Evaluating the Performance of Environmental Streamlining:
Development of a NEPA baseline for Measuring Continuous Performance
4.0 RESULTS

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This section presents the
major results of the statistical analysis of this research. The first set of findings
relate to descriptive statistics on the length of the NEPA process, which is really
the heart of this entire analysis. These descriptive statistics are presented,
first, for the length of the NEPA process for the country as a whole during the
past 30 years, and then for the individual former FHWA regions. Descriptive statistics
for all other factors considered during the course of this analysis are then presented.
Finally, the relationship between length of the NEPA process and other factors
considered are presented, with its relationship to the project development process
given special attention. The relationship between length of the NEPA process and
all other factors are also presented according to the individual former FHWA regions.
It should be noted that this section generally focuses only on those findings
that were found to be statistically significant (i. e. , within 0. 05 level
of significance). If findings are not specifically reported in this section,
it is usually because they were not found to be statistically significant. Even
so, as reported below, some very interesting results were found.
4. 1 Descriptive Statistics on the Length of the NEPA Process
4. 1. 1 Overall Descriptive Statistics
As discussed in the previous section, the length of the NEPA process was denoted
in this analysis as the variable NEPATIME, and was calculated on the basis of
LASTEIS_YR minus BEG_NEPA. In a few cases, NEPATIME was found to exceed 15 years,
generally because a Supplemental EIS process was involved in addition to the
initial EIS process. Since the method for calculating NEPATIME didn't specifically
exclude the time between the initial EIS process and the Supplemental EIS process,
NEPATIME didn't give a true estimate of actual NEPA process time in those few
cases. Therefore, those cases where NEPATIME exceeded 15 years were excluded
from the descriptive analysis, in order to avoid incorrectly skewing the results.
A descriptive analysis of NEPATIME was run. The results, which are presented
both tabularly and graphically in Appendix J, indicate that the mean value of
NEPATIME is approximately 3. 6 years, with a standard deviation of 2. 4 years
and values ranging from 1 to 12 years. There were three cases with NEPATIME
values exceeding 15 years, leaving a valid sample size of 97 cases for analysis.
Theoretically, this information indicates that the average period of time that
has been required to comply with the NEPA process during the past 30 years has
been about 3. 6 years, and the majority of the EISs prepared during that
period have ranged between 1. 2 years and 6. 0 years (based on the standard
deviation).
However, because the NEPATIME variable was found to be not normally distributed,
the mean value may not be a true indicator of central tendency, so other indicators
of central tendency were obtained. For instance, the median value, which is
the midpoint among the full set of NEPATIME values that divides the observations
into two groups of equal numbers, was determined to be about 3. 0 years. The
reason that the median value is somewhat less than the mean value is because
the higher values of NEPATIME that were included in the analysis (i. e. , those
approaching the highest value of 12 years) tend to skew the mean on the high
side. Therefore, in this case, the median value is likely to be a better indicator
of the typical length required for complying with the NEPA process during the
past 30 years.
A KolmogorovSmirnov test comparing the distribution of NEPATIME with a theoretical
normal distribution was also run. As a result of this test, NEPATIME was found
to be significantly different from the normal distribution. For subsequent tests,
a new variable, which was not significantly different from the normal distribution,
was created by taking the square root of NEPATIME (SQRTNTIM). For many of the
subsequent statistical analyses, SQRTNTIM was utilized instead of NEPATIME due
to the more normal distribution of that variable, although equivalent NEPATIME
values are also reported when appropriate.
4. 1. 2 Descriptive Statistics by Region
The next step involved exploratory analysis of NEPATIME categorized according
to the nine former FHWA regions. The results are summarized in Table 3, and
are presented in greater detail in Appendix K. The interesting fact that emerged
from this analysis is that there are apparent large differences between some
FHWA regions with respect to the mean and median NEPATIMEs. The largest difference
was found to be between Region 1 (in the Northeast) and Region 10 (in the Northwest).
In the case of Region 1, which demonstrated the longest NEPATIME values among
the regions, the mean NEPATIME value was approximately 4. 7 years, while the
median NEPATIME value was approximately 4. 5 years. ^{[6]} At the other end of the spectrum, Region
10's mean NEPATIME value was approximately 2. 2 years, although the median value
was only 1. 0 year. Interestingly, the minimum value for Region 10 was also
1. 0 year. The fact that the median and minimum NEPATIME values were both 1.
0 year is reflective of the fact that 6 out of the 11 cases in Region 10 had
a NEPATIME value of 1. 0.
Regions 4 (in the Southeast) and 7 (in the Midwest), which had the next highest
NEPATIME mean values, also appeared to be very different from Region 10. In
the case of Region 4, the mean and median values were approximately 4. 4 and
3. 0 years, respectively. In the case of Region 7, the mean and median values
were approximately 4. 3 and 4. 0 years, respectively.
After Region 10, Region 8 (the Rocky Mountain states) exhibited the next lowest
mean NEPATIME value of approximately 2. 6 years. The median value was 2. 0 years.
Table 3: DESCRIPTIVE STATISTICS OF NEPATIME BY REGION
Region

