Skip to main content
SpringerLink
Log in

Risk management for sulfur dioxide abatement under multiple uncertainties

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmed S (2004). Mean-risk objectives in stochastic programming. Technical report, Georgia Institute of Technology, available at www.optimization-online.org

    Google Scholar 

  • An H, Eheart J W (2007). A screening technique for joint chance-constrained programming for air-quality management. Oper Res, 55(4): 792–798

    Article  Google Scholar 

  • Athanasiadis I N, Mitkas P A (2007). Knowledge discovery for operational decision support in air quality management. Journal of Environmental Informatics, 9(2): 100–107

    Article  Google Scholar 

  • Birbil S I, Frenk J, Kaynar B, Noyan N (2009). Risk measures and their applications in asset management. In: Gregoriou G N, ed. The VaR Implementation Handbook. New York: The McGraw-Hill Companies, 311–337

    Google Scholar 

  • Birge J R, Louveaux F V (1988). A multicut algorithm for two-stage stochastic linear programs. Eur J Oper Res, 34(3): 384–392

    Article  Google Scholar 

  • Byun D W, Kim S T, Chen F Y, Kim S B, Cuclis A, Moon N K (2003). Information infrastructure for air quality modeling and analysis: application to the houston-galveston ozone non-attainment area. Journal of Environmental Informatics, 2(2): 38–57

    Article  Google Scholar 

  • Cai Y P, Huang G H, Tan Q, Yang Z F (2011a). An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part I: methodology. Renewable & Sustainable Energy Reviews, 15: 2779–2790

    Article  Google Scholar 

  • Cai Y P, Huang G H, Tan Q, Liu L (2011b). An integrated approach for climate-change impact analysis and adaptation planning under multilevel uncertainties. Part II. case study. Renewable & Sustainable Energy Reviews, 15: 3051–3073

    Article  Google Scholar 

  • Chen C, Huang G H, Li Y, Li M (2013a). Model of risk analysis on site selection of biomass power plant based on stochastic robust interval method. Transactions of the Chinese Society of Agricultural Engineering, 29(20): 206–213

    Google Scholar 

  • Chen C, Huang G H, Li Y P, Zhou Y (2013b). A robust risk analysis method for water resources allocation under uncertainty. Stochastic Environ Res Risk Assess, 27(3): 713–723

    Article  Google Scholar 

  • Dai C, Li Y, Huang G (2012). An interval-parameter chance-constrained dynamic programming approach for capacity planning under uncertainty. Resour Conserv Recycling, 62: 37–50

    Article  Google Scholar 

  • Dentcheva D, Ruszczynski A (2006). Portfolio optimization with stochastic dominance constraints. J Bank Finance, 30(2): 433–451

    Article  Google Scholar 

  • Dong C, Huang G H, Cai Y P, Liu Y (2012). An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing City. Energy, 37(1): 673–688

    Article  Google Scholar 

  • Fábián C I (2008). Handling CVaR objectives and constraints in two-stage stochastic models. Eur J Oper Res, 191(3): 888–911

    Article  Google Scholar 

  • Haurie A, Kübler J, Clappier A, Van Den Bergh H (2004). A metamodeling approach for integrated assessment of air quality policies. Environ Model Assess, 9(1): 1–12

    Article  Google Scholar 

  • Hsu C P, Huang CW, Paul Chiou WJ P (2012). Effectiveness of copulaextreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets. Rev Quant Finance Account, 39(4): 447–468

    Article  Google Scholar 

  • Huang G, Loucks D (2000). An inexact two-stage stochastic programming model for water resources management under uncertainty. Civ Eng Environ Syst, 17(2): 95–118

    Article  Google Scholar 

  • Huang G H, Baetz B W, Patry G G (1994). Grey dynamic programming for waste-management planning under uncertainty. J Urban Plann Dev, 120(3): 132–156

    Article  Google Scholar 

  • Huang G H, Chang N B (2003). Perspectives of environmental informatics and systems analysis. J Environ Inform, 1(1): 1–6

    Article  Google Scholar 

  • Inuiguchi M, Sakawa M (1998). Robust optimization under softness in a fuzzy linear programming problem. Int J Approx Reason, 18(1–2): 21–34

    Article  Google Scholar 

  • Lejano R P, Ayala P M, Gonzales E A (1997). RESEARCH: optimizing the mix of strategies for control of vehicular emissions. Environ Manage, 21(1): 79–87

