1
AN ANALYSIS OF THE IMPACT OF POLICY AND OTHER FACTORS ON
CARBON MONOXIDE EMISSIONS
Growing environmental concern throughout the world has ignited a number of
controversial views on how environmental cleaning should be achieved. In the context of
developing countries in particular, several options have been put forth. Environmentalists
have held that general level production and consumption are the prime drivers of
environmental pollution and only in reducing economic activity can environmental
performance be improved. Other opinions have favoured a reduction in population
among developing countries as a key step to resolving the environmental dump,
especially in their larger cities. Yet another perspective has emphasised that more than
population and consumption, the answer lies in formulating and implementing
appropriate policies.
To understand the relative significance of each of these opinions, it would be most useful
to directly look at a cross-section of developing countries to see how their environmental
performance has been linked to the above factors. However, a paucity of consistent data
makes this exercise difficult. A possible alternative could be to conduct the relevant data
analysis using information on the US, and to extrapolate, to the extent possible, the broad
implications of the results for developing countries. Data relating to environmental
emissions should ideally comprise information on Nitrogen Oxides, Carbon Monoxide,
Volatile Organic Compounds and Sulfur Dioxide. Using these, a global air quality
variable should be obtained. However, in light of the mathematical complications is
would produce, it is simpler to look at one emission variable.
Response Variable: I will choose Carbon Monoxide (CO) per capita as the response
variable, since this gas has been intimately connected to car related pollution, a source of
growing concern among developing countries as their number of cars increase and pollute
the environment. This data on CO emissions is available at www.epa.gov. I have
divided this data by the population over time to reduce the time effect of emissions to the
extent that population tends to go up over time. I will be using data from 1960 to 1989, as
two policy variables we will be examining occurred during this period. The units of
measurement are in millions of metric tonnes.
COPC
600
500
400
Index
10
20
30
2
The plot of CO per capita versus time shows that for until about 1970, the emission levels
per capita were quite level, they began to reduce consistently after that. This downward
trend post about 1970 has continued through until 1989. The reason for the downturn in
emission per capita could be related to the Clean Air Act of 1970. Interestingly, although
the Clean Air Act of 1963 was in place earlier, according to the above graph, this policy
did not seem to have the same dramatic effect on CO per capita levels.
Among the predictor variables, I chose GNP per capita. I would expect that this general
indicator of the level of economic activity in the country would have some correlation
with the level of CO per capita emissions. A look at the scatter plot of CO per capita and
GNP per capita shows a similar pattern as the time series plot of the response variable,
which is that there is a flat relationship initially, which then becomes quite obviously
negative after about 1972.
600
COPC
COPC
600
500
500
400
400
6000
7000
8000
9000
GNP P.C
10000
11000
0.7
0.8
0.9
1.0
1.1
GCPC
I also chose gasoline consumption per capita over time, since the consumption gasoline
over time (GCPC) is an important indicator of vehicle related pollution. To the extent
that some gasoline may be used for purposes other than for consumption in vehicles, my
result may be biased. However, from what I know, gasoline is used almost entirely for
vehicles, so there should not be any significant issue with respect to this variable.
Once again, we see a similar pattern in the plot between COPC and gasoline consumption
per capita, namely that the relationship remains quite level up to a point, after which it
becomes quite negative. The effect here is a little delayed, since the downturn begins to
occur after 1973. This could perhaps be the result of the policy put into place in 1970,
and after a lag of a few years, adjustments in the industry may have resulted in higher
gasoline consumption per capita being associated with lower levels of CO per capita.
Another predictor variable I chose was the extent of forest fires in the country. This
variable is important because forest fires are known to release substantial amounts of CO
in the air. The data for this predictor variable was obtained from www.usda.gov. and is
measured in million acres of land destroyed by forest fires.
1.2
3
COPC
600
500
400
2
3
4
5
6
7
For.fires
Here we do not see a distinct pattern emerge and it is difficult to guess whether our model
will require this variable. However, it may be good to use it initially and see the results it
gives.
Along with the above variables, I also chose to look at the CAA 1970 and CAA 1963 to
see whether these policies had any dramatic impact on the CO per capita emissions.
600
COPC
COPC
600
500
400
500
400
0.0
0.5
Pol63
1.0
0.0
0.5
1.0
Pol70
I would expect both these variables to have an impact on the level of CO per capita
emissions, although looking at the scatter plots, I would expect CAA of 1970 to be of
greater value in our model.
