A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part I: Single Services

• URL: http://arxiv.org/abs/2006.10831v1
• Date: Thu, 18 Jun 2020 19:55:23 GMT
• Title: A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part I: Single Services
• Authors: Vlad C. Coroam\u{a}, Pernilla Bergmark, Mattias H\"ojer, Jens Malmodin
• Abstract summary: Information and communication technologies (ICT) are increasingly seen as key enablers for climate change mitigation measures. Different initiatives have started to estimate the environmental effects of ICT services. This article identifies the shortcomings of existing methodologies and proposes solutions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information and communication technologies (ICT) are increasingly seen as key enablers for climate change mitigation measures. They can make existing products and activities more efficient or substitute them altogether. Consequently, different initiatives have started to estimate the environmental effects of ICT services. Such assessments, however, lack scientific rigor and often rely on crude assumptions and methods, leading to inaccurate or even misleading results. The few methodological attempts that exist do not address several crucial aspects, and are thus insufficient to foster good as-sessment practice. Starting from such a high level standard from the European Telecommunication Standardisation Institute (ETSI) and the International Telecommunication Union (ITU), this article identifies the shortcomings of existing methodologies and proposes solutions. It addresses several aspects for the assessment of single ICT services: the goal and scope definition (analyzing differences between ICT substitution and optimization, the time perspective of the assessment, the challenge of a hypothetical baseline for the situation without the ICT solution, and the differences between modelling and case studies) as well as the often ignored influence of rebound effects and the difficult extrapolation from case studies to larger populations.

Related papers

• Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [50.67937325077047]
  This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL) Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
  arXiv  Detail & Related papers  (2024-11-15T15:18:57Z)
• Revisiting Technical Bias Mitigation Strategies [0.11510009152620666]
  Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings. We illustrate each limitation with empirical studies focusing on healthcare and biomedical applications.
  arXiv  Detail & Related papers  (2024-10-22T21:17:19Z)
• ICT Sector Greenhouse Gas Emissions -- Issues and Trends [9.715937446025878]
  Information and Communication Technology (ICT) use has become more prevalent. There has been a growing concern in how its associated greenhouse gas emissions will impact the climate. We provide a framework for estimating ICT's carbon footprint and identify some of the issues that impede the task.
  arXiv  Detail & Related papers  (2024-10-22T19:51:37Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [94.2860766709971]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Eliciting the Double-edged Impact of Digitalisation: a Case Study in Rural Areas [1.8707139489039097]
  This paper reports a case study about the impact of digitalisation in remote mountain areas, in the context of a system for ordinary land management and hydro-geological risk control. We highlight the higher stress due to the excess of connectivity, the partial reduction of decision-making abilities, and the risk of marginalisation for certain types of stakeholders. Our study contributes to the literature with: a set of impacts specific to the case, which can apply to similar contexts; an effective approach for impact elicitation; and a list of lessons learned from the experience.
  arXiv  Detail & Related papers  (2023-06-08T10:01:35Z)
• In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation [92.51773744318119]
  This paper empirically investigates the strengths and weaknesses of different model selection criteria. We highlight that there is a complex interplay between selection strategies, candidate estimators and the data used for comparing them.
  arXiv  Detail & Related papers  (2023-02-06T16:55:37Z)
• A Review of Off-Policy Evaluation in Reinforcement Learning [72.82459524257446]
  We primarily focus on off-policy evaluation (OPE), one of the most fundamental topics inReinforcement learning. We provide a discussion on the efficiency bound of OPE, some of the existing state-of-the-art OPE methods, their statistical properties and some other related research directions.
  arXiv  Detail & Related papers  (2022-12-13T03:38:57Z)
• Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes [93.61202366677526]
  We study the offline reinforcement learning (RL) in the face of unmeasured confounders. We propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy.
  arXiv  Detail & Related papers  (2022-09-18T22:03:55Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Holistically Placing the ICT Artefact in Capability Approach [0.0]
  This paper proposes a framework that holistically places the Information and Communication Technology (ICT) Artefact in Capability Approach (CA) The framework harmonises the different conceptualisations of technology within CA-based frameworks in ICT4D.
  arXiv  Detail & Related papers  (2021-08-22T16:49:20Z)
• A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part II: Multiple Services and Companies [0.0]
  ICT companies, industry organizations and international initiatives have started to estimate the environmental effects of ICT services. The few existing methodological attempts are too general to provide guidance to practitioners. This article identifies and addresses shortcomings of existing methodologies and industry practices with regard to multiple services assessment.
  arXiv  Detail & Related papers  (2020-06-18T20:12:26Z)

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.