Rapid urbanization has led to drastic land-use/cover changes (LUCCs) and urban heat islands (UHIs), negatively altering the urban climate and air quality. LUCC’s significant impacts on human health and energy consumption have inspired researchers to develop nature-based solutions to mitigate UHIs and improve air quality. However, integrating GIS-CFD modeling for urban heat mitigation towards climate change adaptation was largely neglected for eco-sustainable urban design in rapidly urbanizing areas. In this study, (1) long-term LUCC and meteorological analysis were conducted in the Wuhan metropolitan area from 1980 to 2016; (2) to mitigate the adverse effects of LUCC under a speedy development process, the role and relevance of optimizing building morphology and urban block configuration were discussed; (3) and particular design attention in strategy towards climate change adaptation for environmental performance improvement was paid in Wuhan’s fast-growing zones. The results show that UHII in 1980 was less severe than in 2016. Air temperature (Ta) increased by 0.4 °C on average per decade in developing areas. This increases the severity of UHII in urban fringes. It is found obligatory for a nature-based design to adopt urban morphology indicators (UMIs) such as average building height (μBH), sky view factors (ψ_SVF), and building density (BD/λ_p = % of built area) towards these changes. Further, on-site measurement revealed that λ_p is the most effective indicator for increasing urban heat around the buildings and boosting UHII. Using UMIs and a combined three-in-one regulation strategy based on μBH of common building types of high-rise (BH^A), mid-rise (BH^B), and low-rise (BH^C) buildings can effectively contribute to regulating Ta and air movement within block configuration. As a result of this study’s strategy, urban heat is mitigated via reinforcing wind in order to adapt to climate change, which impacts the quality of life directly in developing areas.

The wide variation in household characteristics, such as household size, income, and age, can lead to significant differences in carbon footprints. Based on data from 1132 Chinese households in 2021, this study examines the structural differences, multiple influencing factors, and mitigation strategies of household carbon footprints (HCFs) in China. The results indicate that indirect emissions, primarily from energy and food consumption, account for the largest share of household carbon footprints, making up over 65% of total emissions. Households with lower carbon footprints are characterized by a per capita living area of less than 20 square meters, rural residences, and shared living arrangements. Carbon footprints for the elderly and minors are lower than adults, while households with higher monthly incomes have the highest carbon footprints. The Multivariate Analysis of Variance (MANOVA) reveals that the main factors influencing HCF include household size, income, and single status, with a more pronounced impact on affluent households than on average households. High-income households have the potential to reduce their carbon footprints through investments in energy-efficient technologies, whereas low-income households are more susceptible to the effects of household size and geographic location. It is recommended that policymakers adopt differentiated measures, such as setting higher reduction targets for larger and wealthier households while providing incentives and technical support to low-income households to achieve meaningful carbon reductions. More effective and equitable low-carbon policies can be formulated by addressing these structural disparities and leveraging the unique characteristics of different household types.

There are significant differences between expectations and fulfillment in the building delivery process. Many researchers have emphasized the need for design strategies that establish a direct correlation between design proposals and building performance. One of the main objectives is to support performance-driven primary design, which occurs before the design performance modeling (DPM) phase. To achieve this, a case study of retrofitting existing buildings on campus is presented. A normative calculation approach is used to identify the optimal combinations of a dozen retrofit strategies based on the Energy Performance Calculator (EPC) model. This approach reduces or eliminates the impact of parametric uncertainties on modeling assumptions and simplifies calculations, particularly in restrictive studies. These retrofit solutions involve structural and functional zoning renovation, meaning that disparity between expectations and fulfilments is considered, and a timely related information feedback route to architects is achieved. In the first step of the narrative development of the EPC model, EPC-Calib was used to find the optimal combination of input variables in the model that satisfies the desired target and complies with the problem constraints. Secondly, the retrofit study was implemented with EPC-TechOpt, and 16 retrofit solutions for three design performance models were examined based on the local climatic conditions, building features, and retrofit costs. Finally, design schemes were determined, and the cost-optimal mix of the measures was desired with a 40% energy saving.