Forecasting CO2 Emissions from Libya’s Transport Sector
Abstract
This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.
Citation: Eyime, E., & Ben, U. (2024). Forecasting CO2 emissions from Libya’s transport sector. Trends in Renewable Energy, 11(1), 1-23. doi:http://dx.doi.org/10.17737/tre.2025.11.1.00185
Keywords
Full Text:
FULL TEXT (PDF)References
Beitelmal, W. H., Nwokolo, S. C., Meyer, E. L., & Ahia, C. C. (2024). Exploring Adaptation Strategies to Mitigate Climate Threats to Transportation Infrastructure in Nigeria: Lagos City, as a Case Study. Climate, 12(8), 117. doi:https://doi.org/10.3390/cli12080117
Benatallah, M., Bailek, N., Bouchouicha, K., Sharifi, A., Abdel-Hadi, Y., Nwokolo, S. C., ... & M. El-kenawy, E. S. (2024). Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques. International Journal of Engineering Research in Africa, 68, 151-164. doi:https://doi.org/10.4028/p-VH0u4y
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Introduction: Africa’s Net Zero Transition. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 1-13). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_1
Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2024). Reducing transport sector CO2 emissions patterns: Environmental technologies and renewable energy. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100217. doi:https://doi.org/10.1016/j.joitmc.2024.100217
Solaymani, S. (2022). CO2 emissions and the transport sector in Malaysia. Frontiers in Environmental Science, 9, 774164. doi:https://doi.org/10.3389/fenvs.2021.774164
Ahmed, S., Ahmed, K., & Ismail, M. (2020). Predictive analysis of CO 2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Quality, Atmosphere & Health, 13, 1035-1044. doi:https://doi.org/10.1007/s11869-020-00855-1.
Wang, L., Xue, X., Zhao, Z., Wang, Y., & Zeng, Z. (2020). Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model. Environmental Science and Pollution Research, 27, 21762-21776. doi:https://doi.org/10.1007/s11356-020-08627-1.
Nwokolo, S., Eyime, E., Obiwulu, A., & Ogbulezie, J. (2023). Africa's Path to Sustainability: Harnessing Technology, Policy, and Collaboration. Trends in Renewable Energy, 10(1), 98-131. doi:http://dx.doi.org/10.17737/tre.2024.10.1.00166
Nwokolo, S. C., Eyime, E. E., Obiwulu, A. U., Meyer, E. L., Ahia, C. C., Ogbulezie, J. C., & Proutsos, N. (2024). A multi-model approach based on CARIMA-SARIMA-GPM for assessing the impacts of climate change on concentrated photovoltaic (CPV) potential. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103560. doi:https://doi.org/10.1016/j.pce.2024.103560
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Global Investment and Development in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 15-58). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_2
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Remedies to the Challenges of Renewable Energy Deployment in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 59-74). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_3
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Influencing the Scale of Africa’s Energy Transition. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 75-91). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_4
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Technological Pathways to Net-Zero Goals in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 93-210). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_5
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Decarbonizing Hard-to-Abate Sectors in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 211-236). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_6
Alataş, S. (2022). Do environmental technologies help to reduce transport sector CO2 emissions? Evidence from the EU15 countries. Research in Transportation Economics, 91, 101047. doi:https://doi.org/10.1016/j.retrec.2021.101047
Amin, A., Altinoz, B., & Dogan, E. (2020). Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization. Clean Technologies and Environmental Policy, 22, 1725-1734. doi:https://doi.org/10.1007/s10098-020-01910-2
Awan, A., Alnour, M., Jahanger, A., & Onwe, J. C. (2022). Do technological innovation and urbanization mitigate carbon dioxide emissions from the transport sector? Technology in Society, 71, 102128. doi:https://doi.org/10.1016/j.techsoc.2022.102128
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Impacts of Climate Change in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 237-262). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_7.
