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Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon

Author

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  • Yanhua Chang

    (School of Accounting, Shanxi Institute of Business and Technology, Taiyuan 030006, China)

  • Yi Liang

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China
    Strategy and Management Base of Mineral Resources in Hebei Province, Hebei GEO University, Shijiazhuang 050031, China)

Abstract

Ecological agriculture projects have achieved a growing development in the context of low carbon. However, because of the great difference in these issues from traditional types, there exist risks in progression quality and sustainability. To better identify the risk, this paper proposes a novel hybrid approach that integrates the analytic hierarchy process (AHP) with technique for order preference by similarity to an ideal solution (TOPSIS), as well as an improved support vector machine (SVM) based on the brainstorming algorithm (BSO). First, a risk evaluation index framework is developed and elaborated in terms of the natural environment, society, market economy, management, technology, and finance. Then the traditional assessment can be derived from AHP with TOPSIS. In addition, BSO is applied to improve SVM for rapid computation. Finally, a case study is implemented to verify the accuracy of the proposed technique. In this research, based on the low-carbon perspective, artificial intelligence algorithm and risk assessment are introduced into the field of ecological agriculture project management, which is conducive to the rapid and effective evaluation of ecological agriculture project risk. It can improve managers’ risk awareness and risk management ability, reduce investment blindness, and help ecological agriculture projects achieve healthy and sustainable development under the background of low carbon, thus contributing to the development of a low-carbon economy.

Suggested Citation

  • Yanhua Chang & Yi Liang, 2023. "Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5765-:d:1107698
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    1. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    2. Elisa Chaleta & Margarida Saraiva & Fátima Leal & Isabel Fialho & António Borralho, 2021. "Higher Education and Sustainable Development Goals (SDG)—Potential Contribution of the Undergraduate Courses of the School of Social Sciences of the University of Évora," Sustainability, MDPI, vol. 13(4), pages 1-10, February.
    3. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    4. Lombardi, G.V. & Parrini, Silvia & Atzori, R. & Stefani, G. & Romano, D. & Gastaldi, M. & Liu, G., 2021. "Sustainable agriculture, food security and diet diversity. The case study of Tuscany, Italy," Ecological Modelling, Elsevier, vol. 458(C).
    5. Yi Liang & Haichao Wang & Wei-Chiang Hong, 2021. "Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-26, May.
    6. Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
    7. Shuhan Liu & Dongyan Wang & Guoping Lei & Hong Li & Wenbo Li, 2019. "Elevated Risk of Ecological Land and Underlying Factors Associated with Rapid Urbanization and Overprotected Agriculture in Northeast China," Sustainability, MDPI, vol. 11(22), pages 1-18, November.
    8. Xiaoqing Li & Qingquan Jiang & Maxwell K. Hsu & Qinglan Chen, 2019. "Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    9. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    10. Lingjun Wang & Ying Wang & Jian Chen, 2019. "Assessment of the Ecological Niche of Photovoltaic Agriculture in China," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    11. Jian Chen & Lingjun Wang & Yuanyuan Li, 2022. "Research on Niche Evaluation of Photovoltaic Agriculture in China," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    12. Hadi Jaber & Franck Marle & Ludovic-Alexandre Vidal & Ilkan Sarigol & Lionel Didiez, 2021. "A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method," Sustainability, MDPI, vol. 13(6), pages 1-35, March.
    13. Georgios Bartzas & Konstantinos Komnitsas, 2020. "Environmental Risk Assessment in Agriculture: The Example of Pistacia vera L. Cultivation in Greece," Sustainability, MDPI, vol. 12(14), pages 1-20, July.
    14. Xuwei Wang & Zhaojie Li & Yanlei Zhang, 2021. "Model for Predicting the Operating Temperature of Stratospheric Airship Solar Cells with a Support Vector Machine," Energies, MDPI, vol. 14(5), pages 1-14, February.
    15. Haichao Wang & Yi Liang & Wei Ding & Dongxiao Niu & Si Li & Fenghua Wang, 2020. "The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, December.
    16. Alexander Martín-Garin & José Antonio Millán-García & Iñigo Leon & Xabat Oregi & Julian Estevez & Cristina Marieta, 2021. "Pedagogical Approaches for Sustainable Development in Building in Higher Education," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
    17. Weijun Pan & Zhengyuan Wu & Xiaolei Zhang, 2020. "Identification of Aircraft Wake Vortex Based on SVM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-8, May.
    18. Jiawang Zhang & Jianguo Wang & Shengbo Chen & Siqi Tang & Wutao Zhao, 2022. "Multi-Hazard Meteorological Disaster Risk Assessment for Agriculture Based on Historical Disaster Data in Jilin Province, China," Sustainability, MDPI, vol. 14(12), pages 1-25, June.
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