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This research considers a supply chain consisting of a regulator and two symmetric firms where the regulator influences the market by imposing a tax on firms' environmental pollutant emissions. A price competition model is proposed to examine the equilibrium solutions that the two firms can reach in their technology upgrading process, and the effect of the environmental tax is evaluated. The two firms' Nash equilibrium solutions regarding their technology improvement decision show that there is no asymmetric equilibrium. The decision to upgrade or not upgrade may arise in equilibrium, depending on the technology's fixed cost. Besides, a prisoner's dilemma may arise when the two firms do not upgrade their technology, and multiple equilibria may arise when the fixed cost incurred is at a medium level. Technology improvement decision is the dominant strategy when either prisoner's dilemma arises or multiple equilibria arise for the two firms regardless of whether the environmental tax is exogenous given or not. In addition, firms' reactions to environmental tax may be non-monotone. However, the role of the tax on firms' improvement decisions is limited when the regulator further increases the tax. Finally, the optimal tax level for the regulator that can maximise welfare is obtained. [ABSTRACT FROM AUTHOR]

General deep learning methods achieve high-level semantic feature representation by aggregating hierarchical features, which performs well in object detection tasks. However, issues arise with general deep learning methods in UAV-based remote sensing image object detection tasks. Firstly, general feature aggregation methods such as stride convolution may lead to information loss in input samples. Secondly, common FPN methods introduce conflicting information by directly fusing feature maps from different levels. These shortcomings limit the model's detection performance on small and weak targets in remote sensing images. In response to these concerns, we propose an unmanned aerial vehicle (UAV) object detection algorithm, IF-YOLO. Specifically, our algorithm leverages the Information-Preserving Feature Aggregation (IPFA) module to construct semantic feature representations while preserving the intrinsic features of small objects. Furthermore, to filter out irrelevant information introduced by direct fusion, we introduce the Conflict Information Suppression Feature Fusion Module (CSFM) to improve the feature fusion approach. Additionally, the Fine-Grained Aggregation Feature Pyramid Network (FGAFPN) facilitates interaction between feature maps at different levels, reducing the generation of conflicting information during multi-scale feature fusion. The experimental results on the VisDrone2019 dataset demonstrate that in contrast to the standard YOLOv8-s, our enhanced algorithm achieves a mean average precision (mAP) of 47.3%, with precision and recall rates enhanced by 6.3% and 5.6%, respectively. [ABSTRACT FROM AUTHOR]