Digital intelligent supply chains strengthen industrial resilience and optimize economic efficiency in the timber industry. Information asymmetry and low collaboration efficiency remain key challenges across the timber supply chain. This study develops a three-party evolutionary game model examining digital collaboration between timber production and processing enterprises, finished product distribution and retail enterprises, and third-party service providers, introducing third-party service providers alongside traditional production and distribution enterprises. The model incorporates novel parameters including information sharing degree, value-added reliability gains, and free-riding coefficients to reflect real-world circumstances. Through equilibrium simulation and analysis, we identify four possible evolutionary states. The results demonstrate that successful digital collaboration in timber supply chains relies on three conditions: a high level of initial stakeholder involvement accelerates the formation of supply chain digital intelligent collaborative mechanisms, equitable benefit distribution maintains long-term cooperation, and integrated third-party services reduce implementation costs while improving information reliability. These findings provide a new perspective and reference for timber enterprises to implement digital transformation strategies.

A high-quality grain distribution system is the key to guarantee the balance of grain supply and demand, and the reduction of greenhouse gas emissions in grain transportation is the concern of the government and enterprises. In order to clarify the influence of different low-carbon policies and loading modes on the optimization of grain multimodal transport paths, this paper constructs a low-carbon grain multimodal transport path optimization model with the objective of minimizing transportation, cargo loss, time and carbon emission costs. Taking Jiamusi City, the main grain producing area in China, as an example, a heuristic genetic algorithm is used to solve the model to explore the impacts of carbon tax policy (CTP), carbon emission trading scheme (ETS) policy and carbon offset policy (COP) on the transportation schemes of grain in three loading modes, namely, "bagged, bulk and containerized". We analyze the effects of carbon price fluctuations on decision-making under low carbon quota, medium carbon quota and high carbon quota scenarios, and study the effects of different cost preference values on transportation decision-making under the ETS policy. Under the ETS policy, the optimal transportation path of each loading mode has the lowest total cost, and the total cost is reduced by 1% compared with that of carbon tax and carbon offset policy. Among them, the containerized rail-water intermodal transportation scheme has obvious cost and environmental advantages, with the total cost decreasing by 42% and 33% compared to bag and bulk, and the carbon emission decreasing by 27% compared to both. With the overall relaxation of the time window, the transportation scheme is transformed from road-rail intermodal transportation to rail-water intermodal transportation. In addition, when the carbon price is RMB 2/kgCO[Formula: see text] and above, it can promote the transportation transition to low-carbon rail-water intermodal transportation, and the high carbon quota under the ETS policy can motivate enterprises to realize cost reduction and efficiency. The findings of the study can provide reference for grain transportation enterprises to formulate multimodal transportation solutions and provide theoretical support for the government to formulate low-carbon policies.

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain.