Forestry construction is a fundamental issue related to sustainable economic and social development. However, the traditional development of forestry is currently facing the dilemma of insufficient resource supply, rising labor cost, and a low return on forestry investment, which needs to be transformed into high-quality forestry development. The general view is that synergistic development is an important way to achieve high-quality development. Based on this, the strategic planning and behavioral choices of forestry enterprises under synergistic development are explored. With the objectives of minimizing cost and environmental impacts, and maximizing social benefits, a multi-objective sustainable closed-loop supply chain network planning model is developed to study a real case of a forestry supply chain in northeastern China. A robust optimization approach is used to deal with the relevant uncertain parameters, and a weighted generalized epsilon-constraint approach is applied to solve the multi-objective problem, based on which a GA genetic algorithm is used to solve it. Based on the synergistic development perspective, four different scenario assumptions and sensitivity analyses are made to examine the multi-objective calculation results in this closed-loop supply chain network, and then to analyze the strategic decisions and specific measures for forest industry enterprises to achieve high-quality development. The model results show that realizing supply chain synergy is an effective way to achieve efficient business development in the future. Managers should also establish an acceptable balance between sustainability dimensions while focusing on building a collaborative supply chain development model, where small economic benefits can be appropriately ignored to reduce the harmful effects of the production process on the environment.

In recent years, China has achieved notable results with its poverty alleviation program, the focus of which is shifting toward the comprehensive promotion of rural revitalization. The role played by sustainable human resources in agriculture is becoming increasingly prominent. In this context, China’s sustainable talent in agriculture is used as the research object, and a neural network analysis method is applied to construct a prediction model of sustainable agricultural talent to forecast its supply and demand. The prediction aims to provide a scientific basis for the strategic planning of talent development for rural revitalization. Based on the forecast results by region and province, we analyzed the level of coordinated development of talent supply and demand to provide a reference for the coordinated development of supply and demand of sustainable talent in agriculture in China. The results showed that a large sustainable agricultural talent demand gap exists in China. The overall talent supply and demand coupling coordination level is low; we found significant differences among different regions and provinces, characterized by decreasing order of the northeast, central, west, and east. According to the socio-economic development level, agricultural economic foundation, and other factors, we divided the provinces into six types for analysis. To promote the coordinated development of sustainable human agricultural resources, talent policy support at the national level is required to reduce the loss of human resources to other countries; at the regional level, the talent environment for rural revitalization should be optimized to increase the attraction of talent. At the provincial level, agricultural and forestry education resources should be created to increase the supply of sustainable agricultural talent.