Modern educational buildings often meet daylighting needs via higher window-to-wall ratios, yet this may cause excessive solar radiation and increased air-conditioning energy consumption. Amid global low - carbon and sustainable development, the energy efficiency and low-carbon design of educational buildings still lacks sufficient attention. This study proposes a data-driven workflow that combines a genetic algorithm with a Bayesian optimization-based XGBoost model to optimize the retrofit design of educational buildings. Taking the first educational building of Tianjin University as a case study, the study focuses on envelope material selection and shading measures, with optimization objectives including sustainable energy use (Av.LM), useful daylight illuminances (Av.UDI), and building carbon emissions (GWP). Building modeling, performance simulation, and multi-objective optimization were completed through Rhinoceros v8.0, Ladybug Tools v1.6.0 and WallaceiX v2.7 plugins, and data was processed using Python v3.10.0. The XGBoost model based on Bayesian optimization performed well in performance prediction with an accuracy of 0.86, precision of 0.77, recall of 0.86, and F1 score of 0.816, which is a significant advantage over LGBM, AdaBoost, and Random Forest models. Sensitivity analyses show that parameters such as north-facing window-to-wall ratio, facade and roof thicknesses significantly affect Av.LM and Av.UDI, while roof and facade selection and thicknesses have the greatest impact on GWP. The study’s main contribution is a novel data - driven workflow balancing low - carbon goals and daylighting needs in educational buildings, validated through model comparison and sensitivity analyses. It highlights the potential to reduce energy consumption and carbon emissions while enhancing daylighting performance, aiding global low - carbon and sustainable development.

At present, the design and planning of teaching and living areas on university campuses are relatively straightforward but encounter problems, such as poor ventilation, low indoor air quality, and poor sound insulation. In this study, the teaching building and living area cluster at the Tianjin University campus were selected as the research objects. We verified the effectiveness of the simulation results before and after renovation through onsite testing. To improve ventilation, an atrium and patio were added to the teaching building, and the ventilation of the renovated building was studied. The indoor thermal environment intelligent control system regulates carbon dioxide (CO₂) concentration and humidity in the teaching building and changes the thermal comfort of the teaching building. Limiting vehicle speeds near the teaching building and the living area cluster, using muffling materials and muffling equipment, and increasing greenery to reduce noise were factors we studied, considering whether they had a noise-reduction effect. It was found that the average number of air changes in the overall functional space of the first teaching building reaches 6.49 times/h, and the wind speed in the human activity region is below 1 m/s. When using a thermal environment intelligent control system, the indoor temperature throughout the year was within the thermal comfort range 81% of the time. The maximum noise around the teaching building during the daytime was 51.0 dB, the maximum noise at nighttime was 41.5 dB, and the maximum sound level on the facade of the living area cluster was 53 dB. The average noise-reduction rate was 22.63%, which exceeds the noise-reduction rate given in the above research literature.

With the increasing and intensifying effects of global climate change and the rapid development of higher education, energy and resource consumption at university campuses has been rising drastically. This shift has been worsened by campuses’ expanded role in addressing extreme weather hazards and taking on additional cultural and community functions. This article carries out a comprehensive literature review of the low-carbon measures and resilient behaviors implemented on university campuses based on publications published in two major databases, the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Results show that: (1) most existing studies only focus on campus carbon emission reduction from a single perspective, without considering the correlation between carbon emissions in different dimensions on campuses and without analyzing the causes of excessive campus carbon emissions from the perspective of the built environment; (2) current studies have not constructed an assessment system for campus carbon resilience and lack the tools and methods for assessment. After summarizing and analyzing, this study proposes the concept of campus “carbon resilience”, which refers to the ability of campuses to cope with the risks of disasters and uncertainties caused by excessive carbon emissions. The research framework of this study is divided into three parts: connotative characteristics, influencing factors, and optimization strategy. Following this framework, the concept and critical features of campus carbon resilience “carbon minus resilience”, “carbon saving resilience”, “carbon reduction resilience”, and “carbon sequestration resilience” are analyzed and outlined. Next, an integrated impact factor system for campus carbon resilience is proposed. This system incorporates aspects such as land utilization, building operation, landscape creation, and energy regeneration from the perspective of the built environment. Finally, with the core objective of effectively reducing the dynamic range of carbon emissions when dealing with critical disturbances and improving the adaptability and resilience of campuses to cope with excessive carbon emissions, this study proposes an optimization strategy of “setting development goals–establishing an evaluation system–proposing improvement strategies–dynamic feedback and adjustment” to provide ideas and theoretical guidance for responding to university campus carbon risk and planning carbon resilience.

The environmental crisis precipitated by climate change has accelerated the urgency of urban green and low-carbon transformation. In 2024, China’s Action Plan for the National Standardization Development Outline (2024–2025) stipulated requirements for continuously improving the standard system for carbon peaking and carbon neutrality in public institutions. As key venues for knowledge innovation and energy consumption, the low-carbon transformation of higher education institutions holds significant importance for China’s achievement of its dual carbon goals. However, China lacks a systematic evaluation framework specifically designed for university campus carbon emissions. Existing green campus assessment standards often suffer from inadequate indicator adaptability, a lack of update mechanisms, and limited coverage. The STARS sustainability assessment system, widely adopted in North America, offers valuable reference points for establishing campus carbon emissions evaluation frameworks due to its features of indicator adaptability, dynamic update mechanisms, and comprehensive evaluation dimensions. This paper conducts an exploratory comparative case study of Princeton University (USA) and Tianjin University (China)—two leading research-intensive institutions—within the STARS 2.2 framework. It systematically analyses their divergent approaches to carbon management and evaluation, not as representatives of their respective continents, but as exemplars of how advanced universities operationalize low-carbon transitions. Based on this analysis and a review of domestic Chinese standards, it proposes a development pathway for China’s university campus carbon emissions evaluation system: (1) Establish a differentiated indicator system combining ‘universal fundamentals with discipline-specific types’ to enhance adaptability to campus characteristics; (2) Establish a mechanism for periodic version updates to the evaluation standard itself, ensuring alignment with evolving national carbon goals and technological advancements; (3) Develop a comprehensive and transparent carbon accounting framework that integrates direct emissions, purchased energy, and indirect sources. This research provides theoretical foundations and methodological support for institutional development and practical optimization in carbon emissions evaluation within Chinese higher education institutions.