Data Analysis and Hypothesis Testing for Carbon Emission and Climate Change Integration in Business Strategy

This report presents the results of inferential analysis including t-test, ANOVA test, cross tabulation and chi square test, and hypothesis testing for carbon emission and climate change integration in business strategy. The findings suggest that firms who have integrated climate change into their business strategy have a significant decline in carbon emission as compared to those who have not. However, there is no significant difference in carbon emission between firms in different sectors. The spending on energy for firms in different countries is significantly different. Hypothesis testing has been conducted to examine the significant difference in the change in carbon emission for firms who take climate change into consideration while making the business strategy.

MGT723 Research Project
Semester X 20XX
Assessment Task 3: Report
Student Name: XXXXX
Title: XXXXX
Submission Date: XXXXX
(Note that the submission is due by 5:00pm via SafeAssign)
Acknowledgement:
I certify that I have carefully reviewed the university’s academic misconduct policy. I understand
that the source of ideas must be referenced and that quotation marks and a reference are required
when directly quoting anyone else’s words.

Contents
Data Analysis – Inferential..............................................................................................................3
Hypothesis Testing........................................................................................................................10
Discussion......................................................................................................................................12
Limitations.....................................................................................................................................12
Further research.............................................................................................................................12
References......................................................................................................................................13

Data Analysis – Inferential
The results from the inferential analysis have been discussed in the current section. In this
research various inferential analysis have been performed. This includes the t- test, ANOVA test,
cross tabulation and chi square test and the correlation analysis.
T test
The first section of the inferential analysis is devoted to the t-test. In this case the independent
sample t test has been conducted. The main objective of conducting the t test is to examine
whether there is difference in the emission for the firms between two different groups of firm.
The first group is firm is those who have integrated the climate change and the second is the
other group who have not integrated climate change into their business (Kuada 2012; Kumar
2014; Mangal & Mangal 2013).
The results are shown below and as the table suggests, the mean change in the firms who have
integrated climate change into their business is -24.76. In other words on average there is 24 %
decline in the carbon emission as compared to the previous year if the company has integrated.
On the other hand the average decline is only 8 % if the company do not integrate climate change
into their business strategy(Najah & Cotter 2010; Orsato 2017; Winn et al. 2011).
Group Statistics
climate change
integrated into your
business strategy
(Grp_1)
N Mean Std.
Deviation
Std. Error
Mean
emissions in metric
tonnes % change from
previous year(Grp_1)
Yes 30 -24.7627 88.16826 16.09725
No 30 -8.6187 7.38995 1.34921

Table 1 Group statistics for the climate change and the emission change from the previous year
Findings from the Independent sample t test is shown in the table below. As the results suggests,
the Leven’s Test for equality of variance value is 3.157 and the significance value is 0.081. So,
the test is statistically significant as the 95 % confidence interval has been taken into
consideration.
Independent Samples Test
Levene's
Test for
Equality
of
Variances
t-test for Equality of Means
F Sig. t df Sig.
(2-
tailed
)
Mean
Differenc
e
Std. Error
Differenc
e
95% Confidence
Interval of the
Difference
Lower Upper
emissions
in metric
tonnes %
change
from
previous
year(Grp_
1)
Equal
variance
s
assumed
3.15
7
.08
1
-.99
9
58 .322 -
16.14400
16.15369 -
48.4791
3
16.1911
3
Equal
variance
s not
assumed
-.99
9
29.40
7
.326 -
16.14400
16.15369 -
49.1621
4
16.8741
4
Table 2 Results from the independent sample t test
The mean difference is -16 when the equal variance is assumed.
ANOVA test
Apart from the t test the ANOVA test has also been conducted as a part of the inferential
analysis. Since the t test can be conducted only with the two categorical variable, the ANOVA

test has been conducted so that the variable with more than two categories can be taken into
consideration.
ANOVA
emissions in metric tonnes % change from previous year(Grp_1)
Sum of
Squares
df Mean Square F Sig.
Between
Groups
3846.911 2 1923.456 .483 .620
Within Groups 227081.854 57 3983.892
Total 230928.766 59
Table 3 Results from the ANOVA test
The findings from the ANOVA are shown in the above table and it shows that the F value is
0.483 with the 2 degrees of freedom. However the significance value is 0.62, which is not
statistically significant at 95 % confidence interval which has been taken into consideration for
the current research. The first ANOVA has been conducted to examine whether there is
difference in the carbon emission for different countries included in the study. Since, the F
statistics is not significant at given confidence level, so it can be concluded that there is no
statistically significant difference in the carbon emission between the
countries(Phimphanthavong 2013; Hogan Lovells 2014; Skaza, Student & University 2013).
Multiple Comparisons
Dependent Variable: emissions in metric tonnes % change from previous year(Grp_1)
LSD
(I) country
(Grp_1)
(J) country
(Grp_1)
Mean
Difference (I-
J)
Std. Error Sig. 95% Confidence Interval
Lower
Bound
Upper
Bound
Brazil Canada -5.59919 22.97348 .808 -51.6028 40.4044
USA 13.17458 18.93945 .489 -24.7510 51.1002
Canada Brazil 5.59919 22.97348 .808 -40.4044 51.6028

