The Costs of Climate Protection: A Guide for the Perplexed (WRI, 1997, 60 pages) |
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 CO_{2} 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 |
CO_{2} |
percentage reduction in CO_{2} 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 CO_{2} emissions. The regression equation, with GDP as the dependent variable, was specified as a quadratic (through the origin) in the extent of CO_{2} abatement from the projected baseline. The variables listed above were assumed to shift the coefficient of the linear term in CO_{2}, except that the variable for the non-carbon backstop was specified to affect the coefficient of the quadratic term in CO_{2} 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
R^{2}: 0.83
Coefficient |
Standard Error | |
CO_{2} |
-0.02319 |
0.00907 |
(CO_{2})^{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 projection2. 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 R^{2} for a regression of GDP against CO_{2} and (CO_{2})^{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
Notes
1. '1 or 0' signifies that different runs from the same model incorporate alternative assumptions2. 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)