly popular since its introduction in 2008. Transactions in the bitcoin system are stored in a public transaction ledger (‘the blockchain’), which is stored in a decentralized, peer-to-peer network. Bitcoin provides decentralized currency issuance and transaction clearance. The security of the blockchain depends on a compute-intensive algorithm for bitcoin mining, which prevents double spending of bitcoins and tampering with confirmed transactions. This ‘proof-of-work’ algorithm is energy demanding. How much energy is actually consumed, is subject of debate. We argue that this energy consumption currently is in the range of 100–500 MW. We discuss the developments in bitcoin mining hardware. We also briefly outline alternative schemes that are less energy demanding. We finally look at other blockchain applications, and argue that also here energy consumption is not of primary concern.