This study focuses on the core economic zone of East China, utilizing the decoupling model to investigate the relationship between carbon emissions and economic development in the construction industry. Furthermore, it analyzes the driving factors through the application of the logarithmic mean index method. The findings reveal that, firstly, Zhejiang and Jiangsu provinces exhibit higher total carbon emissions in the construction industry. Except for Fujian Province, the other regions exhibit a downward trend after 2019. Secondly, there is considerable spatial variability in carbon emissions in the construction industry within the core economic zone of East China, and it gradually decreases over the study period. While economically developed regions like Zhejiang and Jiangsu provinces tend to concentrate and consume more resources and energy, their impact on surrounding neighboring provinces or cities is relatively limited. Thirdly, carbon emissions from the construction industry in the core economic zone of East China show a development trend shifting from weak decoupling to strong decoupling, indicating a healthy growth in the construction industry. Specifically, different regions show different trends. Lastly, regarding influencing factors, the impact direction of carbon intensity on total carbon emissions shows instability. Energy intensity consistently exhibits inhibitory effects, and the economy and the population scale act as driving forces.

With the increasing trend of office buildings towards high-rise, multifunctional, and structurally complex architecture, the difficulty of engineering cost management has increased. Accurately estimating costs during the decision-making stage is crucial for ensuring the overall project’s financial viability. Therefore, finding straightforward and efficient methods for cost estimation is essential. This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. By employing grey relational analysis and principal component analysis to simplify indicators, six prediction models are developed: BPNN, GA-BPNN, PSO-BPNN, GA-SVM, PSO-SVM, and GSA-SVM models. After considering accuracy, stability, and computation time, the PCA-GSA-SVM model is identified as the most suitable for office building cost prediction. It achieves stable and rapid results, with an average mean square error of 0.024, a squared correlation coefficient of 0.927, and an average percentage error of 5.52% in experiments. Thus, the model proposed in this paper is both practical and reliable, offering valuable insights for decision-making in office building projects.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10⁻³ a^–1. The area within 1 km of roads experienced a higher increase of 3 × 10⁻³ a^–1, while the roadless areas experienced a lower increase of 1.1 × 10⁻³ a^–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

Thermal infrared land-surface temperature (LST) anomalies can support early-stage geothermal reconnaissance, but in humid, vegetated and urbanizing low-temperature regions they are readily affected by land cover, terrain and human activity. This study develops a reliability-aware Landsat 8 framework for Quzhou, eastern China. Landsat 8 Collection 2 winter scenes from 2016 to 2025 were cloud-screened, converted to LST, and composited using a per-pixel median strategy. NDVI, NDBI and NDWI masks reduced vegetation, impervious-surface and water interference. Standardized LST anomalies were integrated with a 3 km local-relief constraint, candidate-patch vectorization, and patch-level scoring based on thermal persistence, terrain context, land-cover reliability, structural proximity and lithology/contact favorability. General and strong winter LST anomalies cover approximately 64.4 and 22.3 km², respectively, with impervious surfaces accounting for 55.97% and 67.65% before reliability screening. The terrain-constrained workflow reduces the candidate area to approximately 22.2 km² and ranks 267 patches, including 27 very-high relative reconnaissance-priority patches. The highest-ranked patches are characterized mainly by combined thermal persistence, lower land-cover interference, favorable fault proximity and favorable lithology/contact settings, indicating relative priority rather than confirmed resources. Integrated reliability screening is therefore essential before using Landsat LST anomalies for geothermal target prioritization.