Special Issue "Future-Oriented LCA: Current Practice, Emerging Topics and Innovative Approaches"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 30 December 2021.

Special Issue Editor

Prof. Dr. Ben Amor
E-Mail Website
Guest Editor
LIRIDE (Interdisciplinary Research Laboratory on Sustainable Engineering and Ecodesign) University of Sherbrooke, Civil and Building Engineering Department, Sherbrooke, Canada
Interests: life cycle assessment; consequential market modelling; temporal modelling, innovative modelling approaches

Special Issue Information

Dear Colleagues,

Life cycle assessment (LCA) is increasingly used as a tool for evaluation of the environmental performance of products and services as well as of the environmental consequences of strategies and policies, both on the institutional and commercial level. Often, LCA studies are used for decision support in the context of long-term investments, consequences of certain policy measures, or development of processes and technologies within a long-time horizon.

However, in current practice, the LCA framework is somewhat limited when it comes to future-oriented LCA: Inventory data are mostly based on current or even past processes, consequences in economic sectors other than the one in focus are hardly and poorly considered, rebound effects are often ignored, and potential future developments—be they economic, social, or environmental—are rarely accounted for in a consistent way. Further, life cycle impact assessment (LCIA) methods usually reflect current or past conditions, and the temporal aspects of different indicators (e.g., GHG emissions) are still not yet integrated. These limitations are often motivated and justified by, e.g., the lack of inventory data reflecting a future state of the economy, by inherent uncertainties in future developments, by difficulties in predicting consequences of decisions, or by lack of consistent economic and technologic scenarios. However, in a context when LCA studies are used for decision support for a long-term perspective, these limitations make reaching such an objective impractical.

This Special Issue aims at presenting current practice and limitations in future- and decision-oriented LCA and at discussing innovative approaches for overcoming these limitations. We welcome papers that report ideas on how to integrate methods from other areas of research into LCA, such as economic modeling, forecasting tools, machine learning tools, agent-based modeling, and scenario analysis.

Prof. Dr. Ben Amor
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Consequential life cycle assessment
  • Life cycle inventory
  • Life cycle impact assessment (LCIA) methods
  • Innovative modelling approaches
  • Economic modeling
  • Forecasting tools
  • Machine learning tools
  • Agent-based modeling
  • Scenario analysis

Published Papers (2 papers)

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Research

Article
Changing Technology or Behavior? The Impacts of a Behavioral Disruption
Sustainability 2021, 13(11), 5861; https://doi.org/10.3390/su13115861 - 23 May 2021
Viewed by 498
Abstract
Transportation is a key factor in the fight against climate change. Consumer behavior changes in transportation are underrepresented in energy policies, even if they could be essential to achieve the fixed GHG emission reduction targets. To help quantify the role of behaviors in [...] Read more.
Transportation is a key factor in the fight against climate change. Consumer behavior changes in transportation are underrepresented in energy policies, even if they could be essential to achieve the fixed GHG emission reduction targets. To help quantify the role of behaviors in energy transition and their implications on the dynamics of an energy system, this study is conducted using the North American TIMES Energy Model, adapted to Quebec (Canada). A behavioral disruption scenario (an increase in carpooling) is introduced in the model’s transportation sector and is compared to a massive electrification scenario. Our results highlight the fact that a behavioral disruption can lead to the same GHG emission reductions (65%) by 2050 as an electrification policy, while alleviating different efforts (such as additional electrical capacity and additional costs) associated with massive electrification. Moreover, the results are sensitive to behavior-related parameters, such as social discount rates and car lifetimes. Full article
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Article
Analyzing Temporal Variability in Inventory Data for Life Cycle Assessment: Implications in the Context of Circular Economy
Sustainability 2021, 13(1), 344; https://doi.org/10.3390/su13010344 - 02 Jan 2021
Cited by 1 | Viewed by 877
Abstract
Life cycle assessment (LCA) is used frequently as a decision support tool for evaluating different design choices for products based on their environmental impacts. A life cycle usually comprises several phases of varying timespans. The amount of emissions generated from different life cycle [...] Read more.
Life cycle assessment (LCA) is used frequently as a decision support tool for evaluating different design choices for products based on their environmental impacts. A life cycle usually comprises several phases of varying timespans. The amount of emissions generated from different life cycle phases of a product could be significantly different from one another. In conventional LCA, the emissions generated from the life cycle phases of a product are aggregated at the inventory analysis stage, which is then used as an input for life cycle impact assessment. However, when the emissions are aggregated, the temporal variability of inventory data is ignored, which may result in inaccurate environmental impact assessment. Besides, the conventional LCA does not consider the environmental impact of circular products with multiple use cycles. It poses difficulties in identifying the hotspots of emission-intensive activities with the potential to mislead conclusions and implications for both practice and policy. To address this issue and to analyze the embedded temporal variations in inventory data in a CE context, the paper proposes calculating the emission intensity for each life cycle phase. It is argued that calculating and comparing emission intensity, based on the timespan and amount of emissions for individual life cycle phases, at the inventory analysis stage of LCA offers a complementary approach to the traditional aggregate emission-based LCA approach. In a circular scenario, it helps to identify significant issues during different life cycle phases and the relevant environmental performance improvement opportunities through product, business model, and supply chain design. Full article
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