Capacity optimization of an innovating firm

Hagspiel, Verena; Kort, Peter M.; Nunes, Cláudia; Pimentel, Rita; Støre, Kristian

doi:10.1016/j.ijpe.2020.108021

Cited by 5 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider a company that is risk‐neutral and discounted with a risk‐free rate

r

, which has the option to invest in EVs with uncertain future revenue streams. Once the traditional manufacturer decides to invest in EVs, the company immediately begins producing EVs, with an investment cost of

I = i K_{1}

, where

K_{1}

represents the capacity level and

i

represents the investment cost of one unit (Della Seta et al, 2012; Hagspiel et al, 2021). To be as realistic as possible, we assume that the production cost of EVs is larger than that of FVs and the company's production reached capacity

K_{1}

.…”

Section: Modelmentioning

confidence: 99%

“…Once the investment is completed, the company will use the new technology to produce EVs immediately and will run at full capacity; thus, the production quantity is always equal to the scale, that is,

q_{t} = K_{1}

. The firm faces an inverse demand function (Chevalier‐Roignant et al, 2011; Hagspiel et al, 2021).

p_{1} = ξ_{1} - a K_{1}

where

ξ_{1}

is the maximum willingness to pay for the EV and

p_{1}

stands for the price of EV.…”

Section: Modelmentioning

confidence: 99%

“…with a > 0 is a constant reflecting the sensitivity of quantity to price, K 0 represents the installed capacity, this multiplicative demand curve is widely used in the literature, e.g., Comincioli et al (2021) and Hagspiel et al (2021), X t is the demand shift parameter assumed to undergo a geometric Brownian shock, and geometric Brownian motion is often used to describe the standard diffusion process of a stochastic variable, e.g., Comincioli et al (2021), Nishihara et al (2019) and Sarkar (2021), as shown in Equation (2).…”

Section: Model Descriptionmentioning

confidence: 99%

“…This analysis splits the automaker's profit flow into two components, one for product sales profits and the other for additional benefits or costs incurred in carbon trading under the dual credit policy (Li et al, 2020). Firstly, we consider a conventional vehicle company that is currently producing an FV with a production cost of

c_{0}

and with a price of

p_{0} = X_{t} ((), ξ_{0} - a K_{0})

with

a > 0

is a constant reflecting the sensitivity of quantity to price,

K_{0}

represents the installed capacity, this multiplicative demand curve is widely used in the literature, e.g., Comincioli et al (2021) and Hagspiel et al (2021),

X_{t}

is the demand shift parameter assumed to undergo a geometric Brownian shock, and geometric Brownian motion is often used to describe the standard diffusion process of a stochastic variable, e.g., Comincioli et al (2021), Nishihara et al (2019) and Sarkar (2021), as shown in Equation ().

d X_{t} = α_{1} X_{t} d t + σ_{1} X_{t} d W_{t 1}

in which

α_{1}

and

σ_{1}

are the FV drift rate and volatility parameter, respectively.…”

Section: Modelmentioning

confidence: 99%

“…The real options approach is commonly used to analyze the investment under uncertainty, mostly for production investments (Hagspiel et al, 2016(Hagspiel et al, , 2021Lukas et al, 2017), green technology investments (Deeney et al, 2021;Wang & Qie, 2018), and venture capital investments (Ferreira & Pereira, 2021;Jeon & Nishihara, 2018;Lukas et al, 2016). However, rarely does theory adopt real options approach to analyze EVs investments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

When to invest in electric vehicles under dual credit policy: A real options approach

et al. 2023

View full text Add to dashboard Cite

This research aims to investigate traditional vehicle manufacturers' green technology investment theory under dual credit policy from the perspective of real options, overcoming earlier investigations of this issue that considered it only from a stability or single uncertainty perspective. An analytical real options model was first provided for traditional automaker investment. Then we solved the analytical solution for the electric vehicle investment threshold based on the uncertainty of credit price and fuel vehicle market scenarios. The optimal electric vehicle investment timing is demonstrated using numerical simulation. Results show that (1) when the fuel vehicle market demand falls to a certain level, automakers will choose to make electric vehicle investments regardless of how the credit price changes in the market; (2) the effect of volatility on the investment threshold depends on the covariance or correlation coefficient; (3) the numerical simulation results revealed that the credit price drift rate, risk‐free rate, correlation parameters, and electric vehicle production cost all have a positive impact on the electric vehicle investment region, whereas the drift rate of fuel vehicle and electric vehicle production cost have a negative impact. These results can be used to make theoretical conclusions about electric vehicle investments.

show abstract

“…We consider a company that is risk‐neutral and discounted with a risk‐free rate

r

I = i K_{1}

, where

K_{1}

represents the capacity level and

i

K_{1}

.…”

Section: Modelmentioning

confidence: 99%

q_{t} = K_{1}

. The firm faces an inverse demand function (Chevalier‐Roignant et al, 2011; Hagspiel et al, 2021).

p_{1} = ξ_{1} - a K_{1}

where

ξ_{1}

is the maximum willingness to pay for the EV and

p_{1}

stands for the price of EV.…”

Section: Modelmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

c_{0}

and with a price of

p_{0} = X_{t} ((), ξ_{0} - a K_{0})

with

a > 0

is a constant reflecting the sensitivity of quantity to price,

K_{0}

represents the installed capacity, this multiplicative demand curve is widely used in the literature, e.g., Comincioli et al (2021) and Hagspiel et al (2021),