4
In addition to the above variables, I will include time as a variable. As the scatter plots
above show, time seems to play an important role in the manner in which the individual
relationships of the predictor variables seem to be related to the response variable.
In running the regressions with CO per capita as my response variable and GNP per
capita, gasoline consumption per capita, area under forest fire and the policies of 1963
and 1970 as my predictor variables, I get the following results:
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
(response is COPC)
(response is COPC)
2
Standardized Residual
2
0
-1
1
0
-1
-2
-2
-30
-20
-10
0
10
20
30
400
Residual
500
Fitted Value
2
1
SRES1
Normal Score
1
0
-1
-2
Index
10
20
30
The errors appear normally distributed. However, the plot of standardized residuals over
time indicates that autocorrelation might possibly be a problem. However, I will not
correct for it here, but will be aware that the results may have an element of bias in them.
Let us then look at our regression results (MINITAB output).
600
5
The regression equation is
COPC = 24443 + 0.0197 GNP P.C + 40.7 Pol63 + 50.9 Pol70 + 3.86 For.fires
+ 78.4 GCPC - 12.3 Year
Predictor
Constant
GNP P.C
Pol63
Pol70
For.fire
GCPC
Year
Coef
24443
0.01970
40.72
50.90
3.859
78.44
-12.256
S = 16.48
StDev
9185
0.03133
11.44
12.69
2.737
81.98
4.758
R-Sq = 95.3%
T
2.66
0.63
3.56
4.01
1.41
0.96
-2.58
P
0.014
0.535
0.002
0.001
0.171
0.348
0.017
VIF
288.9
2.6
4.2
1.4
15.7
206.7
R-Sq(adj) = 94.2%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
GNP P.C
Pol63
Pol70
For.fire
GCPC
Year
DF
1
1
1
1
1
1
DF
6
24
30
SS
132965
6521
139486
MS
22161
272
F
81.57
P
0.000
Seq SS
108698
842
15545
5
6073
1802
Unusual Observations
Obs
GNP P.C
COPC
4
6378
608.57
31
11272
386.29
Fit
637.23
382.17
StDev Fit
10.16
14.21
Residual
-28.66
4.12
St Resid
-2.21R
0.49 X
R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
Durbin-Watson statistic = 1.16
The first thing that strikes me is the high VIF statistics, indicating a major
multicollinearity problem. It is quite likely that GNP per capita, gasoline consumption
per capita and time have a strong correlation. This could explain some of the low tstatistics for the coefficient estimates, while the F statistic is a high 81.57, indicating that
we can strongly reject the hypothesis that this model has no explanatory power. The R
square too is over 94, indicating that about 94 % of the variability in the response
variable can be explained by our model.
Another thing which stands out is the fact that both the policy variables are positively
correlated with CO per capita emissions. This is completely counterintuitive and can
perhaps be explained by the fact that after about 1970, when the second CAA came into
effect, the relationships between several of the explanatory variables and CO per capita
changed. Since the slope of the relationship changed after this period, it may be more
accurate to split the data explicitly into two and run separate regressions for the period
before 1970 and after.
6
For the period before the second CAA act (i.e. between 1960 and 1970), let us look to see
whether our assumptions are violated. The errors do not seem very normally distributed,
but are not very non-normal either. Some outliers do seem to be present and we will
come back to this. The residual shows some cyclical pattern, although this does not seem
to be a major problem. No major increases or decreases in variance are visible.
Residuals Versus the Fitted Values
Normal Probability Plot of the Residuals
(response is COPC)
(response is COPC)
2
1
Normal Score
1
0
-1
0
-1
-2
600
610
620
630
-2
-2
Fitted Value
-1
0
1
2
Standardized Residual
Histogram of the Residuals
(response is COPC)
2
3
Frequency
1
SRES4
Standardized Residual
2
0
-1
-2
Index
2
1
0
2
4
6
8
10
-1.5
-1.0
-0.5
0.0
0.5
1.0
Standardized Residual
Let us then see how our regression results look.
