Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T10:19:22.954Z Has data issue: false hasContentIssue false
  • English
  • Français

Parametrization of intensive global climate change indicators on a level of sovereign states and governments

Published online by Cambridge University Press:  27 May 2019

Micha Tomkiewicz
Micha Tomkiewicz*
Affiliation:
Department of Physics, Brooklyn College of CUNY, Brooklyn, New York 11210, USA; Ph.D Program in Physics and the Ph.D Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, USA
*
a)Address all correspondence to Micha Tomkiewicz at [email protected]
Get access

Abstract

This article will show that within well-defined error margins, carbon intensities and energy intensities are independent of the population and GDP of countries and thus can serve as a convenient parametrization of humanity on a sovereign level.

The IPCC, in its fifth international report, states that the two leading contributions to changes in annual CO2 emissions by decade are the changes in population and changes in GDP/capita, which reflect changes in standard of living. This article will show that within well-defined error margins, carbon intensities, and energy intensities (both with respect to GDP) are independent of the population and GDP of countries and thus can serve as a convenient parametrization of humanity. With some imagination, these parameters can serve as input and output indicators that connect the physical environment with the human environment. The article will show that both indicators fit lognormal distribution well without any outliers. The data are based on the World Bank database. Comparing the global distribution with individual world-bank indicators of world-bank country grouping based on socioeconomic conditions quantifies the contributions of global income distribution.

Type
Review Article
Information
MRS Energy & Sustainability , Volume 6 , 2019 , E7
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Tomkiewicz, M.: Energy and sustainability, from the point of view of environmental physics. MRS Energy Sustain. 2, 1–13 (2015).CrossRefGoogle Scholar
Zalasiewicz, J., Waters, C.N., Summerhayes, C.P., Wolfe, A.P., Barnosky, A.D., Cearreta, A., Crutzen, P., Ellis, E., Fairchild, I.J., Gałuszka, A., Haff, P., Hajdas, I., Head, M.J., Ivar do Sul, J.A., Jeandel, C., Leinfelder, R., McNeill, J.R., Neal, C., Odada, E., Oreskes, N., Steffen, W., Syvitski, J., Vidas, D., Wagreich, M., and Williams, M.: The working group on the Anthropocene: Summary of evidence and interim recommendations. Anthropocene 19, 55 (2017).CrossRefGoogle Scholar
IPCC: Available at: http://IPCC.ch/reports/SR15 (accessed May 15, 2019).Google Scholar
World Bank Indicators: Available at: https://data.worldbank.org/indicator (accessed May 15, 2019).Google Scholar
Green, M.L., Espinal, L., Traversa, E., and Amis, E.J.: Materials for sustainable development. MRS Bull. 37, 303 (2012).CrossRefGoogle Scholar
AR5: Fifth Synthesis Report. Available at: http://www.ipcc.ch/report/ar5 (accessed May 15, 2019).Google Scholar
Tomkiewicz, M.: Available at: http://climatechangefork.blog.brooklyn.edu/2018/02/27/primary-energy-transition-individual-countries-population-gdp/ (accessed May 15, 2019).Google Scholar
Wikipedia: Available at: https://en.wikipedia.org/wiki/log-normal_distribution (accessed May 15, 2019).Google Scholar
Limpert, E., Stahel, W.A., and Abbt, M.: Log-normal distribution across the sciences: Keys and clues. BioScience 51, 341 (2001).CrossRefGoogle Scholar
Sun, K.: Explanation of log-normal distribution and power-law distributions in biology and social science. Available at: http://guava.physics.uiuc.edu/∼nigel/courses/569/Essays_2004/files/sun.pdf (accessed May 15, 2019).Google Scholar
Razumovsky, N.K.: Distribution of metal values in ore deposits. C. R. (Dokl.) Acad. Sci. URSS 9, 814 (1940).Google Scholar
Ahrens, L.H.: The log-normal distribution of the elements. Geochem. Cosmochim. Acta 5, 49 (1954).CrossRefGoogle Scholar
Malance, A., Gaidolfi, L., Pessina, V., and Dallara, G.: Specific activities and radioactive contour maps of natural and anthropogenic radionuclides in beach sand samples (Patong, Kamala, Kata, Karon and Nai Yang) after tsunami disaster in Phuket province, Thailand. J. Environ. Radioact. 30, 55 (1996).Google Scholar
Kondo, K.: The log-normal distribution of the incubation time of exogenous diseases. Jpn. J. Hum. Genet. 21, 217–237 (1977).Google Scholar
Boag, J.W.: Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J. R. Stat. Soc. Ser. B 11, 15 (1949).Google Scholar
Feinleib, M. and McMahon, B.: Variation in the duration of survival of patients with the chronic leukemia. Blood 15, 332 (1960).CrossRefGoogle Scholar
Horner, R.D.: Age at onset of Alzheimer’s disease: Clue toto the relative importance of etiologic factors? Am. J. Epidemiol. 126, 409–414 (1987).CrossRefGoogle Scholar
Preston, F.W.: The commonness and rarity of species. Ecology 29, 254–283 (1948).CrossRefGoogle Scholar
May, R.M.: Patterns in multi-species communities. In Theoretical Ecology: Principles and Applications, May, R.M. and McLean, A.R., eds. (Blackwell, Oxford, U.K., 1981); pp. 197–227.Google Scholar
Magurran, A.E.: Ecological Diversity and its Measurement (Croom Helm, London, U.K., 1988).CrossRefGoogle Scholar
Williams, C.B.: A note on the statistical analysis of sentence length as a criterion of literary style. Biometrika 31, 356–361 (1940).Google Scholar
Preston, F.W.: Pseudo-log-normal distributions. Ecology 62, 355–364 (1981).CrossRefGoogle Scholar
Allanson, P.: Farm size structure in England and Wales. J. Agric. Econ. 43, 137–148 (1992).CrossRefGoogle Scholar
UN Conference on Sustainable Development: Available at: https://en.wikipedia.org/wiki/United_Nations_Conference_on_Sustainable_Development (accessed May 15, 2019).Google Scholar
Kuznets, S.: Economic growth and income inequality. Am. Econ. Rev. 45, 1–28 (1955).Google Scholar
Selden, T. and Song, D.: Environmental quality and development: Is there a Kuznets curve for air pollution emissions? J. Environ. Econ. Manage. 27, 147–162 (1994).CrossRefGoogle Scholar
Hank Hilton, F.G. and Levinson, A.: Factoring the environmental Kuznets curve: Evidence from automotive lead emissions. J. Environ. Econ. Manage. 35, 126–141 (1998).CrossRefGoogle Scholar
John Wood, P.: Climate change and game theory. Ann. N. Y. Acad. Sci. 1219, 153–170 (2011).CrossRefGoogle Scholar
EIS: Available at: http://web.evs.anl.gov/uranium/eis/whatiseis/index.cfm (accessed May 15, 2019).Google Scholar
CEQ: Available at: https://www.lexology.com/library/detail.aspx?g=977ede42-9176-4315-bdaa-6b5a75edeb59 (accessed May 15, 2019).Google Scholar
Park, A. and Ravenel, C.: Integrating sustainability into capital markets. Bloomberg LP and ESG’s quantitative legitimacy. J. Appl. Corp. Finance 25, 62 (2013).Google Scholar
National Academies of Sciences, Engineering, and Medicine: Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide (The National Academies Press, Washington, DC, 2017). doi: https://doi.org/10.17226/24651.Google Scholar