| تعداد نشریات | 24 |
| تعداد شماره‌ها | 930 |
| تعداد مقالات | 8,346 |
| تعداد مشاهده مقاله | 16,808,299 |
| تعداد دریافت فایل اصل مقاله | 14,119,700 |
Predicting the impacts of climate change on the production of the agricultural sector and the Iranian economy using artificial intelligence models | ||
| Iran Agricultural Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 خرداد 1405 اصل مقاله (1.84 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2026.54699.1725 | ||
| نویسندگان | ||
| Zakaria Farajzadeh* ؛ Alireza Keshavarz | ||
| Department of Agricultural Economics, School of Agriculture, Shiraz University, Shiraz, I. R. Iran | ||
| چکیده | ||
| Climate change is especially severe in developing countries such as Iran, which have a low capacity to address this issue. A variety of complex models and methodologies which are reliant on extensive datasets have been employed to evaluate the economic and environmental impacts. In other words, alongside the primary variable indicating climate change, specifically temperature anomalies, numerous economic and environmental variables and datasets must be incorporated. This study was undertaken with the objective of evaluating the viability of discovering alternative models through the application of machine learning (ML) and artificial intelligence (AI)-based frameworks. The data utilized is derived from the related literature for the period 2021-2060, which outlined the production effects of climate change. The temperature change scenarios encompass SSP2-4.5, SSP3-7.0, and SSP5-8.5. A wide array of AI-based models was employed. The study's findings indicated that by utilizing the temperature anomaly variable, it is feasible to forecast the decline in Iran's gross domestic product (GDP) with a prediction error of fewer than 5% through multilayer perceptron (MLP) and random forest (RF) models, as well as their combinations. For agricultural subsectors, using the hybrid model, this error was assessed to be below 3%. Concerning agricultural products, it was found that the combined models are capable of predicting accurately the extent of loss even at higher levels of temperature anomaly. The high sensitivity of agricultural activities to climate change, coupled with the complex and multifaceted nature of climate change itself, has made it essential to use large datasets and sophisticated models. In this regard, ML and AI offer a promising, though not flawless, solution. Given the highly favorable accuracy and rapid access to reliable predictions afforded by AI-based models, the implementation of these models, particularly the combinations of MLP and RF, is recommended. | ||
| کلیدواژه‌ها | ||
| Temperature anomaly؛ Output loss؛ Machine learning | ||
| مراجع | ||
|
Amjad, M., Ahmad, I., Ahmad, M., Wróblewski, P., Kamiński, P., & Amjad, U. (2022). Prediction of pile bearing capacity using XGBoost algorithm: modeling and performance evaluation. Applied Sciences, 12(4), 2126. https://doi.org/10.3390/app12042126
Bozoglu, M., Başer, U., Eroglu, N. A., & Topuz, B. K. (2019). Impacts of climate change on Turkish agriculture. Journal of International Environmental Application and Science, 14(3), 97-103. https://izlik.org/JA27ZN99WF
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Calzadilla, A., Zhu, T., Rehdanz, K., Tol, R. S., & Ringler, C. (2013). Economy-wide impacts of climate change on agriculture in Sub-Saharan Africa. Ecological Economics, 93, 150-165. https://doi.org/10.1016/j.ecolecon.2013.05.006
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA. (pp. 785-794). https://doi.org/10.1145/2939672.2939785
Climate Change Knowledge Portal (CCKP). (2023). Köppen-Geiger Climate Classification. Retrieved from: https://climateknowledgeportal.worldbank.org/country/iran-islamic-rep.
Demirhan, H. (2020). Impact of increasing temperature anomalies and carbon dioxide emissions on wheat production. Science of the Total Environment, 741, 139616. https://doi.org/10.1016/j.scitotenv.2020.139616
Farajzadeh, Z., Ghorbanian, E., & Tarazkar, M. H. (2024). Sectoral impacts of climate change in Iran: A dynamic analysis with emphasis on agriculture. Sustainable Production and Consumption, 49, 571-588. https://doi.org/10.1016/j.spc.2024.07.020
Farajzadeh, Z., & Nematollahi, M. A. (2023). Components and predictability of pollutants emission intensity. Global Journal of Environmental Science and Management, 9(2), 241-260. https://doi.org/10.22034/gjesm.2023.02.05
Farajzadeh, Z., Ghorbanian, E., & Tarazkar, M. H. (2022). The shocks of climate change on economic growth in developing economies: Evidence from Iran. Journal of Cleaner Production, 372, 133687. https://doi.org/10.1016/j.jclepro.2022.133687
Footprint network data. (2022). Ecological footprint vs biocapacity (gha per person). Retrieved from: https://data.footprintnetwork.org/#/.
