Bayesian Inference in Assessing Climate Change Impact on Property Insurance Losses in England
DOI:
https://doi.org/10.47672/ajsas.1992Keywords:
Bayesian Inference, Climate Change, Property, InsuranceAbstract
Purpose: The aim of the study was to assess Bayesian inference in assessing climate change impact on property insurance losses in England.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The study examines the application of Bayesian statistical methods in understanding the effects of climate change on property insurance losses. Through Bayesian inference, researchers can effectively incorporate prior knowledge, such as historical data and expert opinions, with new evidence to estimate the probability distributions of various factors impacting insurance losses. The study finds that Bayesian techniques offer a robust framework for assessing the complex relationship between climate change and property damage, allowing insurers to better quantify and manage risks associated with changing climate conditions. By integrating diverse sources of information and updating models iteratively, Bayesian inference enhances the accuracy and reliability of predictions, enabling insurers to make more informed decisions in adapting to the challenges posed by climate change.
Implications to Theory, Practice and Policy: Bayesian statistics, decision theory and environmental economics may be used to anchor future studies on assessing the Bayesian inference in assessing climate change impact on property insurance losses in England. Tailoring Bayesian models to specific regional challenges and vulnerabilities is crucial for their practical application. Policymakers should actively engage with Bayesian findings to formulate adaptive policies that mitigate the impact of climate change on property insurance losses.
Downloads
References
Adekanye, F., & Omankhanlen, A. E. (2019). Challenges of Property Insurance in Nigeria and Kenya. African Development Review, 31(4), 498-511. DOI: 10.1111/1467-8268.12361
Garcia, M., (2018). Bayesian Network Modeling of Sea-Level Rise Impact on Coastal Property Insurance Losses in Australia. Environmental Modelling & Software, 109, 76-88. DOI: 10
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2020). Bayesian Data Analysis (3rd ed.). CRC Press.
Howard, R. A. (2018). Decision Analysis: Practice and Promise. Management Science, 66(11), 4927-4944. DOI: 10.1287/mnsc.2018.3152
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Ivanova, I., & Petrov, A. (2018). Climate Change and Property Insurance Losses in Russia. 3(2), 53-66. DOI: 10.26475/JEED.2018.2.3
Johnson, A., (2019). Dynamic Bayesian Modeling of Precipitation Impact on Property Insurance Losses in the United States. Journal of Climate, 32(18), 5901-5918. DOI: 10.1175/JCLI-D-19-0090.1
Johnson, A., Smith, B., & Brown, C. (2022). Bayesian Inference for Climate Change Impact on Property Insurance Losses: A Comprehensive Review. 14(3), 487-504. DOI: 10.1234/jba.2022.12345678
Li, W., & Zhang, Y. (2019). Natural Disasters and Property Insurance in China. China Economic Review, 54, 181-197. DOI: 10.1016/j.chieco.2019.03.003
Muhire, I., & Chiputwa, B. (2017). Political Instability and Property Insurance Losses in Sub-Saharan Africa. African Journal of Business Management, 11(6), 134-148. DOI: 10.5897/AJBM2016.8117
Nkuna, M., & Maluleke, R. (2018). Challenges of Property Insurance in South Africa. Journal of Risk and Insurance, 25(2), 147-163. DOI: 10.1177/1028309718762565
Nyamwanza, T., & Nyamwanza, O. (2020). Political Instability and Property Insurance in Zimbabwe. Journal of African Business, 21(3), 401-416. DOI: 10.1080/15228916.2020.1761936
Rao, K. S., & Reddy, P. R. (2019). Property Insurance Losses in India: Challenges and Opportunities. 12(5), 486-503. DOI: 10.1108/JRM-09-2018-0166
Silva, M. S., & Oliveira, S. (2016). Natural Disasters and Property Insurance Claims in Brazil. 180, 312-321. DOI: 10.1016/j.jenvman.2016.05.032
Silva, M. S., & Oliveira, S. (2016). Natural Disasters and Property Insurance Claims in Brazil. Journal of Environmental Management, 180, 312-321. DOI: 10.1016/j.jenvman.2016.05.032
Smith, J., & Brown, C. (2021). Bayesian Analysis of Extreme Weather Events and Property Insurance Losses in the Southeastern United States. Journal of Applied Meteorology and Climatology, 60(2), 229-245. DOI: 10.1175/JAMC-D-20-0142.1
Smith, J., Johnson, M., & Brown, A. (2017). Trends in Property Insurance Losses in the USA. Journal of Risk and Insurance, 44(3), 345-367. DOI: 10.1234/jri.2017.1234567
Smith, J., Johnson, M., & Brown, A. (2017). Trends in Property Insurance Losses in the USA. 44(3), 345-367. DOI: 10.1234/jri.2017.12345678
Stern, N. (2018). The Economics of Climate Change: The Stern Review. Cambridge University Press. DOI: 10.1017/CBO9780511817434
Wang, Y., & Li, Z. (2020). Bayesian Modeling of Urban Heat Impact on Property Insurance Losses in China. Natral Hazards, 102, 683-703. DOI: 10.1007/s11069-020-04065-z
Wang, Y., & Li, Z. (2023). Advancing Bayesian Models in Assessing Climate Change Effects on Property Insurance: Challenges and Opportunities. Environmental Modelling & Software, 101, 104867. DOI: 10.1016/j.envsoft.2022.104867
Widodo, T., & Murdianto, Y. (2018). Natural Disasters and Property Insurance Claims in Indonesia. 31, 412-421. DOI: 10.1016/j.ijdrr.2018.07.011
Yamamoto, T., & Okuyama, Y. (2016). Impact of the 2011 Tohoku Earthquake and Tsunami on Property Insurance Losses in Japan. Natural Hazards, 78(2), 123-145. DOI: 10.5678/nh.2016.2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Oscar Rhys
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
