A REVIEW OF CARBON FOOTPRINT PREDICTION MODELS: METHODS, ADVANCES, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.54314/sk7jx792Keywords:
carbon footprint prediction, method, machine learning, sustainability, future directionsAbstract
Abstract: This study provides a systematic review of carbon footprint prediction models, examining methodological developments, existing challenges, and future research directions. As a crucial tool for climate change mitigation and sustainable environmental management, carbon emission prediction has attracted growing academic attention. A comprehensive literature search was conducted in leading databases, including Scopus, Web of Science, and Google Scholar, focusing on peer-reviewed articles from 2018 to 2024. The review covers a wide range of modeling approaches, such as process-based, input-output, hybrid, grey system, and AI-based models. Although significant progress has been made, issues concerning data quality, model complexity, scalability, and transparency persist. The study also highlights emerging technologies, including AI, big data, IoT, and blockchain, which enhance prediction accuracy and adaptability. Furthermore, it underscores the importance of standardized methods, Explainable AI (XAI), and flexible modeling frameworks for future research. Overall, this review offers valuable insights to inform future studies and policymaking in carbon footprint prediction and climate action.
Keyword: carbon footprint prediction, method, machine learning, sustainability, future directions
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Acheampong, A. O., & Boateng, E. B. (2019). Modelling carbon emission intensity: Application of artificial neural network. Journal of Cleaner Production, 225, 833–856. https://doi.org/10.1016/j.jclepro.2019.03.352
Akyol, M., & Uçar, E. (2021). Carbon footprint forecasting using time series data mining methods: the case of Turkey. Environmental Science and Pollution Research, 28(29), 38552–38562. https://doi.org/10.1007/s11356-021-13431-6
Ayman Naji Khallaf. (2024). Using Ai to Help Reduce the Effect of Global Warming. Power System Technology, 48(1), 1927–1947. https://doi.org/10.52783/pst.464
Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing, 599(June), 128096. https://doi.org/10.1016/j.neucom.2024.128096
Bussaban, K., Kularbphettong, K., & Boonseng, C. (2023). Prediction of Co 2 Emissions Using Machine Learning. 7250.
Cai, J., Xu, K., Zhu, Y., Hu, F., & Li, L. (2020). Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Applied Energy, 262(November 2019), 114566. https://doi.org/10.1016/j.apenergy.2020.114566
Chen, C. Y., Chai, K. K., & Lau, E. (2021). AI-Assisted approach for building energy and carbon footprint modeling. Energy and AI, 5(May), 100091. https://doi.org/10.1016/j.egyai.2021.100091
Chen, J., Cui, Y., Zhang, X., Yang, J., & Zhou, M. (2024). Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences (Switzerland), 14(20). https://doi.org/10.3390/app14209213
Chiu, Y. J., Hu, Y. C., Jiang, P., Xie, J., & Ken, Y. W. (2020). A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/8829948
Fang, Z., Xu, X., Bilal, M., & Jolfaei, A. (2024). Renewable prediction-driven service offloading for IoT-enabled energy systems with edge computing. Wireless Networks, 30(5), 3721–3733. https://doi.org/10.1007/s11276-021-02740-w
Gao, M., Yang, H., Xiao, Q., & Goh, M. (2021). A novel fractional grey Riccati model for carbon emission prediction. Journal of Cleaner Production, 282, 124471. https://doi.org/10.1016/j.jclepro.2020.124471
Gardezi, S. S. S., & Shafiq, N. (2019). Operational carbon footprint prediction model for conventional tropical housing: a Malaysian prospective. International Journal of Environmental Science and Technology, 16(12), 7817–7826. https://doi.org/10.1007/s13762-019-02371-x
Geevaretnam, J. L., Megat Mohd. Zainuddin, N., Kamaruddin, N., Rusli, H., Maarop, N., & Wan Hassan, W. A. (2022). Predicting the Carbon Dioxide Emissions Using Machine Learning. International Journal of Innovative Computing, 12(2), 17–23. https://doi.org/10.11113/ijic.v12n2.369
Han, J., Tan, Z., Chen, M., Zhao, L., Yang, L., & Chen, S. (2022). Carbon Footprint Research Based on Input–Output Model—A Global Scientometric Visualization Analysis. International Journal of Environmental Research and Public Health, 19(18). https://doi.org/10.3390/ijerph191811343
Hans, M., Hikmawati, E., & Surendro, K. (2023). Predictive Analytics Model for Optimizing Carbon Footprint From Students’ Learning Activities in Computer Science-Related Majors. IEEE Access, 11(July), 114976–114991. https://doi.org/10.1109/ACCESS.2023.3324725
Hidayat, A. (2024). Predictions and Policies on Carbon Footprint Release Data at the College of Vocational Studies, IPB University (SV IPB) Based on the Contribution of Campus Operational Activities. Environment and Ecology Research, 12(1), 40–53. https://doi.org/10.13189/eer.2024.120105