1.5
2.0
7
The regression equation is
COPC = 14592 + 0.0077 GNP P.C - 0.024 For.fires + 259 GCPC - 7.25 Year
+ 11.5 Pol63
Predictor
Constant
GNP P.C
For.fire
GCPC
Year
Pol63
Coef
14592
0.00772
-0.0235
258.98
-7.254
11.515
S = 2.047
StDev
3937
0.01115
0.8061
65.04
2.034
3.420
R-Sq = 98.2%
T
3.71
0.69
-0.03
3.98
-3.57
3.37
P
0.014
0.520
0.978
0.011
0.016
0.020
VIF
168.0
2.9
105.4
108.6
7.1
R-Sq(adj) = 96.4%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
GNP P.C
For.fire
GCPC
Year
Pol63
DF
1
1
1
1
1
DF
5
5
10
SS
1144.08
20.95
1165.04
MS
228.82
4.19
F
54.60
P
0.000
Seq SS
1001.07
45.22
19.03
31.27
47.50
Unusual Observations
Obs
GNP P.C
COPC
Resid
11
8134
627.796
2.07R
Fit
StDev Fit
Residual
625.946
1.842
1.850
St
R denotes an observation with a large standardized residual
Durbin-Watson statistic = 2.35
The regression seems to show a reasonably good fit. The R square indicates that about
96 % of the variability in CO per capita emission can be explained by our model. We
should be aware that some of the fit may possibly be a result of an autocorrelation
problem which may be inflating the fit somewhat. The F-statistic indicates that we can
strongly reject the null that the model has no explanatory power. Besides GNP per capita
and forest fires show a high enough p-value to not reject the null that these variables have
no explanatory power on their own.
In carrying out the regression diagnostics, no major outliers or leverage points were
apparent.
8
Also, multicollinearity seems to be a problem with the model. Some of this must
8000
GNP P.C
0.9
0.8
7000
6000
0.7
1960
1965
1970
1960
Year
1965
Year
continue to be due to the strong relationship between GNP per capita and gasoline
consumption per capita.
8000
GNP P.C
GCPC
1.0
7000
6000
0.7
0.8
0.9
1.0
GCPC
Clearly, the above plots indicate a very strong relationship between GNP per capita and
time, gasoline consumption per capita and time and GNP per capita and gasoline
consumption per capita. It would seem that the overall consumption level in the
economy, as embodied by the GNP per capita data may be less useful than the gasoline
consumption data, which is likely to relate more directly to CO emission. Therefore, GNP
per capita can be dropped. Since time trend seems to be an important issue in this model,
1970
9
let us keep it. We therefore run a regression with CO per capita as the response and area
under forest fire, CAA, 1963, per capita gasoline consumption and time as predictor
variables. Before discussing the results, let us see whether our errors are more or less in
keeping with our assumptions.
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
(response is COPC)
(response is COPC)
2
1
Normal Score
1
0
0
-1
-1
-2
-2
-2
600
610
620
-1
0
630
1
Standardized Residual
Fitted Value
Histogram of the Residuals
(response is COPC)
2
3
1
2
SRES4
Frequency
Standardized Residual
2
1
0
-1
-2
0
-1.5
-1.0
-0.5
-0.0
0.5
1.0
1.5
Index
2
4
6
8
10
Standardized Residual
The errors look reasonably well distributed. There does not seem to be a problem of
heteroskedasticity. The plot of standard residuals versus time does not indicate a serious
autocorrelation problem either.
2
10
Let us now analyse the results and see whether this is an improvement on the previous
one.
Regression Analysis
The regression equation is
COPC = 13020 + 12.8 Pol63 + 288 GCPC - 6.44 Year - 0.392 For.fires
Predictor
Constant
Pol63
GCPC
Year
For.fire
Coef
13020
12.790
287.64
-6.438
-0.3916
S = 1.956
StDev
3073
2.753
47.92
1.584
0.5787
R-Sq = 98.0%
T
4.24
4.65
6.00
-4.06
-0.68
P
0.005
0.004
0.001
0.007
0.524
VIF
5.0
62.7
72.1
1.7
R-Sq(adj) = 96.7%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
Pol63
GCPC
Year
For.fire
DF
1
1
1
1
DF
4
6
10
SS
1142.08
22.96
1165.04
MS
285.52
3.83
F
74.62
P
0.000
Seq SS
192.48
876.22
71.62
1.75
Durbin-Watson statistic = 2.22
As the results indicate, the multicollinearity problem is reduced now, but definitely not
eliminated. Clearly, the relationship between time and gasoline consumption per capita
has to do with this. However, since we are not breaking any assumptions of the model,
we will stick with both the variables since they both seem to provide us with valuable
information.