Mean

Median

Minimum

Maximum

Standard Deviation

1

4.7

4.5

1

9

2.6

3

3.7

3.5

2

6

1.2

4

4.4

3.0

1

10

3.0

5

3.5

3.0

1

9

2.5

6

4.0

4.0

1

12

3.1

7

4.3

4.0

1

9

2.8

8

2.6

2.0

1

7

2.3

9

3.2

3.5

1

5

1.7

10

2.2

1.0

1

5

1.7

Source: The Louis Berger Group, Inc. , 2000.
It should be noted that the regional comparisons provided above are based entirely
on a review of the descriptive statistics provided in Appendix K. Actual statistical
testing of the difference in the mean values of NEPATIME between regions is
discussed in Section 4. 3. 3 below.
4. 2 Descriptive Statistics on Other Factors Considered
Similar to the NEPATIME variable, descriptive statistics were also analyzed
for the other factors against which correlations would be made with NEPATIME
or its more normally distributed squareroot value, SQRTNTIM. Highlights of
those descriptive statistics, which include number of valid cases in the sample,
minimum value, maximum value, mean, and standard deviation, are presented in
Table 4. Of particular interest is the total length of the project development
process (OPMINSTR, or YR_OPEN minus PROSTART) during the 30year NEPA period.
Based on the sample, it can be concluded that the mean time for the entire project
development process is approximately 13. 1 years, although the process has ranged
from a little as 3. 0 years to as many as 36 years. The standard deviation of
approximately 6. 7 years indicates that the vast majority of the cases ranged
between 6. 4 years and 19. 8 years.
Frequencies for some of the key variables that could be considered to be either
continuous or categorical in nature are presented in Appendix L. These frequencies
indicate the number of responses for each categorical response that were received
within the sample. For instance, in the case of the LAND_USE variable, 45 out
of the 97 valid responses were identified as being rural in character, while
22 were identified as being suburban and 30 were identified as urban. Similar
frequencies are presented in the Appendix for 26 additional variables as well.
These frequencies were then used to identify the appropriate types of further
analyses to be performed (i. e. , whether to treat them as continuous or categorical
variables in the subsequent testing).
4. 3 Statistical Relationship of the Length of the NEPA Process with Other
Factors
Having developed the descriptive statistics for the length of the NEPA process
and other factors, the next step was to refine the search for correlations and
relationships between NEPATIME (or its surrogate) and other variables in the
data set. The surrogate variable for NEPATIME that was found to be the most
effective in making correlations was SQRTNTIM, which represents the square root
of NEPATIME. The reason that SQRTNTIM was generally more effective in making
correlations was because it more closely approximates a normal distribution
than NEPATIME. Even so,NEPATIME equivalents are generally provided as well,
since these values are more readily understood than the square root values.
Table 4: Descriptive Statistics for Key Variables

