While a majority of French protein intake still comes from animal-based sources, concerns about environmental, social and ethical issues of current food systems are increasingly pressing towards more sustainable and circular alternatives. In response to this, the plant based protein is emerging as a promising alternative to the meat sector with the potential of playing a crucial role in the country’s agri-food transition, in alignment with both environmental objectives and national food sovereignty goals. While numerous studies have examined consumers’ behavior in relation to plant-based protein, there remain unanswered questions regarding the challenges faced by the actors up the value chain. In view of the above, this study aims to explore the main barriers and enabling factors influencing the development of the plant-based protein sector in France with a particular focus on coordination between stakeholders and the ecological transition. Based on semi-structured qualitative interviews with experts from academia and industry, this document identifies fragmentation among stakeholders, market instability, limited political and financial support, and cultural barriers as the main obstacles. These findings highlight the importance of local governance, collaborative innovation ecosystems and education in order to drive the sector forward. By adopting a systemic perspective, this research contributes to the diagnosis of the current state of the plant protein sector in France and offers ideas for strengthening the protein plant-based value chain.

Climate change and increasingly stringent carbon regulations are accelerating the need for sustainable and carbon-efficient supply chain engineering solutions that balance environmental responsibility with economic performance. In response, this study develops an integrated multi-period supply chain engineering framework that embeds Carbon Capture and Storage (CCS) within a cap-and-trade regulated system. The proposed optimization model simultaneously considers production, inventory, transportation, carbon trading, emission caps, and CCS infrastructure decisions, enabling a holistic evaluation of operational and environmental trade-offs in carbon-constrained environments. Through a comprehensive set of carbon policy and engineering scenarios varying carbon prices, emission caps, and CCS deployment strategies the results demonstrate that carbon pricing and cap levels significantly influence profitability and emission-management decisions. Importantly, integrating CCS within supply chain design provides a cost-effective mitigation pathway that reduces reliance on carbon purchasing while enhancing regulatory compliance and long-term decarbonization performance. Sensitivity analysis further indicates that carbon price and core production costs are the dominant drivers of system profitability, whereas logistics and CCS unit costs have comparatively moderate impacts. Overall, the study presents a sustainable and carbon-efficient supply chain engineering framework that supports resilient industrial system design under evolving carbon constraints and contributes toward advancing circular and low-carbon engineering practices.

The rapid diffusion of electric vehicles (EVs) has intensified concerns about the sustainable management of end-of-life (EoL) batteries and the transition toward a circular economy. This study develops an evolutionary game-theoretic model to analyze long-run EoL strategy selection among electric vehicle manufacturers (EVMs) under technological, economic, and environmental constraints. Firms choose between two strategies: remanufacturing, before recycling residual waste or recycling-only. A fraction of batteries remains non-recoverable and is landfilled. Firms differ in their technological capability for remanufacturing, which shapes their strategic choice between remanufacturing and recycling rate. The distribution of strategies evolves over time, motivating the use of an evolutionary game-theoretic approach. The framework is extended to a two-player evolutionary game by incorporating government, which chooses between policy support (taxes and subsidies) and no intervention. Replicator dynamics are used to derive stability conditions and evolutionary stable strategies (ESS). The results show that recycling becomes the stable long-run equilibrium when it yields higher net returns than remanufacturing, even in the presence of policy incentives. In contrast, remanufacturing emerges as evolutionarily stable only when technological progress and policy design jointly ensure superior net profitability relative to recycling. Moreover, misaligned subsidy–tax differentials may generate cyclical dynamics rather than stable convergence, implying that stronger incentives alone do not guarantee a successful transition. Overall, the findings identify the economic and policy thresholds required for remanufacturing to become self-sustaining and provide guidance for designing coordinated technological and regulatory strategies to support circular EV battery management.

The construction industry is a major contributor to global carbon emissions, resource extraction, and waste generation, making Design-for-Disassembly (DfD) a critical strategy for circular economy transitions. However, DfD decisions must be made under uncertainty, as parameters such as disassembly times and material reuse probabilities are unknown at the design stage. In this paper, a quantitative decision support framework is proposed and evaluated that integrates machine learning prediction with multi-objective mixed-integer linear programming (MILP) for component selection in DfD. The framework is formalized as a predict-then-optimize (PtO) pipeline, and five methods are systematically evaluated: Random Forest, a two-stage neural network, and three decision-focused learning variants (SPO+, Perturbed Optimizer, and a hybrid approach). A comprehensive experimental study was conducted across five levels of prediction difficulty, comprising over 4,000 optimization instances. The results demonstrate that all methods achieve low decision regret (approximately 2%). Notably, the two-stage neural network provided a robust baseline that is not significantly outperformed by decision-focused alternatives. An analysis of the structural properties of the DfD optimization problem reveals that the partition constraint structure and component independence limit the potential gains from end-to-end decision-focused training. These findings provide practical guidance for practitioners implementing DfD optimization systems and contribute to the understanding of when decision-focused learning yields benefits over conventional two-stage approaches.

Metal Additive Manufacturing (AM) encompasses a long-established field of processes, characterized by the building of metal products through the selective adding and fusing of metal material, typically in the form of powder or wire. These techniques have seen rapid industrial adoption due to the freedom of design they offer compared to existing subtractive manufacturing techniques such as CNC machining. Within this field, there have been great advances in fabrication at smaller scales, reaching micrometer-level precision with techniques like Powder Bed Fusion. Beyond the freedom in geometric design for these manufacturing methods is the freedom in material composition. Multi-Material Additive Manufacturing (MMAM) describes the AM processes that allow for the controlled deposition of different unique materials within one fabrication process and has been extensively researched and introduced to industry for larger-scale techniques, down to the millimeter scale using Directed Energy Deposition. Investigation is still ongoing for micrometer-scale multi-material production, where current research efforts are largely focused on adapting existing high-resolution processes to enable the same level of material composition at this scale. In contrast, this paper provides the proposition of a new process, termed “MadeCold”, where electrostatic fields are used to accelerate charged metal powders to achieve a process best described as a directed Cold Spray. As a standalone process, “MadeCold” has the potential to enable higher resolution in metal deposition, but proving the feasibility of this process will also allow for extremely precise control in the placement of specific metal powder of any material, in any location of choice, enabling full freedom in material composition for metal AM at micron and potentially even sub-micrometer scales.