The model contained two values which were influential points: one was the year 1960
and the other, the year 1970. I ran a regression without these points and did not see a
dramatic change in my results. Also, because I have so few points in my data set, I do
not wish to lose further information and will keep the data from these two years intact.
The adjusted R square of close to 97 % indicates that a very high proportion of the
variability in the CO per capita emission can be explained by our model. The F-statistic
indicates that we can strongly reject the null hypothesis that the model has no predictive
power. In addition, the low p-values of all the explanatory variables, except the forest
fire variable indicate that individually, they do contribute to explaining variability in CO
per capita emissions. As we had seen in the scatter plot of CO per capita and forest fire,
11
no apparent relationship seemed to exist between the two variables. This is now borne
out by the high p value of 0.524. It may be worthwhile to remove this variable as it
seems to be adding noise to our model. Hence we run a regression with CO per capita as
response variable and gasoline consumption per capita, time and CAA, 1963 as
predictors.
Before we look at the results, let us see whether our assumptions are violated.
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
(response is COPC)
(response is COPC)
2
2
1
Normal Score
0
-1
0
-1
-2
-2
-3
-3
600
610
620
-2
-1
630
0
1
2
Residual
Fitted Value
Histogram of the Residuals
2
(response is COPC)
1
SRES3
4
3
Frequency
Residual
1
0
-1
2
1
-2
Index
2
4
6
8
0
-3
-2
-1
0
1
2
Residual
The graphs look fairly reasonable, except that once again, an autocorrelation problem
may perhaps exist in our model. This means that our estimates may have some element of
bias in them.
10
12
Looking at the regression results, we note that removing the forest fire variable has
improved our model in the sense that all our predictor variables now have p values which
show that independently, the variables have explanatory power. The adjusted R square
is still very high. It indicates that about 97 % of the variability in the data may be
explained by the model. However, some of this high R square may perhaps be due to a
slight autocorrelation problem in our model. The F statistic of 107 indicates clearly that
the model does have explanatory power.
Regression Analysis
The regression equation is
COPC = 11956 + 271 GCPC + 11.6 Pol63 - 5.89 Year
Predictor
Constant
GCPC
Pol63
Year
Coef
11956
271.25
11.629
-5.890
S = 1.879
StDev
2536
39.72
2.069
1.308
R-Sq = 97.9%
T
4.71
6.83
5.62
-4.50
P
0.002
0.000
0.001
0.003
VIF
46.7
3.1
53.3
R-Sq(adj) = 97.0%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
GCPC
Pol63
Year
DF
1
1
1
DF
3
7
10
SS
1140.32
24.71
1165.04
MS
380.11
3.53
F
107.67
P
0.000
Seq SS
1028.79
39.91
71.62
Durbin-Watson statistic = 2.00
There continue to be two influential points in the data: these are the end points: 1960 and
1970. When I removed these in my present model, it gave me a better fit, although, as
stated earlier, the compromise was that I lost valuable information because I have so few
data points.
13
Regression Analysis
The regression equation is
COPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year
Predictor
Constant
GCPC
Pol63
Year
Coef
7893
200.92
12.198
-3.794
S = 1.047
StDev
2409
35.00
1.826
1.241
R-Sq = 99.4%
T
3.28
5.74
6.68
-3.06
P
0.022
0.002
0.001
0.028
VIF
64.5
4.7
84.3
R-Sq(adj) = 99.0%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
GCPC
Pol63
Year
DF
1
1
1
DF
3
5
8
SS
895.24
5.48
900.72
MS
298.41
1.10
F
272.31
P
0.000
Seq SS
828.30
56.70
10.24
Durbin-Watson statistic = 2.52
Now, as we can see the adjusted R square is an even higher 99 %.
The model:
COPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year
states that as gasoline consumption per capita increases by 1 unit, CO per capita emission
increases by 201 million tonnes provided other variables are held constant. The time
variable coefficient indicates that with the passage of one year, CO per capita reduced by
3.79 million tonnes. Interestingly, the CAA 1963 variable is positively correlated with
CO per capita. This indicates that the first CAA act did not really help curb the CO
emission problem, which explains why the second one was introduced some years later!