N

Minimum

Maximum

Mean

Std. Deviation

INCEPTION

68

46.
00

88.
00

68.
04

9.
13

NEWLANES

95

0.
00

6.
00

2.
97

1.
63

LENGTH

91

0.
36

25.
90

6.
52

5.
54

LAND_AQ

61

0.
00

861.
00

153.
08

211.
77

FMISTOTL

97

$461,000.
00

$196,138,563.
00

$23,474,301.
51

$36,113,545.
82

FMIS_FED

97

$282,292.
00

$170,917,977.
00

$18,681,508.
42

$30,197,536.
57

NEPATIME

97

1.
00

12.
00

3.
61

2.
43

SQRTNTIM

97

1.
00

3.
46

1.
79

0.
63

FED_AGEN

97

1.
00

14.
00

6.
51

2.
97

ST_AGEN

97

0.
00

15.
00

4.
76

2.
84

EISPP

97

0.
00

1160.
00

265.
59

246.
74

PUBLICM

87

0.
00

11.
00

2.
51

2.
20

AGNCYMTG

34

0.
00

130.
00

8.
53

23.
38

PROJALT

97

0.
00

36.
00

3.
66

3.
97

RELHH

95

0.
00

981.
00

29.
60

106.
73

RELBUS

94

0.
00

106.
00

5.
78

12.
76

ACWETLI

97

0.
00

85.
00

2.
62

10.
36

OPMINSTR

97

3.
00

36.
00

13.
10

6.
66

PENG99

61

$2,056.
00

$31,811,256.
00

$2,648,349.
77

$4,813,666.
35

CENG99

62

$1,738.
00

$32,504,258.
00

$3,220,449.
29

$5,269,030.
55

ROW99

58

$8,637.
00

$115,309,521.
00

$7,753,319.
71

$16,687,161.
70

CON99

79

$180,139.
00

$188,493,520.
00

$26,966,478.
32

$38,332,523.
70

INCEP2OP

68

6.
00

50.
00

21.
94

9.
95

ALLAGENC

97

1.
00

25.
00

11.
27

4.
87

PENGTIME

61

1.
00

28.
00

8.
56

7.
35

CONTIME

78

1.
00

18.
00

5.
53

4.
11

CENGTIME

62

1.
00

16.
00

5.
85

4.
12

ROWTIME

57

1.
00

26.
00

6.
63

5.
36

PENG2CON

49

1.
00

36.
00

10.
35

7.
97

INCP2CON

55

5.
00

50.
00

21.
42

10.
15

Valid N (listwise)

6






Source: The Louis Berger Group, Inc. , 2000.

4. 3. 1 Relationship of the Length of the NEPA Process to the Entire Project
Development Process
The first statistical relationship established with the length of the NEPA
process (NEPATIME) was a comparison to total project length (YR_OPEN minus PROSTART).
Based on mean and median values, it was found that the NEPA process comprises
or, more likely, coincides with approximately 28% or 27%, respectively, of the
total project development time. In other words, between the time that either
preliminary engineering or the NEPA process begins, whichever is first, and
the end of construction, the entire NEPA process generally comprises only about
27  28% of that total period.
A correlation analysis using a Pearson (parametric) test was then run in order
to identify any significant relationship between the length of the NEPA process
and the length of the project development process. The result of that test yielded
a correlation value of 0. 495 and a significance level of 0. 000. Of all variables
tested against NEPATIME, the correlation with YR_OPEN minus PROSTART is the
greatest. This finding is not surprising since the length of the NEPA process
accounts for 27  28% of the total project development process and would, therefore,
likely have some influence on the total length of that process. Even so, the
relatively weak correlation value indicates that NEPATIME only accounts for
only a relatively small portion of the total variation in length of the project
development process, further indicating that other factors would also likely
influence the total process length.
4. 3. 2 Relationship of the Length of the NEPA Process to Other Factors
For continuous data variables, Pearson (parametric) and Spearman (nonparametric)
correlation tests were run against NEPATIME and SQRTNTIM (i. e. , the square
root of NEPATIME). Weak but significant correlations between SQRTNTIM / NEPATIME
were found with the variables shown in Table 5. Of the variables included in
the table, the strongest correlations were found with: the length of the project
development process from project inception to project opening (INCP2CON, 0.
44); the length of the project development process from authorization of preliminary
engineering to completion of construction (PENG2CON, 0. 43); and the number
of total agencies involved (ALLAGENC, 0. 43). All of these correlations were
weaker than that found between SQRTNTIM / NEPATIME and YR_OPEN  PROSTART.
It should be noted that the results of all correlation tests between SQRTNTIM
/ NEPATIME and other variables, as well as between the other variables themselves,
are presented in Appendix M.
LEISDEC is a derived variable that is based on dividing the LASTEIS_YR into
decades. Using the decades as a categorical variable, an ANOVA showed that there
is a difference in the length of the NEPA process among the categories representing
the 1970s, 1980s, and 1990s. Post hoc testing indicated that the significant
difference occurred between the 1970s and the remaining two decades, whereas
the 1980s and 1990s did not differ significantly from each other.
Table 5: CORRELATIONS BETWEEN LENGTH OF THE NEPA PROCESS AND OTHER VARIABLES
Variable