Wang, C., Wood, J., Wang, Y., Geng, X., & Long, X. (2020). CO2 emission in transportation sector across 51 countries along the Belt and Road from 2000 to 2014. Journal of Cleaner Production, 266, 122000. doi:https://doi.org/10.1016/j.jclepro.2020.122000
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Scenarios that Could Give Rise to an African Net-Zero Energy Transition. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 263-298). Cham: Springer Nature Switzerland. doi: https://doi.org/10.1007/978-3-031-44514-9_8
Nwokolo, S. C., & Ogbulezie, J. C. (2018). A quantitative review and classification of empirical models for predicting global solar radiation in West Africa. Beni-Suef University Journal of Basic and Applied Sciences, 7(4), 367-396. doi:https://doi.org/10.1016/j.bjbas.2017.05.001
Nwokolo, S. C., & Ogbulezie, J. C. (2018). A qualitative review of empirical models for estimating diffuse solar radiation from experimental data in Africa. Renewable and Sustainable Energy Reviews, 92, 353-393. doi:https://doi.org/10.1016/j.rser.2018.04.118
Samuel Chukwujindu, N. (2017). A comprehensive review of empirical models for estimating global solar radiation in Africa. Renewable and Sustainable Energy Reviews, 78, 955-995. doi:https://doi.org/10.1016/j.rser.2017.04.101
Barman, P., Dutta, L., Bordoloi, S., Kalita, A., Buragohain, P., Bharali, S., & Azzopardi, B. (2023). Renewable energy integration with electric vehicle technology: A review of the existing smart charging approaches. Renewable and Sustainable Energy Reviews, 183, 113518. doi:https://doi.org/10.1016/j.rser.2023.113518
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Africa's Path to Net-Zero. Cham: Springer Nature Switzerland; 2023. doi:https://doi.org/10.1007/978-3-031-44514-9
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Africa’s Awakening to Climate Action. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 299-310). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_9
Nwokolo, S., Eyime, E., Obiwulu, A., & Ogbulezie, J. (2023). Exploring Cutting-Edge Approaches to Reduce Africa's Carbon Footprint through Innovative Technology Dissemination. Trends in Renewable Energy, 10(1), 1-29. doi:http://dx.doi.org/10.17737/tre.2024.10.1.00163
Borysova, T., Monastyrskyi, G., Zielinska, A., & Barczak, M. (2019). Innovation activity development of urban public transport service providers: multifactor economic and mathematical model. Marketing and Management of Innovations, 4, 98-109. doi:https://doi.org/10.21272/mmi.2019.4-08
Fontanot, T., Kishore, R., Van den Kerkhof, S., Blommaert, M., Peremans, B., Dupon, O., Kaaya, I., Tuomiranta, A., Duerinckx, F., & Meuret, Y. (2024). Multi-physics based energy yield modelling of a hybrid concentrated solar power/photovoltaic system with spectral beam splitting. Solar Energy, 278, 112753. doi:https://doi.org/10.1016/j.solener.2024.112753
Godil, D. I., Yu, Z., Sharif, A., Usman, R., & Khan, S. A. R. (2021). Investigate the role of technology innovation and renewable energy in reducing transport sector CO2 emission in China: a path toward sustainable development. Sustainable Development, 29(4), 694-707. doi:https://doi.org/10.1002/sd.2167
Gulagi, A., Alcanzare, M., Bogdanov, D., Esparcia, E., Ocon, J., & Breyer, C. (2021). Transition pathway towards 100% renewable energy across the sectors of power, heat, transport, and desalination for the Philippines. Renewable and Sustainable Energy Reviews, 144, 110934. doi:https://doi.org/10.1016/j.rser.2021.110934
Hens, L., Melnyk, L. H., Matsenko, O. M., Chygryn, O. Y., & Gonzales, C. C. (2019). Transport economics and sustainable development in Ukraine. Marketing and Management of Innovations, 3, 272-284. doi:https://doi.org/10.21272/mmi.2019.3-21
