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Article

Photovoltaic Solar Energy in Forest Nurseries: A Strategic Decision Based on Real Options Analysis

by
Qüinny Soares Rocha
1,
Rafaele Almeida Munis
1,
Richardson Barbosa Gomes da Silva
1,
Elí Wilfredo Zavaleta Aguilar
2 and
Danilo Simões
1,*
1
School of Agriculture, São Paulo State University (Unesp), Botucatu 18610-034, Brazil
2
Campus of Itapeva, São Paulo State University (Unesp), Itapeva 18409-010, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(5), 3960; https://doi.org/10.3390/su15053960
Submission received: 10 October 2022 / Revised: 7 January 2023 / Accepted: 20 February 2023 / Published: 22 February 2023
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Abstract

:
With the growing demand for investment projects in renewable energy, it is essential for the economic feasibility analysis to consider the inherent uncertainties of these projects and enable more accurate investment decisions. In this way, we analyze whether investment projects in photovoltaic panels to produce electrical energy in a forest nursery are economically viable through the analysis of real options. We consider the energy demand of a forest nursery with an initial investment of USD 90,417 in photovoltaic panels. The managerial flexibilities of deferral, expansion of 30.0% of the energy production capacity, and the project’s abandonment were inserted into the binomial model of the decision tree, built in discrete time. The project’s expanded net present value was 79.2% higher than the traditional net present value, capturing the value of flexibilities for managers. The deferral option was the one that most impacted the expanded net present value. Investment projects in photovoltaic panels to produce electricity in a forest nursery are economically viable when analyzed through real options.

1. Introduction

It is of global interest to replace non-renewable sources of energy to reduce the emission of greenhouse gases. The popularization of photovoltaic technology in several countries has increased the demand for the use of solar energy. Even with the favorable conditions of solar incidence in Brazil, there are incipient studies that address the economic feasibility of investment projects to produce photovoltaic solar energy.
The world’s demand for energy is constantly expanding and intensifying. By the year 2030, a 40% growth in global energy demand is projected. In this context, the production and use of renewable energies, such as solar, wind, biomass, geothermal, and oceanic, have gained more and more supporters in the substitution of non-renewable energy sources [1,2,3,4,5].
Among these, sunlight is one of the most abundant sources of renewable energy available; in addition, photovoltaic technology shows greater increase in installation to produce electricity. It is noteworthy that the interest in producing electricity from renewable sources is attributed to favorable environmental externalities, sustainability, and, above all, economic results [6,7,8,9,10,11].
The technological advance of photovoltaic modules has increased the efficiency of energy production, making photovoltaic solar energy advantageous for preventing electrical blockages and reducing transmission losses. In addition to reducing electricity costs, it has lower maintenance costs and free availability of the generating source, being economically viable, as opposed to other energy sources [12,13,14,15].
In the forestry sector, nurseries are structures responsible for producing the seedlings that support forestry programs in Brazil, requiring a significant amount of electrical energy. Some authors [16,17,18,19] emphasize that for the economic development of this sector, the search for innovations is fundamental. In this context, electricity production projects using photovoltaic technology can be a viable alternative.
Photovoltaic technology can be used in greenhouses, irrigation, and fertigation systems, among other stages of seedling production [20,21,22,23].However, the main limitation of investment projects in photovoltaic solar energy is the stochastic nature of the monetary and productive resources.
Among these resources, the capital invested in the acquisition of photovoltaic modules, the disparity in the value of the kilowatt-hour (kWh) paid to electrical energy concessionaires, and the characteristics of photovoltaic solar energy production stand out. These combined uncertainties limit the accuracy of economic analysis results by traditional methods [24,25,26,27,28,29].
In these uncertain environments, the analysis of real options can be applied to decision making for evaluating the value of managerial flexibility, whether it is exercised or not. Derived from the theory of financial options, real options give the manager the right, but not the obligation, to buy or sell an asset at a fixed price in a predetermined period [30,31,32,33].
The analysis of real options provides a trade-off in time between the benefits and costs arising from the uncertainties and managerial flexibilities inherent to investment projects. By allowing managers to modify initial planning in response to market information, real options reduce the number of wrong decisions as they capture new information and opportunities, limit investment risks, and have become a methodology increasingly accessible to forest managers [34,35,36,37,38].
While still in the planning phase, considering the managerial flexibilities that occur naturally in investment projects can guide the manager in decision making. With the deferral option, which corresponds to an American call option, the decision to invest can be made in the present or the future, when new market information will be acquired. In a world of uncertainty, the opportunity to invest can be more valuable than immediate investment or a delayed commitment because it gives management the flexibility to defer undertaking the investment until circumstances turn more favorable or back out altogether if they become unsatisfactory [39,40,41]
Another American call option, the expansion option, provides the capture of additional assets on top of the base asset. If market conditions are more favorable than expected, the firm can expand the scale of production or accelerate resource utilization. Furthermore, the option to expand may also be of strategic importance, especially if it enables the firm to capitalize on future growth opportunities [42,43].
If the market environment is totally unfavorable, the option to abandon limits the risks. In most real-life projects, the required investment is not incurred as a single up-front outlay. The staging of capital investment as a series of outlays over time creates valuable options to “default” at any given stage [44,45,46,47,48,49].
Considering the uncertainties associated with the production of photovoltaic solar energy and the managerial flexibilities, through the analysis of real options, we analyze whether the investment project in photovoltaic panels to produce solar electrical energy in a forest nursery is economically viable.

