Can carbon reduction policies promote sustainable construction development? Evidence from China's green building market.

Carbon emissions have become a global challenge, and China, as the world's largest developing country, has a serious emissions problem. Developing green buildings is an important way of reducing carbon emissions. China's low-carbon city pilot policy may be an effective way of promoting green building development and reducing these emissions. This study uses the low carbon city pilot policy as a quasi-natural experiment and employs the staggered difference-in-differences method to investigate its impact on green building development. The results show that the low-carbon city pilot policy promotes green building development, and this policy promotes it by enhancing regional green innovation capacity, improving green total factor productivity at the firm and regional levels, and reducing the financing constraints of firms in the construction and real estate sectors. In addition, the promotion effect of the policy on green building development is stronger in western and non-resource-based regions and large-scale cities in China. This study contributes to the literature related to environmental policy, green building, and carbon emissions and supports the promotion of green building development and the reduction of carbon emissions.

Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision.

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.