With global ecological problems and uncertainties in the international economy, the resilience of the wood supply chain is becoming important for the sustainable development of the wood industry. Heilongjiang Province is a key region for wood production and consumption in China. However, it faces many internal and external challenges in its wood supply chain. This paper uses a system dynamics method to build a wood supply chain model for Heilongjiang Province with Anylogic. It includes resilience factors from infrastructure, economic, and ecological perspectives. The goal is to find the system’s dynamics and how different factors influence and interact with each other. The results show that relying on overseas raw materials makes supply chain management more difficult. Logistics information platforms are essential for better coordination. The manufacturing sector does not respond well, so improving manufacturing efficiency is necessary to increase the system’s efficiency. The sales sector is affected by changes in market demand. Balancing the consumption of domestic and international customer groups can help stabilize production and sales. This study explains the current situation of the wood supply chain in Heilongjiang Province. It provides a theoretical basis for regional wood supply chain management and offers useful insights for improving supply chain resilience in other similar regions.

In order to solve the problem of the optimization of delivery and pickup cargoes’ routes with time windows under the constraints of load and road, the method and algorithm were designed and researched based on these factors. Firstly, the model was built with the constraints of truck number, load quality, load capacity, maximum distance, and customer service time in order to minimize the transportation cost and time cost. Then, the Floyd Algorithm was used to determine the moving path and the shortest distance between each point with the road constraint. 0-1 integer programming method was used to determine the number and type of vehicles. Tabu search algorithm was used to determine the order of delivery and pickup points and the distribution route based on the influence of job requirements of pickup cargoes, the time cost, and the transportation cost. Finally, the case was given to verify the effectiveness of the proposed method. This method can be used to improve the efficiency of delivery and pickup cargoes and the level of distribution service.

In the context of global timber supply chains facing policy adjustments, resource fluctuations, and market uncertainties, this study focuses on the resilience of the Sino-Russian timber supply chain. A system dynamics (SD) model is developed to analyze the dynamic evolution of the key segments. By integrating the entropy weight–TOPSIS method, the research quantitatively assesses overall supply chain resilience by synthesizing data from four capability dimensions—Russian logistics and transportation capability, Russian primary wood processing capability, Sino-Russian timber import–export capability, and Heilongjiang furniture sales capability—over the 2017–2033 period. Results indicate a “first decline, then rise” trajectory for resilience, with a minimum normalized resilience index of 0.1549 recorded in 2021, followed by a gradual recovery and sustained strengthening thereafter. Among evaluated segments, Russian logistics demonstrates the strongest short-term shock resistance (36.2% reduction in minimum resilience), while Heilongjiang’s sales segment exhibits optimal long-term recoverability (the normalized resilience index increased by an average of 0.0363 units per year during the recovery phase). Based on these findings, a “short-term logistics enhancement–long-term demand-driven” strategy is proposed to improve resilience, providing actionable insights for the high-quality development of the Sino-Russian timber supply chain.

Under the “dual-carbon goals” (carbon peaking and carbon neutrality), the accelerated global transition toward green and low-carbon development has become an irreversible trend. As a key carbon-emitting sector in China, the transportation industry accounts for approximately 10% of the nation’s total carbon emissions. Transportation decarbonization is therefore critical not only for addressing global climate change challenges and fulfilling international emission reduction commitments but also for driving the industry’s green transformation, optimizing energy structures, enhancing public livelihood and ecological environment quality, and, ultimately, achieving sustainable development. Taking Chengdu as a case study, systematically, this paper (1) objectively summarizes the current status and achievements of green development in Chengdu’s transportation sector; (2) identifies bottlenecks hindering its green transition; (3) calculates carbon emissions across various transport modes in recent years using the GHG Protocol framework to identify key influencing factors; and (4) proposes targeted strategies to establish Chengdu as a national pioneer in developing innovative “dual-carbon” pathways for transportation. The findings are intended to provide decision-making support for building a low-carbon transportation system that aligns with Chengdu’s megacity development goals.

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.

Urban freight systems in megacities must simultaneously address carbon reduction targets and emergency preparedness requirements. This study develops a systems-oriented optimization framework for truck-drone collaborative delivery that integrates both objectives. Using Chengdu as a case study, we propose a dual-mode routing model that explicitly incorporates carbon costs as a carbon tax into the objective function. The model is solved using an Adaptive Large Neighborhood Search algorithm enhanced with Q-learning (ALNS-RL), featuring carbon-sensitive destruction and battery-constrained repair operators. We calibrate the model using Chengdu’s emission inventory across nine transport modes (2020–2025) and simulate it under carbon tax scenarios of 80, 150, and 300 CNY/ton. Results show that the drone usage ratio increases from 10.3% to 25.0% as the carbon tax rises, with a marginal incentive threshold near 150 CNY/ton. Under emergency mode, drone usage increases by 20%, while total cost and carbon emissions decrease by 4.3% and 4.2%, respectively. Overall, the proposed method reduces total cost by 31.0% and carbon emissions by 29.2% relative to a pure truck baseline. This study provides a practical decision-support framework for designing low-carbon and resilient urban logistics systems in megacities.