Hickman, R., Ashiru, O., & Banister, D. (2009). Achieving carbon-efficient transportation: backcasting from London. Transportation Research Record, 2139(1), 172-182. doi:https://doi.org/10.3141/2139-20
Nwokolo, S. C., Meyer, E. L., & Ahia, C. C. (2024). Exploring the Interactive Influences of Climate Change and Urban Development on the Fraction of Absorbed Photosynthetically Active Radiation. Atmosphere, 15(3), 253. doi:https://doi.org/10.3390/atmos15030253
Proutsos, N., Liakatas, A., Alexandris, S., Nwokolo, S. C., Solomou, A. D., & Amadi, S. O. (2024). Assessing the impact of atmospheric attributes on the effectiveness of solar irradiance for photosynthesis of urban vegetation in Attica, Greece. Theoretical and Applied Climatology, 155(2), 1415-1427. doi:https://doi.org/10.1007/s00704-023-04700-0
Nwokolo, S., Obiwulu, A., Amadi, S., & Ogbulezie, J. (2023). Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems. Trends in Renewable Energy, 9(2), 120-136. doi:http://dx.doi.org/10.17737/tre.2023.9.2.00156
Nwokolo, S. C., Proutsos, N., Meyer, E. L., & Ahia, C. C. (2023). Machine learning and physics-based hybridization models for evaluation of the effects of climate change and urban expansion on photosynthetically active radiation. Atmosphere, 14(4), 687. doi:https://doi.org/10.3390/atmos14040687
Agbor, M., Udo, S., Ewona, I., Nwokolo, S., Ogbulezie, J., Amadi, S., & Billy, U. (2023). Effects of Angstrom-Prescott and Hargreaves-Samani Coefficients on Climate Forcing and Solar PV Technology Selection in West Africa. Trends in Renewable Energy, 9(1), 78-106. doi:http://dx.doi.org/10.17737/tre.2023.9.1.00150
Agbor, M. E., Udo, S. O., Ewona, I. O., Nwokolo, S. C., Ogbulezie, J. C., & Amadi, S. O. (2023). Potential impacts of climate change on global solar radiation and PV output using the CMIP6 model in West Africa. Cleaner Engineering and Technology, 13, 100630. doi:https://doi.org/10.1016/j.clet.2023.100630
Nwokolo, S. C., Singh, R., Khan, S., Kumar, A., & Luthra, S. (2023). Technological Pathways to Net-Zero Goals in Africa. In Africa's Path to Net-Zero: Exploring Scenarios for a Sustainable Energy Transition (pp. 93-210). Cham: Springer Nature Switzerland. doi:https://doi.org/10.1007/978-3-031-44514-9_5
Li, X., Ren, A., & Li, Q. (2022). Exploring patterns of transportation-related CO2 emissions using machine learning methods. Sustainability, 14(8), 4588. doi:https://doi.org/10.3390/su14084588
Ağbulut, Ü. (2022). Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Consumption, 29, 141-157. doi:https://doi.org/10.1016/j.spc.2021.10.001
Klemm, C., & Vennemann, P. (2021). Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches. Renewable and Sustainable Energy Reviews, 135, 110206. doi:https://doi.org/10.1016/j.rser.2020.110206
IPCC. (2021).Summary for Policymakers. In Climate Change 2021: The Physical Science Basis, https://www.ipcc.ch/report/ar6/wg1/chapter/summary-for-policymakers/ (Accessed on 9/22.2024).
Proutsos, N., Tigkas, D., Tsevreni, I., Alexandris, S. G., Solomou, A. D., Bourletsikas, A., ... & Nwokolo, S. C. (2023). A thorough evaluation of 127 potential evapotranspiration models in two mediterranean urban green sites. Remote Sensing, 15(14), 3680. doi:https://doi.org/10.3390/rs15143680
Schmidt Rivera, X. C., Topriska, E., Kolokotroni, M., & Azapagic, A. (2018). Environmental sustainability of renewable hydrogen in comparison with conventional cooking fuels. Journal of Cleaner Production, 196, 863-879. doi:https://doi.org/10.1016/j.jclepro.2018.06.033.
