Mathematical Modelling of Carbon Dioxide Emissions in Agricultural Systems
DOI:
https://doi.org/10.5614/cbms.2025.8.2.2Keywords:
CO2 emission, boundedness, stability, delay, hopf-bifurcation, sensitivityAbstract
This study formulates a dynamic mathematical model to investigate the interplay between human activities and CO2 emissions within the context of agriculture. The model incorporates a system of differential equations describing the interactions among human population growth (H1), human economic activities (H2), atmospheric CO2 concentration (H3), forest biomass density (H4), and vehicle population (H5). Key processes include the effects of deforestation, economic activities, and vehicle emissions on CO2 levels, as well as the mitigating role of forest biomass.The model parameters account for natural growth rates, carrying capacities, and interaction coefficients that represent both the exacerbation and alleviation of CO2 emissions. The delay parameter ? captures the temporal lag in the effects of population growth and deforestation. This framework aims to provide insights into the dynamic interactions and feedback loops influencing CO2 emissions, with a particular emphasis on sustainable practices and policies to mitigate environmental degradation in agricultural contexts.
References
Abdelfatah, A., Mokhtar, S.A. and Sheta, A., Forecast global carbon dioxide emission using swarm intelligence, International Journal of Computer Applications, 77(12), pp. 1-5, 2013.
Ann, J., Shin, D., Kim, K. and Yang, J., Indoor air quality analysis using deep learning with sensor data, Sensors, 17(11), p. 2476, 2017.
Chen, S., Mihara, K. and Wen, J., Time series prediction of CO2, TVOC and HCHO based on machine learning at different sampling points, Building and Environment, 146, pp. 238-246, 2018.
Fang, D., Zhang, X., Yu, Q., Jin, T.C. and Tian, L., A novel method for carbon dioxide emission forecasting based on improved Gaussian process regression, Journal of Cleaner Production, 173, pp. 143-150, 2018.
Global Carbon Atlas, 2025. www.globalcarbonatlas.org, Accessed on November, 2025.
Kardani, M.N., Baghban, A., Sasanipour, J., Mohammadi, A.H. and Habibzadeh, S., Group contribution methods for estimating CO2 absorption capacities of imidazolium- and ammonium-based polyionic liquids, Journal of Cleaner Production, 203, pp. 601-618, 2018.
Khatibi, R., Ghorbani, M.A. and Pourhosseini, F.A., Streamflow predictions using nature-inspired firefly algorithms and a multiple model strategy: Directions of innovation towards next-generation practices, Advanced Engineering Informatics, 34, pp. 80-89, 2017.
Khatibi, R., Ghorbani, M.A., Naghshara, S., Aydin, H. and Karimi, V., A framework for inclusive multiple modelling with critical views on modelling practices: Applications to modelling water levels of Caspian Sea and lakes Urmia and Van, Journal of Hydrology, 587, p. 124923, 2020.
Zhang, L., Shen, Q., Wang, M., Sun, N., Wei, W., Lei, Y. and Wang, Y., Driving factors and predictions of CO2 emission in China?s coal chemical industry, Journal of Cleaner Production, 210, pp. 1131-1140, 2019.
Lin, C.S., Liou, F.M. and Huang, C.P., Grey forecasting model for CO2 emissions: A Taiwan study, Applied Energy, 88(11), pp. 3816-3820, 2011.
Pao, H.T., Fu, H.C. and Tseng, C.L., Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model, Energy, 40(1), pp. 400-409, 2012.
Lotfalipour, M.R., Falahi, M.A. and Bastam, M., Prediction of CO2 emissions in Iran using grey and ARIMA models, International Journal of Energy Economics and Policy, 3(3), pp. 229-237, 2013.
Samsami, R., Application of ant colony optimization (ACO) to forecast CO2 emission in Iran, Bulletin of Environment, Pharmacology and Life Sciences, 2(6), pp. 95-99, 2013.
Taghavifar, H., Taghavifar, H., Mardani, A., Mohebbi, A., Khalilarya, S. and Jafarmadar, S., Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane-fueled engine, Journal of Cleaner Production, 112, pp. 1729-1739, 2016.
Moazami, S., Noori, R., Amiri, B.J., Yeganeh, B., Partani, S. and Safavi, S., Reliable prediction of carbon monoxide using developed support vector machine, Atmospheric Pollution Research, 7(3), pp. 412-418, 2016.
Saleh, C., Dzakiyullah, N.R. and Bayu, J., Carbon dioxide emission prediction using support vector machine, Materials Science and Engineering Conference Series, 114(1), p. 012148, 2016.
Sun, W. and Liu, M., Prediction and analysis of the three major industries and residential consumption CO2 emissions based on the least squares support vector machine in China, Journal of Cleaner Production, 122, pp. 144-153, 2016.
Yu, Y., Deng, Y.R. and Chen, F.F., Impact of population aging and industrial structure on CO2 emissions and emissions trend prediction in China, Atmospheric Pollution Research, 9(3), pp. 1-10, 2017.
Libo, Y., Tingting, Y., Jielian, Z., Guicai, L., Yanfen, L. and Xiaoqian, M., Prediction of CO2 emissions based on multiple linear regression analysis, Energy Procedia, 105, pp. 4222-4228, 2017.
Sangeetha, A. and Amudha, T., A novel bio-inspired framework for CO2 emission forecast in India, Procedia Computer Science, 125, pp. 367-375, 2018.
Xu, G., Schwarz, P. and Yang, H., Determining China?s CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis, Energy Policy, 128, pp. 752-762, 2019.
Wang, Z.X. and Li, Q., Modeling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithmbased grey Verhulst model, Journal of Cleaner Production, 207, pp. 214-224, 2019.
Behrang, M.A., Assareh, E., Assari, M.R. and Ghanbarzadeh, A., Using bees algorithm and artificial neural network to forecast world carbon dioxide emission, Energy Sources Part A: Recovery, Utilization and Environmental Effects, 33(19), pp. 1747-1759, 2011.
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