• Energy Research
• engineering and technology close
• JP close
• TH close
• Sustainability close

China’s research on and specific implementation of energy saving for buildings are mainly concentrated in urban areas, but according to 2016 statistics, the rural population accounts for 42.65% of the total population, so rural housing has considerable energy-saving potential. However, the degree of attention to the energy consumption of rural houses needs to be improved. Regarding the research on and implementation of passive energy-saving strategies for residences, compared with centralized urban high-rise residences, rural residences mainly have independent courtyards, with a flexible layout and easier transformation. In this study, a system that uses the common cold lanes in traditional villages and buildings’ exterior walls was constructed, and the indoor spaces of courtyard buildings in southern Shaanxi were completely passively cooled in summer. This system can be completely separated from the supply of artificial energy by relying on the accumulation and buoyancy effects of air in patios and cold lanes and the hot-pressure ventilation in buildings to cool the buildings and greatly improve indoor ventilation efficiency. As the building is ventilated and cooled, the air wall formed in the system can effectively prevent direct contact between the outdoor and indoor temperatures and reduce the impact of thermal wall radiation on the interior. In previous studies on the passive design of courtyard houses, scholars considered the effect of thermal wall radiation on indoor temperature in simulations. Therefore, in this study, we also separately calculated whether to consider the difference between the situation with and without wall heat radiation (WHR) when simulating thermal conversion. The final results show that when the cooling system was adopted, the annual cooling load of the whole building was 4786.494 kW·h without WHR. However, with WHR, the cooling load reduction was 2989.128 kW·h, a difference of 1797.336 kW·h.

This study aims to investigate the behavior of ground surface settlement in TBM double-line tunnels constructed under existing buildings and to devise a calculative representation for that behavior. Numerical simulation and field monitoring methods were used to examine the Zhongcong Tunnel in Chongqing Metro Line 9. The ground surface settlement was analyzed using an orthogonal test of 3D numerical simulation methods. The results showed that ground surface settlement was influenced by TBM tunneling parameters and the location of the existing building in the following manner. The existing building reduced the settlement trough width. Surface settlement was increased by frictional and palm surface thrust forces but reduced by grouting pressure. The settlement trough width of the first excavation iz correlated with that of the last excavation iy. To accommodate the influence of existing buildings, the tilt factor of the settlement trough TR was introduced to improve the formula for calculating the ground surface settlement of TBM double-line tunnels. The improved formula was validated by comparing the calculated results with actual measurements.

Can shrinking cities harness population decline to improve their sustainability by repurposing land use, for example, for localizing food production? Whether such a transition is feasible depends on the pre-shrinkage state of urban agricultural land use, including ongoing trends in local land use change. This study examined agricultural land use from 2007–2017 in Kyoto City, Japan. Kyoto is on the brink of a large projected population decline (~190,000 or ~13% until 2040) and serves as a representative for a large number of regional Japanese cities in a similar situation. Analysis was based on a public 2007 land use data set, aerial and satellite imagery and ground truthing. Results showed a decline of 209 ha or 10% in agricultural land use over ten years, but also highlight the diversity of ongoing agricultural land use types not captured by standard categories. The main post-agricultural land uses were residential (40%) and vacant land (28%). These results have implications for planning and policy. Kyoto City is currently not set to benefit from the projected shrinking process through localizing food production, despite a tradition of vegetable production. Future research should analyze drivers of change for observed agricultural land use.

