Evaluation of Carbon Disclosure Scores for Different Company Sectors and Countries

This research evaluates the differences in carbon disclosure scores between different company sectors and countries. Chi square test and correlation and regression analysis are conducted to test the hypotheses. Limitations and further research are discussed.

Running Head: MGT723 RESEARCH PROJECT
MGT723 Research Project
Name of the Strudent
Name of the University
Author Note

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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.

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Data Analysis – Inferential
The main initiative of this research is to evaluate the differences in the carbon disclosure
scores between different company sectors and different countries. Further the relationship
between the company factors on carbon emission on the disclosure scores are also of interest to
evaluate. Thus, in order to find out the stated concerns, the following hypothesis can be framed:
Null Hypothesis (H01): There are no significant differences in the carbon disclosure scores with
respect to different sectors of companies.
Alternate Hypothesis (HA1): There are significant differences in the carbon disclosure scores
with respect to different sectors of companies.
Null Hypothesis (H02): There are no significant differences in the carbon disclosure scores with
respect to disclosure status.
Alternate Hypothesis (HA2): There are significant differences in the carbon disclosure scores
with respect to disclosure status.
Null Hypothesis (H03): There are no significant relationship between the carbon emission
conditions of the companies with their disclosure scores.
Alternate Hypothesis (HA3): There are significant relationship between the carbon emission
conditions of the companies with their disclosure scores.
Hypothesis Testing
In order to test the first and the second hypothesis, chi square test of association has been
conducted and a correlation and regression analysis has been conducted in order to test the third
stated hypothesis.

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Chi Square Test
The results of the chi square test for testing the first hypothesis are provided in table 1. It
can be seen from the table that the value of the chi square statistic with 105 degrees of freedom
has been obtained as 116.7 with a significance value of 0.205. The significance value of the chi-
square test is higher than the estimated level of significance (0.05). This indicates that the test of
association is insignificant (Seber 2015). Thus, the null hypothesis (H01) is rejected.
The results of the chi square test for testing the second hypothesis are provided in table 2.
It can be seen from the table that the value of the chi square statistic with 21 degrees of freedom
has been obtained as 60.000 with a significance value of 0.000. The significance value of the chi-
square test is lower than the estimated level of significance (0.05). This indicates that the test of
association is significant (Olive 2014). Thus, the null hypothesis (H02) is accepted.
Table 1: Chi-Square Tests for different Sectors
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 116.700a 105 .205
Likelihood Ratio 96.181 105 .719
Linear-by-Linear Association 1.436 1 .231
N of Valid Cases 60
a. 132 cells (100.0%) have expected count less than 5. The minimum expected count is .10.
Table 2: Chi-Square Tests for Disclosure Status
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 60.000a 21 .000
Likelihood Ratio 23.822 21 .302
Linear-by-Linear Association 36.604 1 .000
N of Valid Cases 60
a. 41 cells (93.2%) have expected count less than 5. The minimum expected count is .05.
Correlation Analysis

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In order to test the third hypothesis, which is to establish the relationship between the
variables disclosure scores and the carbon emission reduction initiatives of the companies, a
correlation and regression analysis has been performed. It can be seen from the correlation
matrix in table 3 that the dependent variable disclosure scores have a positive relationship with
all the independent variables except with the first independent variable, with which there is a
negative relationship (Brandt 2014).
Table 3: Correlations Matrix
Disclosure_Scores_2015 IV1 IV2 IV3 IV4 IV5 IV6
Disclosure_Scores_2015
Pearson
Correlation 1 -.177 .028 .079 .215 .181 .209
Sig. (2-tailed) .176 .831 .550 .100 .166 .108
N 60 60 60 60 60 60 60
IV1
Pearson
Correlation -.177 1 -.082 -.016 .055 .294* .055
Sig. (2-tailed) .176 .534 .900 .677 .023 .674
N 60 60 60 60 60 60 60
IV2
Pearson
Correlation .028 -.082 1 .132 -.349** -.341*
* -.352**
Sig. (2-tailed) .831 .534 .313 .006 .008 .006
N 60 60 60 60 60 60 60
IV3
Pearson
Correlation .079 -.016 .132 1 .060 .057 .057
Sig. (2-tailed) .550 .900 .313 .648 .667 .667
N 60 60 60 60 60 60 60
IV4
Pearson
Correlation .215 .055 -.349*
* .060 1 .954** 1.000**
Sig. (2-tailed) .100 .677 .006 .648 .000 .000
N 60 60 60 60 60 60 60
IV5
Pearson
Correlation .181 .294* -.341*
* .057 .954** 1 .954**
Sig. (2-tailed) .166 .023 .008 .667 .000 .000
N 60 60 60 60 60 60 60
IV6 Pearson
Correlation
.209 .055 -.352*
*
.057 1.000** .954** 1

