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Research on value and factors of the guarantee payment in the energy performance contracting in China

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Abstract

Mistrust of the Energy Using Organisation (EUO) in Energy Services Company (ESCO) is a key factor that hinders the development of Energy Performance Contracting Projects (EPCPs) in China, especially the EPCP is the shared savings type. A payment guarantee that is able to hedge the credit risk of the EUO is the rational choice for the ESCO. Considering an EPCP payment guarantee with a limit on the total amount that is essentially a multi-period European put option portfolio with multiple uncertain exercise prices that the guarantor sells to the ESCO, this paper constructs a valuation formula of an EPCP payment guarantee, in which the exercise price follows the geometric Brownian motion with a Poisson jump, and analyses the effects of the factors on the value of the payment guarantee. The results revealed the following. First, the value of the payment guarantee is affected by the total guaranteed amount, uncertainty in ESCO’s revenue, and uncertainty in EUO’s net cash flow, and consideration of the impact of unexpected shocks on the exercise price can avoid overrating the value. Second, the value of the payment guarantee for each period within the scope of the full guarantee increases with an increase in the ESCO’s initial revenue flow, proportion of the revenue sharing and expected revenue growth rate, and it decreases with an increase in the risk-adjusted discount rate and EUO’s initial net cash flow; the value of the payment guarantee for each period within the scope of the full guarantee first decreases and subsequently increases with an increase in the risk-level of ESCO’s revenue and the EUO’s net cash flow. Third, the results of the Sobol’s sensitivity analysis indicate that, within the scope of the full guarantee, the ESCO’s initial revenue flow, proportion of the revenue sharing, the risk-adjusted discount rate and EUO’s initial net cash flow play relatively important roles in changing the EPCP payment guarantee and its exercise prices. According to the above conclusions, the payment guarantee contract can provide the ESCO with a basis for decision-making in the formulation of a strategy to safeguard energy conservation revenues in accordance with the relationships embodied by the value of the payment guarantee. Finally, the policy recommendations about relieving the mistrust between EUOs and ESCOs are provided.

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Notes

  1. Prospective Industry Research Institute. Report of Development Prospects & Investment Strategy Planning Analysis on China Energy Managent Contract Industry(2018–2023).

  2. In 2010, Beijing Huatong Xingyuan Heat Supply Energy Conservation Technology Co., Ltd., paid a high price in terms of time and money to ultimately win a lawsuit against the EUO, Huaqing Property Management Company, for midway refusal to pay the energy conservation revenue money; showing the importance of performance clauses in a contract concerning the payment of energy conservation revenue under the large backdrop of imperfect industry standards.

References

  • Angoua, P., Lai, V. S., & Soumaré, I. (2008). Project risk choices under privately guaranteed debt financing. The Quarterly Review of Economics and Finance, 48(1), 123–152.

    Article  Google Scholar 

  • Ansar, J., & Sparks, R. (2009). The experience curve, option value, and the energy paradox. Energy Policy, 37(3), 1012–1020.

    Article  Google Scholar 

  • Backlund, S., & Eidenskog, M. (2013). Energy service collaborations—it is a question of trust. Energy Efficiency, 6(3), 511–521. https://doi.org/10.1007/s12053-012-9189-z.

    Article  Google Scholar 

  • Bakken, H., Lindset, S., & Olson, L. H. (2006). Pricing of multi-period rate of return guarantees: the Monte Carlo approach. Insurance: Mathematics and Economics, 39(1), 135–149.

    MathSciNet  MATH  Google Scholar 

  • Bannai, M., Tomita, Y., Ishida, Y., Miyazaki, T., Akisawa, A., & Kashiwagi, T. (2007). Risk hedging against the fuel price fluctuation in energy service business. Energy, 32(11), 2051–2060.

    Article  Google Scholar 

  • Barndorff-Nielsen, O. E., & Shephard, N. (2001). Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. Journal of the Royal Statistical Society, 63(2), 167–241.

    Article  MathSciNet  MATH  Google Scholar 

  • Bertoldi, P., & Boza-Kiss, B. (2017). Analysis of barriers and drivers for the development of the ESCO markets in Europe. Energy Policy, 107, 345–355.

    Article  Google Scholar 

  • Bertoldi, P., Rezessy, S., & Vine, E. (2006). Energy service companies in European countries: current status and a strategy to foster their development. Energy Policy, 34(14), 1818–1832.

