Halkos, George and Kitsos, Christos (2018): Mathematics vs. Statistics in tackling Environmental Economics uncertainty.
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Abstract
In this paper the appropriate background in Mathematics and Statistics is considered in developing methods to investigate Risk Analysis problems associated with Environmental Economics uncertainty. New senses of uncertainty are introduced and a number of sources of uncertainty are discussed and presented. The causes of uncertainty are recognized helping to understand how they affect the adopted policies and how important their management is in any decision-making process. We show Mathematical Models formulate the problem and Statistical models offer possible solutions, restricting the underlying uncertainty, given the model and the error assumptions are correct. As uncertainty is always present we suggest ways on how to handle it.
Item Type: | MPRA Paper |
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Original Title: | Mathematics vs. Statistics in tackling Environmental Economics uncertainty |
Language: | English |
Keywords: | Uncertainty; Environmental Economics; Mathematics; Statistics. |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C60 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General > Q00 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q50 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q58 - Government Policy |
Item ID: | 85280 |
Depositing User: | G.E. Halkos |
Date Deposited: | 19 Mar 2018 14:34 |
Last Modified: | 27 Sep 2019 04:33 |
References: | Alefed G. and Mayer G. (2000). Interval Analysis: theory and applications. Journal of Computational and Applied Mathematics, 121, 421-464. Alves F.I. and Rosario P. (2015). Parametric and Semi-parametric Approaches to Extreme Rainfall Modelling. In: Theory and Practice of Risk Assessment, by Kitsos C.P., Oliveira A.T., Rigas A. and Gulati S. (Eds), pp. 279-291. Bernando J.M. and Smith A.F.M. (1994). Bayesian Theory. Wiley. New York. Bliss C.I. (1934). The methods of probits. Science, 79. 38-39. Bliss C.I. (1939). The toxicity of poisons applied jointly. Ann of Appl. Biology, 26, 585-615. Breslow N.E. and Day N.E. (1980). Statistical methods in cancer research. Volume 1: The analysis of case-control studies. Lyon: International Agency for Research on Cancer (SARC). Broadwater R.P., Shaalan H.E., Fabrycky W.J. and Lee R.E. (1994). Decision evaluation with interval mathematics: a power distribution system case study, IEEE Trans. Power Delivery, 9(1), 59-67. Chiang A.C., Wainwright K. (2005). Fundamental Methods of Mathematical Economics. McGraw-Hill Irwin. pp. 3–4. Coyle D. (2003). Who’s better not best: approach probabilistic uncertainty analysis. International Journal of Technology Assessment in Health Care, 19, 540-545. Davis, H. (1954). Political Statistics. The Principia Press of Illinois, Inc. Dempster A.P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, Series B 30, 205-247. Diggle J.P. and Richardson S. (1993). Epidemiological Studies of Industrial Pollutants: An Introduction. International Statistical Review, 61, 201-206. Draper N.R. and Smith H. (1998). Applied Regression Analysis. 3rd Edition. Wiley. Edler L. and Kitsos C.P. (Eds) (2005). Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment. Wiley. Chichester, England. Feller W. (1950). An introduction to Probability Theory and its Applications. Vol I. John Wiley & Sons, Inc. New York. Gomes M.I., Brilhante M.F. and Pestana D. (2015). A Mean-of-Order-p Class of Value-at-Risk Estimators. In: Theory and Practice of Risk Assessment, by Kitsos, C. P., Oliveira, A.T., Rigas, A. Gulati, S., Edts,, pg 305-320. Halkos G. (1992). Economic Perspectives of the Acid Rain Problem in Europe. DPhil thesis, Department of Economics and Related Studies, University of York, UK. Halkos G. (1993). Sulphur abatement policy: Implications of cost differentials, Energy Policy, 21(10), 1035-1043. Halkos G. (1994). Optimal abatement of sulphur emissions in Europe. Environmental & Resource Economics, 4(2), 127-150. Halkos G. (1996). Incomplete information in the acid rain game, Empirica, 23(2), 129-148 . Halkos G. (2006). Econometrics: Theory and practice. Giourdas Publications. Halkos G. (2011). Econometrics: Theory and practice: Instructions in using Eviews, Minitab, SPSS