Turning now to the second part of the data (1970-1990), let us first look at the residual
plots which come from running a regression of GNP per capita, forest fires, gasoline
consumption per capita and time. I will not include the policy variable of 1970, since it
was in place throughout the period we will be looking at.
Normal Probability Plot of the Residuals
(response is COPC)
14
2
1
Normal Score
Histogram of the Residuals
(response is COPC)
5
0
-1
Frequency
4
3
-2
-1
0
2
1
Standardized Residual
1
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Standardized Residual
We first look at the error structure to determine whether or not any of our assumptions
Residuals Versus the Fitted Values
(response is COPC)
2
1
1
0
SRES1
Standardized Residual
2
0
-1
-1
450
500
550
600
Fitted Value
Index
5
10
15
are violated.
The errors seem reasonably normally distributed. Some autocorrelation may possibly be
present in our data, and we have to be aware of this factor while evaluating our results.
The errors seem to have a dispersion which indicates that heterskedasticity is not a
problem in this model.
The regression result is presented below.
The regression equation is
COPC = 20472 + 0.0044 GNP P.C + 5.10 For.fires + 1.1 GCPC - 10.1 Year
2
15
Predictor
Constant
GNP P.C
For.fire
GCPC
Year
Coef
20472
0.00440
5.104
1.14
-10.110
S = 14.62
StDev
9375
0.03159
3.120
94.90
4.851
R-Sq = 93.9%
T
2.18
0.14
1.64
0.01
-2.08
P
0.047
0.891
0.124
0.991
0.056
VIF
65.3
1.3
2.7
62.7
R-Sq(adj) = 92.2%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
GNP P.C
For.fire
GCPC
Year
DF
1
1
1
1
DF
4
14
18
SS
46206
2994
49199
MS
11551
214
F
54.02
P
0.000
Seq SS
43802
141
1334
929
Unusual Observations
Obs
GNP P.C
COPC
Resid
2
8562
615.16
2.07R
18
11186
478.24
2.03R
Fit
StDev Fit
Residual
587.68
6.19
27.47
453.38
7.97
24.86
St
R denotes an observation with a large standardized residual
Durbin-Watson statistic = 1.49
As in the previous model, there is a problem of multicollinearity again. This can explain
why none of the coefficient estimates appear to be statistically significant, even though
the F statistic is over 54. This time, it seems largely between GNP per capita and time.
To check for this, let us look at the scatter plots. Unlike the previous case, the scatter plot
between gasoline per capita and time and GNP per capita and gasoline consumption per
capita do not show a strong correlation. Hence, to reduce our multicollinearity problem,
it may be a good idea to drop the GNP per capita variable. Once again, the gasoline per
capita consumption will continue to capture the car-related pollution.
Looking at the regression model after dropping GNP per capita, we first note that the
errors are reasonably well distributed and there does not seem to be serious
heteroskedasticity problem. However, once again, some autocorrelation may possibly be
16
11500
GNP P.C
10500
9500
8500
1970
1980
1990
Year
present in the data..
Normal Probability Plot of the Residuals
(response is COPC)
Residuals Versus the Fitted Values
2
(response is COPC)
1
Normal Score
Standardized Residual
2
1
0
-1
0
-2
-1
-1
0
1
Standardized Residual
450
500
550
600
Fitted Value
Histogram of the Residuals
2
(response is COPC)
5
SRES2
1
Frequency
4
0
3
-1
2
1
Index
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
Standardized Residual
1.5
2.0
5
10
15
2
17
Now we can look at the regression result.
Regression Analysis
The regression equation is
COPC = 19178 + 5.06 For.fires + 11.5 GCPC - 9.44 Year
Predictor
Constant
For.fire
GCPC
Year
Coef
19178
5.061
11.54
-9.4400
S = 14.14
StDev
1301
3.002
56.71
0.6631
R-Sq = 93.9%
T
14.75
1.69
0.20
-14.24
P
0.000
0.113
0.841
0.000
VIF
1.3
1.0
1.3
R-Sq(adj) = 92.7%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
For.fire
GCPC
Year
DF
1
1
1
DF
3
15
18
SS
46201
2998
49199
MS
15400
200
F
77.06
P
0.000
Seq SS
4986
716
40500
Unusual Observations
Obs
For.fire
COPC
Resid
2
2.60
615.16
2.13R
18
5.70
478.24
2.10R
Fit
StDev Fit
Residual
587.84
5.89
27.32
453.21
7.61
25.03
St
R denotes an observation with a large standardized residual
Durbin-Watson statistic = 1.49
Now the multicollinearity problem seems to taken care of. Interestingly, the forest fire
and the gasoline consumption coefficients continue to not be significantly different from
zero.
