The ecological (EOD) model embodies the concept that green mountains and clear waters are as valuable as mountains of gold and silver. It is a powerful measure to develop new forms of ecological and environmental protection industry, guide diversified investment, and promote the coordinated development of ecological environment and economy. This paper aims to explore the application of the Ecological Oriented Development (EOD) model in the Anshun prickly pear industry, and how to promote a win-win situation between ecological protection and economic development through the EOD model. By analyzing the current situation and challenges faced by the Anshun prickly pear industry, as well as the theoretical basis of the EOD model, this study will propose specific strategies aimed at achieving sustainable development of the prickly pear industry and protecting the ecological environment.

Jin Qiu, Wenjia Yang. Ecological EOD: Achieving a win-win model for the prickly pear industry. Academic Journal of Business & Management (2024) Vol. 6, Issue 10: 181-191. https://doi.org/10.25236/AJBM.2024.061027.

[1] Cao Yizhi, Lei Guodong, Zheng Bo. (2023) Thoughts on Policy based Financial Support for Ecological Oriented Development (EOD) Model. Agricultural Development and Finance, (05): 77-81 

[2] Zhong Chenglin, Zhou Zichen. (2022) Research on the Ecological and Green Integrated Regional Economic Development Model (EOD) in the Yangtze River Delta Guided by Ecological Environment. Shangzi Qinsi: Compilation of Achievements from Shangzi Think Tank (2022) Social Governance Research Center of Shangzi Economic Development Research Institute; DOI:10.26914/c.cnkihy. 2022 .038902. 

[3] Jin Xiaofei. (2023) Anshun Economic Development Zone builds a demonstration zone for the integration of the golden prickly pear industry through the "four links and four modernizations". Guizhou Ethnic Daily. DOI:10.28302/n.cnki.ngzmz.2023.000057.

[4] Luo M, Du B, Zhang W, et al. (2023) Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 24(4):3868-3881. doi:10.1109/TITS.2022.3233422

[5] Sun, Y., & Ortiz, J. (2024). An AI-Based System Utilizing IoT-Enabled Ambient Sensors and LLMs for Complex Activity Tracking. Academic Journal of Science and Technology, 11(3), 277-281. DOI:https://doi.org/10.54097/dj2pt496

[6] Zhong, Z., and Li, X., (2024), Re-Visiting the Green Puzzle: The Effect of Eco-Positioning on Inertial Consumers. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4138686

[7] Zhang, H., Guo, J., Li, K., Zhang, Y., & Zhao, Y. (2024), Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder. arXiv E-Prints, arXiv:2409.19754. doi:10.48550/arXiv.2409.19754

[8] Zhong, K., Jiang, Z., Ma, K., & Angel, S. (2020). A file system for safely interacting with untrusted {USB} flash drives. In 12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20).

[9] X Chen, K Li, T Song, J Guo (2024); Few-shot name entity recognition on stackoverflow, arXiv preprint arXiv:2404.09405

[10] X Chen, K Li, T Song, J Guo (2024), Mix of Experts Language Model for Named Entity Recognition, arXiv preprint arXiv:2404.19192