The construction industry and the built environment accounts for 38% of global greenhouse gases. Significant efforts are being implemented across stakeholder categories to provide supportive guidelines and ways to address the negative impact; however, market developers need to be engaged to create the scale of impact due to large portfolios. Unfortunately, the short-term interests of private developers in real estate are to maximize profits and not to invest in long-term climate mitigation strategies. This paper will address the barriers and opportunities to incentivize, regulate real estate developers, and account for the market to adopt the lens of the B-Corp movement’s triple bottom line business practices, using business to address social and environmental challenges. Academically, accepted theories addressed through a literature review will be analyzed by a socially-oriented developer in Montreal and demonstrated through an eco-district case study. This study will identify the key stakeholders and address the life cycle thinking process to tackle the carbon impacts in the building development sector through the lens of real estate developers. This literature review will be complemented by the empirical study of one of the authors being a private developer, to link academic best practices with the market realities of real estate development. The findings of the process will outline possible solutions to real estate development that suggest cities have the opportunity to play the role of an educator, mediator, regulator, and incentivizing body to private real estate developers. Generally, critical factors of collaboration and capacity building through business modelling lists of barriers and opportunities could promote positive adoption opportunities for large-scale green development projects with a high impact on climate mitigation strategies, which could transform how the construction industry adapts to building green and socially inclusive communities.

Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model’s performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model’s behavior and optimize the model’s performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study’s findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale.