Abstract
We study the long-run connection between atmospheric carbon dioxide (CO2) concentration and the probability of hydrometeorological disasters using a panel of 193 countries over the period 1970–2016 providing annual disaster projections to the year 2040 for each of these countries. Generating accurate predictions on where hydrometeorological disasters have greater chances to occur, may facilitate preparedness and adaption to such disasters, thus helping to reduce their high human and economic costs. We estimate the probabilities of hydrometeorological disasters at country levels using Bayesian sampling techniques. We decompose the probability of country disaster into the effects of country-specific factors, such as climatological and socio-demographic factors, and factors associated with world climate, which we denote global probability of disaster (GPOD). Finally, we subject these GPOD time paths to a cointegration analysis with CO2 concentration and provide projections to the year 2040 of the GPOD conditional on nine Shared Socioeconomic Pathways scenarios. We detect a stable long-term relation between CO2 accumulation and the GPOD that allows us to determine projections of the latter process conditional on the former. We conclude that readily available statistical data on global atmospheric concentrations of CO2 can be used as a conceptually meaningful, statistically valid and policy useful predictor of the probability of occurrence of hydrometeorological disasters.
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Data files can be found at: https://www.dropbox.com/s/t2nelg9epd6jvga/data.rar?dl=0. A guide for replicating results is available at: https://www.dropbox.com/scl/fi/577iz845crmlwufcnasxn/guide_for_replicating_results.docx?dl=0&rlkey=uyfvatkdv02odu3fa85mba672. A preprint version of this manuscript is available at https://doi.org/10.21203/rs.3.rs-528668/v1.
Notes
Climatological models typically build upon physical principles and laws, fluid mechanics and/or chemistry relations, used for running computer simulations of the earth climate system.
This definition was also adopted by López et al. (2020) and accords with the one by the United Nations Office for Disaster Risk Reduction: “Process or phenomenon of atmospheric, hydrological or oceanographic nature that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage” (UNISDR, 2009).
Iso-region or iso-subregion are used depending on the selected model and refer to a geographical classification of countries. Iso-region corresponds to continent (5 categories total) and iso-subregion to a within continent subdivision (21 categories). For aggregating observations over time, we allow for random year and decade effects.
The Metropolis–Hastings (also known as MCMC) algorithm is a recursive method to simulate multivariate distributions. It can be shown that the simulated distribution converges to the target distribution, a detailed description of the algorithm can be found in Chib and Greenberg (1995). BayesX software utilizes a generalized version of the algorithm with distribution specific iteratively weighted least squares proposal densities.
To put the effect estimates into perspective, consider the descriptive statistics in Table 1 using two country examples. Vietnam had a precipitation deviation of 46.74 mm/month in 1990 and -78.13 mm/month in 2010; Zambia -81.82 mm/month in 1990 and 37.64 mm/month in 2010. These changes account for -0.67 (Vietnam) and 0.65 (Zambia) standard deviations. Hence, other things equal, this precipitation changes account for a reduction of the probability of at least one disaster by 3.78% for Vietnam, and an increase of 3.62% for Zambia.
As our model selection process favored growth rates instead of log levels for these controls, a direct interpretation of the correlation results in the sense of the ones in Wu et al. (2018), Mora et al. (2018), or López et al (2020) cannot be made here. Taking GDP per capita as an example, we have that on the one hand neoclassical theory suggests conditional higher growth rates for lower income (more vulnerable) countries, while on the other hand growth has been shown to be sensitive to disasters (Loayza et al. 2012, Cavallo et al. 2013 and others). Hence, we refrain from making any causal interpretation.
See Kremers et al. (1992) for a discussion of single equation cointegration and error-correction models.
Under the null hypothesis of no cointegration, testing the significance of \({\alpha }_{2}\) requires non-standard critical values (Kremers et al. 1992). Appendix AB.1 provides the results with these values.
We have also applied these exercises to two other related trending variables as placebo, namely Global Temperature and World GDP per capita. Both variables appear to be also correlated in the long term with the GPOD, but both trending variables fail to be weakly exogenous (see Appendix B.2). Hence, for the conditioning of GPOD projections Global Temperature and World GDP per capita lack an essential qualification.
These scenarios include the five high-priority scenarios for the Sixth Assessment report by the IPCC (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5 and SSP1-1.9), the three scenarios that complete the Tier 2 list suggested by O’Neill et al. (2016) (SSP4-6.0, SSP4-3.4, SSP5-3.4-OS) and a variation of the SSP3-7.0 scenario (Meinshausen et al., 2020). For a comprehensive description of the SSPs we refer the reader to the work of Riahi et al. (2016).
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This research has been partially financed by the authors’ academics institutions, particularly for the research stays of Helmut Herwartz at the Department of Economics of the University of Chile in 2018 and 2019.
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Andrés Fortunato involved in data acquisition, software programming, tables and figures, literature review, model description and manuscript co-editing. Helmut Herwartz participated in econometric model designing and result interpretation, model description, text review. Ramón López involved in Initial conception, manuscript editing, text review, result interpretation, and supervision. Eugenio Figueroa B. participated in Research coordination, literature review, and manuscript writing and editing.
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Fortunato, A., Herwartz, H., López, R.E. et al. Carbon dioxide atmospheric concentration and hydrometeorological disasters. Nat Hazards 112, 57–74 (2022). https://doi.org/10.1007/s11069-021-05172-z
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DOI: https://doi.org/10.1007/s11069-021-05172-z