The theory on the carbon footprint of agriculture can systematically evaluate the carbon emissions caused by artificial factors from the agricultural production process, which is the theoretical basis for constructing low-carbon agriculture and has important guiding significance for realizing low-carbon agriculture. Based on farm production survey data from Jilin Province in 2014, this paper aims to obtain a clear understanding of the carbon footprint of maize production through the following method: (1) one ton of maize production was evaluated systematically by using the Life Cycle Assessment (LCA); (2) the carbon emissions of the whole system were estimated based on field measurement data, (3) using the emission factors we estimated Jilin’s carbon footprint for the period 2006–2013, and forecasted it for the period from 2014 to 2020 using the grey system model GM (1, 1).

In the context of China’s agricultural labor shortage and the pressure of aging, this paper uses the land fragmentation index and the intermediary efficiency model to measure the degree of land fragmentation based on farmer-level data from the main garlic producing area in Lanling County in Shandong Province in 2020. The direct effect of labor structure on land-use efficiency and the mediating effect through land fragmentation are analyzed. The research results show that: (1) the average land-use efficiency of the sample farmers is relatively low; (2) the change in labor structure has an “inverted U”-shaped direct effect on land-use efficiency; and (3) the change in land fragmentation in the labor structure has a direct effect on land-use efficiency. The influence of land-use efficiency played a nonlinear mediating effect. The change in labor structure with the degree of land fragmentation showed an “inverted U”-shaped relationship, and the degree of land fragmentation and land-use efficiency had a “U”-shaped relationship. In order to improve land-use efficiency, two aspects of policy support should be increased: encouraging farmers to integrate land and supporting specialized and diversified planting.

Disassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation.

While the aging of agricultural labor force and its impact on agricultural production have been attracting extensive attention, little is known about the relationship between aging of agricultural labor force and technical efficiency in the garlic production. Based on the survey data of garlic growers in Lanling County, Shandong Province, the Data envelopment analysis (DEA) model is used to measure the production technical efficiency of garlic growers in 84 villages in Lanling County, Shandong Province, with the aging of the labor force. The distribution characteristics and changing trends of garlic production technology efficiency are analyzed in terms of family characteristics and garlic planting characteristics, and regression analysis is performed on the differences of the results. The study found the following: (1) Garlic growers in Lanling County, Shandong Province, accounted for a high proportion of laborers over 60 years old, and their physical health status was weaker than that of young laborers, which reduced the technical efficiency of garlic planting and production. (2) The number of garlic varieties grown by garlic growers and the number of garlic planted in acres have a significant negative impact on the technical efficiency of garlic planting by farmers. (3) There is a significant positive correlation between the annual household income of farmers and the production technology efficiency of garlic growers; when the number of garlic training is not more than three times, it has a positive impact on the production technology efficiency of garlic planting by farmers, and more than three times are related to garlic production technology. The efficiency is negatively correlated.