Abstract
A series of Poisson distributions are fit to sets of global cost-of-impact data representing large-scale accidents and anthropogenic catastrophes. The fits are used to build a function representing data means and are designated the Inverse Poisson Functional. Climate and environmental data have been used to develop a cost-frequency population distribution and to estimate the expected time between events. On a global scale, we show that expected wait- or reaction- times can be estimated using the Poisson density function. The functional is generated, representing the locus of means (peaks) from the individual Poisson distributions from different impact costs. Past (ex-post) forecasts relate to a range of natural and anthropogenic disasters; future (ex-ante) forecast presents global CO2 emissions. This paper shows that a substantial reaction to global climate change (CO2 emissions extremum) will occur in 55 to 120 years (95% CI) with a model prediction of 80 years.
Highlights
According to the International Panel on Climate Change[1] and the U.S Department of Energy[2], CO2 emissions are expected to rise at least through 2030
Some specific natural events and man-made accidents are indicated in each figure
17% of the anthropogenic data had low statistics and when applying these two cuts, (SD) improved from s = 10.0 to s = 6.22, and combined data improved from s = 14.6 to s = 6.23
Summary
Some examples include sulfur-induced acid rain[10], lead pollution resulting from the combustion of petroleum fuel containing lead[11], and the stratospheric ozone hole resulting from chlorofluorocarbons (CFCs)[12]. These problems have been addressed in both regional and international mitigation agreements. Regarding CO2 emissions and their impact, IAMs have long been used to perform simulations of the climate using reduced-order approximations to various Earth-systems These models include simulations of both the social science side (e.g., demographics, political, and economic modeling that affect greenhouse gas emissions) and the physical side (e.g., energy transfer, atmospheric, land, and ocean models that determine how and to what degree temperatures rise).
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