and excel. Gutenberg: Athens Halkos G. and Kitsos C. (2005). Optimal pollution level: a theoretical identification. Applied Economics, 37(13): 1475-1483. Halkos G. and Kitsos C.P. (2010). Exploring Greek Innovation activities: The adoption of Generalized Linear Models. MPRA Paper No 24392. http://mpra.ub.uni-muenchen.de/24392/ Halkos G. and Kitsos C.P (2012). Relative risk and innovation activities : The case of Greece. Innovation: Management, policy & practice, 14(1), 156-159. Halkos G. and Kitsou D. (2015). Uncertainty in optimal pollution levels: modelling and evaluating the benefit area, Journal of Environmental Planning and Management, 58(4), 678-700. Halkos G. and Kitsou D. (2018). Weighted location differential tax in environmental problems, Environmental Economics and Policy Studies, 20(1), 1-15. Halkos G. and Papageorgiou G. (2008). Extraction of non-renewable resources: a differential game approach, MPRA Paper 37596, University Library of Munich, Germany. Halkos G and Papageorgiou G. (2016). Dynamical Methods Applied in Natural Resource Economics, Journal of Economics and Political Economy, 3(1), 12-31. Halkos G. (1994). Optimal acid rain abatement policy in Europe, MPRA Paper 33943, University Library of Munich, Germany. Hutton J.P. and Halkos G. (1995) Optimal acid rain abatement policy in Europe: an analysis for the year 2000, Energy Economics, 17(4), 259-275 Jousselme A.L. and Maupin P. (2012). Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning, 53(2), 118–145. Kitsos, C. P. (2002). The Simple Linear Calibration Problem a an Optimal Experimental Design. Communications. Statistics – Theory and Methods, 31, 1167-1177. Kitsos C.P. (2005). Environmental and Economical Effects of Pollution: A Statistical Analysis. In CCMS/NATO meeting Risk Assessment of Chernobyl Accident Consequences: Lessons learned for the future. Kiev, June 1-4, 2005. Kitsos C.P. (2011). Invariant Canonical Form for the Multiple Logistic Regression. Mathematics in Engineering, Science and Aerospace (MESA), Vol 2(3), pg 267-275. Kitsos C.P. (2012). Cancer Bioassays: A Statistical Approach. LAMPERT Academic Publishing. Kitsos C.P. and Tavoularis K.N. (2009). Logarithmic Sobolev Inequalities for Information Measures. IEEE TRANSACTIONS ON INFORMATION THEORY, Vol 55, 6, June 2009, 2554-2561. Kitsos C.P. and Toulias LT (2010). New information measures for the generalized normal distribution. Information, 1, 13–27. Kitsos C.P., Oliveira T.A., Rigas A., Gulati S. (Eds) (2015). Theory and Practice of Risk Assessment. Springer. Klir G. and Yan, B. (1995). Fuzzy Sets and Fuzzy Logic. Prentice Hall. New Jersey. Knight, F. (1921). Risk, Uncertainty and Profit. Hart, Schaffner & Marx. Boston. Knight F.H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin: New York McCullagh P. (2002). What is a Statistical Model?. Ann. Math. 30(5), 1225-1310. Pfeiffer, P. E. (1978). Concepts of Probability Theory. Dover Pub. Inc. New York. Pierre D.A. (1986). Optimization Theory with Applications. Dover Pub. Inc. New York. Rehacek J. and Hradil Z. (2004). Uncertainty relation from Fisher information. J. of Modern Optics, 51, 979-982. Shafer G. (1976). A Mathematical Theory of Evidence. Princeton University Press. Shannon C.E. (1948). A mathematical theory of communication, Bell Syst. Tech. J., 27, 379–423, 623–656. Tan W.Y. (1991). Stochastic models of carcinogenesis, Marchel Dekler, New York. Toulias T.L and Kitsos C.P. (2014). On the properties of the Generalized Normal Distribution. Discussiones Mathematicae Probability and Statistics 34 , pp 35-49. Toulias T.L, Vassiliadis V. and Kitsos C.P. (2014). MLE for the γ-order Generalized Normal Distribution. Discussiones Mathematicae Probability and Statistics 34 , pp 143-158. Write Q. (1942). Study of War. The University of Chicago Press. Wolfe M.A. (2000). Interval Mathematics, algebraic equations and optimization. J.of Comp. and Appl. Mathematics, 124, 263-280. Zarikas V. And Kitsos C.P. (2005). Risk Analysis with Reference Class Forecasting Adopting Tolerance Regions. In: Theory and Practice of Risk Assessment, by Kitsos, C. P., Oliveira, A.T., Rigas, A. Gulati, S., Edts,, pg 235-247. Zeldovich Y. and Novikov I.D (1983). The Structure and Evolution of the Universe. The University of Chicago Press. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/85280 |