Goldberg, M. H., van der Linden, S., Ballew, M. T., Rosenthal, S. A., Gustafson, A., & Leiserowitz, A. (2019). The experience of consensus: Video as an effective medium to communicate scientific agreement on climate change. Science Communication, 41(5), 659-673. https://doi.org/10.1177/1075547019874361
Haj Seyed Javady, S. M. R., Heydari, R., & Abbasi, F., (2023). Forecasting the future price of pistachio in agricultural commodity exchange using of the hybrid model of Wavelet-XGBoost. Agricultural Economics, 17(1), 79-108. https://doi.org/10.22034/iaes.2023.1974208.1959
Hoegh-Guldberg, O., & Bruno, J. F. (2010). The impact of climate change on the world’s marine ecosystems. Science, 328(5985), 1523-1528. https://doi.org/10.1126/science.1189930
Howard, P. H., & Sterner, T. (2017). Few and not so far between: A meta-analysis of climate damage estimates. Environmental and Resource Economics, 68(1), 197-225. https://doi.org/10.1007/s10640-017-0166-z
Intergovernmental Panel on Climate Change (IPCC). )2014(. Climate Change 2014: Impacts, adaptation, and vulnerability. Contribution of working group II to the fifth Assessment report of the intergovernmental panel on climate change IPCC. Cambridge, United Kingdom and New York. Retrieved from: www.ipcc.ch/report/ar5/wg2/
Intergovernmental Panel on Climate Change (IPCC). )2022(. Climate Change 2022: Impacts, adaptation, and vulnerability. Cambridge, United Kingdom and New York. Retrieved from: https://www.ipcc.ch/report/ar6/wg2/
Jahangirpour, D., & Zibaei, M. (2022). Cropping pattern optimization in the context of climate-smart agriculture: A case study for Doroodzan Irrigation Network-Iran. Journal of Agricultural Economics and Development, 35(4), 407-422. https://doi.org/10.22067/JEAD.2021.73325.1095
Jia, J. S., Zhao, J. Z., Deng, H. B., & Duan, J. (2010). Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China. Ecological Indicators, 10(2), 538-544. https://doi.org/10.1016/j.ecolind.2009.06.007
Karmalkar, A. V., & Bradley, R. S. (2017). Consequences of global warming of 1.5 C and 2 C for regional temperature and precipitation changes in the contiguous United States. PloS one 12(1), e0168697. https://doi.org/10.1371/journal.pone.0168697.g001
Keshavarz, A., & Farajzadeh, Z. (2025). Exploring the impacts of climate change on ecological footprint in Iran: A dynamic input-output analysis. Environmental Development, 55, 101217. https://doi.org/10.1016/j.envdev.2025.101217
Keshavarz, A., Farajzadeh, Z., & Tarazkar, M. H. (2021). Indicators for natural capital: Changes and forecasts. Agricultural Economics Research, 13(1), 235-260. (In Persian). https://doi.net/dor/20.1001.1.20086407.1400.13.1.11.0
Kompas, T., Pham, V. H., & Che, T. N. (2018). The effects of climate change on GDP by country and the global economic gains from complying with the Paris climate accord. Earth's Future, 6(8), 1153-1173. https://doi.org/10.1029/2018EF000922
Liu, L., & Lei, Y. (2018). An accurate ecological footprint analysis and prediction for Beijing based on SVM model. Ecological Informatics, 44, 33-42. https://doi.org/10.1016/j.ecoinf.2018.01.003
Maghrebi, M., Noori, R., Partani, S., Araghi, A., Barati, R., Farnoush, H., & Torabi Haghighi, A. (2021). Iran's groundwater hydrochemistry. Earth and Space Science, 8(8), e2021EA001793. https://doi.org/10.1029/2021EA001793
Malakootikhah, Z., & Farajzadeh, Z. (2020). Climate change impact on agriculture value added. Agricultural Economics and Development, 28(3), 1-30. (In Persian). https://doi.org/10.30490/aead.2020.305725.1093
Mansouri Daneshvar, M. R., Ebrahimi, M., & Nejadsoleymani, H. (2019). An overview of climate change in Iran: Facts and statistics. Environmental Systems Research, 8(1), 1-10. https://doi.org/10.1186/s40068-019-0135-3
Mehrara, M., Moeini, A., Ahrari, M., & Bahrami, Z. (2009). Forecasting ammonia price based on fundamental, technical analysis and neural network. Journal of Quantitative Economics (Quarterly Journal of Economics review), 6(1 (20)), 51-75.
