The use of life cycle assessment (LCA) allows work to go beyond the traditional scope of urban nature-based solutions (NBS), in which ecosystem services are provided to citizens, to include environmental impacts generated over the entire life cycle of the NBS, i.e., from raw material extraction, through materials processing, production, distribution, and use stages, to end-of-life management. In this work, we explored how LCA has been applied in the context of NBS through a critical analysis of the literature. Systems under review were not restricted to one typology of NBS or another, but were meant to cover a broad range of NBS, from NBS on the ground, water-related NBS, building NBS, to NBS strategies. In total, 130 LCA studies of NBS were analysed according to several criteria derived from the LCA methodology or from specific challenges associated with NBS. Results show that studies were based on different scopes, resulting in the selection of different functional units and system boundaries. Accordingly, we propose an innovative approach based on the ecosystem services (ES) concept to classify and quantify these functional units. We also identify and discuss two recent and promising approaches to solve multifunctionality that could be adapted for LCA of NBS.

In a traditional building design process (TDP), design variables are fixed sequentially, as opposed to integrated design process (IDP) which tends to avoid sequential design phases to create more sustainable buildings. First, a reference building is introduced and an energy model based on TRNSYS is presented to determine the energy consumption and comfort in the building. The model is validated based on energy bills, certified simulations and literature. Then, the paper performs an extended sensitivity analysis (SA) of 30 design variables with respect to different performance criteria related to energy consumption and comfort, based on a TRNSYS model. Three SA techniques were used, namely standard regression coefficients (SRC), partial rank correlation coefficients (PRCC) and Sobol indices. Results show that all three techniques yielded a similar ranking of the importance of the variables for most model outputs. Interactions between variables were identified with second-order Sobol indices. In the second part of this paper, a traditional design framework was adopted in which sets of variables were fixed sequentially. A SA was performed at each phase of the process, assuming fixed values for parameters chosen in previous design phases. Results show that fixing variables during the phases of a traditional design process tends to reduce the probabilities of finding low-energy consumption designs. Moreover, the influence of some variables was found to change during the design phases.

Integrated design processes are currently pushed forward in order to achieve net-zero energy building designs at affordable cost. Through a case study of a residential building, this paper compares a sequential versus a holistic design approach based on multi-objective optimization. In the holistic approach, 39 design variables related to the architecture and HVAC systems are simultaneously optimized. In the sequential approach, the architecture variables are first optimized; several optimal solutions are then selected for the second phase optimization of the heating system parameters. Carbon footprint, life cycle cost and thermal comfort are optimized by the algorithm NSGA-II. With only 100 computational hours, the holistic approach found 59% of the optimal solutions, whereas it took 765 hours to find 41% of the optimal solutions with the sequential approach. This comparison shows the negative effects of making irreversible variable selections in the early phase of a design process, as it reduces the ability to find optimal solutions in the end.

The emergence of building performance optimization is recognized as a way to achieve sustainable building designs. In this paper, the problem consists in minimizing simultaneously the emissions of greenhouse gases (GHG) related to building energy consumption and those related to building materials. This multi-objective optimization problem involves variables with different hierarchical levels, i.e. variables that can become obsolete depending on the value of the other variables. To solve it, NSGA-II is compared with an algorithm designed specifically to deal with hierarchical variables, namely sNSGA.Evaluation metrics such as convergence, diversity and hypervolume show that both algorithms handle hierarchical variables, but the analysis of the Pareto front confirms that in the present case, NSGA-II is better to identify optimal solutions than sNSGA. All the optimal solutions are made of buildings with wooden envelopes and relied either on heat pumps or on electrical heaters for proving heating.