2. Materials and Methods

2.1. Electrical Capacity of the Forest Nursery

We carried out this study in a suspended and sectored forest nursery, with an installed capacity of 17.6 million seedlings per year, located in the São Paulo State, Brazil, at coordinates 22°28’ S 48°58’ W. Annually, they produced an average 14.1 million of Eucalyptus spp. cuttings.
We calculated the number of photovoltaic panels and inverters based on the average daily electricity consumption of the forest nursery, which was 550 kWh day−1. The inclination of the plates, with an angle of 21° from flat, and the incidence of average daily solar irradiation of 5.07 kWh m−2 day−1 were calculated according to [50,51]. According to [52,53,54], the hour of the full sun was obtained from the division between the daily average of the solar irradiation incidence and the standard solar irradiance 1 kW m−2.
The peak power of the photovoltaic panels required was 143 kWp, based on the overall efficiency of the on-grid system of 76.2% and to substitute 70.0% (~100.0 kWp) of the energy demand of the forest nursery [55,56]. Based on this percentage, we installed 190 solar photovoltaic panels of 540 watts (model Tiger Pro, Jinko Solar Holding Co., Ltd., Shanghai, China), with 144 monocrystalline silicon cells, 2274 mm × 1134 mm × 35 mm (including the structure), with an efficiency of 20.9%. Four on-grid photovoltaic solar inverters with a nominal output power of 25 kW (model MID 25KTL3-X1, Growatt New Energy Technology Co., Ltd., Shenzhen, China) with three-phase nominal voltage 127/220 V and maximum efficiency of 98.8% were needed.

2.2. Deterministic Economic Analysis

Cash flow was characterized as conventional [57]. The useful life of the inverters and photovoltaic panels was 10 and 25 years, respectively; however, we adopted a planning horizon of 20 years. We consider capital expenditure (CAPEX) and operational expenditure (OPEX) inherent to photovoltaic panels and inverters, as well as system installation costs and gross revenue.
We deduct taxes in force in the country referring to the Social Integration Program (1.6%), contribution for the financing of social security (7.6%), tax on the circulation of goods and services (18.0%), tax on corporate income (25.0%), and social contribution on net income (9.0%), according to [58,59,60,61].

2.3. Opportunity Cost Rate

We used the weighted average cost of capital (Equation (1)) to adjust the opportunity cost rate (i) of the project, considering the participation of third-party capital and a proxy for the systematic market risk [62].
i = ( 1 − T ) ( D V d ) + ( E V e )
where T is the marginal corporate tax rate of the company that owns the forest nursery that considers the tax deductibility of debt interest; D is the market value of the company’s debt; V is the company’s market value; d is the company’s average cost of debt; E is the market value of the company’s equity; e is its cost of equity.
We applied the financial asset pricing model [63] to calculate the cost of equity (Equation (2)), thus determining the net worth.
e = R f + β   ( R m − R f ) + R B
where e is the cost of equity; R f is the risk-free rate; β is the systematic risk proxy of the investment project; R m is the expected market return; R B is the country risk premium.
We add the 2.4% spread to the cost of third-party capital according to Brazil’s current speculative credit classification Ba2 [64]. The 5.1% risk-free return rate was obtained from the geometric mean of the 10-year rate on treasury bonds between 2 January 1962 and 1 February 2022 according to data provided by [65]. Nevertheless, we obtained a cost of third-party capital of 7.5%. The predilection for long periods resulted in the absence of a trend over time in accordance with [66].
The estimation of the systematic risk coefficient (Equation (3)) was performed empirically according to [67].
β = C o v ( r i , t , r m , t ) V a r ( r m , t )
where C o v ( r i , t , r m , t ) is the covariance between the return on asset and the return on the market portfolio; V a r ( r m , t ) is the variance of the portfolio return.
We used the sector’s beta of 0.28 as a proxy for the systematic risk of the investment project, estimated through the average of the unleveraged systematic risk of companies in the Brazilian wood, pulp, and paper sector, namely Dexco S.A., Eucatex S.A. Indústria e Comércio, Companhia Melhoramentos de São Paulo, Klabin S.A. e Suzano S.A., listed on B3 S.A.—Brasil, Bolsa, Balcão [68]. When weighing the dimension of the asset financed by third parties (51.8%) according to the balance sheet of the company that owns the forest nursery, we determined the average systematic re-leveraged risk of 0.37.
Based on the S&P Global Timber & Forestry Index [69] and the risk-free asset’s rate of return, we calculated the market risk premium, which resulted in 2.8%, by means of the difference between the expected rate of return for the forest market portfolio, which was 7.9%. The country risk premium, obtained through the geometric average of the historical series of Brazil risk between 29 April 1994 and 1 February 2022, and the Emerging Markets Bond Index Plus, released by J.P. Morgan [70], was 4.0%.
The cost of equity rate was 10.1%. We considered the proportion of equity capital (48.2%), taking into account the proportion of debt capital, which allowed us to estimate the opportunity cost rate of 7.5%.

2.4. Traditional Net Present Value

The traditional valuation methodology based on net present value [71] acted as an input in the dynamic modeling (Equation (4)).
N P V T r a d = ∑ t = 0 n B t − C t ( 1 + i ) t
where N P V T r a d is the sum of net benefit evaluated at a present value up to the n-time interval; B t and C t are, respectively, benefit and cost in the t-year; and i is the discount rate for the entire planning horizon.

