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Modeling energy and air pollution health damaging: a two-stage DEA approach

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Abstract

Various pollutants can cause serious health problems. One of these pollutants is sulfur dioxide which is associated with respiratory problems in humans. In this study, two-stage DEA method is used to estimate the efficiencies of 23 countries for the time period 1990–2017, using capital, labor, and energy consumption as inputs, GDP as a desirable intermediate which is going out of the system, and sulfur oxides as an undesirable intermediate and respiratory disease deaths as an undesirable output. The results showed Iceland, Luxembourg, and New Zealand as the most overall efficient countries in the time period 1990–2017. Previous works have used the additive Chen et al. (2009) two-stage DEA model in order to handle undesirable variables. The contribution of this study is the use of a modified multiplicative Kao and Hwang (2008) two-stage DEA model under the existence of undesirable variables.

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References

  • Australian Government Department of the Environment and Energy. Sulfur Dioxide fact sheet. Melbourne: Department of Environment and Energy (2005) http://www.environment.gov.au/protection/publications/factsheet-sulfur-dioxide-so2. Accessed 5 Apr 2020

  • Balmes JR, Fine JM, Sheppard D (1987) Symptomatic bronchoconstriction after short-term inhalation of sulfur dioxide. Am Rev Respir Dis 136:1117–1121

    Article  CAS  Google Scholar 

  • Bian YW, Yang F (2010) Resource and environment efficiency analysis of province in China: a DEA approach based on Shannon’s entropy. Energy Police 38:1909–1917

    Article  Google Scholar 

  • Cavalieri M, Guccio C, Rizzo I (2017) On the role of environmental corruption in healthcare infrastructures: an empirical assessment for Italy using DEA with truncated regression approach. Health Policy 121(5):515–524

    Article  Google Scholar 

  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Nav Res Logis Q 9:181–186

    Article  Google Scholar 

  • Charnes A, Cooper WW (1984) The non-Archimedean CCR ratio for efficiency analysis: a rejoinder to Boyd and Färe. Eur J Oper Res 15:333–334

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1979) Short communication: measuring the efficiency of decision making units. Eur J Oper Res 196:1170–1176

    Google Scholar 

  • Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Info Technol Manag J 5(1–2):9–22

    Article  Google Scholar 

  • Chen Y, Cook WD, Li N, Zhu J (2009) Additive efficiency decomposition in two-stage DEA. Eur J Oper Res 196:1170–1176

    Article  Google Scholar 

  • Curtis L, Rea W, Willis PW, Fenyves E, Pan Y (2006) Adverse health effects of outdoor air pollutants. Environ Int 32(6):815–830

    Article  CAS  Google Scholar 

  • Dincer I (1998) Energy and environmental impacts: present and future perspectives. Energy Sources 20(4–5):427–453

    Article  CAS  Google Scholar 

  • Dincer I (1999) Environmental impacts of energy. Energy Policy 27(14):845–854

    Article  Google Scholar 

  • Dyckhoff H, Allen K (2001) Measuring ecological efficiency with data envelopment analysis (DEA). Eur J Oper Res 132:312–325

    Article  Google Scholar 

  • Färe R, Grosskopf S (2004) Modeling undesirable factors in efficiency evaluation: comment. Eur J Oper Res 157:242–245

    Article  Google Scholar 

  • Färe R, Grosskopf S, Lovell CAK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71:90–98

    Article  Google Scholar 

  • Filippini M, Masiero G, Steinbach S (2019) The impact of ambient air pollution on hospital admissions. Eur J Health Econ 20:919–931

    Article  Google Scholar 

  • French JG, Lowrimore G, Nelson WC, Finklea JF, English T, Hertz M (1973) The effect of sulfur dioxide and suspended sulfates on acute respiratory disease. Arch Environ Health Int J 27(3):129–133

    Article  CAS  Google Scholar 

  • Halkos GE, Tzeremes NG, Kourtzidis SA (2014) A unified classification of two-stage DEA models. Surv Oper Res Manag Sci 19(1):1–16

    Google Scholar 

  • Hawdon D (2003) Efficiency, performance and regulation of the international gas industry – a bootstrap DEA approach. Energy Policy 31:1167–1178

    Article  Google Scholar 

  • Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192:949–962

    Article  Google Scholar 

  • Kao C, Hwang S-N (2008) Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. Eur J Oper Res 185:418–429

    Article  Google Scholar 

  • Keikha-Javan S, Rostamy-Malkhalifeh M (2016) Efficiency measurement of ndea with interval data. Int J Math Model & Comput 6(3 (SUMMER)):199–210

    Google Scholar 

  • Korhonen PJ, Luptacik M (2004) Eco-efficiency analysis of power plants: An extension of data envelopment analysis. Eur J Oper Res 154:437–446

    Article  Google Scholar 

  • Liang L, Yang F, Cook WD, Zhu J (2006) DEA models for supply chain efficiency evaluation. Ann Oper Res 145(1):35–49

    Article  Google Scholar 

  • Liang L, Cook W, Zhu J (2008) DEA models for two-stage processes: game approach and efficiency decomposition. Nav Res Logist 55:643–653

