ESG-szempontok a klímakockázat előrejelzésében
DOI:
https://doi.org/10.14267/VEZTUD.2021.09.02Keywords:
climate risk, neural networks, sustainability, ESG indicatorAbstract
Sustainability has been playing an increasingly important role in finance, as well as the measurement of climate risk. In this research, the authors examine the relationship between ESG indicators (Environmental, Social and Governance) and the Global Climate Risk Index (CRI), focusing on the role of environmental factors and countries with different income categories. This analysis uses the World Bank’s countrywide ESG and Germanwatch climate risk databases, which uses linear regression and neural network methodology to predict CRI, which attempts to quantify the consequences of extreme weather events and related socioeconomic data. An essential result of the article is that climate risk is less predictable by environmental indicators, social and governmental factors are more predictive, and countries’ income levels are inversely proportional to climate sensitivity. The results may allow international organizations and institutional investors to pay more attention to ESG indicators in low-income countries with a significant value.
Downloads
References
Barr, R., Fankhauser, S., & Hamilton, K. (2010). Adaptation investments: A resource allocation framework. Mitigation & Adaptation Strategies for Global Change, 15(8), 843–858. https://doi.org/10.1007/s11027-010-9242-1
Berlinger, E., Keresztúri, J.,L., & Tamásné Vőneki, Zs. (2018). A cross-country analysis of operational risk: The effect of the freedom of press. In Dömötör Barbara (Ed.), PRMIA Hungary Chapter Éves Konferenciája, 2018: A Magyar kockázatkezelési kutatások legújabb eredményei (p. 8). Budapest: PRMIA Hungary.
Bertolotti, A. (2020). Effectively managing risks in an ESG portfolio. Journal of Risk Management in Financial Institutions, 13(3), 202–211.
Brooks, N., Neil Adger, W., & Mick Kelly, P. (2005). The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Global Environmental Change Part A: Human & Policy Dimensions, 15(2), 151–163. https://doi.org/10.1016/j.gloenvcha.2004.12.006
Busby, J. W., Smith, T. G., & Krishnan, N. (2014). Climate security vulnerability in Africa mapping 3.0. Political Geography, 43, 51–67. https://doi.org/10.1016/j.polgeo.2014.10.005
Choi, D., Gao, Z., & Jiang, W. (2020). Attention to global warming. The Review of Financial Studies, 33(3), 1112-1145. https://doi.org/10.2139/ssrn.3180045
Christiansen, N. H., Voie, P. E. T., Winther, O., & Høgsberg, J. (2014). Comparison of neural network error measures for simulation of slender marine structures. Journal of Applied Mathematics, 2014, Article ID 759834. https://doi.org/10.1155/2014/759834
Crabb, J. (2020). ESG: the financial costs of climate change. International Financial Law Review, 2020. https://www.iflr.com/article/b1lmx4n8t9626x/esg-the-financial-costs-of-climate-change
Eckstein, D., Künzel, V. & Schäfer L. (2017). Global Climate Risk Index 2018 [on-line]. Germanwatch. https://germanwatch.org/sites/germanwatch.org/files/publication/20432.pdf
Eckstein, D., Hutfils, M.-L, & Winges, M. (2018). Global Climate Risk Index 2019 [on-line]. Germanwatch. https://germanwatch.org/sites/germanwatch.org/files/Global%20Climate%20Risk%20Index%202019_2.pdf
Eckstein, D., Künzel, V., Schäfer L. & Winges, M. (2019). Global Climate Risk Index 2020 [on-line]. Germanwatch. https://www.germanwatch.org/sites/germanwatch.org/files/20-2-01e%20Global%20Climate%20Risk%20Index%202020_14.pdf
Eriksen, S., & Silva, J. A. (2009). The vulnerability context of a savanna area in Mozambique: Household drought coping strategies and responses to economic change. Environmental Science & Policy, 12(1), 33–52. https://doi.org/10.1016/j.envsci.2008.10.007
Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210–233. https://doi.org/10.1080/20430795.2015.1118917
Glejser, H. (1969). A new test for heteroskedasticity. Journal of the American Statistical Association, 64(325), 316–323. https://doi.org/10.1080/01621459.1969.10500976
Griffin, P. A. (2020). Energy finance must account for extreme weather risk. Nature Energy, 5(2), 98–100. https://doi.org/10.1038/s41560-020-0548-2
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.3386/w25398
Hale, J. (2020). Despite the Downturn, U.S. Sustainable Funds Notch a Record Quarter for Flows. https://www.morningstar.com/articles/977328/despite-the-downturn-us-sustainable-funds-notch-a-record-quarter-for-flows
Hong, H., Karolyi, G. A., & Scheinkman, J. A. (2020). Climate finance. The Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.2139/ssrn.3478737
IPCC. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [on-line]. New York: Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/03/SREX_Full_Report-1.pdf
IPCC (2014). Climate Change 2014 Impacts, Adaptation, and Vulnerability [on-line]. New York: Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-PartA_FINAL.pdf
Kahn, M. E. (2005). The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions. Review of Economics and Statistics, 87(2), 271–284. https://doi.org/10.2139/ssrn.391741
Kavzoglu, T., & Mather, P. M. (2003). The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24(23), 4907–4938. https://doi.org/10.1080/0143116031000114851
Kovács, E. (2014). Többváltozós adatelemzés. Budapest: Typotex.