Correlation

Significance^{1}

Test
Type^{2}

LNROW99

0.342

0.009

P

EISPP

0.397

0.000

P

ALLAGNC

0.433

0.000

P

INCEP2OP

0.261

0.032

P

LNFMISTO

0.240

0.018

P

LNMISFED

0.219

0.031

P

NEWLANES

0.373

0.033

NP

PENG2CON

0.426

0.002

P

INCP2CON

0.443

0.001

P

(^{1}) Significance level 0. 05 or better. (^{2}) P= parametric;
NP= nonparametric.
Source: The Louis Berger Group, Inc. , 2000.
Appendix N for results of the post hoc testing by decade). It was found that
EISs completed in the 1970s required significantly less time to prepare than
those completed in either the 1980s or the 1990s. This is not an unexpected
result since the requirements for preparing EISs evolved and became more complex
over time.
Specifically, it was found that EISs completed in the 1970s took, on average,
approximately 2. 2 years to prepare. By the 1980s, the average mean for
EIS preparation increased to approximately 4. 4 years while the average mean
increased to approximately 5. 0 years in the 1990s. Although an increase is
shown to exist between the 1980s and the 1990s, the difference between those
two decades was not found to be statistically significant (i.e. , the difference
exceeds the 0. 05 level of significance).
Significant relationships were also found between SQRTNTIM and two variables
reflecting other specific regulatory requirements. SECT404 is a nominal variable
indicating whether a given EIS project also involved a Section 404 permit from
the U. S. Army Corps of Engineers. As shown in Table 6, those cases that did
involve Section 404 (denoted as "True" in the table) showed a significantly
longer NEPA process time. The NEPATIME equivalent to SQRTNTM was found to be
about 4. 3 years when a Section 404 permit was involved, but only about 2. 4
years when no Section 404 permit was involved. This finding is not a surprise
given the increased compliance associated with the additional procedural and
technical requirements of Section 404 that would likely have been initiated
during the EIS process.
Table 6: CORRELATIONS BETWEEN LENGTH OF NEPA PROCESS AND OTHER REGULATORY
REQUIREMENTS
SECT404

