The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R² > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.

The influence of different recycled brick coarse aggregate (RBA) substitution rates on the mechanical performance and frost resistance of concrete was observed. The test findings revealed that RBA deteriorated the compressive and flexural properties in concrete and improved the tensile properties and plasticity in concrete to some extent. The frost resistance of concrete can be effectively improved by adding RBA. The influence degree of the RBA concrete frost resistance factor was quantified by gray entropy correlation theory, and the gray entropy correlations between freezing and thawing cycles, natural coarse aggregate substitution rate, recycled brick aggregate substitution rate, and freezing and thawing damage value (D_N) were 0.9979, 0.9914, and 0.9876, respectively. Moreover, the freezing and thawing damage model about GM(1, 1) theory was developed (R² > 0.87), which can better predict the freezing and thawing damage of RBA concrete. The damage mechanism of RBA concrete during freezing and thawing was revealed.

Urban energy consumption is mostly concentrated in industrial regions, and carbon emissions from industrial land use have significantly increased as a result of fast urbanization and industrialization. In the battle against climate change, the affluent regions of developing countries are increasingly being used as models for reducing carbon emissions. Therefore, in order to accomplish global sustainable development, it is crucial to understand how industrial land use and carbon emissions are decoupled in wealthy areas of rising nations. This study investigates the decoupling effects and the factors influencing them in six East Chinese provinces and one city between 2005 and 2020 using the Tapio decoupling model and the LMDI decomposition approach. At the same time, the industrial carbon emissions from 2021 to 2035 were predicted using a BP neural network model combined with scenario analysis. The findings indicate that: (1) From 29.921 million tons in 2005 to 40.2843 million tons in 2020, the carbon emissions from industrial land in the East China area have nearly doubled. Of these, Shandong and Jiangsu emit more than half of the region’s total emissions around East China. (2) The decoupling effect analysis shows the East China region’s decoupling trajectory’s phased characteristics, with the degree of decoupling gradually increasing from weak decoupling (2006–2012) to strong decoupling (2013–2018) and finally to negative decoupling (2019–2020). (3) The primary causes of the rise in carbon emissions in the East China region are the scale of per capita economic output and industrial land use. (4) The overall industrial carbon peak time in East China is roughly distributed between 2028 and 2032. It is expected that Shanghai, Shandong, Jiangsu, and Zhejiang will be among the first to achieve carbon emission peak.

Carbon emissions (CE) from expanding construction land (CL), a vital territory for human production and habitation, have contributed to climate change worldwide. The Yellow River Basin (YRB), an essential economic region and energy supply base in China, is experiencing rapid urbanization, and the contradiction between economic development and ecological protection is increasingly acute. Consequently, a thorough examination of the spatial and temporal change features of carbon emissions from construction land (CECL) and its decoupling from economic growth (EG) is crucial for the maintaining development of the region. This study adopts the IPCC carbon emission coefficient approach for measuring the CECL in the YRB from 2010 to 2021. The temporal and spatial variation features of CECL in the YRB were revealed using ArcGIS software and the standard deviation ellipse (SDE) model. The decoupling effect between CECL and EG was analyzed using the Tapio decoupling model and innovatively combined with the Logarithmic Mean Divisia Index (LMDI) method to explore the influence of five main drivers on the decoupling effect. This study found that: (1) The CECL rose from 2.463 billion tons in 2010 to 3.329 billion tons in 2021. The spatial layout of CECL is “high in the east and low in the west”. (2) The SDE of CECL is distributed in the direction of “northeast to southwest”, and the gravity center’s moving path is “northwest to northeast to northwest”; (3) weak decoupling (WD) is the main decoupling state between CECL and EG; (4) the economic output effect and the construction land (CL) scale effect are the two main factors inhibiting the decoupling of CECL, while the energy intensity effect, the population density effect, and the energy structure effect are the main elements motivating the decoupling of CECL. This study provides specific references and bases for the YRB in China and other countries and regions with similar levels of development in promoting green and ecologically friendly initiatives and achieving low-carbon utilization of regional land and sustainable development.