    Article  Google Scholar 

  • Martins F, Felgueiras C, Smitkova M, Caetano N (2019) Analysis of fossil fuel energy consumption and environmental impacts in European countries. Energies 12(6):964

    Article  CAS  Google Scholar 

  • O’Neill MS, Breton CV, Devlin RB, Utell MJ (2012) Air pollution and health: emerging information on susceptible populations. Air Qual Atmos Health 5:189–201

    Article  Google Scholar 

  • Penard-Morand C, Charpin D, Raherison C, Kopferschmitt C, Callaud D, Lavaud F et al (2005) Long-term exposure to background air pollution related to respiratory and allergic health in schoolchildren. Clin Exp Allergy 35(10):1279–1287

    Article  CAS  Google Scholar 

  • Pryor SC, Crippa P, Sullivan RC (2015) Atmospheric chemistry. Reference Module in Earth Systems and Environmental Sciences. https://doi.org/10.1016/B978-0-12-409548-9.09177-6

  • Seiford LM, Zhu J (1999) Profitability and marketability of the top 55 US commercial banks. Manag Sci 45(9):1270–1288

    Article  Google Scholar 

  • Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. Eur J Oper Res 142:16–20

    Article  Google Scholar 

  • Shahroudi K, Mohammadi G, Taleghani M (2011) Application of two-stage DEA technique for efficiencies measuring of private insurance companies in Iran. Int J Appl Oper Res 1(3):91–105

    Google Scholar 

  • Shi GM, Bi J, Wang JN (2010) Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy 38:6172–6179

    Article  Google Scholar 

  • Song ML, An Q, Zhang W, Wang Z, Wu J (2012) Environmental efficiency evaluation based on data envelopment analysis: a review. Renew Sust Energ Rev 16:4465–4469

    Article  Google Scholar 

  • Sueyoshi T, Yuan Y, Goto M (2017) A literature study for DEA applied to energy and environment. Energy Econ 62:104–124

    Article  Google Scholar 

  • Tani M (2010) Japan’s Environmental Policy http://www.rieti.go.jp/en/special/policyupdate/059.html. Accessed April 5 2020

  • Thompson RG, Lee E, Thrall RM (1992) DEA/AR-efficiency of US independent oil/gas producers over time. Comput Oper Res 19:377–391

    Article  Google Scholar 

  • Wang CH, Gopal RD, Zionts S (1997) Use of data envelopment analysis in assessing information technology impact on firm performance. Ann Oper Res 73:191–213

    Article  Google Scholar 

  • Wang Q, Zhou P, Zhou D (2012) Efficiency measurement with carbon dioxide emissions: the case of China. Appl Energy 90(1):161–166

    Article  Google Scholar 

  • Wang K, Yu S, Zhang W (2013) China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 58(5–6):1117–1127

    Article  CAS  Google Scholar 

  • WHO (2015) Age-standardized mortality rate (per 100000 population). https://www.who.int/data/gho/indicator-metadata-registry/imr-details/78. Accessed April 5, 2020

  • Wu J, Zhu Q, Chu J, Liang L (2015) Two-stage network structures with undesirable intermediate outputs reused: a DEA based approach. Comput Econ 46:455–477

    Article  Google Scholar 

  • Wu J, Yin P, Sun J, Chu J, Liang L (2016) Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: an interest preference perspective. Eur J Oper Res 254(3):1047–1062

    Article  Google Scholar 

  • Zha Y, Liang L (2010) Two-stage cooperation model with input freely distributed among the stages. Eur J Oper Res 205(2):332–338

    Article  Google Scholar 

  • Zhang N, Choi Y (2013) Environmental energy efficiency of China’s regional economies: a non-oriented slacks-based measure analysis. Soc Sci J 50(2):225–234

    Article  Google Scholar 

  • Zhou P, Ang BW (2008) Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy 36:2911–2916

    Article  Google Scholar 

  • Zhou P, Poh KL, Ang BW (2007) A non-radial DEA approach to measuring environmental performance. Eur J Oper Res 178(1):1–9

    Article  Google Scholar 

  • Zhou P, Ang BW, Poh KL (2008a) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189:1–18

    Article  Google Scholar 

  • Zhou P, Ang BW, Poh KL (2008b) Measuring environmental performance under different environmental DEA technologies. Energy Econ 30:1–14

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Thanks are due to the Editor-in-Chief Professor Frank Kelly and to the anonymous reviewers for the helpful and constructive comments on an earlier version of our paper. Any remaining errors are solely the authors’ responsibility.

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  1. Department of Economics, University of Thessaly, Volos, Greece

    George Halkos & Georgia Argyropoulou

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  1. George Halkos
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  2. Georgia Argyropoulou
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Correspondence to George Halkos.

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Halkos, G., Argyropoulou, G. Modeling energy and air pollution health damaging: a two-stage DEA approach. Air Qual Atmos Health 14, 1221–1231 (2021). https://doi.org/10.1007/s11869-021-01012-y

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