Mean SQRTNTIM

NEPATIME Equivalent

Significance

True

2. 073

4. 3

0. 000

False

1. 5439

2. 4


SECT4F




True

2. 1721

4. 7

0. 000

False

1. 6707

2. 8


Source: The Louis Berger Group, Inc. , 2000.
SECT4F is a nominal variable indicating whether a given EIS project involved
approval of a Section 4(f) Statement by FHWA. Similar to the Section 404 finding,
those cases that did involve Section 4(f) approval (denoted as "True"
in Table 6) showed a significantly longer NEPA process time. Specifically, the
NEPATIME equivalent to SQRTNTIM was found to be about 4. 7 years when a Section
4(f) approval was involved, but only about 2. 8 years when no Section 4(f) approval
was involved. This finding makes sense for the same reason that the Section
404 finding makes sense.
Other regulatory variables such as Coast Guard permits (PERMCG) and Section
106 approvals (SECT106) did not show significant differences in the length of
the NEPA process between the with and without scenarios. Therefore,
these variables are not shown in Table 6. However, the results of the tests
performed for assessing correlation between the length of the NEPA process (SQRTNTIM)
and all of the regulatory variables are provided in Appendix O.
AGNCYMTG is the number of meetings held with State and Federal agencies during
the NEPA process. The huge range of values found for this variable (i. e. ,
0  130 meetings) suggested that the variable should be disaggregated into two
separate categories: (1) those below and including the median value of 2; and
(2) those above the median. The resulting test showed a significant difference
between the two groups with respect to the mean length of the NEPA process.
As shown in Table 7, those projects where two or less agency meetings were held
during the course of EIS preparation took approximately 2. 4 years to complete.
When more than two agency meetings were held, it took approximately 4. 5 years
to complete preparation of the EIS.
Table 7: CORRELATIONS BETWEEN LENGTH OF NEPA PROCESS AND NUMBER OF
MEETINGS
AGNCMTG