2.5. Dynamic Modeling

The synergy between the uncertainties that impact investment projects reflects in the complexity of the calculations. Thus, for simplification and interpretation, according to [72], we assume a single source of consolidated uncertainty of the investment project, the kWh value.
Based on the methodology developed by [73,74], we model the underlying asset through Brownian geometric motion and capture the volatility of the stochastic process (Equation (5)).
d p c   = μ c   p c   d t + σ c   p c   d z
where p c   is the kWh value at time t; μ c and σ c represent the drift rate and the volatility rate, respectively, of the kWh value; d z is the independent increment of the Wiener process and follows the normal distribution N   ( 0 ,   t ) .
Using the Monte Carlo method, we estimated the volatility of the project [75], generating the probability distribution of the present value (PV) of the first year of the projected horizon conditioned to the expectations of the present values of the second to the twentieth year. The generation of 100,000 pseudorandom numbers was performed using @Risk Copyright software [76].
In the construction of the binomial decision tree in discrete time, we attribute to the underlying asset, i.e., the investment project free of real options, two possibilities of movement. Furthermore, we calculate the levels of increase u (Equation (6)) and decrease d (Equation (7)) from the risk neutral probability p (Equation (8)) and q (Equation (9)) as its complement [77].
u = e σ δ t
d = u − 1
p = ( e r . δ t − d ) ( u − d )
1 − p
With the underlying asset modeled, we incorporated real options into the investment project through the software Decision Programming Language (DPL) [78]. We emphasize that, although the binomial decision tree was built with the risk-free rate, in obtaining the real probability of executing each option, in line with [79], we apply the opportunity cost rate of the investment project.
We consider the managerial flexibilities of deferral, expansion, and abandonment as real options. The deferral option, which corresponds to an American call option, can wait any number of years to see if output prices justify the investment project in photovoltaic panels. Thus, forest nursery managers would invest in photovoltaic panels only if prices increase significantly, but if prices decline, they would not commit to the investment project and save the planned outlays. The option to wait is particularly interesting due to the value of the investment opportunity. If photovoltaic panel prices or other market conditions turn out more favorable than expected, forest nursery managers can expand the acquisition of photovoltaic panels. If market conditions decline drastically, forest nursery managers can abandon current operations permanently and realize the resale value of photovoltaic panels capital. Thus, building the necessary infrastructure of photovoltaic panels can be considered as an option on the value of subsequent stages.
At a cost of USD 90,417, we considered the option to defer from year zero to year two. In the expansion option, we considered the 30.0% increase in the project’s production capacity in the tenth year at a cost of USD 11,362 for the acquisition of 82 photovoltaic panels, two inverters, and installation costs. We evaluated the abandonment option with a bonus of USD 48,945 in the tenth year of the planning horizon, referring to the residual value of photovoltaic panels (Figure 1).
The deferral allowed the compound exercise of successive real options, abandonment, or expansion. Regarding real options for abandonment and expansion, these were assimilated into the investment project as exclusive real options because of the hierarchy of relationships between real options.
We calculated the value of the real option (Equation (10)), according to [80].
N P V E x p   = N P V T r a d   − R O V
where N P V E x p is the expanded net present value; N P V T r a d is the traditional net present value; R O V is the real options value.

3. Results

Considering CAPEX of USD 90,417, PV and N P V T r a d   were calculated to be USD 129,928 and USD 39,510, respectively. As a result of the Monte Carlo method, the mean PV was USD 532,132 and the standard deviation was ±USD 750,420. The volatility of project was 0.6080 and the random sample of returns was 1.0084. The binomial decision tree parameters were u = 1.8369, d = 0.5444, p = 0.3922, and q = 0.6078.
When combining all real options, the N P V E x p was USD 70,820 and the R O V was USD 31,310. Regarding the investment project in photovoltaic panels, the optimal probabilities of real options indicated the exercise of deferral and abandonment and the non-exercise of the real option of expansion (Figure 2).
Individually calculated, the deferral real option presented an N P V E x p   of UDS 65,468 with a probability of execution of 100.0%. In the adopted configuration, the expansion option was indifferent to the manager, returning the traditional PV itself with a probability of execution of 0.0%. The N P V E x p   of the abandon option in the tenth year was UDS 51,198, with a 61.0% probability of execution. As it is directly related to the risks associated with the investment project, the greater the volatility, the greater the profitability (Figure 3).

4. Discussion

CAPEX can be considered as the strategic value to be invested in the present to create value for its managers [80,81,82,83]. The CAPEX of the investment project in photovoltaic panels to produce solar energy can be decisive for its economic viability, as it represented a significant part of the projected financial contribution.
The analysis of the economic viability of projects in photovoltaic panels is commonly performed by traditional methods, as discussed by [84,85,86]. However, analysis methods that incorporate managerial uncertainties and flexibilities promote more accurate results. Compared to the traditional method, the analysis of economic viability through real options returned the project’s appreciation by 79.2%.
In investment projects, PV can be used as a method of analyzing the time value of money [87,88]. Through the PV, the profitability of the project in photovoltaic panels for the production of solar electrical energy in a forest nursery showed a significant variation reflected in the standard deviation. This result captured the instability of the electricity market, corroborating [89,90].
As a result of fluctuations in the value of the project’s source of uncertainty, the volatility of investment projects in photovoltaic panels directly influenced their economic return. This fact is explained by [91,92,93], as the production of photovoltaic solar energy can be considered, of course, intermittent and relatively difficult to measure.
When considering the analysis of real options, according to [36,94,95], it was possible to obtain the monetary benefits, added by the exercise of flexibilities, helping the selection of economically viable projects. Although the investment project in photovoltaic panels to produce solar electrical energy in a forest nursery was economically viable under the traditional method, the analysis of real options evidenced the aggregation of value of the investment project.
According to [50,96,97], the profit margin can be enhanced if real options are combined. In the investment project in photovoltaic panels to produce electricity in a forest nursery, combining the options of deferral, expansion, and abandonment simulated the strategy that the manager would naturally exercise over the planned horizon. The N P V E x p   of the combined real options was 7.5% higher than the deferral real option and 27.7% higher than the real option and abandonment calculated individually.
When considering the deferral option, combined or not, the optimal probability was that it will always be exercised. Due to its arrangement, the investment project did not pay dividends to its managers. Associated with the purchase configuration of the deferral option, in agreement with [98,99,100], investment never occurs before the maturity date when the underlying assets do not pay dividends.
In the investment project in photovoltaic panels for the production of solar electricity in a forest nursery, the real option of expansion with zero probability was not an option to be exercised. The expansion requires a greater investment than initially planned, increasing the project’s CAPEX. When calculated, the revenue generated by the expansion was not attractive enough to be exercised.
The authors of [101,102,103] classified the abandonment option as a complex call option. The exercise of this option can be associated with its value and market purchase availability. As it is a physical asset, which deteriorates over the planned horizon, exercising this option or not was associated with its residual value.
The undervaluation of investment projects in photovoltaic panels is a function of the limited capacity of traditional methods of economic feasibility analysis to deal with the instability of the electricity market. The real options analysis captured the real value of the investment project and translated the risks to the manager from the calculated return.

