Does Crypto Kill? Relationship between Electricity Consumption Carbon
Footprints and Bitcoin Transactions
- URL: http://arxiv.org/abs/2206.03227v1
- Date: Mon, 16 May 2022 18:03:45 GMT
- Title: Does Crypto Kill? Relationship between Electricity Consumption Carbon
Footprints and Bitcoin Transactions
- Authors: Altanai Bisht, Arielle Wilson, Zachary Jeffreys, Shadrokh Samavi
- Abstract summary: We predict that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources.
We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
- Score: 4.7805617044617446
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cryptocurrencies are gaining more popularity due to their security, making
counterfeits impossible. However, these digital currencies have been criticized
for creating a large carbon footprint due to their algorithmic complexity and
decentralized system design for proof of work and mining. We hypothesize that
the carbon footprint of cryptocurrency transactions has a higher dependency on
carbon-rich fuel sources than green or renewable fuel sources. We provide a
machine learning framework to model such transactions and correlate them with
the electricity generation patterns to estimate and analyze their carbon cost.
Related papers
- Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility.
We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL)
Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
arXiv Detail & Related papers (2024-06-12T05:08:51Z) - Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT)
In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT.
We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
arXiv Detail & Related papers (2024-04-28T05:46:28Z) - Global, robust and comparable digital carbon assets [0.28106259549258145]
We propose a new digital carbon asset (the PACT stablecoin) against which carbon offsetting claims can be transparently verified.
We implement and evaluate the PACT carbon stablecoin on the Tezos blockchain, which is designed to facilitate low-cost transactions.
Our work brings scale and trust to the voluntary carbon market by providing a transparent, scalable, and efficient framework for high integrity carbon credit transactions.
arXiv Detail & Related papers (2024-03-21T17:35:07Z) - Harnessing Web3 on Carbon Offset Market for Sustainability: Framework
and A Case Study [7.312288830305857]
We argue that blockchain's contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.
Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset.
arXiv Detail & Related papers (2023-07-26T02:22:13Z) - Counting Carbon: A Survey of Factors Influencing the Emissions of
Machine Learning [77.62876532784759]
Machine learning (ML) requires using energy to carry out computations during the model training process.
The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source.
We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
arXiv Detail & Related papers (2023-02-16T18:35:00Z) - High Resolution Modeling and Analysis of Cryptocurrency Mining's Impact
on Power Grids: Carbon Footprint, Reliability, and Electricity Price [2.285928372124628]
This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid.
We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions.
arXiv Detail & Related papers (2022-12-29T06:30:22Z) - Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language
Model [72.65502770895417]
We quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle.
We estimate that BLOOM's final training emitted approximately 24.7 tonnes ofcarboneqif we consider only the dynamic power consumption.
We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of machine learning models.
arXiv Detail & Related papers (2022-11-03T17:13:48Z) - Confronting the Carbon-footprint Challenge of Blockchain [5.643032424220467]
We point out an advanced consensus mechanism named Proof of Stake that can eliminate the extensive energy consumption of the current PoW-based blockchain.
We comprehensively elucidate the current and projected energy consumption and carbon footprint of the PoW and PoS based Bitcoin and blockchain platforms.
arXiv Detail & Related papers (2021-12-31T22:10:09Z) - Accounting for carbon emissions caused by cryptocurrency and token
systems [0.0]
This white paper explores different approaches of how to allocate emissions caused by cryptocurrencies and tokens.
Based on our analysis of the strengths and limitations of potential approaches, we propose a framework that combines key drivers of emissions in Proof of Work and Proof of Stake networks.
arXiv Detail & Related papers (2021-11-11T22:03:09Z) - Optimizing carbon tax for decentralized electricity markets using an
agent-based model [69.3939291118954]
Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology.
Carbon taxes have been shown to be an efficient way to aid in this transition.
We use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix.
arXiv Detail & Related papers (2020-05-28T06:54:43Z) - Towards the Systematic Reporting of the Energy and Carbon Footprints of
Machine Learning [68.37641996188133]
We introduce a framework for tracking realtime energy consumption and carbon emissions.
We create a leaderboard for energy efficient reinforcement learning algorithms.
We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
arXiv Detail & Related papers (2020-01-31T05:12:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.