Janković, R., Mihajlović, I., Štrbac, N., & Amelio, A. (2021). Machine learning models for ecological footprint prediction based on energy parameters. Neural Computing and Applications, 33(12), 7073–7087. https://doi.org/10.1007/s00521-020-05476-4
Kaur, G. (2024). Integrating Data Envelopment Analysis and Machine Learning Approaches for Energy Optimization, Decreased Carbon Footprints, and Wheat Yield Prediction Across North-Western India. Journal of Soil Science and Plant Nutrition, 24(1), 1424–1447. https://doi.org/10.1007/s42729-024-01647-7
Li, Y., & Sun, Y. (2021). Modeling and predicting city-level CO2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 28(15), 19260–19271. https://doi.org/10.1007/s11356-020-12294-7
Lim, S. J., Cortez, M. M. E., & Esquivel, R. (2023). Carbon Footprint Prediction in the Philippines Using Time Series in Data Science. 1296–1303. https://doi.org/10.46254/an13.20230375
Luo, J., Zhuo, W., Liu, S., & Xu, B. (2024). The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy under Big Data. IEEE Access, 12(December 2023), 14690–14702. https://doi.org/10.1109/ACCESS.2024.3351468
Marçal, M. F. M., de Souza, Z. M., Tavares, R. L. M., Farhate, C. V. V., Oliveira, S. R. M., & Galindo, F. S. (2021). Predictive models to estimate carbon stocks in agroforestry systems. Forests, 12(9). https://doi.org/10.3390/f12091240
Meng, Y., & Noman, H. (2022). Predicting CO2 Emission Footprint Using AI through Machine Learning. Atmosphere, 13(11), 1–15. https://doi.org/10.3390/atmos13111871
Mosso, D., Colucci, G., Lerede, D., Nicoli, M., Piscitelli, M. S., & Savoldi, L. (2024). How much do carbon emission reduction strategies comply with sustainable development of the power sector? Energy Reports, 11(March), 3064–3087. https://doi.org/10.1016/j.egyr.2024.02.056
Nguyen, Q., Diaz-Rainey, I., & Kuruppuarachchi, D. (2021). Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach. Energy Economics, 95, 105129. https://doi.org/10.1016/j.eneco.2021.105129
Ni, K., Hu, Y., Ye, X., AlZubi, H. S., Goddard, P., & Alkahtani, M. (2018). Carbon footprint modeling of a clinical lab. Energies, 11(11). https://doi.org/10.3390/en11113105
Norton, A. J., Anthony Bloom, A., Parazoo, N. C., Levine, P. A., Ma, S., Braghiere, R. K., & Smallman, T. L. (2023). Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance. Biogeosciences, 20(12), 2455–2484. https://doi.org/10.5194/bg-20-2455-2023
Pirson, T., & Bol, D. (2021). Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach. Journal of Cleaner Production, 322(September), 128966. https://doi.org/10.1016/j.jclepro.2021.128966
Spring, A., Dunkl, I., Li, H., Brovkin, V., & Ilyina, T. (2021). Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle. Earth System Dynamics, 12(4), 1139–1167. https://doi.org/10.5194/esd-12-1139-2021
Sun, W., & Ren, C. (2021). Short-term prediction of carbon emissions based on the EEMD-PSOBP model. Environmental Science and Pollution Research, 28(40), 56580–56594. https://doi.org/10.1007/s11356-021-14591-1
Thilakarathna, P. S. M., Seo, S., Baduge, K. S. K., Lee, H., Mendis, P., & Foliente, G. (2020). Embodied carbon analysis and benchmarking emissions of high and ultra-high strength concrete using machine learning algorithms. Journal of Cleaner Production, 262(February 2019), 121281. https://doi.org/10.1016/j.jclepro.2020.121281
Tong, M., Duan, H., & He, L. (2021). A novel Grey Verhulst model and its application in forecasting CO2 emissions. Environmental Science and Pollution Research, 28(24), 31370–31379. https://doi.org/10.1007/s11356-020-12137-5
Wang, Y., & Wang, H. (2023). Forecasting CO2 emissions using a novel fractional discrete grey Bernoulli model: A case of Shaanxi in China. Urban Climate, 49. https://doi.org/10.1016/j.uclim.2023.101452
Wen, L., & Yuan, X. (2020). Forecasting CO2 emissions in China's commercial department through BP neural network based on random forest and PSO. Science of the Total Environment, 718, 137194. https://doi.org/10.1016/j.scitotenv.2020.137194
Wilkes, A., Wassie, S., Odhong’, C., Fraval, S., & van Dijk, S. (2020). Variation in the carbon footprint of milk production on smallholder dairy farms in central Kenya. Journal of Cleaner Production, 265, 121780. https://doi.org/10.1016/j.jclepro.2020.121780
Yunis, R., Andri, A., & Djoni, D. (2024). Hybridization Model for Air Pollution Prediction Using Time Series Data. CogITo Smart Journal, 10(1), 422–435. https://doi.org/10.31154/cogito.v10i1.619.422-435
Zhang, C. (2023). Carbon Footprint Prediction of Thermal Power Industry under the Dual-Carbon Target: A Case Study of Zhejiang Province, China. Sustainability (Switzerland), 15(4).
Zhou, W., Zeng, B., Wang, J., Luo, X., & Liu, X. (2021). Forecasting Chinese carbon emissions using a novel grey rolling prediction model. Chaos, Solitons and Fractals, 147, 110968. https://doi.org/10.1016/j.chaos.2021.110968
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