Liu, C., Golding, D., & Gong, G. (2008). Farmers’ coping response to the low flows in the lower Yellow River: A case study of temporal dimensions of vulnerability. Global Environmental Change Part A: Human & Policy Dimensions, 18(4), 543–553. https://doi.org/10.1016/j.gloenvcha.2008.09.002
Matos, P. (2020). ESG and Responsible Institutional Investing Around the World: A Critical Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3668998
Monasterolo, I., Roventini, A., & Foxon, T. J. (2019). Uncertainty of climate policies and implications for economics and finance: An evolutionary economics approach. Ecological Economics, 163, 177–182. https://doi.org/10.1016/j.ecolecon.2019.05.012
Naffa, H., & Fain, M. (2020). Performance measurement of ESG-themed megatrend investments in global equity markets using pure factor portfolios methodology. PLoS ONE, 15(12), 1–34. https://doi.org/10.1371/journal.pone.0244225
Németh-Durkó, E. (2020). Környezet és pénzügyek: A pénzügyi fejlettség emissziót befolyásoló szerepe. Gazdaság és Pénzügy, 7(4), 434-449. https://doi.org/10.33926/gp.2020.4.4
Obersteiner, M., Azar, C., Kossmeier, S., Mechler, R., Moellersten, K., Nilsson, S., ... Yan, J. (2001). Managing climate risk. Science, 294(5545), 1282. https://doi.org/10.1126/science.294.5545.1282
Ostrom, E. (2009). A polycentric approach for coping with climate change. World Bank Policy Research Working Paper No. 5095. http://dx.doi.org/10.2139/ssrn.1934353
Prechelt, L. (1998). Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks: The Official Journal of the International Neural Network Society, 11(4), 761–767. https://doi.org/10.1016/s0893-6080(98)00010-0
Primecz, H., Havran, D., & Lakatos, Z. (2019). How Does Female Presence on the Management and Supervisory Boards Impact the Performance in CEE? Academy of Management Annual Meeting Proceedings, 2019(1), 1–1. https://doi.org/10.5465/ambpp.2019.10602abstract
ROBECOSAM (2020). Country ESG Ranking Update – July 2020. Retrieved from https://www.robecosam.com/media/a/5/6/a565154e6efcd25e1197da40b69c1238_202008-robecosam-country-esg-ranking-robecosam_tcm1011-25282.pdf
Rubin, O., & Rossing, T. (2012). National and Local Vulnerability to Climate-Related Disasters in Latin America: The Role of Social Asset-Based Adaptation. Bulletin of Latin American Research, 31(1), 19–35. https://doi.org/10.1111/j.1470-9856.2011.00607.x
Sarkodie, S. A. (2018). The invisible hand and EKC hypothesis: What are the drivers of environmental degradation and pollution in Africa? Environmental Science and Pollution Research International, 25(22), 21993–22022. https://doi.org/10.1007/s11356-018-2347-x
Shive, S. A., & Forster, M. M. (2020). Corporate governance and pollution externalities of public and private firms. The Review of Financial Studies, 33(3), 1296-1330. https://doi.org/10.2139/ssrn.3339517
Tamásné Vőneki, Z., & Lamanda, G. (2020). Content analysis of bank disclosures related to ESG risks. Economy and Finance: English-Language Edition of Gazdaság és Pénzügy, 7(4), 412-424. https://doi.org/10.33908/EF.2020.4.3
Tato, A. & Nkambou R. (2018). Improving Adam Optimizer [online]. ICLR 2018 Workshop Submission. https://openreview.net/pdf?id=HJfpZq1DM
The World Bank (2019). Environment, Social and Governance Data [on-line]. New York: The Word Bank. https://datacatalog.worldbank.org/dataset/environment-social-and-governance-data
Thomas, V., Albert, J. R., & Perez, R. (2013). Climate-related disasters in Asia and the Pacific. Asian Development Bank Economics Working Paper Series, (358). Mandaluyong: Asian Development Bank. https://doi.org/10.2139/ssrn.2295783
Thomas, V. (2017). Climate change and natural disasters: transforming economies and policies for a sustainable future. New York: Routledge Taylor & Francis. https://doi.org/10.4324/9781315081045
Toya, H., & Skidmore, M. (2007). Economic Development and the Impacts of Natural Disasters. Economics Letters, 94(1), 20–25. https://doi.org/10.1016/j.econlet.2006.06.020
UNEP (2016). The Adaptation Finance Gap Report 2016 [online]. New York: United Nations Environment Programme (UNEP). https://climateanalytics.org/media/agr2016.pdf
van Dijk, M. A. (2020). Assessing climate risk for investment portfolios. Rotterdam: Rotterdam School of Management, Erasmus University. https://www.rsm.nl/fileadmin/Images_NEW/Erasmus_Platform_ for_Sustainable_Value_Creation/Assessing_climate_ risk_for_investment_portfolios__oct20.pdf
Volz, U., Beirne, J., Ambrosio Preudhomme, N., Fenton, A., Mazzacurati, E., Renzhi, N., & Stampe, J. (2020). Climate change and sovereign risk. London: SOAS Centre for Sustainable Finance, SOAS University of London. https://eprints.soas.ac.uk/33524/1/Climate%20Change%20and%20Sovereign%20Risk_final.pdf
Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897. https://doi.org/10.3390/app10051897
Wheeler, D. (2011). Quantifying vulnerability to climate change: Implications for adaptation assistance. Washington, D.C.: Center for Global Development. https://doi.org/10.2139/ssrn.1824611
WHO (2011). The Social Dimensions of Climate Change [online]. World Health Organization (WHO). https://www.who.int/globalchange/mediacentre/events/2011/social-dimensions-of-climate-change.pdf
World Economic Forum (2020). The Global Risks Report 2020 [online]. http://www3.weforum.org/docs/WEF_Global_Risk_Report_2020.pdf
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Vezetéstudomány / Budapest Management Review
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors assign copyright to Vezetéstudomány / Budapest Management Review. Authors are responsible for permission to reproduce copyright material from other sources.