    Article  Google Scholar 

  • Li Y, Huang G, Nie X, Nie S (2008). An inexact fuzzy-robust two-stage programming model for managing sulfur dioxide abatement under uncertainty. Environ Model Assess, 13(1): 77–91

    Article  Google Scholar 

  • Li Y, Huang G H, Veawab A, Nie X, Liu L (2006). Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management. J Air Waste Manag Assoc, 56(8): 1070–1082

    Article  Google Scholar 

  • Lim C, Sherali H D, Uryasev S (2010). Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization. Comput Optim Appl, 46(3): 391–415

    Article  Google Scholar 

  • Liu L, Huang G, Liu Y, Fuller G, Zeng G (2003). A fuzzy-stochastic robust programming model for regional air quality management under uncertainty. Eng Optim, 35(2): 177–199

    Article  Google Scholar 

  • Liu Y, Zou R, Guo H (2011). Risk explicit interval linear programming model for uncertainty-based nutrient-reduction optimization for the Lake Qionghai Watershed. J Water Resour Plann Manage, 137(1), 83–91

    Article  Google Scholar 

  • Lv Y, Huang G H, Li Y P, Yang Z F, Sun W (2011). A two-stage inexact joint-probabilistic programming method for air quality management under uncertainty. J Environ Manage, 92(3): 813–826

    Article  Google Scholar 

  • Ma X, Zhang F (2002). A genetic algorithm based stochastic programming model for air quality management. J Environ Sci (China), 14(3): 367–374

    Google Scholar 

  • Marti K, Ploöchinger E (1990). Optimal step sizes in semi-stochastic approximation procedures. I. Optimization, 21(1): 123–153

    Article  Google Scholar 

  • Miller N, Ruszczynski A (2008). Risk-adjusted probability measures in portfolio optimization with coherent measures of risk. Eur J Oper Res, 191(1): 193–206

    Article  Google Scholar 

  • Noyan N (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Comput Oper Res, 39(3): 541–559

    Article  Google Scholar 

  • Rockafellar R T, Uryasev S (2000). Optimization of conditional valueatrisk. Journal of Risk, 2: 21–42

    Google Scholar 

  • Ruszczyński A (1993). Parallel decomposition of multistage stochastic programming problems. Math Program, 58(1–3): 201–228

    Article  Google Scholar 

  • Schultz R (2011). Risk aversion in two-stage stochastic integer programming. Stochastic Programming: 165–187

    Google Scholar 

  • Schultz R, Tiedemann S (2006). Conditional value-at-risk in stochastic programs with mixed-integer recourse. Math Program, 105(2–3): 365–386

    Article  Google Scholar 

  • Seifi A, Hipel K (2001). Interior-point method for reservoir operation with stochastic inflows. J Water Resour Plan Manage, 127(1): 48–57

    Article  Google Scholar 

  • Shao L G, Qin X S, Xu Y (2011). A conditional value-at-risk based inexact water allocation model. Water Resour Manage, 25(9): 2125–2145

    Article  Google Scholar 

  • Tan Q, Huang G H, Wu C Z, Cai Y P (2011). IF-EM: an interval-parameter fuzzy linear programming model for environment-oriented evacuation planning under uncertainty. J Adv Transport, 45(4): 286–303

    Article  Google Scholar 

  • Tong X J, Qi L Q, Wu F, Zhou H (2010). A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset. Appl Math Comput, 216(6): 1723–1740

    Article  Google Scholar 

  • Turner D B (1970). Workbook of atmospheric dispersion estimates. Washington D C: Office of Air Programs, Publication No. AP-26, U. S. Environmental Protection Agency

    Google Scholar 

  • Wang L, Milford J B (2001). Reliability of optimal control strategies for photochemical air pollution. Environ Sci Technol, 35(6): 1173–1180

    Article  Google Scholar 

  • Zolezzi T (2001). Well-posedness and optimization under perturbations. Ann Oper Res, 101(1/4): 351–361

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Environmental Science and Engineering, Peking University, Beijing, 100871, China

    C. Dai, Y. Liu, W. T. Lu & H. C. Guo

  2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan, S4S 7H9, Canada

    W. Sun & Q. Tan

  3. College of Water Resources & Civil Engineering, China Agricultural University, Beijing, 100083, China

    Q. Tan

Authors
  1. C. Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Q. Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. W. T. Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. H. C. Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Q. Tan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, C., Sun, W., Tan, Q. et al. Risk management for sulfur dioxide abatement under multiple uncertainties. Front. Earth Sci. 10, 87–107 (2016). https://doi.org/10.1007/s11707-015-0495-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-015-0495-6

Keywords

Search

Navigation