With accelerating climate change and the urgent need to cut carbon emissions, global focus has turned to the existing building stock and its renovation. Sustainable renovation helps to achieve better energy performance and gain wider sustainability benefits, such as increased value of a building, improved indoor and outdoor comfort, reduced carbon emissions, and the higher satisfaction and better emotional state of inhabitants. Numerous systems and tools have been developed worldwide to assist with decision making in the choice of preferred modernisation scenarios and alternatives. However, social aspects are often neglected in the existing systems, and emotions of inhabitants are rarely analysed. To close this gap, the present study proposes an innovative decision-making framework for sustainable renovation solutions, based on emotion recognition. The framework makes it possible to assess various renovation alternatives against sustainability criteria and real-time measurements of the emotional states of inhabitants. Based on the proposed framework, an intelligent multi-criteria decision support system was developed by integrating COPRAS and the facial action coding system, the method of automatic facial expression recognition, and the continuous calibration and participant methods. The system was tested in the case study of renovation solutions for a building located in Ukraine. The research results revealed that the proposed renovation solutions had a positive impact on the emotional state of inhabitants, especially when visual materials such as drawings were presented. Some case studies were analysed together with the application of decision system tools and building information modelling (BIM) subsystem integration as a multidiscipline application of various applied sciences for representation and data analysis. The authors of this research have been analysing human emotional, affective and physiological states for many years and collected over a billion of these data in Vilnius city during the H2020 ROCK, SAVAS and BIM4REN projects. Data acquired during measurements in Vilnius were used to determine correlations and trends for the case study. The proposed methodology and findings of the study can be useful for researchers who use the evaluation and analysis of human emotions when there is a need to choose appropriate renovation measures or find alternative solutions.

Green products, clean energy, energy union, green buildings, eco-innovations, environment-related, and similar initiatives and policies have become very popular and widely applied all over the world. A pleasant built environment (parks, flowerbeds, beautiful buildings) and a repulsive environment (noise, polluted surroundings) influence a buyer’s outlook on an advertisement differently. An aesthetic, comfortable, and clean built environment evokes positive emotional states, not only at the time of housing selection and purchase but during the building’s life cycle as well. Potential housing buyers always feel comfortable in certain built environments, and they are inclined to spend more time there. The issues needing answers are how to measure the segmentation/physiological indicators (crowd composition by gender and age groups), as well as the emotional (happy, sad, angry, valence) and physiological (heart rate) states of potential homebuyers realistically, to produce an integrated evaluation of such data and offer buyers rational, green, and energy efficient housing alternatives. To achieve this purpose, the Multisensory, green and energy efficient housing neuromarketing method was developed to generate the necessary conditions. Here, around 200 million multisensory data recordings (emotional and physiological states) were accumulated, and the environmental air pollution (CO, NO₂, PM₁₀, volatile organic compounds) and noise pollution were investigated. Specific green and energy efficient building case studies appear in this article to demonstrate the developed method clearly. The obtained research results are in line with those from previous and current studies, which state that the interrelation of environmental responsiveness and age forms an inverse U and that an interest in green and energy efficient housing depends on age.