Healthcare contributes up to 8.5 percent of global carbon emissions, amongst other environmental impacts including intensive water consumption, plastics in our environment and pharmaceutical associated pollution. Healthcare supply chains are the largest contributor of environmental impact, accounting for up to 80 percent of total health sector emissions. Health technology assessment (HTA) can help support the mitigation of this impact by integrating sustainability considerations into policy making. Current literature indicates emission values are being included in some procurement decisions, and some HTA agencies are collecting environmental data or developing environmental domains in their strategies. However, there are no HTA agencies globally that officially include environmental outcomes as part of the decision-making framework. Adapting HTA to include environmental impacts will require integrating multidisciplinary approaches to make decisions that promote environmental sustainability. A scoping review was undertaken to identify such multidisciplinary methods. Multiple databases and gray literature sources were searched to identify relevant studies. The identified methods were appraised based on predefined criteria for HTA suitability, focusing on comprehensiveness, feasibility, and transparency. Key methods and frameworks identified include environmental life cycle assessment, cost–benefit analysis, and discrete choice experiments. Frameworks were ranked based on their potential suitability for implementing environmental impacts into HTA. Approaches such as One Health and circular economy provide possible foundations for environmental sustainability frameworks in HTA. Combining assessment methods to support trade-off considerations may strengthen decision making, although further research should examine stakeholder perspectives and implementation barriers.

Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.