Agyekum, E. B., Nutakor, C., Khan, T., Adegboye, O. R., Odoi-Yorke, F., & Okonkwo, P. C. (2024). Analyzing the research trends in the direction of hydrogen storage – A look into the past, present and future for the various technologies. International Journal of Hydrogen Energy, 74, 259-275. doi:https://doi.org/10.1016/j.ijhydene.2024.05.399
Okonkwo, P. C., Islam, M. S., Taura, U. H., Barhoumi, E. M., Mansir, I. B., Das, B. K., Ali Sulaiman, M. M. B., Agyekum, E. B., & Bahadur, I. (2024). A techno-economic analysis of renewable hybrid energy systems for hydrogen production at refueling stations. International Journal of Hydrogen Energy, 78, 68-82. doi:https://doi.org/10.1016/j.ijhydene.2024.06.294
Chukwujindu Nwokolo, S., Ogbulezie, J. C., & Umunnakwe Obiwulu, A. (2022). Impacts of climate change and meteo-solar parameters on photosynthetically active radiation prediction using hybrid machine learning with Physics-based models. Advances in Space Research, 70(11), 3614-3637. doi:https://doi.org/10.1016/j.asr.2022.08.010
Nwokolo, S. C., Obiwulu, A. U., Ogbulezie, J. C., & Amadi, S. O. (2022). Hybridization of statistical machine learning and numerical models for improving beam, diffuse and global solar radiation prediction. Cleaner Engineering and Technology, 9, 100529. doi:https://doi.org/10.1016/j.clet.2022.100529
Nwokolo, S. C., Amadi, S. O., Obiwulu, A. U., Ogbulezie, J. C., & Eyibio, E. E. (2022). Prediction of global solar radiation potential for sustainable and cleaner energy generation using improved Angstrom-Prescott and Gumbel probabilistic models. Cleaner Engineering and Technology, 6, 100416. doi:https://doi.org/10.1016/j.clet.2022.100416
Yang, B., Xie, R., Duan, J., & Wang, J. (2023). State-of-the-art review of MPPT techniques for hybrid PV-TEG systems: Modeling, methodologies, and perspectives. Global Energy Interconnection, 6(5), 567-591. doi:https://doi.org/10.1016/j.gloei.2023.10.005
Shah, K. J., Pan, S.-Y., Lee, I., Kim, H., You, Z., Zheng, J.-M., & Chiang, P.-C. (2021). Green transportation for sustainability: Review of current barriers, strategies, and innovative technologies. Journal of Cleaner Production, 326, 129392. doi:https://doi.org/10.1016/j.jclepro.2021.129392
Georgopoulou, E., Mirasgedis, S., Sarafidis, Y., Giannakopoulos, C., Varotsos, K. V., & Gakis, N. (2024). Climate Change Impacts on the Energy System of a Climate-Vulnerable Mediterranean Country (Greece). Atmosphere, 15(3), 286. doi:https://doi.org/10.3390/atmos15030286.
