SearcharxivSearch

arXiv · 1404.6227

A Multi-Entity Input Output (MEIO) Approach to Sustainability - Water-Energy-GHG (WEG) Footprint Statements in Use Cases from Auto and Telco Industries

Abstract

A new Input-Output model, called the Multi-Entity Input-Output (MEIO) model, is introduced to estimate the responsibility of entities of an ecosystem on the footprint of each other. It assumed that the ecosystem is comprised of end users, service providers, and utilities. The proposed MEIO modeling approach can be seen as a realization of the Everybody-in-the-Loop (EitL) framework, which promotes a sustainable future using behaviors and actions that are aware of their ubiquitous eco-socio-environment impacts. In this vision, the behavioral changes could be initiated by providing all actors with their footprint statement, which would be estimated using the MEIO models. First, a naive MEIO model is proposed in the form of a graph of actions and responsibility by considering interactions and goods transfers among the entities and actors along four channels. Then, the unnormalized responsibility and also the final responsibility among the actors are introduced, and then are used to re-allocate immediate footprint of actors among themselves. The footprint in the current model is limited to three major impacts: Water, Energy, and GHG emissions. The naive model is then generalized to Provider-perspective (P-perspective) and End User-perspective (E-perspective) MEIO models in order to make it more suitable to cases where a large number of end users are served by a provider. The E-perspective modeling approach particularly allows estimating the footprint associated to a specific end user. In two use cases from the auto and Telco industries, it has been observed that the proposed MEIO models are practical and dependable in allocating footprint to the provider and also to the end user, while i) avoiding footprint leakage to the end users and ii) handling the large numbers end users. In addition, it will be shown that the MEIO models could be sued to integrate Scope-3 and LCA approaches.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Reza Farrahi Moghaddam, Fereydoun Farrahi Moghaddam, Mohamed Cheriet. 2014-04-28. A Multi-Entity Input Output (MEIO) Approach to Sustainability - Water-Energy-GHG (WEG) Footprint Statements in Use Cases from Auto and Telco Industries. https://arxiv.org/abs/1404.6227

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers

The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.

q-fin.GN

Measuring DeFi Risk

Decentralized finance (DeFi) lending has grown from nonexistent in 2017 to nearly 40 billion US Dollars in deposited funds in May 2022. Using cryptocurrency as collateral, the platforms match speculative margin trading with yield-seeking depositors lending coins pegged to the dollar (stable coins). Depositors receive claims guaranteed by a basket of collateral, akin to new stable coins. We develop a framework requiring only knowledge of aggregate deposits and borrowings to measure overall system risks to lenders and borrowers. Using evidence from major protocols, the measures identify an increase in system fragility beyond prudent levels around mid 2021, with a potential loss of peg for extreme variations in coin prices. Overall, the model offers an easily implementable aggregate risk metric capturing the perspectives of synthetic investors and offers early warning signals as the industry is moving from deposits guaranteed by collateral to fiat money.

q-fin.GN

Historical Reflections on Interest Rates and the Emergence of the Yield Curve

This text grew out of a historical introduction initially written for a study of interest rates in cryptocurrency markets. The difficulty of defining a term structure for a currency without a conventional bond market led naturally to a more fundamental question: under what historical conditions does a yield curve become observable at all? Credit existed long before modern money, and interest-bearing loans are documented as early as ancient Mesopotamia. For much of history, the surviving evidence lacks the institutional features that facilitate reliable comparisons of interest rates by maturity: standardised debt instruments, sufficiently homogeneous borrowers, regular issuance over a range of maturities, observable market prices, and liquid secondary markets. We trace the gradual emergence of these conditions from ancient Mesopotamia, Greece, and Rome, through medieval and early modern Europe, to the development of modern sovereign debt markets in the nineteenth and twentieth centuries.

q-fin.GN