5. Conclusions

The investment project in photovoltaic panels to produce electricity in a forest nursery is economically viable when analyzed through real options.
Real options analysis overcomes the limitations of traditional methods of economic analysis by quantifying the additional value of managerial flexibilities in investment projects in photovoltaic panels to produce electrical energy in a forest nursery.
As it provides greater security to managers of investment projects in photovoltaic panels to produce electricity in a forest nursery, the deferral American option is exercised with 100% probability.
The null probability of the real option of expansion is not attractive for investment projects in photovoltaic panels to produce electrical energy in a forest nursery because its profitability does not supply the investment demanded. Starting from the residual value of the photovoltaic panels, the abandonment as an American put option added 29.6% to the value of the investment project in relation to the traditional method.

Author Contributions

Conceptualization, D.S.; methodology, Q.S.R., R.A.M., E.W.Z.A. and D.S.; software, Q.S.R., R.A.M. and D.S.; validation, E.W.Z.A. and D.S.; formal analysis, D.S.; investigation, Q.S.R. and R.A.M.; resources, E.W.Z.A. and D.S.; data curation, D.S.; writing—original draft preparation, Q.S.R., R.A.M., R.B.G.d.S. and D.S.; writing—review and editing, Q.S.R., R.A.M., R.B.G.d.S. and D.S.; visualization, R.B.G.d.S. and D.S.; supervision, D.S.; project administration, D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data are already provided in the main manuscript. Contact the corresponding author if further explanation is required.