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Sig. (2-tailed) .108 .674 .006 .667 .000 .000
N 60 60 60 60 60 60 60
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Regression Analysis
The effect of the independent variables on the dependent variable cannot be estimated
from the correlation analysis and thus, regression analysis has been performed. The results are
given in tables 4, 5 and 6. The R Square value and the F statistic indicate the goodness of fit of
the model. From the R Square value, it is clear that 17 percent of variability in independent
variable can be explained by dependent variables which is very low (Sullivan III 2015).
Table 4: Regression Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .421a .177 .084 24.171
a. Predictors: (Constant), IV6, IV1, IV3, IV2, IV5, IV4
The value of the F-statistic in table 5 comes with a significance value of 0.097 which
indicates insignificance. Thus, the cumulative impact of the independent variables on the
dependent variable is insignificant (Draper and Smith 2014).
Table 5: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 6682.139 6 1113.690 1.906 .097b
Residual 30963.861 53 584.224
Total 37646.000 59
a. Dependent Variable: Disclosure_Scores_2015
b. Predictors: (Constant), IV6, IV1, IV3, IV2, IV5, IV4

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The coefficients of the independent variable have been found to be insignificant except
for the effect of the independent variables 4 and 6. Thus, the other 4 independent variables can
be eliminated from the model (Chatterjee and Hadi 2015).
Table 6: Regression Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 81.291 8.980 9.052 .000
IV1 -5.064E-005 .000 -.416 -1.958 .055
IV2 1.784 5.814 .042 .307 .760
IV3 7.450E-011 .000 .005 .038 .970
IV4 9.576 4.670 13.034 2.051 .045
IV5 .633 .461 .973 1.372 .176
IV6 -10.084 4.755 -13.713 -2.121 .039
a. Dependent Variable: Disclosure_Scores_2015
Discussion
From the chi square analysis conducted, it can be said that the carbon disclosure scores of
the companies does not depend on which type of sector they belong to but the disclosure scores
have a significant relationship with countries. Thus, the carbon disclosure scores differ at
different countries. This might be due to the difference in the climates at different regions. Thus,
it can be said that climatic change is a very significant factor in evaluating the carbon disclosure
scores for different companies (Gary, Saunders and Goregaokar 2012). The increase in the
disclosure scores will be putting the shareholders of the companies at a lesser risk of investing in
the companies. This will in turn be a benefit for the agency. Thus, it can be said that the agency
theory is not directly related to the carbon disclosure scores but is related with the help of the
stakeholder theory to the carbon disclosure scores.

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The correlational analysis has found significant positive relationship between the pre-
defined independent variables except for the additional intensity of carbon emission figures, with
which negative relationship has been obtained (Macdonald and Headlam 2010). This indicates
that the factors that are considered here are helpful in increasing the carbon disclosure scores
(Guo 2014). Thus, considering these factors and taking actions accordingly will be beneficial for
the companies (Orsato 2017).
The regression analysis has shown that the scope 2 figures and the direction of change in
the carbon emission this year from the previous year has been of significant effect in predicting
the carbon disclosure scores (Winn et al. 2011). The positive impact of these two factors will be
helpful in increasing the carbon disclosure scores and will in turn be of benefit to the companies
as well as to the environment in reducing the environmental pollution (Saunders, Lewis and
Thornhill 2009).
Limitations
In this study, the effects of climatic changes have not been considered which has been
identified as one of the most important factors responsible for the differences in the carbon
disclosure scores of the companies. The research was mainly aimed at finding out the factors that
will be useful in reducing the carbon emissions of the companies. The factors that can be
responsible for this change has not been put into light properly. Moreover, only 60 companies
have been selected for this study which is a very little number as compared to the total number of
industries that are present in this whole world. Thus, a sample of this size might not be sufficient
in interpreting the correct effects of the selected factors on the carbon disclosure scores and in
reducing carbon emissions.

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Further Research
The matter of finding out other important factors has to be evaluated in a much detailed
manner to find out the factors that will be responsible for the carbon emissions and so that the
company can take necessary measures to have control over all those issues. So far, some
companies must have already adopted necessary measures in order to reduce the carbon
emissions. The emissions of the companies before and after adopting the measures can also be
assessed. The difference in the emissions before and after adoption of the techniques can be
tested using appropriate techniques. This will be helpful in indicating whether the factors
adopted are sufficient in reducing the emissions or some other factors are still responsible and
have not been put into light. This will give a better interpretation to the study conducted in this
research.

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References
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lessons for UK SMEs. Surrey: University of Surrey.
Guo, Y., 2014. Climate Change Disclosure : Determinants and impact. University of Hawai.
Macdonald, S. and Headlam, N., 2010. Research Methods Handbook. Manchester.
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Sullivan III, M., 2015. Fundamentals of statistics. Pearson.

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Winn, M., Kirchgeorg, M., Griffiths, A., Linnenluecke, M. K. and Günther, E., 2011. Impacts
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