The indoor environment of buildings affects people’s daily life. Indoor harmful gases include volatile organic gas and greenhouse gas. Therefore, the detection of harmful gas by gas sensors is a key method for developing green buildings. The reasonable design of SnO₂-sensing materials with excellent structures is an ideal choice for gas sensors. In this study, three types of hierarchical SnO₂ microspheres assembled with one-dimensional nanorods, including urchin-like microspheres (SN-1), flower-like microspheres (SN-2), and hydrangea-like microspheres (SN-3), are prepared by a simple hydrothermal method and further applied as gas-sensing materials for an indoor formaldehyde (HCHO) gas-sensing test. The SN-1 sample-based gas sensor demonstrates improved HCHO gas-sensing performance, especially demonstrating greater sensor responses and faster response/recovery speeds than SN-2- and SN-3-based gas sensors. The improved HCHO gas-sensing properties could be mainly attributed to the structural difference of smaller nanorods. These results further indicate the uniqueness of the structure of the SN-1 sample and its suitability as HCHO- sensing material.

The building sector plays a pivotal role in climate change mitigation. By regulating the demand for services and products from supply sectors, building sector can contribute to decarbonization. To assess the decarbonization and cost-saving potential of demand-side solutions for China's residential building sector, this study develops a demand-side solution framework and an end-use technology model. The model covers the building sector and major supply sectors, considering the heterogeneous impacts of demand-side solution measures on different supply sectors. Here we show that the most optimistic cost-effective demand-side solution can reduce cumulative CO₂ emissions by 47% (42.21 Gt CO₂-eq), while achieving a 16% saving in the net present value of costs over the period 2020-2060. Additionally, results indicate that the demand-side solution enable China's rural residential buildings to achieve carbon neutrality without carbon dioxide remove options, while simultaneously mitigating uncertainties in reaching carbon neutrality targets.

Mega infrastructure projects (MIPs) have become increasingly important to the realization of sustainable development in China. Sustainable development is a process of dynamic balance, and coordinating the triple bottom line (the environmental, social, and economic dimensions) will enable more sustainable development of MIPs. However, previous studies have lacked consideration of coordination when applying sustainable development principles to the systematic identification of risks to MIPs. The goals of this study were to clarify the definition and dimensions of the sustainable development of MIPs and to identify the key risks of MIPs. A literature review was performed to extend the definition of sustainable development of MIPs by combining the triple bottom line with a fourth coordination dimension. A conceptual model of MIP risk identification was then proposed from an extended sustainable development perspective, 22 sustainability elements and 75 risk factors were identified, and the key risk factors were determined based on the interview responses and fuzzy set theory. The results show that economic risks have a high probability, social risks have a high loss, environmental risks have an intermediate probability and loss, and coordination risks have the greatest impact. In addition, the three most important key risk factors were found to be construction and installation cost overruns, land acquisition and resettling cost overruns, and information sharing with the public. Identifying key risk factors can provide information to help stakeholders understand the risk factors associated with MIPs and formulate reasonable risk response strategies.

Mega infrastructure projects (MIPs) are exposed to numerous interdependent risks of various natures which pose difficulties in risk management. Thus far, the research on the risk interactions of MIPs has been focused on developing static risk networks within a single category of risks, at certain stages of the project. It is essential to understand the risk interactions at various stages of MIPs to identify the key risks and key risk relationships that jeopardise their success. This is especially relevant nowadays, as MIPs are expected to be delivered sustainably. Therefore, to analyse the dynamic risk interaction of MIPs, initially, through literature analysis and expert interviews, combined with the four dimensions of sustainable development and the four stages of MIPs, 98 risk factors of MIPs were identified. Subsequently, semi-structured interviews were conducted to determine risk relationships and weights. Risk networks were developed for each stage of MIPs, and improved social network analysis was applied to these risk networks. Finally, the key risks and key risk relationships in each stage of MIPs were identified by analysing the changes of multi-level network indicators. This aided in determining risk control strategies. The results demonstrate that the key risks and key risk relationships are different for each stage of MIPs. Furthermore, the risks of different dimensions of sustainable development have different relationships at different stages. This research is the first to identify the risk relationships involved in MIPs by taking into consideration the whole project life cycle and its sustainable development. This research provides theoretical support for the risk management of MIPs, and strategic suggestions for controlling the risks at each stage of the project.

A thorough understanding of residents’ demands plays an important role in realizing the rational distribution of urban retail (UR) and promoting the habitability of cities. Unfortunately, these demands for UR are currently under-researched. To solve this problem, this study aims to quantify the capitalization effect of UR on housing prices and explores the impact of heterogeneity in housing structure characteristics, price quantile, and space on the residents’ demands for UR according to the hedonic price model, quantile regression, and geographically weighted regression in Chengdu. The results of these models show the following: (1) good property management and building sound insulation can reduce the negative influence of UR on residents’ lives; (2) only the owners of low-price houses are willing to pay a premium for UR; and (3) residents’ demands for UR increase from the central area to the peripheral area of Chengdu, and an inverted U-shaped relationship was found between housing prices and the UR level. A comprehensive analysis of the heterogeneity of residents’ demands for UR can provide a reference for planning departments, real-estate developers, and UR owners and promote the sustainable development of UR.

Understanding the historical peak situation and the rules for CO₂ emissions from buildings helps to formulate reasonable building mitigation strategies, accelerating the achievement of the Chinese government’s carbon peak goal. As developed regions, cities in the Guangdong–Hong Kong–Macao Great Bay Area (GBA) provide valuable reference cases. This study quantified the historical building CO₂ emissions of GBA cities and analyzed the contribution of driving factors using the Kaya identity and logarithmic mean Divisia index. Furthermore, we assessed the historical peak situation using the MK trend test method and discussed the reasons behind the inter-city difference in the peak situation shown by the environmental Kuznets curve. The results indicate that the building-related CO₂ emissions of the GBA will slowly increase to 96.90 Mt CO₂ by 2020 and that P&C buildings accounted for a larger proportion of emissions. Emission factors and population made the largest positive and negative contributions, respectively, to this total. At the city level, Guangzhou, Shenzhen, and Hong Kong ranked as the top three sources of building CO₂ emissions. Hong Kong peaked, Dongguan and Macao plateaued, and other cities maintained either slow or quick growth. CO₂ emissions unit area, per capita building CO₂ emissions, and building CO₂ emissions reached a peak in that order. This study provides a valuable reference for formulating a city-level path showing building CO₂ emissions peaks.