Acknowledgments

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations World Water Assessment Programme. The United Nations World Water Development Report 2017. In Wastewater: The Untapped Resource.; UNESCO: Paris, France, 2017; ISBN 9789231002014. [Google Scholar]
  2. Bandyopadhyay, S. Pinch Analysis for Economic Appraisal of Sustainable Projects. Process Integr. Optim. Sustain. 2020, 4, 171–182. [Google Scholar] [CrossRef]
  3. Gutiérrez, R.E.; Haro, P.; Gómez-Barea, A. Techno-economic and operational assessment of concentrated solar power plants with a dual supporting system. Appl. Energy 2021, 302, 117600. [Google Scholar] [CrossRef]
  4. Kapitonov, I.A.; Patapas, A. Principles regulation of electricity tariffs for the integrated generation of traditional and alternative energy sources. Renew. Sustain. Energy Rev. 2021, 146, 111183. [Google Scholar] [CrossRef]
  5. Sabyrzhan, A.; Balgimbekova, G.; Shestak, V. Economic and legal regulation of the use and development of renewable energy sources. Int. Environ. Agreements Polit. Law Econ. 2021, 21, 595–610. [Google Scholar] [CrossRef]
  6. Fuqiang, W.; Ziming, C.; Jianyu, T.; Yuan, Y.; Yong, S.; Linhua, L. Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review. Renew. Sustain. Energy Rev. 2017, 79, 1314–1328. [Google Scholar] [CrossRef]
  7. Modi, A.; Bühler, F.; Andreasen, J.G.; Haglind, F. A review of solar energy based heat and power generation systems. Renew. Sustain. Energy Rev. 2017, 67, 1047–1064. [Google Scholar] [CrossRef] [Green Version]
  8. Carvalho, D.B.; Guardia, E.C.; Marangon Lima, J.W. Technical-economic analysis of the insertion of PV power into a wind-solar hybrid system. Sol. Energy 2019, 191, 530–539. [Google Scholar] [CrossRef]
  9. Islam, M.T.; Huda, N.; Saidur, R. Current energy mix and techno-economic analysis of concentrating solar power (CSP) technologies in Malaysia. Renew. Energy 2019, 140, 789–806. [Google Scholar] [CrossRef]
  10. REN21 Renewables 2020 Global Status Report; Secretariat: Paris, France, 2020; ISBN 978-3-948393-00-7.
  11. Awan, A.B.; Zubair, M.; Memon, Z.A.; Ghalleb, N.; Tlili, I. Comparative analysis of dish Stirling engine and photovoltaic technologies: Energy and economic perspective. Sustain. Energy Technol. Assess. 2021, 44, 101028. [Google Scholar] [CrossRef]
  12. Gürtürk, M. Economic feasibility of solar power plants based on PV module with levelized cost analysis. Energy 2019, 171, 866–878. [Google Scholar] [CrossRef]
  13. Leite, G.d.N.P.; Weschenfelder, F.; Araújo, A.M.; Ochoa, Á.A.V.; Prestrelo Neto, N. da F.; Krajc, A. An economic analysis of the integration between air-conditioning and solar photovoltaic systems. Energy Convers. Manag. 2019, 185, 836–849. [Google Scholar] [CrossRef]
  14. De Doyle, G.N.D.; Rotella Junior, P.; Rocha, L.C.S.; Carneiro, P.F.G.; Peruchi, R.S.; Janda, K.; Aquila, G. Impact of regulatory changes on economic feasibility of distributed generation solar units in Brazil. Sustain. Energy Technol. Assess. 2021, 48, 101660. [Google Scholar] [CrossRef]
  15. Solarin, S.A.; Bello, M.O.; Bekun, F.V. Sustainable electricity generation: The possibility of substituting fossil fuels for hydropower and solar energy in Italy. Int. J. Sustain. Dev. World Ecol. 2021, 28, 429–439. [Google Scholar] [CrossRef]
  16. Loučanová, E.; Paluš, H.; Báliková, K.; Dzian, M.; Slašťanova, N.; Šálka, J. Stakeholder’s perceptions of the innovation trends in the Slovak forestry and forest based sectors. J. Bus. Econ. Manag. 2020, 21, 1610–1627. [Google Scholar] [CrossRef]
  17. Trigkas, M.; Anastopoulos, C.; Papadopoulos, I.; Lazaridou, D. Business model for developing strategies of forest cooperatives. Evidence from an emerging business environment in Greece. J. Sustain. For. 2020, 39, 259–282. [Google Scholar] [CrossRef]
  18. Guerrero, J.E.; Hansen, E. Cross-sector collaboration in Oregon’s forest sector: Insights from owners and CEOs. Int. Wood Prod. J. 2021, 12, 135–143. [Google Scholar] [CrossRef]
  19. Weiss, G.; Hansen, E.; Ludvig, A.; Nybakk, E.; Toppinen, A. Innovation governance in the forest sector: Reviewing concepts, trends and gaps. For. Policy Econ. 2021, 130, 102506. [Google Scholar] [CrossRef]
  20. Singh, V. Influence of Manually Adjustable Photovoltaic Array on the Performance of Water Pumping Systems. Glob. Chall. 2019, 3, 1900009. [Google Scholar] [CrossRef] [Green Version]
  21. Siraganyan, K.; Perera, A.T.D.; Scartezzini, J.-L.; Mauree, D. Eco-SiM: A parametric tool to evaluate the environmental and economic feasibility of decentralized energy systems. Energies 2019, 12, 776. [Google Scholar] [CrossRef] [Green Version]
  22. Magadley, E.; Teitel, M.; Kabha, R.; Dakka, M.; Friman Peretz, M.; Ozer, S.; Levi, A.; Yasuor, H.; Kacira, M.; Waller, R.; et al. Integrating organic photovoltaics (OPVs) into greenhouses: Electrical performance and lifetimes of OPVs. Int. J. Sustain. Energy 2022, 41, 1005–1020. [Google Scholar] [CrossRef]
  23. Mindú, A.J.; Capece, J.A.; Araújo, R.E.; Oliveira, A.C. Feasibility of utilizing photovoltaics for irrigation purposes in moamba, mozambique. Sustainability 2021, 13, 10998. [Google Scholar] [CrossRef]
  24. Rocha, L.C.S.; Aquila, G.; de Oliveira Pamplona, E.; de Paiva, A.P.; Chieregatti, B.G.; Lima, J.d.S.B. Photovoltaic electricity production in Brazil: A stochastic economic viability analysis for small systems in the face of net metering and tax incentives. J. Clean. Prod. 2017, 168, 1448–1462. [Google Scholar] [CrossRef]
  25. Liu, Z. What is the future of solar energy? Economic and policy barriers. Energy Sources, Part B Econ. Planning, Policy 2018, 13, 169–172. [Google Scholar] [CrossRef]
  26. Georgitsioti, T.; Pearsall, N.; Forbes, I.; Pillai, G. A combined model for PV system lifetime energy prediction and annual energy assessment. Sol. Energy 2019, 183, 738–744. [Google Scholar] [CrossRef] [Green Version]
  27. Li, H.; Zhao, J.; Li, M.; Zhong, S.; Wang, F.; He, F.; Zhu, J. Determining the economic design radiation for a solar heating system through uncertainty analysis. Sol. Energy 2020, 195, 54–63. [Google Scholar] [CrossRef]