    Article  Google Scholar 

  • Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. The Journal of Political Economy, 81, 637–654.

    Article  MathSciNet  MATH  Google Scholar 

  • Cannavo, F. (2012). Sensitivity analysis for volcanic source modeling quality assessment and model selection. Computers & Geosciences, 44, 52–59. https://doi.org/10.1016/j.cageo.2012.03.008.

    Article  Google Scholar 

  • Chang, C.-C., Chung, S.-L., & Yu, M.-T. (2006). Loan guarantee portfolios and joint loan guarantees with stochastic interest rates. The Quarterly Review of Economics and Finance, 46(1), 16–35.

    Article  Google Scholar 

  • Cox, J. C., & Ross, S. A. (1976). The valuation of options for alternative stochastic processes. Journal of Financial Economics, 3(1), 145–166.

    Article  Google Scholar 

  • Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty (Vol. 15). Princeton: Princeton University Press.

    Google Scholar 

  • Dreessen, T. (2003). Advantages and disadvantages of the two dominant world ESCO models; shared savings and guaranteed savings. In Paper presented at the Proceedings of the First Pan-European Conference on Energy Service Companies.

  • EVO (2009). International Energy Efficiency Financing Protocol: Standardized Concepts.

  • Fan, Y., & Zhang, L. (2014). Research on the development strategy of building energy efficiency service industry. In Paper presented at the Proceedings of the 18th International Symposium on Advancement of Construction Management and Real Estate.

  • Fischer, S. (1978). Call option pricing when the exercise price is uncertain, and the valuation of index bonds. The Journal of Finance, 33(1), 169–176.

    Article  Google Scholar 

  • General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China (2010). General technical rules for energy performance contracting (Vol. GB/T24915-2010) (In Chinese).

  • Issaka, A., & Sengupta, I. (2017). Analysis of variance based instruments for Ornstein–Uhlenbeck type models: swap and price index. Annals of Finance, 13, 401–434.

    Article  MathSciNet  MATH  Google Scholar 

  • Jones, E. P., & Mason, S. P. (1980). Valuation of loan guarantees. Journal of Banking & Finance, 4(1), 89–107.

    Article  Google Scholar 

  • Kostka, G., & Shin, K. (2013). Energy conservation through energy service companies: Empirical analysis from China. Energy Policy, 52(0), 748–759.

    Article  Google Scholar 

  • Lin, T. T., & Huang, S. L. (2010). An entry and exit model on the energy-saving investment strategy with real options. Energy Policy, 38(2), 794–802.

    Article  Google Scholar 

  • Lin, T. T., & Huang, S.-L. (2011). Application of the modified Tobin’s q to an uncertain energy-saving project with the real options concept. Energy Policy, 39(1), 408–420. https://doi.org/10.1016/j.enpol.2010.10.018.

    Article  Google Scholar 

  • Lin, J., Goldman, C., Levine, M., & Hopper, N. (2004). Developing an energy efficiency service industry. Shanghai: Lawrence Berkeley National Laboratory.

    Book  Google Scholar 

  • Lindset, S. (2003). Pricing of multi-period rate of return guarantees. Insurance: Mathematics and Economics, 33(3), 629–644.

    MathSciNet  MATH  Google Scholar 

  • Marino, A., Bertoldi, P., Rezessy, S., & Boza-Kiss, B. (2011). A snapshot of the European energy service market in 2010 and policy recommendations to foster a further market development. Energy Policy, 39(10), 6190–6198.

    Article  Google Scholar 

  • Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4, 141–183.

    Article  MathSciNet  MATH  Google Scholar 

  • Merton, R. C. (1977). An analytic derivation of the cost of deposit insurance and loan guarantees an application of modern option pricing theory. Journal of Banking & Finance, 1(1), 3–11.

    Article  Google Scholar 

  • Mody, A., & Patro, D. (1995). Methods of loan guarantee valuation and accounting. World Bank.

  • Patari, S., Annala, S., Jantunen, A., Viljainen, S., & Sinkkonen, A. (2016). Enabling and hindering factors of diffusion of energy service companies in Finland-results of a Delphi study. Energy Efficiency, 9(6), 1447–1460. https://doi.org/10.1007/s12053-016-9433-z.

    Article  Google Scholar 

  • Qian, D., & Guo, J. E. (2014). Research on the energy-saving and revenue sharing strategy of ESCOs under the uncertainty of the value of energy performance contracting projects. Energy Policy, 73(5), 710–721.

