Green Building Practices (GBPs) are gaining prominence in many countries around the world. However, in many developing countries, Tanzania inclusive, little progress has been made in achieving its implementation. The current study sought to examine the factors attributable to low uptake, focusing on an appraisal of the stakeholders’ knowledge of GB design features, triggers, and the pathways for uptake. The study was conducted in Dar es Salaam City in Tanzania, involving different stakeholders sampled from the construction industry, including architects, engineers, quantity surveyors, and property managers. Valid data were collected through 412 questionnaires. The study revealed a general consensus among the respondents that GBs are environmentally friendly and cost effective. However, it appeared that the ranking score of GB design features according to the respondents did not correlate with their ranking in existing GB rating systems. It was concluded that there exist misconceptions among the constructions industry stakeholders regarding what green building practices entail and the essential requirements for their implementation. Therefore, the study recommends pragmatic educational trainings to stakeholders in the construction sector of Tanzania on GBPs. Mandatory legislation of green building codes and regulations is also suggested as a potential pathway for enhancing GB practices in Tanzania.

Retrofitting existing buildings is a cornerstone of Europe’s strategy for a sustainable built environment. Therefore, accurate short-term forecasts to evaluate policy impacts and inform future strategies are needed. This study investigates the short-term predictive modelling of renovation activity in Belgium, focusing on overall renovation activity (RA) and energy-specific renovation activity (EA). Using data from 2012 to 2023, linear modelling was employed to analyze the relationships between RA/EA and macroeconomic indicators, market confidence, building permits, and loan data, with model performance evaluated using Mean Absolute Percentage Error (MAPE). Monthly data and time lags of up to 24 months were considered. The three best-performing models for RA achieved MAPE values between 2.9% and 3.1%, with validated errors ranging from 0.1% to 4.1%. For EA, the best models yielded MAPE values between 4.4% and 4.6% and validated errors between 8.9% and 14%. Renovation loans and building permits emerged as strong predictors for RA, while building material prices and loans were more relevant for EA. The time lag analysis highlighted that renovation processes typically span 15–24 months following loan approval. However, the low accuracy observed for EA underscores the need for further refinement. This explorative effort forms a solid base, inviting additional research to enhance our predictive capabilities and improve short-term modelling of the (green) residential renovation market.