  28. Tang, T.; Ding, H.; Nojavan, S.; Jermsittiparsert, K. Environmental and Economic Operation of Wind-PV-CCHP-Based Energy System Considering Risk Analysis Via Downside Risk Constraints Technique. IEEE Access 2020, 8, 124661–124674. [Google Scholar] [CrossRef]
  29. Vargas, C.; Chesney, M. End of life decommissioning and recycling of solar panels in the United States. A real options analysis. J. Sustain. Financ. Investig. 2020, 11, 82–102. [Google Scholar] [CrossRef]
  30. Dixit, A.K.; Pindyck, R.S. Investment Under Uncertainty; Princeton University Press: Princeton, NJ, USA, 1994. [Google Scholar]
  31. Chesney, M.; Gheyssens, J.; Troja, B. Market uncertainty and risk transfer in REDD projects. J. Sustain. For. 2017, 36, 535–553. [Google Scholar] [CrossRef]
  32. Glensk, B.; Madlener, R. Energiewende @ Risk: On the continuation of renewable power generation at the end of public policy support. Energies 2019, 12, 3616. [Google Scholar] [CrossRef] [Green Version]
  33. Pringles, R.; Olsina, F.; Penizzotto, F. Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method. Renew. Energy 2020, 151, 846–864. [Google Scholar] [CrossRef]
  34. Farzan, F.; Mahani, K.; Gharieh, K.; Jafari, M.A. Microgrid investment under uncertainty: A real option approach using closed form contingent analysis. Ann. Oper. Res. 2015, 235, 259–276. [Google Scholar] [CrossRef]
  35. Martín-Barrera, G.; Zamora-Ramírez, C.; González-González, J.M. Application of real options valuation for analysing the impact of public R&D financing on renewable energy projects: A company′s perspective. Renew. Sustain. Energy Rev. 2016, 63, 292–301. [Google Scholar] [CrossRef]
  36. Guo, K.; Zhang, L.; Wang, T. Optimal scheme in energy performance contracting under uncertainty: A real option perspective. J. Clean. Prod. 2019, 231, 240–253. [Google Scholar] [CrossRef]
  37. Penizzotto, F.; Pringles, R.; Olsina, F. Real options valuation of photovoltaic power investments in existing buildings. Renew. Sustain. Energy Rev. 2019, 114, 109308. [Google Scholar] [CrossRef]
  38. Simões, D.; Gil, J.F.S.; da Silva, R.B.G.; Munis, R.A.; da Silva, M.R. Stochastic economic analysis of investment projects in forest restoration involving containerized tree seedlings in brazil. Forests 2021, 12, 1381. [Google Scholar] [CrossRef]
  39. Trigeorgis, L. Real Options: A Primer BT—The New Investment Theory of Real Options and Its Implication for Telecommunications Economics; Alleman, J., Noam, E., Eds.; Springer: Boston, MA, USA, 1999; Volume 13, ISBN 0792377346. [Google Scholar]
  40. Odetayo, B.; MacCormack, J.; Rosehart, W.D.; Zareipour, H. A real option assessment of flexibilities in the integrated planning of natural gas distribution network and distributed natural gas-fired power generations. Energy 2018, 143, 257–272. [Google Scholar] [CrossRef]
  41. Nunes, L.E.; de Lima, M.V.A.; Davison, M.; Leite, A.L. da S. Switch and defer option in renewable energy projects: Evidences from Brazil. Energy 2021, 231, 120972. [Google Scholar] [CrossRef]
  42. Weibel, S.; Madlener, R. Cost-effective design of ringwall storage hybrid power plants: A real options analysis. Energy Convers. Manag. 2015, 103, 871–885. [Google Scholar] [CrossRef] [Green Version]
  43. Herder, P.M.; De Joode, J.; Ligtvoet, A.; Schenk, S.; Taneja, P. Buying real options—Valuing uncertainty in infrastructure planning. Futures 2011, 43, 961–969. [Google Scholar] [CrossRef] [Green Version]
  44. Inthavongsa, I.; Drebenstedt, C.; Bongaerts, J.; Sontamino, P. Real options decision framework: Strategic operating policies for open pit mine planning. Resour. Policy 2016, 47, 142–153. [Google Scholar] [CrossRef]
  45. Song, N.; Xie, Y.; Ching, W.K.; Siu, T.K. A real option approach for investment opportunity valuation. J. Ind. Manag. Optim. 2017, 13, 1213–1235. [Google Scholar] [CrossRef]
  46. Kim, B.; Kim, C.; Han, S.U.; Bae, J.H.; Jung, J. Is it a good time to develop commercial photovoltaic systems on farmland? An American-style option with crop price risk. Renew. Sustain. Energy Rev. 2020, 125, 109827. [Google Scholar] [CrossRef]
  47. Oliveira, A.; Couto, G.; Pimentel, P. Uncertainty and flexibility in infrastructure investments: Application of real options analysis to the Ponta Delgada airport expansion. Res. Transp. Econ. 2021, 90, 100845. [Google Scholar] [CrossRef]
  48. Yue, Y.; Ying, Y. Real option analysis for emission reduction investment under the sulfur emission control. Sustain. Energy Technol. Assess. 2021, 45, 101055. [Google Scholar] [CrossRef]
  49. Zhang, X.; Yin, J. Assessment of investment decisions in bulk shipping through fuzzy real options analysis. Marit. Econ. Logist. 2021, 23, 1–18. [Google Scholar] [CrossRef]
  50. Liu, B.Y.H.; Jordan, R.C. The long-term average performance of flat-plate solar-energy collectors. Sol. Energy 1963, 7, 53–74. [Google Scholar] [CrossRef]
  51. Klein, S.A. Calculation of Monthly Average Insolation on Tilted Surfaces. Sol. Energy 1977, 19, 325–329. [Google Scholar] [CrossRef] [Green Version]
  52. Borhanazad, H.; Mekhilef, S.; Saidur, R.; Boroumandjazi, G. Potential application of renewable energy for rural electrification in Malaysia. Renew. Energy 2013, 59, 210–219. [Google Scholar] [CrossRef]
  53. Alsharif, M.H.; Nordin, R.; Ismail, M. Energy optimisation of hybrid off-grid system for remote telecommunication base station deployment in Malaysia. Eurasip J. Wirel. Commun. Netw. 2015, 2015, 1–15. [Google Scholar] [CrossRef] [Green Version]
  54. Rosas-Flores, J.A.; Zenón-Olvera, E.; Gálvez, D.M. Potential energy saving in urban and rural households of Mexico with solar photovoltaic systems using geographical information system. Renew. Sustain. Energy Rev. 2019, 116, 109412. [Google Scholar] [CrossRef]
  55. Ekici, S.; Kopru, M.A. Investigation of PV system cable losses. Int. J. Renew. Energy Res. 2017, 7, 807–815. [Google Scholar] [CrossRef]
  56. Al-Badi, A.; Yousef, H.; Al Mahmoudi, T.; Al-Shammaki, M.; Al-Abri, A.; Al-Hinai, A. Sizing and modelling of photovoltaic water pumping system. Int. J. Sustain. Energy 2018, 37, 415–427. [Google Scholar] [CrossRef]