Gaetani, M., Huld, T., Vignati, E., Monforti-Ferrario, F., Dosio, A., & Raes, F. (2014). The near future availability of photovoltaic energy in Europe and Africa in climate-aerosol modeling experiments. Renewable and Sustainable Energy Reviews, 38, 706-716. doi:https://doi.org/10.1016/j.rser.2014.07.041
Mango, M., Casey, J. A., & Hernández, D. (2021). Resilient Power: A home-based electricity generation and storage solution for the medically vulnerable during climate-induced power outages. Futures, 128, 102707. doi:https://doi.org/10.1016/j.futures.2021.102707
Kany, M. S., Mathiesen, B. V., Skov, I. R., Korberg, A. D., Thellufsen, J. Z., Lund, H., Sorknæs, P., & Chang, M. (2022). Energy efficient decarbonisation strategy for the Danish transport sector by 2045. Smart Energy, 5, 100063. doi:https://doi.org/10.1016/j.segy.2022.100063
Abbas, S., Saqib, N., & Shahzad, U. (2024). Global export flow of Chilean copper: The role of environmental innovation and renewable energy transition. Geoscience Frontiers, 15(3), 101697. doi:https://doi.org/10.1016/j.gsf.2023.101697
International Energy Agency. (2022). World Energy Outlook 2022. https://iea.blob.core.windows.net/assets/830fe099-5530-48f2-a7c1-11f35d510983/WorldEnergyOutlook2022.pdf (Accessed on 9/22/2024)
Khurshid, A., Khan, K., & Cifuentes-Faura, J. (2023). 2030 Agenda of sustainable transport: Can current progress lead towards carbon neutrality? Transportation Research Part D: Transport and Environment, 122, 103869. doi:https://doi.org/10.1016/j.trd.2023.103869
Xia, X., Li, P., Xia, Z., Wu, R., & Cheng, Y. (2022). Life cycle carbon footprint of electric vehicles in different countries: A review. Separation and Purification Technology, 301, 122063. doi:https://doi.org/10.1016/j.seppur.2022.122063
Khurshid, A., Khan, K., Chen, Y., & Cifuentes-Faura, J. (2023). Do green transport and mitigation technologies drive OECD countries to sustainable path? Transportation Research Part D: Transport and Environment, 118, 103669. doi:https://doi.org/10.1016/j.trd.2023.103669
Khurshid, A., Rauf, A., Qayyum, S., Calin, A. C., & Duan, W. (2023). Green innovation and carbon emissions: the role of carbon pricing and environmental policies in attaining sustainable development targets of carbon mitigation—evidence from Central-Eastern Europe. Environment, Development and Sustainability, 25(8), 8777-8798. doi:https://doi.org/10.1007/s10668-022-02422-3
Liu, M., Chen, Z., Sowah, J. K., Ahmed, Z., & Kirikkaleli, D. (2023). The dynamic impact of energy productivity and economic growth on environmental sustainability in South European countries. Gondwana Research, 115, 116-127. doi:https://doi.org/10.1016/j.gr.2022.11.012
Hassan, M. A., Bailek, N., Bouchouicha, K., & Nwokolo, S. C. (2021). Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks. Renewable Energy, 171, 191-209. doi:https://doi.org/10.1016/j.renene.2021.02.103
Obiwulu, A. U., Erusiafe, N., Olopade, M. A., & Nwokolo, S. C. (2020). Modeling and optimization of back temperature models of mono-crystalline silicon modules with special focus on the effect of meteorological and geographical parameters on PV performance. Renewable Energy, 154, 404-431. doi:https://doi.org/10.1016/j.renene.2020.02.103
Obiwulu, A. U., Chendo, M. A. C., Erusiafe, N., & Nwokolo, S. C. (2020). Implicit meteorological parameter-based empirical models for estimating back temperature solar modules under varying tilt-angles in Lagos, Nigeria. Renewable Energy, 145, 442-457. doi:https://doi.org/10.1016/j.renene.2019.05.136
Hassan, M. A., Bailek, N., Bouchouicha, K., Ibrahim, A., Jamil, B., Kuriqi, A., ... & El-kenawy, E. S. M. (2022). Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions. Theoretical and Applied Climatology, 150(1), 715-729. doi:https://doi.org/10.1007/s00704-022-04166-6
Obiwulu, A. U., Erusiafe, N., Olopade, M. A., & Nwokolo, S. C. (2022). Modeling and estimation of the optimal tilt angle, maximum incident solar radiation, and global radiation index of the photovoltaic system. Heliyon, 8(6). doi:https://doi.org/10.1016/j.heliyon.2022.e09598
Nwokolo, S. C., Meyer, E. L., & Ahia, C. C. (2023). Credible pathways to catching up with climate goals in Nigeria. Climate, 11(9), 196. doi:https://doi.org/10.3390/cli11090196
Sylvia John-Jaja, A., Abdullah, A. R., & Samuel Nwokolo, C. (2017). Genetic Analysis of Egg Quality Traits in Bovan Nera Black Laying Hen under Sparse Egg Production Periods. Iranian Journal of Applied Animal Science, 7(1), 155-162.
