# Presentation: 370013

## Measuring the Contribution to the Economy of Investments in Renewable Energy: Estimates of Future Welfare Gains

- by
- Molly Macauley*, Joel Darmstadter*, Jhih-Shyang Shih*,
- Emily Aronow*, David Austin**, and Tom Bath***
*We thank the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, for their support. *
** Resources for the Future*
***now at the U.S. Congressional Budget Office*
**** independent engineering consultant*
*http://www.rff.org/**disc_papers/PDF_files/0205.pdf** *

## Approach

**Theoretical Framework**
- Cost-index based measure of expected consumer welfare gains from innovation
- Use of counterfactual and its own technological change
- Adoption rate
- External effects
- Uncertainty (data, forecasts)
- Discount rate

**Our Application**
- Electricity generation technologies:
- 	--Renewable (PV, solar thermal, biomass, wind, geothermal)
-       --Fossil (Combined cycle gas turbine -- conventional and advanced designs)
- Two geographic regions (CNV, MAPP)
- Weibull (“fast,” “slow”)
- Carbon dioxide, thermal effluent
- Time period: 2000-2020

## Derived Demand for Renewable Energy Technologies:Illustration of Net Surplus Change 

## Derived demand for renewable energy technologies: illustration of net surplus change with external costs

## Derivation of estimating relationships

-         *C***dt** **=  *_*E* (u*__*dt*__*, P*__*dt*__*, W*__*dt*__*)*_
- 		           *E* (u**dt**, P**I** , W**RE**)*
-   and *C***I**  = *_*E* (u*__*I*__*, P*__*dt*__*, W*__*dt*__*)*_ 		      (1)
-                      *E* (u**I**, P**I** , W**RE**)*
*1⁄2 ln (C***dt**  **x C***I** )=1⁄2 (s**dt**+s**I**) ln (W**dt**/ W**RE**) *(2)
*(Bresnahan, AER 1986)*
*W**RE **= **** W**I** + (1- ****) W**dt                                              *(3)_*               *_

## Relationship between expenditures, cost index

***Relationship between expenditures, cost index***

## Using the index to estimate the present value of consumer surplus

- 	*Interpretation of the index*: “how much better off are we (that is, society in general) as a result of investment in renewables, taking into account the alternative (conventional technology) and differences in the social benefits and costs between renewables and conventional technology?”

## Model Framework

**Aggregate Consumer Surplus**
- Discount rate

**Private Generation Costs **
- PV, ST, GEO, BIO, Wind, CCGT, A-CCGT
*(DOE/EIA (2000), DOE/EPRI (1997), authors’ adjustments)*

**Externality Costs**** **
- Carbon (CCGT) *(Krupnick et al. (1996))*
- Thermal H2O (CCGT, Biomass, ST) *(Authors’ estimates)*

**Private and Social**
**Generation Costs**

**Cost Indices**

**Private Consumption Expenditures**
** ***(DOC (2001), authors’ forecasts)*

**“****Market Conditions”**** **
- Adoption rates
- Electricity prices    *(DOE/EIA (2000))*

-  Triangular and normal distributions combined with Monte Carlo draws characterize uncertainty.

## Diversity of renewable energy resources in the United States.

## Electricity Market Module Supply Regions

***Electricity Market Module Supply Regions***
- Source: EIA

## Weibull Adoption Rate Curves 

## Examples of External Effects	

- Carbon
- Water
- Land use
- Avian and other ecological resources
- Lifecycle (such as manufacturing)
- Distinguishing pecuniary from technological effects

## The present value of benefits from 2000 to 2020 for Wind Class 6 from scenario 1 for CNV

## Discounted incremental net benefits from 2000 to 2020 for Wind 6 from scenario 1 for CNV

***D******iscounted incremental net benefits from 2000 to 2020 for Wind 6 from scenario 1 for CNV***

## Slide 15

## Illustrative Results (45 scenarios)Median Present Value, 2000-2020, 5% discount rate

- CNV Region
- Largest “loss”
*Fast adoption of PV w/*
*A-CCGT and no ext. *$11.9 B
- Largest “gain”
*Fast adoption of WC6 w/*
*C-CCGT and ext. *$4.6 B.
*~ $556/household*

- MAPP Region*
- $4.9 B.
- $4.4 B.
*~ $600/household*
- * No HGT, ST

## Slide 17

## Other Results

- Externalities
-    -- Carbon and thermal externalities enhance  relative surplus associated with renewable technologies
-     -- Thermal externality reduces relative surplus associated with ST, BIO (even though externality is also linked with CCGT)
- Innovation
- 	-- Innovation in CCGT reduces median relative surplus associated with renewables by ~ 5%

## Other results, continued

- Uncertainty
- 	-- Uncertainty can lead to values +/- 20-40% of median at the 5% and 95% confidence intervals
- Portfolio
-     -- Equal portfolio weights create negative surplus estimates
- 	-- Variable portfolio weights can yield positive surplus estimates but smaller than single technology surplus estimates

## Conclusions	

- Limits
- 	-- Pairwise or exogenously specified portfolio comparisons rather than optimization
-  	-- Data gaps (external effects) and assumptions (“GenCo”; deregulation; state/local policies)
- 	-- Gross not net of public and private investment or other expenditures to attain cost goals, adoption rates

## Conclusions, continued

- Findings
- 	-- Large differences among technologies
- 	-- Regional differences
- 	-- Adoption push
- 	-- Externality internalization
- 	-- Useful estimates result from model

## Conclusions, continued

- Contribution
- 	-- Offers conceptually grounded measurement approach; alternative to data-intensive econometric models; appeal of “cost index” analogy with Consumer Price Index; tool for program managers
- 	-- Allows for uncertainty, externalities, policy simulation
- 	-- Could extend to include “green preferences” (data? Are they verified?); state and local policies
- 	-- Could extend to NRC-defined benefits including commercialization, knowledge, option values