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Energy consumption has dramatically increased in buildings over the past decade. A synthetic urban building energy consumption dataset can be used to estimate energy demand and anthropogenic carbon emission, which overcomes the constraints of real-world datasets, including difficulties in the collection of energy uses from different sources, expense, and time. In this study, a 24-h building energy consumption dataset was designed and established in a hot and humid subtropical region, based on ENVI-met, Energyplus™ and Access software. The dataset considers 72 conditions from three aspects including six built-up local climate zones, seven building categories (five public and two residential buildings), and four widely-used air conditioning systems. A total of 17,400 building energy consumption data were collected. We analyzed the hourly variations of building energy consumption under different conditions, and the factors influencing the building energy consumption. Finally, based on the dataset, taking Tianhe District in Guangzhou as an example, we further explored the hourly spatio-temporal distribution patterns of building energy consumption and daily total building energy consumption distribution using ArcGIS software. This study provides a method for establishing an urban building energy consumption dataset from a local-scale view, which shows a new light on developing the National Building Energy Consumption Database response to global low-carbon action. [Display omitted] • Propose a bottom-up building energy consumption (BEC) accounting method • Establish a LCZ-based BEC dataset based on a series of coupling factors • Present spatio-temporal visualization maps of local-scale BEC • A LCZ-based BEC dataset contributes to design "smart energy network" • The established BEC dataset can be applied to guide smart urban planning [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to determine the long-term effects of one-week self-guided internet cognitive behavioral treatments for insomnia (CBTI) on situational insomnia during the COVID-19 pandemic. Patients and Methods: The participants with situational insomnia (n = 194) were recruited from March 2020 to April 2020 in Guangzhou, China. The insomnia severity index (ISI), pre-sleep arousal scale (PSAS), and hospital anxiety and depression scale (HADS) were evaluated at baseline and a one-week internet CBTI program was delivered to all individuals. The participants were divided into the complete treatment group (the participants completed all seven modules of the CBTI course, n = 75), and the incomplete treatment group (the participants completed 0–6 modules of the CBTI course, n = 119). A total of 135 participants completed the post-intervention assessments. At 3 months follow-up, a total of 117 participants (complete treatment group: n = 51; incomplete treatment group: n = 66) completed the assessments of the ISI, PSAS and HADS. The transition rate from situational insomnia to chronic insomnia (duration of insomnia ≥ 3 months and ISI ≥ 8) was calculated in the two groups. Linear mixed effect model was used to investigate the effect of group (between the two groups), time (baseline vs. follow-up), and interaction (group x time) on various questionnaire score. Results: The transition rate from situational insomnia to chronic insomnia was significantly lower in the complete treatment group compared to the incomplete treatment group (27.5%, 14/51 vs. 48.5%, 32/66, p = 0.023). There were significant differences in group effect (p = 0.032), time effect (p = 0.000) and group × time effect (p = 0.048) between the two groups in the ISI total score. The ISI total scores decreased in both groups during follow-up compared to their baseline values, with a greater magnitude of decrease in the complete treatment group. There were no significant group x time effects between the two groups in the PSAS-total score, PSAS-somatic, PSAS-cognitive score, HADS total score, HADS anxiety score or HADS depression score. Conclusion: Our results suggested that one-week self-guided internet CBTI prevented the development of chronic insomnia from situational insomnia during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]