discrete choice modeling of a firm s decision to adopt photovoltaic technology n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology PowerPoint Presentation
Download Presentation
Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology

Loading in 2 Seconds...

play fullscreen
1 / 19

Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology - PowerPoint PPT Presentation


  • 83 Views
  • Uploaded on

Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology. Chrystie Burr May 2, 2011. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology' - mitch


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
discrete choice modeling of a firm s decision to adopt photovoltaic technology

Discrete Choice Modeling of a Firm’sDecision to Adopt Photovoltaic Technology

Chrystie Burr

May 2, 2011

TexPoint fonts used in EMF.

Read the TexPoint manual before you delete this box.: AA

research aims

Develop an understanding of how firms respond differently to upfront subsidies and production subsidies.

Develop a policy optimization framework for solar technology (policy target).

Research Aims
background global market share
Background Global Market Share

Solar PV Existing Capacity, 2009 (source: REN21)

driver for the pv boom

Lower cost

Government Incentive Programs

Driver for the PV boom
slide11

Annual installed capacity (2002-2008) by states: Larry Sherwood (IREC)

Subsidy: Dollar amount recovered from DSIRE database

Electricity price: EIA

Solar Irradiation: NREL

# businesses: US small business admin.

Data
assumptions

Potential market: 30%

Annual discount rate: 8%

System lifespan: 20 years

Average PV size: 20kW

Elec. escalation rate: 10 year average

Maintenance cost: $0.01/kWh

Inverter cost: $0.75/W

Annual degradation factor: 1%

Solar electricity conversion factor: 76%

Net metering: null

Company located in the largest metropolitan area in a state

Assumptions
discrete choice model

At each time period, a non-residential unit (commercial firm) can choose to install an average sized PV panel or not adopt PV technology

Decision is based on the annual revenue generated by the system and the upfront cost, both affected by the incentive programs.

The purchasers leave the market.

Discrete Choice Model
model

Firm’s profit function

Model

if not installed

if installed

  • τuf: Upfront subsidy (% based)
  • ξmt: Fixed effect
  • f(ε) = eε/(1+ eε)
  • R: NPV of the future benefit and costs
    • Avoided utility cost
    • Production incentive
  • FC: Upfront installed cost
model1
Model

if not installed

if installed

  • CAC: Avoided electricity cost for next 20 years
    • Local solar Irradiation
    • Electricity price
  • τp: Production subsidy
  • X: Increased revenue from
  • improved brand image
  • PAV: Ave. cost of 20kW system
  • W: State wage deviation from national mean
  • L: Learning effect. f(cum. install)
  • Code: Building codes depend on seismic activity and hurricane
estimation hierarchical bayesian approach
EstimationHierarchical Bayesian approach
  • Let A = , Bi = [ ]T ~ lognormal(b, D),
  • Prior: b ~ N(0, s) s ∞, D ~ IW(3, V0)
  • Likelihood:
  • Posterior: K(Bi, b, D| Y)
  • Conditional posterior:
estimation bayesian procedure on blp model
EstimationBayesian Procedure on BLP model

Yang, S., Y. Chen, and G. Allenby (2003), ‘Bayesian analysis of simultaneous demand and supply’, Quantitative Marketing and Economics 1.

Jiang, R., P. Manchanda, and P. Rossi (2009), ‘Bayesian analysis of random coefficient logit models using aggregate data’, Journal of Econometrics149(2).