John-Jaja, S. A., Abdullah, A.-R., & Nwokolo, S. C. (2016). Effects of age variance on repeatability estimates of egg dimensions of Bovan Nera Black laying chickens. Journal of Genetic Engineering and Biotechnology, 14(1), 219-226. doi:https://doi.org/10.1016/j.jgeb.2016.06.003
John-Jaja, S. A., Udoh, U. H., & Nwokolo, S. C. (2016). Repeatability estimates of egg weight and egg-shell weight under various production periods for Bovan Nera Black laying chicken. Beni-Suef University Journal of Basic and Applied Sciences, 5(4), 389-394. doi:https://doi.org/10.1016/j.bjbas.2016.11.001
Ituen, E. E., Esen, N. U., Nwokolo, S. C., & Udo, E. G. (2012). Prediction of global solar radiation using relative humidity, maximum temperature and sunshine hours in Uyo, in the Niger Delta Region, Nigeria. Advances in Applied Science Research, 3(4), 1923-1937.
Sunday, E., Agbasi, O., & Samuel, N. (2016). Modelling and estimating photosynthetically active radiation from measured global solar radiation at Calabar, Nigeria. Physical Science International Journal, 12, 1-12. doi:https://doi.org/10.9734/PSIJ/2016/28446
Etuk, S. E., Nwokolo, S. C., Okechukwu, E. A., & John-Jaja, S. A. (2016). Analysis of photosynthetically active radiation over six tropical ecological zones in Nigeria. Journal of Geography, Environment and Earth Science International, 7(4), 1-15. doi:https://doi.org/10.9734/JGEESI/2016/27945
Nwokolo, S., & Otse, C. (2019). Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate. Trends in Renewable Energy, 5(3), 307-332. doi:http://dx.doi.org/10.17737/tre.2019.5.3.00107
Amadi, S., Dike, T., & Nwokolo, S. (2020). Global Solar Radiation Characteristics at Calabar and Port Harcourt Cities in Nigeria. Trends in Renewable Energy, 6(2), 111-130. doi:http://dx.doi.org/10.17737/tre.2020.6.2.00114
Nwokolo, S. C., Obiwulu, A. U., & Ogbulezie, J. C. (2023). Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production. Physics and Chemistry of the Earth, Parts A/B/C, 130, 103389. doi:https://doi.org/10.1016/j.pce.2023.103389
Nwokolo, S. C., Ogbulezie, J. C., & Ushie, O. J. (2023). A multi-model ensemble-based CMIP6 assessment of future solar radiation and PV potential under various climate warming scenarios. Optik, 285, 170956. doi:https://doi.org/10.1016/j.ijleo.2023.170956
Qiao, Q., Eskandari, H., Saadatmand, H., & Sahraei, M. A. (2024). An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector. Energy, 286, 129499. doi:https://doi.org/10.1016/j.energy.2023.129499
Emami Javanmard, M., Tang, Y., Wang, Z., & Tontiwachwuthikul, P. (2023). Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector. Applied Energy, 338, 120830. doi:https://doi.org/10.1016/j.apenergy.2023.120830
Giannakis, E., Serghides, D., Dimitriou, S., & Zittis, G. (2020). Land transport CO2 emissions and climate change: evidence from Cyprus. International Journal of Sustainable Energy, 39(7), 634-647. doi:https://doi.org/10.1080/14786451.2020.1743704
Yasin Çodur, M., & Ünal, A. (2019). An estimation of transport energy demand in Turkey via artificial neural networks. Promet-Traffic&Transportation, 31(2), 151-161. doi:https://doi.org/10.7307/ptt.v31i2.3041
Galeazzi, C., Steinbuks, J., & Cust, J. (2020). Africa’s Resource Export Opportunities and the Global Energy Transition (English). Live wire knowledge note series,no. 2020/111 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/431621608028194772/Africa-s-Resource-Export-Opportunities-and-the-Global-Energy-Transition (Accessed on 9/22/2024)
DOI: http://dx.doi.org/10.17737/tre.2025.11.1.00185
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Eyime E Eyime, Unoh Florence Ben
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright @2014-2025 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)