Constructing and exploring the evolution mechanism of an industrial innovation ecosystem in resource-based cities (RBCs) is the most effective way to solve the contradiction between economic development, energy shortage, and environmental degradation. Taking 10 typical RBCs in Shanxi Province as examples, this paper used the method of system dynamics (SD) to build a model of the industrial innovation ecosystem of RBCs and set up scenarios to simulate and predict the evolution of the industrial innovation ecosystem of RBCs. The results showed that the industrial innovation ecosystem of RBCs is a complex system composed of four subsystems: innovation players, innovation content, innovation resources, and innovation environment. In innovation players, the increase in the amount of talent has a more obvious effect on technology level and GDP than R&D funding. In innovation content, the improvement of management level has a slow and continuous positive impact on GDP. Technology achievements, once implemented, will improve GDP more than management progress does. In innovation resources, human capital has greater potential for an increase in GDP and per capita consumption expenditure. In innovation resources, technology level plays an important role in slowing down the deterioration of the ecological environment. This study enriched the theoretical paradigm of the research on the industrial innovation ecosystem, and provided effective strategies to solve the development problems of RBCs.

In scenes with low lighting at night, the outline of objects that need to be recognized, such as vehicles, is not clear, and cannot be accurately recognized by the automatic driving system. At present, there are many researches on instance segmentation models, but there are few researches on the instance segmentation application of automatic driving night scenes. According to BDD100K dataset, the automatic driving daytime scene dataset is marked. First, we perform data augmentation by using gamma correction to simulate the night driving scene in the training phase. Then we use our improved low-light enhancement algorithm with gradient increment based on RetinexNet in the prediction phase to brighten night driving scene images. Furthermore, we evaluated our proposed method on YOLACT++ model. The results show that the improved YOLACT++ automatic driving night segmentation ability has been significantly improved, the segmentation of vehicles at night is more accurate and robust, and it has better application value in night automatic driving scenarios.

China’s implementation of the Comprehensive Commercial Logging Ban in All Natural Forests is deemed as disrupting the stability of both Chinese and international timber markets and has raised widespread concerns about deforestation leakage on a global scale. Clarifying the impact of the logging ban on the Chinese and international timber markets is essential for formulating effective policies and taking collaborative actions to improve the stability of both timber markets and promote the sustainable development of global forest resources. This study examines the causal effects of the logging ban on Chinese and international timber markets by conducting a counterfactual analysis of Chinese domestic and imported timber prices with the synthetic control method. Unlike most previous studies that revealed significant price increases in both markets as a result of the logging ban, our results show that there are no significant causal effects between the logging ban and the price changes in Chinese and international timber markets. As China made extensive efforts in plantation cultivation and harvesting and substantially improved its domestic timber supply capacity, the logging ban has only produced a limited impact on the Chinese domestic timber market and has not disrupted the international timber market through trade. Therefore, China’s logging ban policy has not protected its own forest resources at the expense of deforestation in other countries, and it has provided a practical reference for the formulation of forest protection policies and sustainable forest management.

Forests have substantial economic and ecological value. However, the overemphasis on economic values raises concerns on the sustainable development of forest resources. This study examines whether the forestry industry can realize coordinated development with forest resources based on forestry industry complexity, and explains the mechanism behind it from a product space perspective. We measure the development status of the global forestry industry using the economic complexity approach and investigate the impact of forestry industry development on forest resource sustainability with a varying coefficient panel data model. Finally, we utilize a complex network to explain the causal mechanism for their relationship. The results indicate that forestry industry development significantly promotes forest resource sustainability globally, but that the relationship varies across different countries. For countries with comparative advantages in labor-intensive industries like furniture manufacturing, development of the forestry industry is detrimental to the sustainability of forest resources. Conversely, for countries possessing comparative advantages in skill-intensive industries like forest cultivation and pulp manufacturing, development of the forestry industry is beneficial to the sustainability of forest resources. This is primarily determined by the input–output relationship between timber and products. Based on the evolution of product space, the transition of forestry industry from labor-intensive to skill-intensive industries is conducive to both the improvement of forestry industry complexity and the sustainability of forest resources. Thus, promoting the cultivation and sustainable management of forest resources through industrial development is a principal policy strategy for achieving a synergistic relationship between forestry industry development and forest resource sustainability.