Urban renewal is extensively practiced around the world and has attracted substantial attention among scholars and the public. To ensure that urban renewal is directed toward sustainable development goals, sustainability assessments for urban renewal projects have become critical topics. Simultaneously, the ex ante evaluation research of urban renewal projects has not received enough academic attention, and most results have not fully considered the localization of criteria and the internal correlation between criteria/dimensions. Therefore, this paper proposes an ex ante decision model for the sustainability assessment of urban renewal projects based on a hybrid multiple-attribute decision-making (MADM) approach, which includes 3 dimensions and 16 criteria. It uses a case in Guangzhou to assess the sustainable potential of the project and explore relevant improvement strategies. Empirical results from the Decision Testing and Evaluation Laboratory (DEMATEL) indicate that the economic ($D_1$) and environment ($D_2$) dimensions both impact the social and cultural ($D_3$) dimensions, with the environment ($D_2$) dimension being impacted by the economic ($D_1$) dimension. The criteria occupying the “cause” position include reducing construction costs and materials expenses ($C_5$), increasing greening configuration and open space ($C_6$), reducing resource consumption and waste ($C_8$), improving existing land-use efficiency ($C_2$), promoting the biodiversity of space ($C_9$), and strengthening the safety of pedestrians and residents ($C_{15}$), where increasing greening configurations and open space ($C_6$) and reducing resource consumption and waste ($C_5$) are considered the key criteria based on the results of DEMATEL and the DEMATEL-Analytic Network Process (DANP). The modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method revealed that the economic ($D_1$) dimension has poor performance, and its improvement needs to be prioritized. Further improvement strategies are proposed based on the influence network relationship graph (INRM). In conclusion, our results show that urban community renewal projects in Guangzhou are steadily progressing toward a sustainable vision.

Urban computing powered by high-resolution spatial data offers promising solutions for mitigating complex thermal issues in rapidly developing cities. This study presents a hybrid optimization framework that integrates the generative adversarial network (GAN) with an adaptive large neighbourhood search (ALNS) algorithm to optimize urban land use layouts for land surface temperature (LST) reduction. The GAN model is trained to learn spatial correlations between urban land use configurations and thermal distributions, enabling fast and flexible LST prediction from layout inputs. Building on this predictive capability, the ALNS algorithm explores a modularized nine-dimensional parameter space representing key urban land elements – such as vegetation, buildings, roads, and water bodies – to iteratively search for optimized configurations. Unlike traditional single-variable or standalone metaheuristic approaches, this framework captures complex non-linear spatial interactions and escapes local optima through adaptive operator selection. The optimization outcomes are validated through comparative experiments, demonstrating superior performance over baseline methods in identifying land use layouts with improved thermal characteristics. Additionally, the framework supports interactive visual mapping between inputs and outputs, allowing integration into early-stage urban planning processes. This work contributes a data-driven methodology for enhancing urban thermal resilience in planning and design workflows.

Nearly half of the global population lives in urban areas, which will be increasingly urban-focusing the sustainability challenge on the city and raising awareness about urban biodiversity and re-interaction with nature. For the common goal of encouraging broad-based public participation for biodiversity conservation, there is need for design professionals forging new collaborations in a transdisciplinary model in which research projects are integrated with design applications and strategic planning. This paper examines the urban ecology in the southern Chinese urban area and provides a green infrastructure (GI) design with decision-support toolkits to determine whether and how design can contribute to its sustainable future viability. The research was conducted through long-term and extensive fieldwork, and through cooperation with the ornithological research institute and related organizations, two distinct approaches to sustainability are identified: an innovative, modular, and multi-functional GI design, and the multidisciplinary cooperation practice with a series of decision-making toolkits around the design of the GI. This paper also shows how to promote the relationship between residents, urban creatures, and the ecological landscape. Through collaborative design practices, demographic characteristics were quantified, which strongly influenced our design results and suggested that quantitative indicators should be introduced in the practice of multidisciplinary collaboration to explore the sustainable relationship between various subjects in the urban environment.