Building energy consumption accounts for a significant proportion of total societal energy consumption, and reducing building energy consumption is critical to the global mission of reducing emissions. Windows are regarded as the least energy-efficient component of a building’s envelope. This study examines service-area buildings fitted with high-performance glass in Chinese cities across various climates and employs the non-dominated sorting genetic algorithm II (NSGA-II) genetic algorithm for multi-objective optimization. In considering design variables such as building orientation and wall insulation, advanced passive design strategies, including electrochromic and aerogel glass, are incorporated into the optimization process to minimize construction costs and operational carbon emissions. Sensitivity analyses were conducted to evaluate the impact of each design variable on building operational carbon emissions. The optimal solution within the Pareto optimal set was further evaluated using the technique for order preference by similarity to ideal solution (TOPSIS) decision-making method, and the preferred energy-saving solution was quantitatively analyzed. The results indicate that optimization leads to a reduction of approximately 7.70–10.50% in annual operational carbon emissions for service-area buildings across different regions, compared to the base case, with a payback period ranging from 4.90 to 13.56 years. The proposed method contributes to sustainable building design by jointly quantifying carbon-emission reduction, construction cost, and payback period, thereby supporting climate-responsive and economically feasible low-carbon envelope decisions for service-area buildings.

Effective bike-sharing planning requires understanding how built-environment associations with usage vary across locations and day types. Existing research remains limited by comparing the relative contributions and spatial variation in macro-scale and street-view attributes within one framework, distinguishing an attribute’s general contribution from geographic context and its interaction with location, or directly assessing weekday–weekend differences. Using Manhattan Citi Bike data and 24 environmental indicators, this research applies an explainable machine learning approach incorporating GeoShapley to examine nonlinear, spatially varying associations and distinguish environmental variable contributions, geographic context, and their interactions. Geographic context ranked first, and station supply had the largest environmental variable contribution in both periods. Metro accessibility, building density, and commercial land use were more important on weekdays, particularly in central and southern Manhattan; residential land use, cycling route density, and station supply were more important on weekends. Among street-view features, visible buildings were more important on weekdays, while sidewalk visibility had a broader positive contribution on weekends; greater sky visibility was generally associated with lower cycling volume. These findings show how built environment contributions vary across locations and day types and inform context-sensitive bike-sharing and cycling infrastructure planning.

The increasing demand for sustainable development and carbon neutrality highlights the need to improve the energy efficiency of infrastructure, particularly in highway service areas. This study explores the application of green roofs as a low-carbon technology to reduce energy consumption across buildings located in different climate zones in China. A combination of theoretical modeling and simulation-based analysis was used to evaluate various green roof configurations in five representative cities: Harbin, Beijing, Wuhan, Guangzhou, and Kunming. The results show that green roofs can reduce annual building energy consumption by up to 2.02%, depending on climate and plant species. For example, fern roofs in Guangzhou reduced heating demand by 16.35%, while grass roofs in Wuhan lowered the daytime roof surface temperature by 31.82 °C. Furthermore, optimizing the building orientation to 60° led to energy savings of up to 7.73% in Kunming. These findings suggest that tailored greening strategies based on regional climate can effectively improve building energy performance and support the development of sustainable service infrastructure.

Highway service-area buildings are characterized by long operating hours, diverse functional spaces, and considerable energy consumption, resulting in significant life-cycle carbon emissions. This study quantifies life-cycle carbon emissions of the buildings in highway service areas. A life-cycle accounting framework was established, and net emissions were further evaluated by considering the contributions of photovoltaic (PV) electricity and vegetation carbon sinks. Five representative service areas covering hot-summer/cold-winter, severe-cold, cold, temperate, and hot-summer/warm-winter zones were investigated through field surveys and indoor thermal environment measurements to obtain envelope properties, equipment configurations, and operating profiles. Results revealed that life-cycle carbon emissions vary substantially across climatic regions, ranging from 4.31 × 10³ to 3.06 × 10⁴ tCO₂e. The operational stage accounts for the largest share of total emissions, approximately 61–84%. Heating demand dominates operational emissions in severe-cold and cold regions, whereas cooling and lighting loads become increasingly important in warm and temperate climates. The orthogonal analysis reveals significant differences in the sensitivity of design parameters across climatic regions. After implementing climate-adaptive optimization measures, life-cycle carbon emissions are reduced by 40.35–87.94% in four service areas. In the hot-summer/warm-winter region, the combined effects of PV electricity generation and vegetation carbon sinks maintain a net-negative carbon balance. The findings provide evidence for sustainable highway service-area design by linking life-cycle accounting, climate-specific design priorities, and renewable-energy substitution.