USA 18.77377 21.06725 .377 -23.4127 60.9602
USA Brazil -13.17458 18.93945 .489 -51.1002 24.7510
Canada -18.77377 21.06725 .377 -60.9602 23.4127
Table 4 Results from the Multiple comparison under ANOVA test
The results from the multiple comparison also show that there is no significant difference
between the countries. For example in the first row the different in carbon emission for Canada
and Brazil is shown. For this the significance value is 0.808 which is higher than the minimum
value of 0.05. So, there is no statistically significant difference. One of the main reason for such
results can be the similar environmental regulations in these countries and also the similar type
of industries taken into the sample. Since the similar types of the firms are expected to have
similar carbon emission, which has led to similar carbon emission in these countries.
Sector and emission change from previous year
Another important ANOVA test for the current research has been conducted to examine whether
there is statistically significant difference in the carbon emission for the firms in the different
sector. This will give an idea whether the different sectors have different carbon emission. The
results from the ANOVA are shown in the table below. For the current section, the firms were
divided into different sectors, so that the analysis can be conducted and comparison can be made.
ANOVA
emissions in metric tonnes % change from previous year(Grp_1)
Sum of
Squares
df Mean Square F Sig.
Between
Groups
329.322 3 109.774 1.630 .193
Within Groups 3703.960 55 67.345
Total 4033.281 58
Table 5 Results from the ANOVA test

Results from the ANOVA test suggest that the F value is 1.63 with the sum of squares between
the groups as 329.322. However the significance value for the F statistics is not statistically
significant as the value is higher than the minimum value taken into consideration for current
research. So, it can be concluded that the emission of carbon is not statistically significant
different for firms in the different sectors. However on the basis of the previous research and the
findings, it was expected that the manufacturing firms have higher carbon emission as compared
to the firms in the service industry.
Multiple Comparisons
Dependent Variable: emissions in metric tonnes % change from previous year(Grp_1)
LSD
(I) Sector (Grp_1) (J) Sector (Grp_1) Mean
Difference
(I-J)
Std.
Error
Sig. 95% Confidence
Interval
Lower
Bound
Upper
Bound
Air Freight
transportation and
Logistics*
Banks, Diverse
Financials, and
Insurance
4.01825 3.89263 .306 -3.7828 11.8193
Manufacturing -1.76625 3.43298 .609 -8.6461 5.1136
Other services -2.67280 3.40954 .436 -9.5057 4.1601
Banks, Diverse
Financials, and
Insurance
Air Freight
transportation and
Logistics*
-4.01825 3.89263 .306 -11.8193 3.7828
Manufacturing -5.78450 3.17832 .074 -12.1540 .5850
Other services -6.69105* 3.15299 .038 -13.0098 -.3723
Manufacturing Air Freight
transportation and
Logistics*
1.76625 3.43298 .609 -5.1136 8.6461
Banks, Diverse
Financials, and
Insurance
5.78450 3.17832 .074 -.5850 12.1540

Other services -.90655 2.56401 .725 -6.0449 4.2318
Other services
Air Freight
transportation and
Logistics*
2.67280 3.40954 .436 -4.1601 9.5057
Banks, Diverse
Financials, and
Insurance
6.69105* 3.15299 .038 .3723 13.0098
Manufacturing .90655 2.56401 .725 -4.2318 6.0449
*. The mean difference is significant at the 0.05 level.
Table 6 Results from the multiple comparison
The comparison of each sector with the other sector has been shown in the above table and as per
the expectation the mean difference between the manufacturing and the service industry is
statistically significant as the significance value is less than 0.1. However, for the current case
the significance level of 95 % has been taken, so this cannot be taken as the significant result.
The sample taken into consideration for the study may have affected the results. If similar study
is conducted on other firms the results may be different.
Percentage spending on energy and country
Another important analysis which has been conducted using the ANOVA is to test whether there
is statistically significant difference in the spending on the energy for different countries.
ANOVA
percentage of your total operational spends on energy (Grp_1)
Sum of
Squares
df Mean Square F Sig.
Between
Groups
1440.705 2 720.352 11.619 .000

