Accounting for one third of global energy-related carbon emissions, the construction and operation of buildings are crucial for mitigating climate change. Decarbonization potentials of embodied and operational energy use in buildings are worth exploring from a life-cycle perspective. This paper focuses on the individual building level and collects the latest cases, to offer a comprehensive and timely understanding of the assessment and reduction of building life cycle carbon emissions (LCCEs). As for the collected cases, the operational process accounts for the largest share of building LCCEs, averaging 67%, followed by the production and construction phase, averaging 31%. Carbon emissions from the demolition process are relatively low, averaging 2%. The most commonly used method for assessing LCCEs is process-based, combining the activity level and carbon emission factors. Advanced technologies such as building information modelling and building performance simulation have been employed in recent years to assess embodied and operational carbon emissions effectively. Different approaches are proposed for the decarbonization of each stage in the building life cycle. In the production stage, the effective approaches could be optimizing the building structure, improving the material performance, and using bio-based materials, etc. Prefabrication technology is helpful to decarbonize the construction process. Energy conservation and electrification, renewable energy integration, and smart energy management can effectively reduce the building's operational carbon emissions. Beyond the life cycle, recycling waste materials is proven to have great environmental benefits. Further studies are suggested to trade off the embodied and operational carbon, to fully explore building life-cycle decarbonization potentials.

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R² of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage.

This paper proposes a decentralized cooling system combined with a traditional computer room air conditioning unit and server-level heat pipe exchangers for thermal environment optimization in a data center. Two cooling strategies, with heat exchangers installed above and below the servers respectively, are proposed and compared with the original CRACs system in terms of thermal environment. The simulation results of the original data center model are in good agreement with the on-site measurement results, and thus its reliability can be validated. The results show that a decentralized cooling system can effectively improve the thermal environment in data centers. To obtain the highest cooling efficiency, altogether 18 cases, where heat pipe exchangers were installed at different locations and heights, are analyzed and compared. The results show that the thermal environment is optimal when heat pipe exchangers are installed 0.01 m below each server. The local hotspot temperature is reduced by 6.8 °C, and the temperature distribution of the rack is the most uniform, which can effectively reduce the heat accumulation in data centers.

In response to the call for global carbon peaking and neutrality, this study mainly focuses on the comparison of energy-related carbon emissions and the performance of two promising heating, ventilation, and air-conditioning technologies (a ground source heat pump (GSHP) and cogeneration systems) over both short (2021–2030) and long (2031–2050) periods, considering the UK decarbonization plans. The simulation model of the building with the GSHP system is validated by the actual building heating energy data in 2020 and 2021, with yearly deviations of only 0.4–0.5%. The results show that the cogeneration system performed better than the GSHP system in a scenario when there was no electricity decarbonization plan in the future. However, under all of the MARKet ALlocation (MARKAL) scenarios, the GSHP system performed much better than the cogeneration system in terms of carbon reduction in both periods, which can achieve 47.8–84.4% and maximum 97.5% carbon emission savings in short and long-term periods, respectively, compared with the cogeneration system. Due to the truth that electricity decarbonization plans will be optimized and executed in the future, the GSHP system is more promising and recommended compared with cogeneration system in both short- and long-term periods in terms of only decarbonization potentials (e.g., reducing carbon emission and achieving carbon-related environmental protection).