    Article  Google Scholar 

  • Saltelli, A. (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145(2), 280–297. https://doi.org/10.1016/s0010-4655(02)00280-1.

    Article  MathSciNet  MATH  Google Scholar 

  • Scoleri, S., Bianchetti, M., & Kucherenko, S. (2017). Application of quasi Monte Carlo and global sensitivity analysis to option pricing and Greeks. Social Science Electronic Publishing

  • Sengupta, I. (2016). Generalized BN–S stochastic volatility model for option Pricing. International Journal of Theoretical & Applied Finance, 19(02), 1650014.

    Article  MathSciNet  MATH  Google Scholar 

  • Sobol, I. M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280. https://doi.org/10.1016/s0378-4754(00)00270-6.

    Article  MathSciNet  MATH  Google Scholar 

  • Soumaré, I. (2006). Investment incentives in project finance in the presence of partial loan guarantees. Research in Finance, 22, 161–186.

    Google Scholar 

  • Toomey, D. E., & McNulty, T. (2002). Surety bonds: a basic user's guide for payment bond claimants and obligees. Construction Law, 22, 5.

    Google Scholar 

  • Van Lai, S., & Yu, M.-T. (1999). An accurate analysis of vulnerable loan guarantees. Research in Finance, 17, 103–138.

    Google Scholar 

  • Wang, G., Wang, Y., & Zhao, T. (2008). Analysis of interactions among the barriers to energy saving in China. Energy Policy, 36(6), 1879–1889. https://doi.org/10.1016/j.enpol.2008.02.006.

    Article  Google Scholar 

  • Xu, Y. C., & He, J. (2007). Option pricing of credit guaranty based on minimal net cash flow requirement (in Chinese). Systems Engineering, 06, 121–123.

    Google Scholar 

  • Xu, Y. C., & Yang, J. G. (2008). Option pricing of credit guarantee under the condition of counter-guarantee provided by third party (in Chinese). Journal of Shanghai Jiaotong University, 42(9), 1566–1569.

    MathSciNet  Google Scholar 

  • Xu, P., Chan, E. H.-W., & Qian, Q. K. (2011). Success factors of energy performance contracting (EPC) for sustainable building energy efficiency retrofit (BEER) of hotel buildings in China. Energy Policy, 39(11), 7389–7398. https://doi.org/10.1016/j.enpol.2011.09.001.

    Article  Google Scholar 

  • Zhang, P. G. (1998). Exotic options. World Scientific.

Download references

Acknowledgments

The authors gratefully acknowledge financial support from the National Science Foundation of China (Grant no. 71603209, 71473193, 71503200, 41602336); the Natural Science Basic Research Program of Shaanxi Province (2018JQ7006); and the Fundamental Research Funds for the Central Universities (Grant No. 2016RWYB04, 2016RWYB03). We would also like to thank the editors and anonymous reviewers.

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Appendix

Appendix

Derivation process of Formula (15):

According to the basic assumptions and Eq. (14), make \( {\tilde{G}}_{1i}=\max \left({V}_i-{\tilde{D}}_i,0\right) \) the European call option corresponding to G1i and \( {\tilde{G}}_{0i} \) its current value. As with Merton (1973), a zero investment portfolio Ω is constructed, i.e. the total amount of investment in Ω is zero, which is constituted by the underlying asset Vi (used to hedge the changes in the underlying asset), the hedge security Hi (used to hedge the uncertainty of execution price \( {\tilde{D}}_i \)) and the call option\( {\tilde{G}}_{1i} \). The structure of Ω is as follows:

$$ \varOmega ={\tilde{G}}_{1i}+{\varDelta}_1{V}_i+{\varDelta}_2{H}_i $$
(42)

Where Δ1 and Δ2 are respectively invested in the share of Vi and Hi. Since Ω is the zero portfolio, according to the no-arbitrage principle, there is  = 0, thus:

$$ d{\tilde{G}}_{1i}+{\varDelta}_1d{V}_i+{\varDelta}_2d{H}_i=0 $$
(43)

In addition, according to the Itô lemma, there is:

$$ {\displaystyle \begin{array}{l}d{\tilde{G}}_{1i}=\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}d{V}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}d{H}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial i} di\\ {}+\frac{1}{2}\left[\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}{\left(d{V}_i\right)}^2+2\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i{H}_i}\left(d{V}_id{H}_i\right)+\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}{\left(d{H}_i\right)}^2\right]\end{array}} $$
(44)