Modarres, R., Sarhadi, A., & Burn, D. H. (2016). Changes of extreme drought and flood events in Iran. Global and Planetary Change, 144, 67-81. https://doi.org/10.1016/j.gloplacha.2016.07.008
Molua, E. L. (2009). An empirical assessment of the impact of climate change on smallholder agriculture in Cameroon. Global and Planetary Change, 67(3-4), 205-208. https://doi.org/10.1016/j.gloplacha.2009.02.006
Monfreda, C., Wackernagel, M., & Deumling, D. (2004). Establishing national natural capital accounts based on detailed ecological footprint and biological capacity assessments. Land Use Policy, 21(3), 231-246. https://doi.org/10.1016/j.landusepol.2003.10.009
Montoya, M. A., Allegretti, G., Bertussi, L. A. S., & Talamini, E. (2021). Renewable and non-renewable in the energy-emissions-climate nexus: Brazilian contributions to climate change via international trade. Journal of Cleaner Production, 312, 127700. https://doi.org/10.1016/j.jclepro.2021.127700
Moore, F. C., Baldos, U., Hertel, T., Diaz, D. (2017). New science of climate change impacts on agriculture implies higher social cost of carbon. Nature Communications 8(1), 1-9. https://doi.org/10.1038/s41467-017-01792-x Nahid, N., Lashgarara, F., Farajolah Hosseini, S. J., Mirdamadi, S. M., & Rezaei-Moghaddam, K. (2021). Determining the resilience of rural households to food insecurity during drought conditions in Fars province, Iran. Sustainability, 13(15), 8384. https://doi.org/10.3390/su13158384
Nasrnia, F., & Ashktorab, N. (2021). Sustainable livelihood framework-based assessment of drought resilience patterns of rural households of Bakhtegan basin, Iran. Ecological Indicators, 128, 107817. https://doi.org/10.1016/j.ecolind.2021.107817
Newell, R. G., Prest, B. C., & Sexton, S. E. (2021). The GDP-temperature relationship: Implications for climate change damages. Journal of Environmental Economics and Management, 108, 102445. https://doi.org/10.1016/j.jeem.2021.102445
Ouedraogo, M., Some, L., & Dembele, Y. (2006). Economic impact assessment of climate change on agriculture in Burkina Faso: A Ricardian Approach. Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31, 6639-6649. https://dl.acm.org/doi/10.5555/3327757.3327770
Rashidi, T., Pakravan-Charvadeh, M. R., Gholamrezai, S., & Rahimian, M. (2024). Unveiling the nexus of climate change, adaptation strategies, and food security: Insights from small-scale farmers in Zagros Mountains in Iran. Environmental Research, 252, 118691. https://doi.org/10.1016/j.envres.2024.118691
Reid, C. E., & Gamble, J. L. (2009). Aeroallergens, allergic disease, and climate change: impacts and adaptation. EcoHealth, 6(3), 458-470. https://doi.org/10.1007/s10393-009-0261-x
Sheng, Y., & Xu, X. (2019). The productivity impact of climate change: Evidence from Australia's Millennium drought. Economic Modelling, 76, 182-191. https://doi.org/10.1016/j.econmod.2018.07.031
Shirley, R., Pope, E., Bartlett, M., Oliver, S., Quadrianto, N., Hurley, P., Duivenvoorden, S., Rooney, P., Barrett, A., Kent, C., and & Bacon, J. (2020). An empirical, Bayesian approach to modelling crop yield: Maize in USA. Environmental Research Communications, 2(2), 025002. https://doi.org/10.1088/2515-7620/ab67f0
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Tsigaris, P., & Wood, J. (2019). The potential impacts of climate change on capital in the 21st century. Ecological Economics, 162, 74-86. https://doi.org/10.1016/j.ecolecon.2019.04.009
Wang, Z., Yang, L., Yin, J., & Zhang, B. (2018). Assessment and prediction of environmental sustainability in China based on a modified ecological footprint model. Resources, Conservation and Recycling, 132, 301-313. https://doi.org/10.1016/j.resconrec.2017.05.003
Wiedmann, T., Wood, R., Minx, J. C., Lenzen, M., Guan, D., & Harris, R. (2010). A carbon footprint time series of the UK– results from a multi-region input–output model. Economic Systems Research, 22(1), 19-42. https://doi.org/10.1080/09535311003612591
Yamany, W., Tharwat, A., Hassanin, M. F., Gaber, T., Hassanien, A. E., & Kim, T. H. (2015). A new multi-layer perceptrons trainer based on ant lion optimization algorithm. In: 2015 Fourth international conference on information science and industrial applications (ISI) (pp. 40-45). IEEE. https://doi.org/10.1109/ISI.2015.9
Zhao, Z. J., Chen, X. T., Liu, C. Y., Yang, F., Tan, X., Zhao, Y., & van Ruijven, B. J. (2020). Global climate damage in 2° C and 1.5° C scenarios based on BCC_SESM model in IAM framework. Advances in Climate Change Research, 11(3), 261-272. https://doi.org/10.1016/j.accre.2020.09.008
| ||
|
آمار تعداد مشاهده مقاله: 99 تعداد دریافت فایل اصل مقاله: 256 |
||