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close this bookThe Costs of Climate Protection: A Guide for the Perplexed (WRI, 1997, 60 pages)
View the document(introduction...)
View the documentACKNOWLEDGMENTS
View the documentFOREWORD
View the documentINTRODUCTION
View the document1. MODELS, ASSUMPTIONS, AND CONCLUSIONS
View the document2. HOW THE MODELS ASSUMPTIONS DETERMINE WHAT ECONOMIC IMPACTS THEY PREDICT
Open this folder and view contents3. MORE DETAIL ON THE KEY ASSUMPTIONS
Open this folder and view contents4. TRADE AND EQUITY ISSUES
View the document5. CONCLUSIONS AND RECOMMENDATIONS
View the documentREFERENCES
View the documentANNEX
View the documentADDITIONAL REFERENCES
View the documentABOUT THE AUTHORS
View the documentBOARD OF DIRECTORS
View the documentWORLD RESOURCES INSTITUTE
View the documentWORLD RESOURCES INSTITUTE CLIMATE PROTECTION INITIATIVE

ANNEX

DETAILS OF THE STATISTICAL ANALYSIS

The data set is made up of 162 simulations derived from 16 models. From each simulation, the percentage change in CO2 emissions and the percentage change in GDP, both relative to the baseline projections for the terminal year of the simulation, were recorded. In addition, a set of descriptive variables was recorded for each model simulation, representing basic structural assumptions and policy assumptions embedded in the model. These were represented either as binary dummy variables or as discrete numerical variables. All the variables considered in the analysis are presented in Table A.1 and include the following:

GDP

percentage change in real GDP relative to projected baseline, in terminal year

CO2

percentage reduction in CO2 emissions relative to projected baseline, in terminal year

MACRO

1 if a macro model, 0 if a CGE model

NCBACK

1 if there is a constant cost, non-carbon backstop

RECYCLING

1 if revenues from policy instrument are used to reduce existing distorting taxes

CLIMATE

1 if averted climate change damages are modeled

NON-CLIMATE

1 if averted air pollution damages are modeled

JI

1 if joint implementation or global emissions trading is modeled

PRODUCTION

1 if the model allows for product substitution

FUELS

the number of primary fuel types recognized for possible inter-fuel substitution

YEARS

the number of years available to meet the abatement target

Using the data, multiple regression analysis was performed to show how these modeling assumptions affected the predicted relationship between the change in GDP and the change in CO2 emissions. The regression equation, with GDP as the dependent variable, was specified as a quadratic (through the origin) in the extent of CO2 abatement from the projected baseline. The variables listed above were assumed to shift the coefficient of the linear term in CO2, except that the variable for the non-carbon backstop was specified to affect the coefficient of the quadratic term in CO2 because the presence of a backstop fuel source mainly affects the curvature of the abatement cost curve at high levels of abatement.

FORM OF REGRESSION:


REGRESSION RESULTS

No. of observations: 162
R2: 0.83


Coefficient

Standard Error

CO2

-0.02319

0.00907

(CO2)2

-0.00079

0.00011

MACRO

-0.05548

0.01395

NCBACK

0.00051

0.00005

RECYCLING

0.04427

0.00652

CLIMATE

0.00943

0.00399

NON-CLIMATE

0.03823

0.00778

JI

0.02337

0.00327

PRODUCTION

0.00378

0.00365

FUELS

0.00018

0.00116

YEARS

0.00005

0.00006

Notes

1. Including an intercept term in the regression adds little to the regression's overall explanatory power. Moreover the assumption that the regression passes through the origin is consistent with the models' definition of a baseline projection

2. The inclusion of dummy variables to represent the individual models adds little to the explanatory power of the regression beyond that provided by variables representing underlying modeling assumptions

3. The R2 for a regression of GDP against CO2 and (CO2)2 terms alone was 0.35.

4. Typically, the number of primary fuel types differs substantially from the number of energy sectors and energy technologies included in the models


TABLE A 1 - SUMMARY OF DATA

Notes

1. '1 or 0' signifies that different runs from the same model incorporate alternative assumptions

2. The number of observations from each model varies with the number of alternative assumptions and end dates considered by the model

3. Despite its name, the Markal-Macro model has optimizing characteristics and is not a macro model according to the distinction made in Box 1

4. Because the Boyd et al model is a comparative static model, the terminal years have been interpolated

5. The results from the IIAM model come from the model's Single Open Economy Section of the model which does not include the effects of JI that appear in the Multi-Regional Trade (MRT) section

6 The IIAM model is characterized as having no backstop because the backstop does not affect the time frame for which results were available

7. Although the Edmonds-Reilly-Barns model has several non-carbon fuel sources all are subject to increasing marginal costs, inconsistent with the definition of a backstop energy source adopted here (see Section 3 D for discussion)