Agriculture is a significant source of carbon emissions, which have a substantial environmental impact. The digital economy plays a vital role in mitigating these emissions through innovative digital solutions. As a leading agricultural nation, China faces substantial pressure to reduce its agricultural carbon emissions(ACE). This paper aims to thoroughly examine the relationship between the growth of the rural digital economy and ACE. To achieve this, we utilize an extensive panel dataset covering China's provinces from 2011 to 2020, analyzing the dynamic and spatial effects of digital economy development on ACE. The key findings of this research are as follows: (1) The rapid expansion of the digital economy significantly reduces ACE. (2) The impact of digital economic development on lowering ACE varies spatially, with a clear progression from eastern to western regions. (3) The digital economy helps reduce ACE through three specific channels: fostering technological innovation, enhancing scale efficiency management, and providing agricultural financial incentives. Based on these findings, this study proposes policy recommendations to improve digital infrastructure, promote balanced regional development in the digital economy, and optimize the management of agricultural science and technology. These policy insights aim to transform agriculture and achieve the goal of reducing ACE, thereby contributing to broader environmental sustainability.

Preserving ancient buildings can be improved using Building Information Modelling (BIM) models created from high-quality point-cloud data. The problems arise from the need for automatic extraction of the characteristics required to meet various security criteria from a high-fidelity point cloud. BIM for Traditional Wooden Architecture (TWA) constructions requires collaboration across various research fields. Two crucial concerns are needed to overcome the current gap and enhance the use of BIM: an automated model for the major components that smartly combines historical information and a Smart Modelling Framework (SMF) to represent these components. First, a parametric model for the usual components, highlighting similarities and properties, was created using a TWA structure as the basis. The next step is creating an automated modelling approach to determine the component type and hidden dimensions automatically. Conservation initiatives for traditional wooden structures will benefit greatly from this research results. The experimental results demonstrate that the suggested technique accomplishes better efficiency, reliability, and effectiveness than other existing technologies.

Polyester fibers tend to generate static electricity during the weaving and application processes, posing a threat to their production. Enhancing the water absorbency and electrical conductivity of polyester fibers themselves is an effective approach to improving their antistatic properties. In this study, multifunctional chitosan (CS), sodium phytate (SP), and Cu²⁺ were loaded on polyester fibers through layer-by-layer (LBL) self-assembly. The antistatic and water absorption capability of the modified polyester fibers was investigated by designing different process parameters combined with a surface resistance test and water contact angle tests. The antistatic property test results confirmed the positive effect of CS and Cu²⁺ on discharging electrostatic charge. Within a definite scope, with the increase in the number of assembly layers, assembly duration, and the concentration of the assembly substances, the wettability of the modified polyester fibers became more favorable and the antistatic effect became more remarkable.