The adjusted R square indicates that about 92% of the variability in CO emission per
capita can be explained by the model and the F statistic indicates clearly that we can
reject the null hypothesis that the model has no explanatory power.
We note that two points at the top and two at the bottom of the normal plot stand out as a
little different. Conducting a regression diagnostic, I find that these points have the
highest standard residuals in this data. However, since these residuals range from about –
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1.46 to 2.12, these are not potent enough to remove. Their Hi values and Cook’s distance
values too are within the norm.
The next thing to do would be to remove the variables for forest fire and gasoline
consumption and see how this affects the model.
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
(response is COPC)
(response is COPC)
2
2.5
Standardized Residual
2.0
1
Normal Score
1.5
1.0
0.5
0.0
-0.5
0
-1
-1.0
-1.5
-2
-2.0
-2.0
450
500
550
-1.5
600
-1.0
-0.5
0.0
0.5
1.0
1.5
Standardized Residual
Fitted Value
Checking for assumptions, we note the same issues as earlier, which is that the errors are
normally distributed and do not seem to demonstrate heteroskedasticity. However,
Histogram of the Residuals
(response is COPC)
2
7
6
1
SRES2
Frequency
5
4
0
3
2
-1
1
0
Index
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
5
10
15
2.5
Standardized Residual
autocorrelation may be present here too. Four points stand out the above graphs as
2.0
2.5
19
unusual. However, conducting a regression diagnostic, none of them turn out to be major
outliers, leverage or influential points.
We now look at the regression model.
Regression Analysis
The regression equation is
COPC = 18234 - 8.95 Year
Predictor
Constant
Year
S = 14.48
Coef
18234
-8.9475
StDev
1201
0.6067
R-Sq = 92.8%
T
15.18
-14.75
P
0.000
0.000
R-Sq(adj) = 92.3%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
17
18
SS
45633
3566
49199
Unusual Observations
Obs
Year
COPC
Resid
18
1988
478.24
2.38R
MS
45633
210
F
217.53
P
0.000
Fit
StDev Fit
Residual
446.72
5.88
31.52
St
R denotes an observation with a large standardized residual
Durbin-Watson statistic = 1.72
As we can see, the univariate model appears to explain about 92 % of the variation in CO
per capita emission. The F statistic is high and shows that we can strongly reject the null
hypothesis that the model has no explanatory power.
We can interpret the results of this model to mean that after 1970, an increase of a year
was associated on an average with a drop of 9 million tonnes in CO per capita.
CONCLUSION
Splitting the data into a period pre CAA 1970 and post 1970 shows that two different
models were useful for understanding how CO per capita emission evolved over time.
The models are:
COPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year
For the initial period, and for the second period:
COPC = 18234 - 8.95 Year
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Clearly, in the first period, level of gasoline consumption per capita did have a significant
impact on the level of CO emission per capita, as did the time. In the second period,
however, only the time trend seems to be relevant. This could be indicative of the fact
that the policy of 1979 had a major impact on the CO per capita emission, which is
captured in the time variable.
From these findings we can then conclude that gasoline consumption ( and therefore
vehicle usage) is significantly linked with higher levels of CO per capita emission, if the
policy in place is not a very effective one. Hence, in the developing country context, this
means that restricting consumption of output is not necessarily the path to environmental
salvation.
On the other hand, if a strong and effective policy is put into place, while giving industry
and individuals sufficient time to adapt to the regulation, this is likely to be strongly
linked with a sustained reduction in CO per capita emissions. Another significant factor
to be kept in mind is that the level of technology in a developing country must be
adequately advanced to incorporate the changes desired by the policy makers. It should
however be kept in mind that the decline in emission may be gradual, as the old cars on
the road are replaced by new ones, for instance. This is perhaps what also explains the
declining trend over time in the US.
If these factors are kept in view, then the chances are high they will show results similar
to the ones we have obtained for the US.
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