We undertook an autopsy of the drivers of individual foreign real estate investment ‘bust’ in Australia through a new theoretical lens of ‘habitus’. Our autopsy data drew contours around the individual foreign real estate capital ‘boom and bust’ cycle, as well as the long-term commitment of professionals in the real estate sector to Australia’s real estate market. More specifically, we showed that the foreign capital ‘boom and bust’ cycle began in earnest in about 2010 (starting at A$8.7 billion), grew to A$72.4 billion in 2016–2017, and then declined to A$12.5 billion in 2017–2018. This decline in foreign capital into Australian real estate occurred within a domestic real estate market in Sydney that also started to slow in 2017. Based on 20 semi-structured interviews with real estate professionals in Sydney and public material culture data, we found out that the off-the-plan apartment sales and global policy landscape changes contributed to the decline of foreign real estate investment in Australia. The three possible implications for Sydney’s future residential real estate development: (1) The loss of investors, (2) the evolution of the labor force, and (3) the diversification of housing products, have been raised as part of a future research road map.

The objective of this study is to map direct and indirect seasonal urban carbon emissions using spatial micro Big Data, regarding building and transportation energy-use activities in Sumida, Tokyo. Building emissions were estimated by considering the number of stories, composition of use (e.g., residence and retail), and other factors associated with individual buildings. Transportation emissions were estimated through dynamic transportation behaviour modelling, which was obtained using person-trip surveys. Spatial seasonal emissions were evaluated and visualized using three-dimensional (3D) mapping. The results suggest the usefulness of spatial micro Big Data for seasonal urban carbon emission mapping; a process which combines both the building and transportation sectors for the first time with 3D mapping, to detect emission hot spots and to support community-level carbon management in the future.

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

Large-scale population mobility has an important impact on the spatial layout of China’s urban systems. Compared with traditional census data, mobile-phone-based travel big data can describe the mobility patterns of a population in a timely, dynamic, complete, and accurate manner. With the travel big dataset supported by Tencent’s location big data, combined with social network analysis (SNA) and a semiparametric geographically weighted regression (SGWR) model, this paper first analyzed the spatiotemporal patterns and characteristics of mobile-data-based population mobility (MBPM), and then revealed the socioeconomic factors related to population mobility during the Spring Festival of 2019, which is the most important festival in China, equivalent to Thanksgiving Day in United States. During this period, the volume of population mobility exceeded 200 million, which became the largest time node of short-term population mobility in the world. The results showed that population mobility presents a spatial structure dominated by two east–west main axes formed by Chengdu, Nanjing, Wuhan, Shanghai; and three north–south main axes formed by Guangzhou, Shenzhen, Shanghai, Wuhan, and Chengdu. The major cities in the four urban agglomerations in China occupy an absolute core position in the population mobility network hierarchy, and the population mobility network presents typical “small world” features and forms 11 closely related groups. Semiparametric geographically weighted regression model results showed that mobile-data-based population mobility variation is significantly related to the value-added of secondary and tertiary industries, foreign capital, average wage, urbanization rate, and value-added of primary industries. When the spatial heterogeneity and nonstationarity was considered, the socioeconomic factors that affect population mobility showed differences between different regions and cities. The patterns of population mobility and determinants explored in this paper can provide a new reference for the balanced development of regional economy.

One major concern about foreign direct investment (FDI) is the potential negative environmental impact due to increased CO2 emissions. However, there is a possibility that FDI mitigates CO2 emissions through green innovation and creates a cleaner environment. In the existing literature, there is no significant empirical evidence on the linkage among FDI, green innovation and CO2 emissions in the context of BRICS countries. Hence, this study aims to analyze the impact of FDI and green innovation on the environmental quality of BRICS economies for 1990–2014. The study employed Augmented Mean Group (AMG) estimators for empirical data analysis. The study’s findings depict that foreign direct investment, energy use, and economic growth have a significant and positive impact on the CO2 emissions of BRICS economies. Moreover, green innovation has a significant inverse impact on CO2 emissions. The results show bidirectional causalities between CO2 emissions and green innovation, trade openness and CO2 emissions, energy use and CO2 emissions, and urbanization and CO2 emissions. Additionally, the findings reveal a one-way causality from CO2 emissions to GDP and CO2 emissions to urbanization. This study offers essential policy recommendations for the environmental sustainability of BRICS countries through green innovation.