Mean SQRTNTM

NEPATIME Equivalent

Significance

2 and below

1. 5506

2. 4

0. 008

more than 2

2. 1169

4. 5


PUBLICM




2 and below

1. 6313

2. 7

0. 002

more than 2

2. 0484

4. 2


Source: The Louis Berger Group, Inc. , 2000.
A similar process was followed with the variable PUBLICM, which represents
the number of public meetings held. The distribution of this variable also showed
the median number of meetings to be 2. The 97 valid cases were then divided
into those involving 2 or less meetings and those involving more than 2. Once
again, there was a significant difference in the length of the NEPA process
between these two groups, with an increased mean for NEPA process length associated
with an increase in the number of public meetings. Specifically, as shown in
Table 7, an average of 2. 7 years has been required to prepare an EIS when the
two or less meetings have been involved while the average increases to 4. 2
years when more than two meetings have been involved.
Complete statistics for both variables, AGNCYMTG and PUBLICM, and their correlations
with SQRTNTIM are presented in Appendix P.
It is important to note that neither the number of agency meetings nor the
number of public meetings necessarily is a determinant of the length of time
required to comply with NEPA. In fact, it is likely that the number of meetings
of either type are actually determined by the complexity of the project and,
therefore, are more of a byproduct of the length of the NEPA process rather
than vice versa. The statistical analyses performed, however, do not specifically
identify which variable is dependent on the other, so this theory is based more
on speculation than fact.
A number of variables reflecting a variety of environmental issues were also
considered for correlating against the length of the NEPA process. Most of these
variables were found to be distributed so heavily in favor of the "False"
response, that analysis would not have been appropriate or meaningful. Only
one issue, ISSUNOIS (i. e. , which reflects noise as an issue), was somewhat
more equitably distributed (30 True, 67 False responses). Therefore, this variable
was tested against the length of the NEPA process. The result was that a significant
difference in length of NEPA existed between those projects where noise was
an issue and those projects where they were not. Projects involving noise issues
had a longer mean NEPA process time than those without noise issues. Specifically,
the mean NEPA process time was found to be only 3. 2 years when noise was not
an issue while the mean increased to 4. 4 years when noise was an issue. Complete
statistics for correlations between ISSUNOIS and length of the NEPA process
(i. e. , SQRTNTIM / NEPATIME) are provided in Appendix Q.
Examination of other variables yielded no information of interest. No significant
correlations or differences in NEPA process time were found with any other variable
not specifically discussed above. It should be particularly noted in this regard
that, other than the cost of rightofway acquisition which was found to have
a weak correlation with length of the NEPA process (see Table 5), no other cost
variables were found to have any significant relationship with NEPA. It is very
possible that this apparent lack of correlation may be attributed more to missing
values and shortcomings in the FMIS database related to the cost variables (see
Section 3. 3. 3 for further details on this data collection issue).
4. 3. 3 Relationship of the Length of the NEPA Process to Individual FHWA
Regions
The next step involved an ANOVA, in which the transformed NEPATIME variable,
SQRTNTIM, was tested against the entire group of former FHWA regions to see
if any differences actually existed among the regions. As reported in Section
4. 1. 2, there were a number of apparent differences between some of the regions
based on a review of the descriptive statistics. A determination as to whether
or not these differences were statistically significant, however, was made following
the use of ANOVA. This test works by examining the entire spectrum of values
and categories. It looks at each category (i. e. , FHWA region) within the context
of the full range of categories and tests if any category means are out of line
with the others. If a significant difference is detected by the ANOVA, then
Post Hoc testing, which analyzes where the differences exist between regions,
would then also be conducted.
When applied to all regions, the results indicated that there was no significant
difference among them (see Appendix R). Intuitively, however, the difference
between Regions 1 and 10, which was the most extreme difference found in the
descriptive statistics analysis, should have been significant. Following this
reasoning, an Independent Samples ttest was run, using just the results from
Region 1 and Region 10 (see Appendix S). In this scenario, a significant difference
was revealed between the two regions in terms of SQRTNTIM. Similar results were
obtained when the test was run using the raw variable (NEPATIME). These results
confirm that, at a minimum, Regions 1 and 10 have historically been truly different
from each other in terms of the length of the NEPA process. It is possible that
some of the other individual regions may be significantly different from each
other as well, but only Regions 1 and 10 were tested in this regard.
Since Regions 1 and 10 represented the greatest difference in mean values for
SQRTNTIM, an attempt was also made to determine if any other variables exhibited
differences between Regions 1 and 10. In this regard, Independent Samples ttests
were run in most cases while Crosstabulation analyses were run in two cases.
Several variables were found to vary significantly between the two regions,
with Region 1 exhibiting significantly greater values than Region 10 in all
cases, which is consistent with the trend in NEPATIME. The following variables
exhibited significant differences in this regard, as presented in Appendix T:
 NEWLANES (Number of new lanes added by construction of the project);
 ALLAGENC (Number of Federal and state agencies commenting on EIS);
 LNROWA99 (Transformed variable — ROW costs indexed to 1999 dollars);
 LNCON99 (Transformed variable — construction cost indexed to 1999 dollars);
 LNFMISFE (Transformed variable — FMIS Federal dollars indexed to 1999
dollars);
 LNCENG99 (Transformed variable — construction engineering costs indexed
to 1999 dollars);
 SECT4F (EIS involving Section 4(f)); and
 PUBLICM (Number of public meetings).
4. 4 Use of Results as a Baseline for Evaluating Future Environmental Streamlining Initiatives
Although most of the relationships established through this analysis are between
the length of the NEPA process and variables that would not be known until after
the project has been largely completed, some useful relationships have been
established which could be useful in evaluating the success of future environmental
streamlining efforts. First, there appears to be a positive relationship between
the length of the NEPA process and length of the total project development process.
Second, the average length of time for completion of the NEPA process appears
to comprise approximately 27  28% of the total project development time. This
could be a useful benchmark for evaluating whether or not the NEPA process is
efficiently carried forward in a given project. More importantly, shifts away
from this average may indicate gradual loss or improvement in efficiency, or
changes in the requirements that have changed the length of the process.
Other potentially useful relationships include the positive correlations between
the number of state and local agencies that comment on an EIS and the length
of time for the entire NEPA process. The same is true of agency and public meetings,
the magnitude of rightof way acquisitions and the presence of special permitting
or regulatory studies. This is not to suggest that any of these factors can
or even should be limited. They do, however, signal the probability of more
extensive time required to fully comply with the process. This should be of
value to administrators.
It would be premature on the basis of the present data set and analysis to
attempt to construct a model of the length of the NEPA process. At most, this
analysis could serve as a baseline from which additional analysis can be started
and to which additional data can be added. In other words, the data set lends
itself well to be used as a baseline against which to evaluate the relative
degree of success of future environmental streamlining initiatives, but its
usefulness as a predictive model for determining either the length of the NEPA
process based on certain project or environmental factors or for determining
the length of the overall project development process based on the length of
the NEPA process is limited at this point in time.

