Search

Curbing the increasing carbon emissions from residential and commercial buildings would be of great importance for achieving China's NDC (Nationally Determined Contributions) target. However, studies on city-level emission pathways and mitigation strategies aiming at building sector are still far from sufficient, especially for regions in Central China. Considering the large potential for further building energy demand increase within this area, this study focuses on Hubei, one of the first batch low-carbon pilot provinces to investigate the possibility of earlier emission peak and reduction potentials by prefecture-level city. When downscaling provincial energy use and emissions estimated from local government plans and academic research, statistical data was applied to exploring the relationship between building energy consumption and socioeconomic variables, then spatial allocation factor could be generated after combining with projections of grid-level GDP and population. Results show that by replacing coal and natural gas (NG) that are currently the main heating & cooking energy sources with more electricity and renewable energy, emissions from the building sector would peak around 2025, which is especially true for three major big cities with higher emission growth rate due to population and urban expansions. Moreover, when considering additional improvement on building energy efficiency, provincial reductions burden would be more evenly distributed among cities. It is implied that by formulating tiered mitigation policies for cities at various economic development levels, including decarbonization of energy structure and improvement of energy efficiency, more cities would peak their emissions from the building sector before 2030. This analytical framework innovatively create a multi-city platform for the simulation of building sector mitigation pathway to facilitate cities' low-carbon development. Image 1 • A spatial downscaling framework was developed for building energy consumption and emission projections on city-level. • Building sector mitigation potential for a low-carbon region with limited supporting data was quantified with this method. • Emission pathway and reduction contribution of the building sectors of multiple cities were estimated. • Electrification and greater renewable energy use in buildings would make emissions peak earlier for more developed cities. • Further building energy efficiency improvement helps increase the mitigation contribution from the less developed cities. [ABSTRACT FROM AUTHOR]

• Hybrid bottom-up model to integrate geo-referenced and synthetic stock models. • Demonstrate multi-scale and long-term transitional workflow for commercial building stock. • Identify drivers and determinants of energy usage in the commercial building stock. • 32% cumulative performance gap if concurrent consideration of physical and technical factors is disregarded. • Introducing the unit block concept to determine minimum viable requirements. For energy modelling of commercial building stock (CBS), several methodologies were developed focusing on technology alternatives and retrofits for climate change mitigation, but they often have limitations to involve a differentiated description of practices and policies across a range of scales and sectors. This paper presents a novel hybrid model by integrating spatial and synthetic modelling approaches to facilitate the concurrent consideration of multiple building-oriented elements. The model is further implemented on Japanese building stock to demonstrate multi-scale and long-term framework for energy modelling of CBS. The results indicate the following findings: the scale choice introduces significant variability in energy consumption estimation with a relative error of 10–17% mainly depending on building geometry and plug loads; the long-term model exhibits a higher scale-bounded uncertainty in terms of energy saving potential that varies by 2–3 times when a non-representative scale is applied; disregarding physical and technical factors results in a cumulative performance gap of up to 32%; and the accuracy and extent of the model across the stock are more sensitive to technical factors, such as building system stock dynamics and the heterogeneity of HVAC systems, than physical factors. The model provides a multi-tier framework using spatial intelligence building stock approach to develop long-term energy efficiency monitoring strategies for CBS. [ABSTRACT FROM AUTHOR]

Building energy use is becoming increasingly important in China. Despite a rapid growth in recent years, energy use intensity in China is still relatively low compared to other advanced economies; thus, there is still substantial room for it to increase as living standards and industrial services are improved. It is therefore important to focus on the future development of building energy use by considering new trends in the building and energy sectors, as well as the role of occupant behavior. This study uses the China Building Energy Model (CBEM) to model China's building energy consumption and carbon emissions up to 2050 for different scenarios based on these considerations. The results indicate that building energy use will be 80% higher than the current situation if the strategies of the 13th Five-Year Plan are maintained and approximately 10% higher if stronger strategies toward energy efficiency are employed. Carbon emissions are predicted to peak around 2020 to 2035. The contributions of key strategies in different subsectors are also discussed. This research suggests that, through the use of suitable strategies and policies, energy use and carbon emissions in China's building sector can achieve the combined goals of energy revolution and climate change mitigation. • China's building energy consumption and carbon emissions up to 2050 were modeled. • China's building energy use will be 10%–80% higher under different scenarios. • The contributions of key strategies in different subsectors were discussed. • China's building sector is able to develop sustainably with suitable strategies. [ABSTRACT FROM AUTHOR]