Real option-based optimization for financial incentive allocation in infrastructure projects under public–private partnerships

Abstract

Financial incentives that stimulate energy investments under public–private partnerships are considered scarce public resources, which require deliberate allocation to the most economically justified projects to maximize the social benefits. This study aims to solve the financial incentive allocation problem through a real option-based nonlinear integer programming approach. Real option theory is leveraged to determine the optimal timing and the corresponding option value of providing financial incentives. The ambiguity in the evolution of social benefits, the decision-maker’s attitude toward ambiguity, and the uncertainty in social benefits and incentive costs are all considered. Incentives are offered to the project portfolio that generates the maximum total option value. The project portfolio selection is formulated as a stochastic knapsack problem with random option values in the objective function and random incentive costs in the probabilistic budget constraint. The linear probabilistic budget constraint is subsequently transformed into a nonlinear deterministic one. Finally, the integer non-linear programming problem is solved, and the optimality gap is computed to assess the quality of the optimal solution. A case study is presented to illustrate how the limited financial incentives can be optimally allocated under uncertainty and ambiguity, which demonstrates the efficacy of the proposed method.

Keywords

financial incentives / public–private partnerships / energy infrastructure projects / real option / optimization / uncertainty

References

[1] Almassi A, McCabe B, Thompson M (2013). Real options–based approach for valuation of government guarantees in public–private partnerships. Journal of Infrastructure Systems, 19(2): 196–204

[2] Arnold U, Yildiz Ö (2015). Economic risk analysis of decentralized renewable energy infrastructures—a Monte Carlo simulation approach. Renewable Energy, 77: 227–239

[3] Ben-Tal A, Nemirovski A (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, 88(3): 411–424

[4] Cai J, Li S, Cai H (2019). Empirical analysis of capital structure determinants in infrastructure projects under public–private partnerships. Journal of Construction Engineering and Management, 145(5): 04019032

[5] Casparri M T, Fronti J G (2012). An stochastic model of the precautionary principle. In: Methods for Decision Making in Uncertain Environment: Proceedings of the XVII SIGEF Congress, Reus, Spain, World Scientific

[6] Ceseña E M, Mutale J, Rivas-Dávalos F (2013). Real options theory applied to electricity generation projects: a review. Renewable & Sustainable Energy Reviews, 19: 573–581

[7] Chen Z, Epstein L (2002). Ambiguity, risk, and asset returns in continuous time. Econometrica, 70(4): 1403–1443

[8] Coady D, Parry I W, Sears L, Shang B (2015). How Large Are Global Energy Subsidies? International Monetary Fund

[9] Dixit A K, Pindyck R S (1994). Investment under Uncertainty. Princeton, New Jersey: Princeton University Press

[10] Eckhause J M, Gabriel S A, Hughes D R (2012). An integer programming approach for evaluating R&D funding decisions with optimal budget allocations. IEEE Transactions on Engineering Management, 59(4): 679–691

[11] European PPP Expertise Centre (EPEC) (2011). State Guarantees in PPPs—A Guide to Better Evaluation, Design, Implementation and Management. European PPP Expertise Centre

[12] Fernandes B, Cunha J, Ferreira P (2011). The use of real options approach in energy sector investments. Renewable & Sustainable Energy Reviews, 15(9): 4491–4497

[13] Gao Y, Driouchi T (2013). Incorporating Knightian uncertainty into real options analysis: using multiple-priors in the case of rail transit investment. Transportation Research Part B: Methodological, 55: 23–40

[14] Huang X, Zhao T (2016). Project selection and adjustment based on uncertain measure. Information Sciences, 352-353: 1–14

[15] International Energy Agency (IEA) (2014). World Energy Investment Outlook. Special Report

[16] Jeon C, Lee J, Shin J (2015). Optimal subsidy estimation method using system dynamics and the real option model: photovoltaic technology case. Applied Energy, 142: 33–43

[17] Kim K T, Lee D J, Park S J (2014). Evaluation of R&D investments in wind power in Korea using real option. Renewable & Sustainable Energy Reviews, 40: 335–347

[18] Kroniger D, Madlener R (2014). Hydrogen storage for wind parks: a real options evaluation for an optimal investment in more flexibility. Applied Energy, 136: 931–946

[19] Lee S C (2011). Using real option analysis for highly uncertain technology investments: the case of wind energy technology. Renewable & Sustainable Energy Reviews, 15(9): 4443–4450

[20] Li S, Abraham D, Cai H (2017). Infrastructure financing with project bond and credit default swap under public-private partnerships. International Journal of Project Management, 35(3): 406–419

[21] Li S, Cai H (2017). Government incentive impacts on private investment behaviors under demand uncertainty. Transportation Research Part E, Logistics and Transportation Review, 101: 115–129