  57. Gitman, L.J.; Zutter, C.J. Principles of Managerial Finance, 14th ed.; Pearson: London, UK, 2019. [Google Scholar]
  58. Law No. 7,689, of December 15, 1988. Establishes Social Contribution on the Profit of Legal Entities and Makes Other Arrangements; Official Gazette of the Federative Republic of Brazil: Brasília, Brazil, 1988.
  59. Law No. 9,250, of December 26, 1995. Amends the Legislation of the Income Tax of Individuals and Other Measures; Official Gazette of the Federative Republic of Brazil: Brasília, Brazil, 1995.
  60. Complementary Law No. 87, of September 13, 1996. Provides for the Tax of the States and the Federal District on Operations Related to the Circulation of Goods and on the Provision of Interstate and Intercity and Communication Services, and Other Measures; Official Gazette of the Federative Republic of Brazil: Brasília, Brazil, 1996.
  61. Law No. 10,147, of December 21, 2000. Provides for the Incidence of the Contribution to the Social Integration and Training Programs of the Public Servant’s Heritage—PIS/PASEP, and the Contribution to the Financing of Social Security—COFINS, in the Sales Operations of the Products You Specify; Official Gazette of the Federative Republic of Brazil: Brasília, Brazil, 2000.
  62. Mariani, M.; Pizzutilo, F.; Caragnano, A.; Zito, M. Does it pay to be environmentally responsible? Investigating the effect on the weighted average cost of capital. Corp. Soc. Responsib. Environ. Manag. 2021, 28, 1854–1869. [Google Scholar] [CrossRef]
  63. Zaroni, H.; Maciel, L.B.; Carvalho, D.B.; de Oliveira Pamplona, E. Monte Carlo Simulation approach for economic risk analysis of an emergency energy generation system. Energy 2019, 172, 498–508. [Google Scholar] [CrossRef]
  64. Moody’s Spread. Available online: https://www.moodys.com (accessed on 26 August 2021).
  65. United States Department of the Treasury Daily Treasury Yield Curve Rates. Available online: https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield (accessed on 12 February 2022).
  66. Damodaran, A. Investment valuation: Tools and techniques for determining the value of any asset, 3rd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2012; ISBN 9781118011522. [Google Scholar]
  67. Alexandridis, A.K.; Hasan, M.S. Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets. Int. J. Financ. Econ. 2019, 25, 518–546. [Google Scholar] [CrossRef]
  68. B3, S.A. BRASIL BOLSA Balcão Séries Históricas. Available online: http://www.b3.com.br/pt_br/market-data-e-indices/servicos-de-dados/market-data/historico/mercado-a-vista/series-historicas/ (accessed on 15 January 2021).
  69. S&P Global Timber & Forestry Index Overview. Available online: https://www.spglobal.com/spdji/en/indices/equity/sp-global-timber-and-forestry-index/#overview (accessed on 15 January 2021).
  70. Morgan, J.P. Emerging Markets Bond Index. Available online: https://www.jpmorgan.com/global (accessed on 26 January 2021).
  71. Ihm, S.H.; Seo, S.B.; Kim, Y.O. Valuation of Water Resources Infrastructure Planning from Climate Change Adaptation Perspective using Real Option Analysis. KSCE J. Civ. Eng. 2019, 23, 2794–2802. [Google Scholar] [CrossRef]
  72. Smit, H.T.J.; Trigeorgis, L. Strategic Investment Real Options and Games; Princeton University Press: Princeton, NJ, USA, 2004; ISBN 0691010390. [Google Scholar]
  73. Copeland, T.E.; Antikarov, V. Opções Reais: Um novo Paradigma para Reinventar a Avaliação de Investimentos, 1st ed.; Campus: Rio de Janeiro, Brazil, 2001. [Google Scholar]
  74. Zhang, W.; Dai, C.; Luo, X.; Ou, X. Policy incentives in carbon capture utilization and storage (CCUS) investment based on real options analysis. Clean Technol. Environ. Policy 2021, 23, 1311–1326. [Google Scholar] [CrossRef]
  75. Samuelson, P.A. Proof That Properly Anticipated Prices Fluctuate Randomly. Ind. Manag. Rev. 1965, 6, 41–49. [Google Scholar] [CrossRef]
  76. Palisade Corporation Software Risk Analysis and Risk Mod_eling, vers 7.6.0. Palisade Corporation. Available online: https://www.palisade-br.com/2021 (accessed on 26 January 2021).
  77. Ryu, Y.; Kim, Y.O.; Seo, S.B.; Seo, I.W. Application of real option analysis for planning under climate change uncertainty: A case study for evaluation of flood mitigation plans in Korea. Mitig. Adapt. Strateg. Glob. Chang. 2018, 23, 803–819. [Google Scholar] [CrossRef]
  78. Syncopation Software. DPL—Decision Programming Language. versão 9.02.00 Concord. 2021. Available online: https://www.syncopation.com/ (accessed on 26 January 2021).
  79. Miranda, R.; Fiorentin, L.; Péllico Netto, S.; Juvanhol, R.; Corte, A.D. Prediction system for diameter distribution and wood production of Eucalyptus. Floresta e Ambient. 2018, 25, 1–12. [Google Scholar] [CrossRef]
  80. Durica, M.; Guttenova, D.; Pinda, L.; Svabova, L. Sustainable value of investment in real estate: Real options approach. Sustainability 2018, 10, 4665. [Google Scholar] [CrossRef] [Green Version]
  81. McConnell, J.J.; Muscarella, C.J. Corporate capital expenditure decisions and the market value of the firm. J. financ. econ. 1985, 14, 399–422. [Google Scholar] [CrossRef]
  82. Baig, U.; Khalidi, M.A. A grounded theory exploration of appraisal Process of Capital Investment Decisions—Capex Appraisal Model (CAM). Indep. J. Manag. Prod. 2020, 11, 2778–2804. [Google Scholar] [CrossRef]
  83. Vartiainen, E.; Masson, G.; Breyer, C.; Moser, D.; Román Medina, E. Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility-scale PV levelised cost of electricity. Prog. Photovoltaics Res. Appl. 2020, 28, 439–453. [Google Scholar] [CrossRef] [Green Version]
  84. Choi, G.; Heo, E.; Lee, C.Y. Dynamic economic analysis of subsidies for new and renewable energy in South Korea. Sustainability 2018, 10, 1832. [Google Scholar] [CrossRef] [Green Version]
  85. Sow, A.; Mehrtash, M.; Rousse, D.R.; Haillot, D. Economic analysis of residential solar photovoltaic electricity production in Canada. Sustain. Energy Technol. Assessments 2019, 33, 83–94. [Google Scholar] [CrossRef]