Where \( {\left(d{V}_i\right)}^2={\sigma}_V^2{V_i}^2 di \),\( {\left(d{H}_i\right)}^2={\tilde{\sigma}}_D^2{H_i}^2 di \),\( \left(d{V}_id{H}_i\right)={\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D{V}_i{H}_i di \). Substitute Eqs. (3) and (8) into Eq. (44) to obtain:

$$ {\displaystyle \begin{array}{l}d{\tilde{G}}_{1i}=\left(\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}{\alpha}_v{V}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}{r}_H{H}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial i}\right) di\\ {}+\frac{1}{2}\left(\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}{\sigma}_V^2{V_i}^2+2\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i{H}_i}{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D{V}_i{H}_i+\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}{\tilde{\sigma}}_D^2{H_i}^2\right) di\\ {}+\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}{\sigma}_V{V}_id{z}_t+\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}{\tilde{\sigma}}_D{H}_id{z}_D\end{array}} $$
(45)

Substitute Eq. (45) into Eq. (43) to obtain:

$$ {\displaystyle \begin{array}{l}\left[\left(\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}+{\varDelta}_1\right){\alpha}_v{V}_i+\left(\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}+{\varDelta}_2\right){r}_H{H}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial i}\right] di\\ {}+\frac{1}{2}\left(\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}{\sigma}_V^2{V_i}^2+2\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i{H}_i}{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D{V}_i{H}_i+\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}{\tilde{\sigma}}_D^2{H_i}^2\right) di\\ {}+\left(\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}+{\varDelta}_1\right){\sigma}_V{V}_id{z}_t+\left(\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}+{\varDelta}_2\right){\tilde{\sigma}}_D{H}_id{z}_D\\ {}=0\end{array}} $$
(46)

Since the right side of the above equation is the income of the zero risk portfolio, the coefficient of dzt and dzD on the left side of the above equation must be 0, there is:

$$ {\varDelta}_1=-\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i},{\varDelta}_2=-\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i} $$
(47)

Substituting the above results into Eq. (46) and eliminating di, yields that \( {\tilde{G}}_{1i} \) meets the partial differential equation:

$$ \frac{\partial {\tilde{G}}_{1i}}{\partial i}+\frac{1}{2}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}{\sigma}_V^2{V_i}^2+\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i{H}_i}{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D{V}_i{H}_i+\frac{1}{2}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}{\tilde{\sigma}}_D^2{H_i}^2=0 $$
(48)

It can be seen that Eq. (48) is similar to the multi-asset option Black-Scholes formula, and there is:

$$ {\tilde{G}}_{1i}=\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}{V}_i+\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}{H}_i $$
(49)

Then Eq. (48) can be rewritten as:

$$ \frac{\partial {\tilde{G}}_{1i}}{\partial i}+\frac{1}{2}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}{\sigma}_V^2{V_i}^2+\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i\partial {H}_i}{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D{V}_i{H}_i+\frac{1}{2}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}{\tilde{\sigma}}_D^2{H_i}^2=0 $$
(50)

According to the boundary condition \( {\tilde{G}}_{1i}\left({V}_i,{H}_i,i;{\tilde{D}}_i\right)=\max \left({V}_i-{\tilde{D}}_i,0\right) \), perform the following equation transformation:

$$ {\zeta}_i=\frac{V_i}{H_i{\tilde{D}}_i} $$
(51)
$$ {\tilde{G}}_{1i}\left({V}_i,{H}_i,i;{\tilde{D}}_i\right)={H}_i{\tilde{D}}_iu\left({\zeta}_i,i\right)={H}_i{\tilde{D}}_i\max \left({\zeta}_i-\frac{1}{H_i},0\right) $$
(52)

Then, there are:

$$ {\displaystyle \begin{array}{c}\frac{\partial {\tilde{G}}_{1i}}{\partial {V}_i}={H}_i{\tilde{D}}_i\frac{\partial u}{\partial {\zeta}_i}\frac{1}{H_i{\tilde{D}}_i}=\frac{\partial u}{\partial {\zeta}_i}\\ {}\frac{\partial {\tilde{G}}_{1i}}{\partial {H}_i}={\tilde{D}}_iu-{H}_i{\tilde{D}}_i\frac{\partial u}{\partial {\zeta}_i}\frac{V_i}{H_i^2{\tilde{D}}_i}={\tilde{D}}_i\left(u-{\zeta}_i\frac{\partial u}{\partial {\zeta}_i}\right)\\ {}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V_i}^2}=\frac{\partial^2u}{\partial {\zeta_i}^2}\frac{1}{H_i{\tilde{D}}_i}\\ {}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {V}_i\partial {H}_i}=-\frac{\partial^2u}{\partial {\zeta_i}^2}\frac{V_i}{{H_i}^2{\tilde{D}}_i}=-\frac{\zeta_i}{H_i}\frac{\partial^2u}{\partial {\zeta_i}^2}\\ {}\frac{\partial^2{\tilde{G}}_{1i}}{\partial {H_i}^2}={\tilde{D}}_i\left(-\frac{\partial u}{\partial {\zeta}_i}\frac{V_i}{H_i^2{\tilde{D}}_i}+\frac{V_i}{H_i^2{\tilde{D}}_i}\frac{\partial u}{\partial {\zeta}_i}+{\zeta}_i\frac{V_i}{H_i^2{\tilde{D}}_i}\frac{\partial^2u}{\partial {\zeta_i}^2}\right)=\frac{{\tilde{D}}_i}{H_i}{\zeta_i}^2\frac{\partial^2u}{\partial {\zeta_i}^2}\end{array}} $$

Substitute all of the above into the Eq. (50) and eliminate the expression \( {H}_i{\tilde{D}}_i \) to obtain:

$$ \frac{\partial u}{\partial i}+\frac{1}{2}\left({\sigma}_V^2-2{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D+{\tilde{\sigma}}_D^2\right){\zeta_i}^2\frac{\partial^2u}{\partial {\zeta_i}^2}=0 $$
(53)

Equations (53) and (52) make up a problem with definitive solution, make \( {\widehat{\sigma}}^2={\sigma}_V^2-2{\rho}_{DV}{\sigma}_V{\tilde{\sigma}}_D+{\tilde{\sigma}}_D^2 \), then yields:

$$ u\left({\zeta}_i,i\right)={\zeta}_iN\left\{\frac{\ln {\zeta}_i+\frac{1}{2}{\widehat{\sigma}}^2i}{\widehat{\sigma}\sqrt{i}}\right\}-\frac{1}{H_i}N\left\{\frac{\ln {\zeta}_i-\frac{1}{2}{\widehat{\sigma}}^2i}{\widehat{\sigma}\sqrt{i}}\right\} $$
(54)

Back to the original function, there is:

$$ {\tilde{G}}_{1i}={H}_i{\tilde{D}}_iu\left({\zeta}_i,i\right)={V}_iN\left\{\frac{\ln {\zeta}_i+\frac{1}{2}{\widehat{\sigma}}^2i}{\widehat{\sigma}\sqrt{i}}\right\}-{\tilde{D}}_iN\left\{\frac{\ln {\zeta}_i-\frac{1}{2}{\widehat{\sigma}}^2i}{\widehat{\sigma}\sqrt{i}}\right\} $$
(55)

Combining \( E\left({\tilde{D}}_t\right)={D}_0{e}^{\tilde{\alpha}t} \) with H0 = 1 yields \( {\tilde{G}}_{0i} \) (the present value of \( {\tilde{G}}_{1i} \)):

$$ {\displaystyle \begin{array}{l}{\tilde{G}}_{0i}={V}_0\cdot N\left\{\frac{\ln \left(\frac{V_0}{D_0}\right)+\left({r}_H-\tilde{\alpha}+\frac{{\widehat{\sigma}}^2}{2}\right)i}{\widehat{\sigma}\sqrt{i}}\right\}\\ {}-{D}_0{e}^{-\left({r}_H-\tilde{\alpha}\right)i}\cdot N\left\{\frac{\ln \left(\frac{V_0}{D_0}\right)+\left({r}_H-\tilde{\alpha}-\frac{{\widehat{\sigma}}^2}{2}\right)i}{\widehat{\sigma}\sqrt{i}}\right\}\end{array}} $$
(56)

where N(⋅) is the cumulative distribution function of the standard normal distribution, \( {\widehat{\sigma}}^2 \) represents the variance of the rate of change for \( \frac{V_i}{{\tilde{D}}_i} \).

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Qian, D., Zhu, G. & Guo, J. Research on value and factors of the guarantee payment in the energy performance contracting in China. Energy Efficiency 12, 1547–1575 (2019). https://doi.org/10.1007/s12053-019-09776-0

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