Within Groups 3533.879 57 61.998
Total 4974.583 59
Table 7 Results from the ANOVA test
Results from the analysis show that the F value of 11.619 is statistically significant at 5 %
confidence interval. The Significance value is very close to zero. So, it can be concluded that
firms in the different countries have significantly different spending on the energy.
Multiple Comparisons
Dependent Variable: percentage of your total operational spends on energy (Grp_1)
LSD
(I) country
(Grp_1)
(J) country
(Grp_1)
Mean
Difference (I-
J)
Std. Error Sig. 95% Confidence Interval
Lower
Bound
Upper
Bound
Brazil Canada -10.34188* 2.86590 .001 -16.0807 -4.6030
USA 2.16475 2.36266 .363 -2.5664 6.8959
Canada Brazil 10.34188* 2.86590 .001 4.6030 16.0807
USA 12.50663* 2.62810 .000 7.2439 17.7693
USA Brazil -2.16475 2.36266 .363 -6.8959 2.5664
Canada -12.50663* 2.62810 .000 -17.7693 -7.2439
*. The mean difference is significant at the 0.05 level.
Table 8 Results from the multiple comparison
The difference between the each country is shown in the above table and it shows that the
difference between Brazil and Canada and difference between USA and Canada is highly
significant. However the difference between the Brazil and USA is not statistically significant.
The main reason behind such results may be because of the similar environmental regulations in
these countries. There may be other reasons also (Field 2011; Johnson et al. 2016; Greenland et
al. 2016)

Hypothesis Testing
Hypothesis 1:
H0: There is no significant difference in the change in the carbon emission for the firms who
takes climate change into consideration while making the business strategy from those who do
not takes into consideration.
H1: There is significant difference in the change in the carbon emission for the firms who takes
climate change into consideration while making the business strategy from those who do not
takes into consideration.
Significance level: 95 %
Test conducted: T- test
Result: Since the value of Levene's Test for Equality of Variances significance is more than 0.05,
the null hypothesis cannot be rejected.
Discussion
The current research is aimed to examine the carbon emission among the firms in different
sectors. For the analysis purpose the secondary data was collected among the 60 different firms
from different countries. Also different sectors were taken into consideration while selecting the
sample. Both the inferential and the descriptive analysis has been conducted. The results from
the inferential analysis has shown that there is significant difference in the spending on energy
for different country. Also among some countries the integration of the climate change while
taking the business decision are statistically different.
The results from the data analysis also shows that there is no significant difference in the change
in the carbon emission for different countries taken into consideration for the current research.
However, it was expected that the change in the carbon emission would be different for these
countries. This is because the countries included are developing and the developed countries.
The firms in the developed country are expected to have higher reduction in the carbon emission
as the government regulation in the developed are stricter as the government are more concerned
about the environment and the climate change(Wai 2012; Antonio et al. 2013; Rohde & Muller

2015; Luo et al. 2014). Whereas on the other hand, the government in the developing countries
are less strict about the carbon emission. This is because they are in the growing phase and the
government do not want their firms to have extra regulation for production and reduce the
production. This is especially true for the manufacturing firms as compared to the service sector.
So, the different results in the current research is due to the fact that the manufacturing firms
produces more carbon as compared to the service firms (Hassaballa 2013).
The analysis and the results from the current research have both practical and theoretical
implications. The results can be used by the various government and international organization
working on climate change to track how the carbon emission have changed for firms in different
countries and also in different sectors. Also the areas where there is significant gap can be
identified in terms of energy spending and the carbon emission. Furthermore the results can be
practically use to track the companies which have not taken into consideration climate change in
their business strategies. On the other hand the research can be used by other researchers to
develop the new theories. Also the tests and the techniques used in this research can be used by
other researchers.
Limitations
Some of the limitations of the current research are as follows:
 One of the major limitation of the current research is that there is no qualitative study
included for the analysis.
 The sample size is comparatively less and only few countries have been taken into
account.
 There is no regression analysis included which could have been more effective technique.
 Apart from this there are limitations related to the time and money invested for the
research
Further research

 Further research can be conducted by taking into account other environmental factors as
well as the more corporate governance variables which will provide clearer view.
 Also, the study can be conducted using other and larger sample size.
 The qualitative study can also be included for future research.
 Similar research can be conducted using the regression analysis.
 Similar research can be conducted using data from other countries around the world.
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