  86. Shimbar, A.; Ebrahimi, S.B. Political risk and valuation of renewable energy investments in developing countries. Renew. Energy 2020, 145, 1325–1333. [Google Scholar] [CrossRef]
  87. Gu, Y.; Zhang, X.; Myhren, J.A.; Han, M.; Chen, X.; Yuan, Y. Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method. Energy Convers. Manag. 2018, 165, 8–24. [Google Scholar] [CrossRef]
  88. Li, Y.; Lou, Z.; Zhang, Q.; Zhang, M. Sequential Monte Carlo estimation for Present-Value model. Appl. Econ. Lett. 2021, 2021, 1–7. [Google Scholar] [CrossRef]
  89. Köse, E.; Sauer, A. Reduction of energy costs and grid instability with energy flexible furnaces. Procedia CIRP 2018, 72, 832–838. [Google Scholar] [CrossRef]
  90. Ríos-Ocampo, J.P.; Arango-Aramburo, S.; Larsen, E.R. Renewable energy penetration and energy security in electricity markets. Int. J. Energy Res. 2021, 45, 17767–17783. [Google Scholar] [CrossRef]
  91. Porcu, A.; Sollai, S.; Marotto, D.; Mureddu, M.; Ferrara, F.; Pettinau, A. Techno-economic analysis of a small-scale biomass-to-energy BFB gasification-based system. Energies 2019, 12, 494. [Google Scholar] [CrossRef] [Green Version]
  92. Bari, A. Di A real options approach to valuate solar energy investment with public authority incentives: The Italian case. Energies 2020, 13, 4181. [Google Scholar] [CrossRef]
  93. Abban, A.R.; Hasan, M.Z. Solar energy penetration and volatility transmission to electricity markets—An Australian perspective. Econ. Anal. Policy 2021, 69, 434–449. [Google Scholar] [CrossRef]
  94. Tiwana, A.; Jijie, W.; Keil, M.; Ahluwalia, P. The bounded rationality bias in managerial valuation of real options: Theory and evidence from IT projects. Decis. Sci. 2007, 38, 157–181. [Google Scholar] [CrossRef]
  95. Huang, C.Y.; Hsieh, H.L.; Chen, H. Evaluating the investment projects of spinal medical device firms using the real option and DANP-mV based MCDM methods. Int. J. Environ. Res. Public Health 2020, 17, 3335. [Google Scholar] [CrossRef]
  96. Benitez, G.B.; Lima, M.J. do R.F. The real options method applied to decision making—An investment analysis. Braz. J. Oper. Prod. Manag. 2019, 16, 562–571. [Google Scholar] [CrossRef]
  97. Shi, J.; Duan, K.; Wen, S.; Zhang, R. Investment valuation model of public rental housing PPP project for private sector: A real option perspective. Sustainability 2019, 11, 1857. [Google Scholar] [CrossRef] [Green Version]
  98. Balibrea-Iniesta, J. Economic analysis of renewable energy regulation in France: A case study for photovoltaic plants based on real options. Energies 2020, 13, 2760. [Google Scholar] [CrossRef]
  99. Fattahi, K.; Naeini, A.B.; Sadjadi, S.J. Technology valuation of NTBFs in the field of cleaner production with regard to the investors’ flexibilities and uncertainties in public policy. Sci. Iran. 2020, 27, 3322–3337. [Google Scholar] [CrossRef] [Green Version]
  100. Golub, A.A.; Lubowski, R.N.; Piris-Cabezas, P. Business responses to climate policy uncertainty: Theoretical analysis of a twin deferral strategy and the risk-adjusted price of carbon. Energy 2020, 205, 117996. [Google Scholar] [CrossRef]
  101. Myers, S.C.; Majd, S. Abandonment Value and Project Life. Adv. Futur. Options Res. 1990, 4, 1990. [Google Scholar]
  102. Berger, P.G.; Ofek, E.; Swary, I. Investor valuation of the abandonment option. J. financ. econ. 1996, 42, 259–287. [Google Scholar] [CrossRef] [Green Version]
  103. Savolainen, J. Real options in metal mining project valuation: Review of literature. Resour. Policy 2016, 50, 49–65. [Google Scholar] [CrossRef]
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Figure 1. Binomial decision tree with the combined real options of deferral, expansion, and abandonment in the tenth year of the investment project in photovoltaic panels to produce electrical energy in the forest nursery. Y is the year of the planning horizon; u is the increment level; d is the level of decrease; r is the risk-free rate.
Figure 1. Binomial decision tree with the combined real options of deferral, expansion, and abandonment in the tenth year of the investment project in photovoltaic panels to produce electrical energy in the forest nursery. Y is the year of the planning horizon; u is the increment level; d is the level of decrease; r is the risk-free rate.
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Figure 2. Optimal probabilities of combined real options of deferral, expansion, and abandonment in the tenth year of the investment project in photovoltaic panels to produce electrical energy in a forest nursery.
Figure 2. Optimal probabilities of combined real options of deferral, expansion, and abandonment in the tenth year of the investment project in photovoltaic panels to produce electrical energy in a forest nursery.
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Figure 3. Expected value of investment project in photovoltaic panels to produce solar energy in a forest nursery as a function of volatility.
Figure 3. Expected value of investment project in photovoltaic panels to produce solar energy in a forest nursery as a function of volatility.
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Rocha, Q.S.; Munis, R.A.; da Silva, R.B.G.; Aguilar, E.W.Z.; Simões, D. Photovoltaic Solar Energy in Forest Nurseries: A Strategic Decision Based on Real Options Analysis. Sustainability 2023, 15, 3960. https://doi.org/10.3390/su15053960

AMA Style

Rocha QS, Munis RA, da Silva RBG, Aguilar EWZ, Simões D. Photovoltaic Solar Energy in Forest Nurseries: A Strategic Decision Based on Real Options Analysis. Sustainability. 2023; 15(5):3960. https://doi.org/10.3390/su15053960

Chicago/Turabian Style

Rocha, Qüinny Soares, Rafaele Almeida Munis, Richardson Barbosa Gomes da Silva, Elí Wilfredo Zavaleta Aguilar, and Danilo Simões. 2023. "Photovoltaic Solar Energy in Forest Nurseries: A Strategic Decision Based on Real Options Analysis" Sustainability 15, no. 5: 3960. https://doi.org/10.3390/su15053960

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