Global increases in the demand for energy are imposing strong pressures over the environment while compromising the capacity of emerging economies to achieve sustainable development. In this context, implementation of effective strategies to reduce consumption in residential buildings has become a priority concern for policy makers as minor changes at the household scale can result in major energy savings. This study aims to contribute to ongoing research on energy consumer profiling by exploring the forecasting capabilities of discrete socio-economic factors that are accessible through social housing allocation systems. Accordingly, survey data gathered by the Chilean Ministry of Social Development was used identify key characteristics that may predict firewood usage for space heating purposes among potential beneficiaries of the Chilean social housing program. The analyzed data evidences strong correlations between general household characteristics and space heating behavior in certain climatic zones, suggesting that personalized delivery of energy efficiency measures can potentially increase the effectiveness of initiatives aimed towards the reduction of current patterns of consumption.

Cities are complex sociotechnical systems, of which buildings and infrastructure assets (built stocks) constitute a critical part. As the main global users of primary energy and emitters of associated greenhouse gases, there is a need for the introduction of measures capable of enhancing the environmental performance of built stocks in cities and mitigating negative externalities such as pollution and greenhouse gas emissions. To date, most environmental modeling and assessment approaches are often fragmented across disciplines and limited in scope, failing to provide a comprehensive evaluation. These approaches tend to focus either on one scale relevant to a discipline (e.g., buildings, roads, parks) or particular environmental flows (e.g., energy, greenhouse emissions). Here, we present a framework aimed at overcoming many of these limitations. By combining life cycle assessment and dynamic modeling using a nested systems theory, this framework provides a more holistic and integrated approach for modeling and improving the environmental performance of built stocks and their occupants, including material stocks and flows, embodied, operational, and mobility‐related environmental flows, as well as cost, and carbon sequestration in materials and green infrastructure. This comprehensive approach enables a very detailed parametrization that supports testing different policy scenarios at a material, element, building, and neighborhood level, and across different environmental flows. We test parts of our modeling framework on a proof‐of‐concept case study neighborhood in Melbourne, Australia, demonstrating its breadth. The proposed modeling framework can enable an advanced assessment of built stocks that enhances our capacity to improve the life cycle environmental performance of cities.

Ensuring access to quality social housing is a major challenge for developing countries. The problems of standardized mass housing are well-known. However, this type of provision is ubiquitously used for its advantages when addressing pressing shortages, often resulting in significant mismatches between the attributes of the housing and the requirements of the dwellers. This multidisciplinary study explores linkages between personalized development and residential satisfaction towards informing a mass personalization approach to social housing. In specific, it presents a model that formalizes this relationship using expectancy disconfirmation theory and field information. A housing survey was conducted in four estates located in Concepción, southern Chile, and complemented with environmental performance data generated with simulation software. The analysis of the results suggests that the relationship between occupants and providers (i.e., personalization as a service) can influence the build-up of expectations, while the capacity of the dwellings to meet the requirements of different households (i.e., personalization as a product) can have a significant impact on satisfaction. These outcomes are formalized with a model that acknowledges these links at different stages of occupancy and, therefore, can be used to inform the personalized development of mass social housing.

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