Bernard Yannou

Erwan Ike de Bantel, Thibault Pirson, Gonzalo Puig-Samper et al.

Absolute environmental sustainability assessment (AESA) has gained increasing attention in environmental research and policymaking. However, its reliability is challenged by several sources of uncertainty that remain insufficiently accounted for, as well as by scope inconsistencies within the absolute sustainability ratio (ASR), which compares estimated environmental burdens with allocated carrying capacities for a given human activity. This work introduces UNCASExt, an extension of the UNCASE framework for systematically propagating uncertainty and ensuring scope consistency in AESA, together with a supporting open-source Python package, pyaesa. At country and sector levels, the computational framework formalizes allocation procedures that match the scope of allocated carrying capacities with that of estimated environmental burdens across three dimensions: impact pathway modeling; production-based versus consumption-based accounting; business-to-consumer versus business-to-business activities. It also incorporates temporal dynamics, supporting both retrospective and prospective assessments with either static steady-state or dynamic carrying capacities, including greenhouse gas budgets from the Intergovernmental Panel on Climate Change Sixth Assessment Report under Shared Socioeconomic Pathway transition scenarios. The framework is applied to a case study of electricity consumption in France over the period 2019 to 2060. The results show that mismatches between the functional units of estimated environmental burdens and allocated carrying capacities can lead to substantial underallocation, with a median factor of 4.6x across all available sector-region pairs in EXIOBASE 3.10.2. Overall, UNCASExt and pyaesa provide a scalable solution to support AESA harmonization and a versatile way forward to bridge the gap between methodological guidelines and practical application.

Michael Saidani, Harrison Kim, Nawres Ayadhi et al.

Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: ``What sustainable design insights can be identified or interpreted from online product reviews?''. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.

Michael Saidani, Harrison Kim, Bernard Yannou

The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks - including Python libraries, packages, as well as state-of-the-art algorithms like BERT - are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.

Michael Saidani, Bernard Yannou, Yann Leroy et al.

Combining new insights from both bibliometric and text mining analyses, with prior relevant research conversations on circular economy (CE) and industrial ecology (IE), this paper aims to clarify the recent development trends and relations between these concepts, including their representations and applications. On this basis, discussions are made and recommendations provided on how CE and IE approaches, tools, and indicators can complement each other to enable and catalyze a more circular and sustainable development, by supporting sustainable policy-making and monitoring sound CE strategies in industrial practices.

Tianjun Hou, Bernard Yannou, Yann Leroy et al.

This research set out to identify and structure from online reviews the words and expressions related to customers' likes and dislikes to guide product development. Previous methods were mainly focused on product features. However, reviewers express their preference not only on product features. In this paper, based on an extensive literature review in design science, the authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. Meanwhile, the linguistic patterns describing these aspects of preference are discovered and drafted as annotation guidelines. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement. As high inter-agreement human annotation results are essential for automatizing the online review summarization process with the natural language processing, this study provides materials for the future study of automatization.

François Cluzel, Bernard Yannou, Markus Dihlmann

An Interactive Genetic Algorithm is proposed to progressively sketch the desired side-view of a car profile. It adopts a Fourier decomposition of a 2D profile as the genotype, and proposes a cross-over mechanism. In addition, a formula function of two genes' discrepancies is fitted to the perceived dissimilarity between two car profiles. This similarity index is intensively used, throughout a series of user tests, to highlight the added value of the IGA compared to a systematic car shape exploration, to prove its ability to create superior satisfactory designs and to stimulate designer's creativity. These tests have involved six designers with a design goal defined by a semantic attribute. The results reveal that if "friendly" is diversely interpreted in terms of car shapes, "sportive" denotes a very conventional representation which may be a limitation for shape renewal.