IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5765-d1107698.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5765/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    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. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Liang & Yingying Fan & Yongfang Peng & Haigang An, 2022. "Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    2. Wei Ding & Xuguang Zhao & Weigao Meng & Haichao Wang, 2022. "Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    3. Dahao Guo & Yuancheng Lin & Min Wang & Zirou Huang, 2023. "Spatial Distribution Pattern, Evolution and Influencing Mechanism of Ecological Farms in China," Land, MDPI, vol. 12(7), pages 1-20, July.
    4. Shuai, Jing & Zhao, Yujia & Shuai, Chuanmin & Wang, Jingjin & Yi, Tian & Cheng, Jinhua, 2023. "Assessing the international co-opetition dynamics of rare earth resources between China, USA, Japan and the EU: An ecological niche approach," Resources Policy, Elsevier, vol. 82(C).
    5. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    6. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    7. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    8. Abhirup Khanna & Bhawna Yadav Lamba & Sapna Jain & Vadim Bolshev & Dmitry Budnikov & Vladimir Panchenko & Alexandr Smirnov, 2023. "Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-33, June.
    9. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    11. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    12. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
    13. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    14. Nebojsa Bacanin & Catalin Stoean & Miodrag Zivkovic & Miomir Rakic & Roma Strulak-Wójcikiewicz & Ruxandra Stoean, 2023. "On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting," Energies, MDPI, vol. 16(3), pages 1-21, February.
    15. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    16. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(1), pages 1-19, January.
    17. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Raji, Mojtaba & Zarei, Alireza & Mohammadi, Amir H., 2020. "Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models," Renewable Energy, Elsevier, vol. 158(C), pages 465-473.
    18. Yanyan Jia & Xiaolan Tang & Wei Liu, 2020. "Spatial–Temporal Evolution and Correlation Analysis of Ecosystem Service Value and Landscape Ecological Risk in Wuhu City," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    19. Xu Chen & Yingliang Wu & Rujie Zhong, 2021. "Research on Competitiveness of China’s Social Commerce Enterprises Based on Macro- and Micro-Niche," Sustainability, MDPI, vol. 13(1), pages 1-20, January.
    20. Brayan A. Atoccsa & David W. Puma & Daygord Mendoza & Estefany Urday & Cristhian Ronceros & Modesto T. Palma, 2024. "Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies," Energies, MDPI, vol. 17(5), pages 1-19, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5765-:d:1107698. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.