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Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters. • Holistic framework for evaluating city-scale potential of green roofs. • Prototype buildings to capture various building characteristics at city scale. • GIS-informed scaling up of building-scale energy simulation toward city-scale. • Ophiopogon japonicus performing the best on city-scale green roofs in Xiamen. • Up to 1.83% energy saving and 1.63% peak load shaving by green roofs in Xiamen. [ABSTRACT FROM AUTHOR]

• Build a co-optimization model to optimize supply and demand sides of IES. • Modify stochastic programming to capture both demand and supply sides' scenarios. • Co-optimization model achieves more cost-efficient design considering carbon tax. • Basic and Standard demand-side upgrading are cost-efficient while Premium is not. • Carbon tax accelerates both renewable penetration and demand-side upgrading. Integrated energy systems (IES) can help achieve greater energy efficiency, and then ultimately promote a climate-neutral economy by utilizing local renewable resources. Demand-side energy-saving measures can reduce operational costs associated with energy usage. Most existing IES models, however, focus on supply-side optimization, while the demand-side energy-saving potential and its impacts on whole-system performance are still not clear. The increasing carbon tax makes it even more important to understand the interactions between supply and demand sides to achieve a sustainable system with a minimal carbon charge. Hence, this study proposes a co-optimization model to simultaneously optimize the supply and demand sides of an IES considering the impact of the carbon tax. A selection tree is developed to describe various demand-side envelope upgrading technologies, and a binary tree is established by generating a set of supply-side scenarios with corresponding probabilities. Based on these results, an improved two-stage stochastic programming model is proposed. The robustness of the modeling results was further validated by a simulation–optimization-based uncertainty analysis addressing price uncertainties. A case study in Shanghai indicates that the proposed co-optimization model achieves more cost-efficient solutions than supply-side-only optimization considering carbon tax. Introducing carbon tax can reduce the installed capacity of fuel-based energy technologies by up to 24% and greatly accelerate the penetration of renewables. The increasing carbon tax also promotes the adoption of more advanced energy-saving technologies. Uncertainty analysis reveals acceptable robustness of the optimal demand-side scheme and supply-side configuration with a deviation of less than 5% and a coefficient of variation of 7%. Overall, the observations of the proposed model and case study provide valuable insights for IES design considering an emerging charge of carbon tax. [ABSTRACT FROM AUTHOR]

• Model community-level energy by combining GIS, simulation, and data-driven approaches. • Propose a GIS-based urban building energy modeling method with low computational cost. • Generate various prototype buildings through two-stage clustering process to describe buildings' diversity. • Integrate urban building energy modeling with global sensitivity analysis to discovery urban planning factors. • Identify FAR and BCR as the most influential factors affecting communities' energy use. The residential sector accounts for an increasing amount of global energy use with continued urbanization. Residential energy-informed urban planning offers an economical and easy-to-operate approach to achieve more efficient urban energy utilization. However, quantifying the interactions between residential energy and urban planning remains an open challenge. This study proposes a holistic approach integrating GIS techniques, building energy modeling, and a global sensitivity analysis to prioritize eight key urban planning factors on the community energy performance based on a building energy dataset. The dataset, including urban planning and building information, was first established using GIS techniques and validated using survey data. The residential energy performance model at the community scale was developed using the clustering tree structure of residential building prototypes and building performance simulations. A combined data-driven and global sensitivity analysis approach was further applied to prioritize the impacts of eight vital urban planning factors on energy use intensity and peak load intensity. A case study of 1963 communities in Shanghai revealed that, for the energy performance of residential communities, the floor area ratio and building coverage ratio are the most influential factors, followed by the maximum height and high-rise proportion having a relatively low impact but higher than other factors. Overall, the proposed holistic approach generates robust insights into urban-scale residential energy performance, which can effectively inform urban planners to achieve more energy-efficient regulatory planning. [ABSTRACT FROM AUTHOR]