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To meet the UK's 2050 net zero carbon targets, Whole Life Carbon Assessment (WLCA) is used to calculate the lifetime carbon emissions of building projects. This paper aims to compare different estate regeneration scenarios for meeting the interim 2030 benchmarks for the 2050 target. The research consists of a case study employing co-design and WLCA experiments, and impact evaluation surveys. The results of the WLCA demonstrate the lower operational impacts of fossil-fuel-free scenarios, and lower embodied and overall carbon emissions of retrofitting scenarios. To make an informed decision towards the future of the estates, different regeneration scenarios need to be studied, and the stakeholders should understand the carbon emissions of different materials and systems. The findings of this study can be used to compare regeneration schemes of other building types. Practical application: This study underscores the critical role of early-stage design decisions and engaging with the communities in minimising the carbon footprint of building projects and making informed decisions. The study reinforces the potential superiority of retrofitting over new builds, which serves as a clear call for the inclusion of retrofit options in the analysis of diverse regeneration and redevelopment scenarios. The study's outcomes also highlight the pivotal role of stakeholder engagement in comprehending the Global Warming Potential (GWP) of materials and systems, influencing the participatory development and evaluation of regeneration scenarios. This approach is paramount to devising strategies that align with net zero ambitions, thereby ensuring environmentally and socially responsible estate regeneration. [ABSTRACT FROM AUTHOR]

Saturation is a useful concept for system dynamics, yet it has not been widely explored or integrated into the modeling process. In this article, we describe saturation as a metaphor describing the point at which a conceptual representation of a system meets the study purpose and no longer requires modification. When saturation is reached, additional data about the problem would not offer added information, thus indicating that additional data gathering and analysis would likely be redundant. We discuss two visualization techniques, "saturation curves" and "shared understanding diagrams," for assessing saturation when conceptualizing with causal loop diagrams and show their application in a case example. Using saturation analysis during a system dynamics research process has many advantages, including: (i) identifying model structures potentially needing revisions, (ii) observing the extent to which evidence supports the current conceptualization, (iii) reflecting extensively, (iv) documenting important modeling decisions, and (v) potentially improving the problem statement. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society. [ABSTRACT FROM AUTHOR]

BackgroundPolicy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output.ObjectiveOur objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake.MethodsWe describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty.DiscussionThere is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits.ConclusionModeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health.