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 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.

The shell explosion by hot air drying is a critical step in the processing of Camellia oleifera fruit (COF), which directly affects the degree of the shell explosion, and the separation effect of Camellia oleifera seed and Camellia oleifera shell after the shell explosion of COF. To reveal the characteristics of the COF shell explosion, a hot air drying device was designed based on mass conservation and drying principles. The physical characteristics of COF and the evolution of drying parameters were thoroughly analyzed with a combination method of drying analysis and experimental. Moreover, under the conditions of air temperature 50–70 °C, relative humidity 20–50%, and air velocity 1.3–1.9 m/s, the internal relationship between COF shell explosion formation through hot air drying and the hot air drying medium was systematically investigated by response surface methodology, and a prediction model for the shell explosion rate of COF by hot air drying was constructed using statistical methods. Results demonstrated that decreasing the relative humidity and increasing the temperature and air velocity of the drying medium could reduce the dehydration time of COF. The moisture content of Camellia oleifera shell was found to be 177.45% d.b. (dry basis) at the initial cracking stage of COF. Furthermore, at temperatures ranging from 50 to 70 °C D_eff values of COF were estimated to be within the range of 0.915 × 10⁻⁹ to 1.782 × 10⁻⁹ m²/s. Similarly, at relative humidity levels of 20 to 50%, D_eff values ranged from 1.226 × 10⁻⁹ to 1.501 × 10⁻⁹ m²/s. At an air velocity of 1.3 to 1.9 m/s, D_eff values ranged from 0.956 × 10⁻⁹ to 1.501 × 10⁻⁹ m²/s. The measured values of the shell explosion rate were in close agreement with that calculated using the fitted model, with a correlation coefficient of 0.997 and a root mean square error of 0.9743. This study will provide a theoretical basis for optimizing the shell explosion process and improving shell explosion rate of COF by hot air drying.