[22] Li S, Cai J, Feng Z, Xu Y, Cai H (2019). Government contracting with monopoly in infrastructure provision: regulation or deregulation? Transportation Research Part E, Logistics and Transportation Review, 122: 506–523

[23] Li Z, Madanu S, Zhou B, Wang Y, Abbas M (2010). A heuristic approach for selecting highway investment alternatives. Computer-Aided Civil and Infrastructure Engineering, 25(6): 427–439

[24] Lin B, Wesseh P K Jr (2013). Valuing Chinese feed-in tariffs program for solar power generation: a real options analysis. Renewable & Sustainable Energy Reviews, 28: 474–482

[25] Mak W K, Morton D P, Wood R K (1999). Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations Research Letters, 24(1-2): 47–56

[26] McInerney C, Johannsdottir L (2016). Lima Paris action agenda: focus on private finance—note from COP21. Journal of Cleaner Production, 126: 707–710

[27] Meier H, Christofides N, Salkin G (2001). Capital budgeting under uncertainty—an integrated approach using contingent claims analysis and integer programming. Operations Research, 49(2): 196–206

[28] Morton D P, Wood R K (1998). On a stochastic knapsack problem and generalizations. In: Woodruff D L, eds. Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research. Berlin: Springer, 149–168

[29] Ng M, Zhang Z, Waller S T (2011). The price of uncertainty in pavement infrastructure management planning: an integer programming approach. Transportation Research Part C, Emerging Technologies, 19(6): 1326–1338

[30] Nishimura K G, Ozaki H (2007). Irreversible investment and Knightian uncertainty. Journal of Economic Theory, 136(1): 668–694

[31] Park T, Kim C, Kim H (2014). A real option-based model to valuate CDM projects under uncertain energy policies for emission trading. Applied Energy, 131: 288–296

[32] Reuter W H, Fuss S, Szolgayová J, Obersteiner M (2012). Investment in wind power and pumped storage in a real options model. Renewable & Sustainable Energy Reviews, 16(4): 2242–2248

[33] Ritzenhofen I, Spinler S (2016). Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty—a real options analysis. Energy Economics, 53: 76–89

[34] Santos L, Soares I, Mendes C, Ferreira P (2014). Real options versus traditional methods to assess renewable energy projects. Renewable Energy, 68: 588–594

[35] Schachter J A, Mancarella P (2016). A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems. Renewable & Sustainable Energy Reviews, 56: 261–271

[36] Schröder D (2011). Investment under ambiguity with the best and worst in mind. Mathematics and Financial Economics, 4(2): 107–133

[37] Sefair J A, Méndez C Y, Babat O, Medaglia A L, Zuluaga L F (2017). Linear solution schemes for mean-semi variance project portfolio selection problems: an application in the oil and gas industry. Omega, 68: 39–48

[38] Tavana M, Keramatpour M, Santos-Arteaga F J, Ghorbaniane E (2015). A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert Systems with Applications, 42(22): 8432–8444

[39] Thompson F, Green M T (1998). Handbook of Public Finance. Florida: CRC Press

[40] U.S. Energy Information Administration (2015). Direct Federal Financial Interventions and Subsidies in Energy in Fiscal Year 2013. Report

[41] Wang X, Du L (2016). Study on carbon capture and storage (CCS) investment decision-making based on real options for China’s coal-fired power plants. Journal of Cleaner Production, 112: 4123–4131

[42] Wesseh P K Jr, Lin B (2015). Renewable energy technologies as beacon of cleaner production: a real options valuation analysis for Liberia. Journal of Cleaner Production, 90: 300–310

[43] Wesseh P K Jr, Lin B (2016). A real options valuation of Chinese wind energy technologies for power generation: Do benefits from the feed-in tariffs outweigh costs? Journal of Cleaner Production, 112: 1591–1599

[44] Wibowo A, Kochendoerfer B (2011). Selecting BOT/PPP infrastructure projects for government guarantee portfolio under conditions of budget and risk in the Indonesian context. Journal of Construction Engineering and Management, 137(7): 512–522

[45] Wu D, Yin Y, Lawphongpanich S (2011). Optimal selection of build–operate-transfer projects on transportation networks. Transportation Research Part B: Methodological, 45(10): 1699–1709

[46] Zhang M, Zhou D, Zhou P (2014a). A real option model for renewable energy policy evaluation with application to solar PV power generation in China. Renewable & Sustainable Energy Reviews, 40: 944–955

[47] Zhang X, Wang X, Chen J, Xie X, Wang K, Wei Y (2014b). A novel modeling based real option approach for CCS investment evaluation under multiple uncertainties. Applied Energy, 113: 1059–1067

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