Higher education institutions (HEIs) in both early and mature stages of sustainable development (SD) have been moving toward sustainability. Methods for assessing SD have been developed from global and regional contexts to support sustainability efforts. The purpose of this paper is to formulate guidelines as input to develop a sustainable assessment tool (SAT) for China based on the current SD stage of Chinese HEIs. Through desk research, SATs were selected and analyzed. Fifteen SATs consisting of more than 1000 indicators included in the analysis and based on components for developing SATs were identified, and then the components were selected and discussed through an online workshop engaging a 34-people Chinese research team, in order to formulate the guidelines for Chinese HEIs. The findings reveal that the emphasis of SATs mainly results from their contexts, purposes and stages, backgrounds or focus. Chinese HEIs are in the early SD stage, and the multiple purposes and components of SATs are identified to support local sustainability efforts. Having a clear understanding of the current SD stages of SATs and selecting the components accordingly would enable them to fully reach their potential in practice, especially in the case of early SD HEIs.

Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and traffic accidents, present significant risks to public safety and infrastructure, necessitating real-time monitoring and early warning systems. This study develops Urban Road Anomaly Visual Large Language Models (URA-VLMs), a generative AI-based framework designed for the monitoring of diverse urban road anomalies. The InternVL was selected as a foundational model due to its adaptability for this monitoring purpose. The URA-VLMs framework features dedicated modules for anomaly detection, flood depth estimation, and safety level assessment, utilizing multi-step prompting and retrieval-augmented generation (RAG) for precise and adaptive analysis. A comprehensive dataset of 3034 annotated images depicting various urban road scenarios was developed to evaluate the models. Experimental results demonstrate the system’s effectiveness, achieving an overall anomaly detection accuracy of 93.20%, outperforming state-of-the-art models such as InternVL2.5 and ResNet34. By facilitating early detection and real-time decision-making, this generative AI approach offers a scalable and robust solution that contributes to a smarter, safer road environment.

Higher Education Institutions (HEIs) play an increasingly significant role in the practice of sustainability. For HEIs in their early stages of sustainability, they are still in need of sustainable assessment tools (SATs) that are suitable for their local context and also lead international sustainable development. The purpose of this paper is to develop a two-hierarchy sustainability assessment tool (THSus) for Chinese higher education institutions, including a quick analysis tool (QAT) and an in-depth benchmarking tool (IBT). The QAT provided a general overview of campus sustainability for HEIs to initiate initial actions and screen cases for the IBT. The IBT then provides more targeted analysis to plan long-term strategic changes. Based on the analysis of HEI cases, a 34-person Chinese research team was enlisted to discuss and select characteristics to formulate THSus. Indicators and weightings were developed according to the tool’s purpose and applied to 15 cases to test its effectiveness. Results showed that THSus is suitable for systematically analyzing campus issues, particularly in research areas. It offers a regional solution for Chinese campuses that is adaptable and considers the comprehensive core of sustainability.

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

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.

This article investigates the application and physical mechanism exploration of distributed collaborative optimization algorithms in building multi-energy complementary energy systems, in response to the difficulties in coordinating various subsystems and insufficient dynamic control strategies. On the basis of modeling each subsystem, the Dual Decomposition algorithm is used to decompose the global optimization problem of the system into several independent sub problems, achieving independent optimization of each subsystem. Through an adaptive dynamic scheduling strategy, real-time data and predictive information are continuously updated and controlled, effectively allocating system resources. The experimental results show that compared to the original system before optimization, the improved algorithm in this paper reduces the total energy consumption of the system by 6.9% and 2.8% on typical summer and winter days, respectively. The conclusion shows that the algorithm proposed in this paper can effectively solve the problem of system coordination difficulties, improve system resource allocation and overall operation level, and provide a new perspective for the optimization design and operation control of energy systems.