"Prevent minor issues before they become major problems, and prepare for the future." This study utilizes complex system theory to introduce a nonlinear dynamic system for examining the production and emission reduction strategies of new energy vehicle (NEV) and gasoline vehicle (GV) manufacturers under the dual credit (DC) policy over a long-term game process. By considering production delays, we analyze dynamic behaviors within a duopoly automotive system, including stable regions, bifurcation, chaotic attractors, and the Largest Lyapunov exponent (LLE). The results show that: (1) As production and carbon emission adjustment parameters increase, the decision-making system for both automakers can slip into disorder, posing a risk of disruption within the automotive industry. (2) In stable regions, GVs' carbon emission adjustments do not affect the production of either NEVs or GVs, while NEVs demonstrate greater flexibility in production adjustments compared to GVs. (3) The industry system will likely benefit from delay production decisions that could help stabilize the automobile market. The study provides theoretical support for the smooth transformation of old and new driving forces in the automobile industry.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623478PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314899PLOS

Publication Analysis

Top Keywords

complex system
8
system theory
8
stable regions
8
carbon emission
8
system
6
production
6
complex dynamic
4
dynamic behavior
4
behavior automakers
4
automakers based
4

Similar Publications

The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.

View Article and Find Full Text PDF

Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).

View Article and Find Full Text PDF

Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.

View Article and Find Full Text PDF

Design of integrated radar and communication system based on solvable chaotic signal.

Sci Rep

December 2024

Shaanxi Key Laboratory of Complex System Control and Intelligent Informantion Processing, Xi'an University of Technology, Xi'an 710048, China.

In the integrated radar and communication system (IRCS), the design of signal that can simultaneously satisfy the radar detection and communication transmission is very important and difficult. Recently, some new properties of a class of solvable chaotic system have been studied for wireless applications, such as low bit error rate (BER) wireless communications and low cost target detection. In this paper, a novel IRCS based on the chaotic signal is proposed, and the performance of proposed scheme is analyzed.

View Article and Find Full Text PDF

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!